Sparsely-spread CDMA - a statistical mechanics based analysis
Sparsely-spread CDMA - a statistical mechanics
based analysis
Jack Raymond and David Saad
Neural Computation Research Group, Aston University, Aston Triangle, Birmingham,
B4 7EJ
E-mail: jack.raymond@physics.org
Abstract.
Sparse Code Division Multiple Access (CDMA), a variation on the standard CDMA
method in which the spreading (signature) matrix contains only a relatively small number
of non-zero elements, is presented and analysed using methods of statistical physics. The
analysis provides results on the performance of maximum likelihood decoding for sparse
spreading codes in the large system limit. We present results for both cases of regular
and irregular spreading matrices for the binary additive white Gaussian noise channel
(BIAWGN) with a comparison to the canonical (dense) random spreading code.
PACS numbers: 64.60.Cn, 75.10.Nr, 84.40.Ua, 89.70.+c
AMS classification scheme numbers: 68P30,82B44,94A12,94A14
http://arxiv.org/abs/0704.0098v5
Sparsely-spread CDMA - a statistical mechanics based analysis 2
1. Background
The area of multiuser communications is one of great interest from both theoretical and
engineering perspectives [1]. Code Division Multiple Access (CDMA) is a particular
method for allowing multiple users to access channel resources in an efficient and robust
manner, and plays an important role in the current preferred standards for allocating
channel resources in wireless communications. CDMA utilises channel resources highly
efficiently by allowing many users to transmit on much of the bandwidth simultaneously,
each transmission being encoded with a user specific signature code. Disentangling the
information in the channel is possible by using the properties of these codes and much of
the focus in CDMA research is on developing efficient codes and decoding methods.
In this paper we study a variant of the original method, sparse CDMA, where the
spreading matrix contains only a relatively small number of non-zero elements as was
originally studied and motivated in [2]. While the straightforward application of sparse
CDMA techniques to uplink multiple access communication is rather limited, as it is
difficult to synchronise the sparse transmissions from the various users, the method can be
highly useful for frequency and time hopping. In frequency-hopping code division multiple
access (FH-CDMA), one repeatedly switches frequencies during radio transmission, often
to minimize the effectiveness of interception or jamming of telecommunications. At
any given time step, each user occupies a small (finite) number of the (infinite) M-ary
frequency-shift-keying (MFSK) chip/carrier pairs (with gain G, the total number of chip-
frequency pairs is MG.) Hops between available frequencies can be either random or
preplanned and take place after the transmission of data on a narrow frequency band. In
time-hopping (TH-)CDMA, a pseudo-noise sequence defines the transmission moment for
the various users, which can be viewed as sparse CDMA when used in an ultra-wideband
impulse communication system. In this case the sparse time-hopping sequences reduces
collisions between transmissions.
This study follows the seminal paper of Tanaka [3], and other recent extensions [4],
in utilising the replica analysis for randomly spread CDMA with discrete inputs, which
established many of the properties of random densely-spread CDMA with respect to
several different detectors including Maximum A Posteriori (MAP), Marginal Posterior
Maximiser (MPM) and minimum mean square-error (MMSE). Sparsely-spread CDMA
differs from the conventional CDMA, based on dense spreading sequences, in that any
user only transmits to a small number of chips (by comparison to transmission on all chips
in the case of dense CDMA). The sparse nature of this model facilitates the use of methods
from statistical physics of dilute disordered systems [5, 6] for studying the properties of
typical cases.
The feasibility of sparse CDMA for transmitting information was recently
demonstrated [2] for the case of real (Gaussian distributed) input symbols by employing a
Gaussian effective medium approximation; several results have been reported for the case of
random transmission patterns. In a separate recent study, based on the belief propagation
inference algorithm and a binary input prior distribution, sparse CDMA has also been
Sparsely-spread CDMA - a statistical mechanics based analysis 3
considered as a route to proving results in the densely spread CDMA [7]. In addition, this
study demonstrated the existence of a waterfall phenomenon comparable to the dense code
for a subset of ensembles. The waterfall phenomenon is observed in decoding techniques,
where there is a dynamical transition between two statistically distinct solutions as the
noise parameter is varied. Finally we note a number of pertinent studies concerning the
effectiveness of belief propagation as an MPM decoding method [8, 9, 10, 11], and in
combining sparse encoding (LDPC) methods with CDMA [12]. Many of these papers
however consider the extreme dilution regime – in which the number of chip contributions
is large but not O(N).
The theoretical work regarding sparsely spread CDMA remained lacking in certain
respects. As pointed out in [2], spreading codes with Poisson distributed number of non-
zero elements, per chip and across users, are systematically failing in that each user
has some probability of not contributing to any chips (transmitting no information).
Even in the “partly regular” code [7] ensemble (where each user transmits on the same
number of chips) some chips have no contributors owing to the Poisson distribution in
chip connectivity, consequently the bandwidth is not effectively utilised. We circumvent
this problem by introducing constraints to prevent this, namely taking regular signature
codes constrained such that both the number of users per chip and chips per user take
fixed integer values. Furthermore we present analytic and numerical analysis without
resort to Gaussian approximations of any quantities. Using new tools from statistical
mechanics we are able to cast greater light on the nature of the binary prior transmission
process. Notably the nature of the decoding state space and relative performance of sparse
ensembles versus dense ones across a range of noise levels; and importantly, the question
of how the coexistence of solutions found by Tanaka [3] extends to sparse ensembles,
especially close to the transition points determined for the dense ensemble.
In this paper we demonstrate the superiority of regular sparsely spread CDMA code
over densely spread codes in certain respects, for example, the anticipated bit error rate
arising in decoding is improved in the high noise regime and the solution coexistence
behaviour is less pervasive. Furthermore, to utilise belief propagation for such an ensemble
is certain to be significantly faster and less computationally demanding [13], this also has
power-consumption implications which may be important in some applications. Other
practical issues of implementation, the most basic being non-synchronisation and power
control, require detailed study and may make fully harnessing these advantages more
complex and application dependent.
The paper is organised as follows: In section 2 we will introduce the general framework
and notation used, while the methodology used for the various codes will be presented in
section 3. The main results for the various codes will be presented in section 4 followed
by concluding remarks in section 5.
Sparsely-spread CDMA - a statistical mechanics based analysis 4
i b ξ j d
1τ iτ
jτ kτ lτ K
yb yc yd yN
Figure 1. A bi-partite graph is useful for visually realising a problem. A user node i at
the bottom interacts with other variables through its set of neighbouring factor nodes (∂i)
to which it connects. The factor nodes are determined through a similar neighborhood.
The interaction at each factor (µ) is conditioned on neighbouring gain factors ξµ (the
non-zero components of s), and yµ (which is an implicit function of the noise ωµ, and
neighbouring input bits bµ and gain factors ξµ), assuming a uniform prior on the bits.
The statistical mechanics reconstruction problem associates dynamical variables τ to the
user nodes that interact through the factors. The thermodynamical equilibrium state of
this system then describes the theoretical performance of optimal detectors.
2. The model
We consider a standard model of CDMA consisting of K users transmitting in a bit
interval of N chips. We assume a model with perfect power control and synchronisation,
and consider only the single bit interval. In our case the received signal y is described by
[skbk] + ω , (1)
where the vector components describe the values for distinct chips: sk is the spreading
code for user k, bk = ±1 is the bit sent by user k (binary input symbols) and ω the noise
vector. Appropriate normalisation of the power is through the definition of the signature
matrix (s). It is possible to include a user or chip specific amplitude variation, which may
be due to fading or imperfect power control. We consider a model without these effects.
The spreading codes are sparse so that in expectation only C of the elements in vector sk
are non-zero. If, with knowledge of the signature matrix in use, we assume the signal has
been subject to additive white Gaussian channel noise of variance σ20/β, where σ
0 is the
variance of the true channel noise 〈ω2〉, we can write the posterior for the transmitted bits
τ (unknowns given the particular instance) using Bayes Theorem
P (τ |y) =
[sµk(bk − τk)] + ωµ
P (τ ) , (2)
and from this define bit error rate, mutual information, and other quantities. The
statistical mechanics approach from here is to define a Hamiltonian and partition
Sparsely-spread CDMA - a statistical mechanics based analysis 5
function from which the various statistics relating to this probability distribution may
be determined - and hence all the usual information theory measures. A suitable choice
for the Hamiltonian is
H(τ ) =
[sµk(bk − τk)] + ωµ
hkτk . (3)
We can here identify τ as the dynamical variables in the inference problem (dependence
shown explicitly). The other quenched variables (parameters), describing the instance of
the disorder, are the signature matrix (s), noise (ω) and the inputs (b). The variables
hk describe our prior beliefs about the inputs (the specific user bias), and we can assume
some simple distribution for this such as all users having the same bias hk = H . Maximal
rate transmission corresponds to unbiased bits H = 0, and this is considered throughout
the paper. The properties of such a system may be reflected in a factor (Tanner) graph,
a bipartite graph in which users and chips are represented by nodes (see figure 1).
The calculation we undertake is specific to the case of the thermodynamic limit in
which the number of chips N → ∞ whilst the load α = K/N is fixed. Note that α is
termed β in many CDMA papers, here we reserve β to mean the “inverse temperature”
in a statistical mechanics sense (which defines our prior belief for the noise level and give
rise to the corresponding MAP detector.)
In all ensembles we may identify the parameter L as the mean number of contributions
to each chip, and C as the mean number of contributions per user. As such the following
also holds
. (4)
The case in which α is greater than 1 will be called oversaturated, since more than one
bit is being transmitted per chip.
The calculations presented henceforth are specific to the case of memoryless noise,
drawn from a single distribution of mean zero and mean square σ20
Ω(ω) = P (ωµ = ω) . (5)
Defining normalised spreading codes such that
sk.sk = N , we can identify the “power
spectral density” (PSD) over a chip interval as a measure of the system noise 1/(2σ20) –
the factor two being connected with physical considerations in implementing the model.
2.1. Code Ensembles
We consider several code ensembles we call irregular, partly regular and regular, which
differ in the constraints placed on the factor and variable degree constraints of the signature
matrix s. The probability distribution
P (s) = N
δ(sµk 6= 0)− L̃)
P (L̃)
Sparsely-spread CDMA - a statistical mechanics based analysis 6
δ(sµk 6= 0)− C̃)
P (C̃)
P (sµk) , (6)
where N is a normalising constant, P (L̃) is the factor degree probability distribution of
mean L, P (C̃) is the variable degree probability distribution of mean C, and P (sµk) is the
marginal probability distribution which is common to all ensembles
P (sµk) =
δ(sµk) +
δ(sµk − ξ) . (7)
The form of (6) is then sufficient for the sparse distributions we consider in the large system
limit, and makes explicit the chip and user connectivity properties of the ensembles. The
gain factor ξ, is drawn randomly from a single distribution with zero measure at ξ = 0,
and finite moments, in any instance of a code
φ(ξ) = P (sµk = ξ|sµk 6= 0) . (8)
Unlike the dense case the details of this distribution will effect results, but only in a small
way for reasonable choices [2]. We here investigate the case of Binary Phase Shift Keying
(BPSK) which corresponds to a uniform distribution on {− 1√
}, though the analytic
results presented are applicable to any distribution of mean square = 1/L. Note that
disorder in the gain factors is not a necessity, the case ξ = 1/
L also allows decoding in
sparse ensembles.
The case where P (L̃) and P (C̃) are Poissonian distributed identifies the irregular
ensemble - where the connections between chips and users are independently distributed.
The second distribution called partly regular has P (C̃) = δC,C̃ , in which the chip
connectivity is again Poisson distributed with mean L, but each user contributes to exactly
C chips. This prevents the systematic failure inherent in the irregular ensemble since
therein an extensive number of users fail to transmit on any chips. If in addition to
the aforementioned constraint all chips receive exactly L contributions, P (L̃) = δL,L̃,
the ensemble is called regular. Regular chip connectivity amongst other things prevents
the systematic inefficiency due to leaving some chips unaccessed by any of the users.
The case of Poissonian distributions is that in which there is no global control. In
many engineering applications constraining users individually (non-Poissonian P (C̃)) is
practical, whereas coordination between users (non-Poissonian P (L̃)) is difficult. The
practicalities of implementing the different ensembles we consider are application specific:
the advantages inherent in distributing channel resources more evenly amongst users may
be lost to practical implentation problems.
3. Methodology
3.1. Spectral Efficiency Lower Bound
The inferiority of codes with Poissonian user connectivity has been pointed out previously
(e.g., in [2]), based on the understanding that codes which leave a portion of the users
Sparsely-spread CDMA - a statistical mechanics based analysis 7
disconnected cannot be optimal. Analogously we argue that codes with irregular chip
connectivity must also be inferior in that they leave a fraction of the chips (bandwidth)
unutilised, thus providing a motivation for considering fully regular codes.
In this section we show a particular case in which the regular codes are expected
to outperform any other ensemble by analysing the amount of information that can be
extracted on the sent bits by consideration of only one chip in isolation of the other chips.
This corresponds to a detector reconstructing bits based only on the value of a single chip,
and is independent of the user connectivity.
The spectral efficiency is defined as the mutual information between the received
signal and reconstructed bits per chip. In considering only a single chip (µ) we have
I(τ ; yµ) =
P (τ |yµ)
P (τ )
P0(τ ,yµ)
, (9)
where the subscript zero indicates that the true (generative), rather than model (2),
probability distribution. For brevity we consider the simplest case that the generative
and model probability distributions are the same with unbiased bits and a Gaussian noise
distribution in which case after some rearrangement
I(τ ; yµ) = L̃−
exp(−Hµ(τ µ))
τ µ exp(−Hµ(τ µ))
P0(τ µ,yµ)
, (10)
where τ µ are the bits connected to chip µ, and the chip Hamiltonian is
ξiτi + yµ
, (11)
labelling each interacting (non-zero) component on the chip by i, L̃ being the chip
connectivity.
Working from this description we wish to compare the performance of ensembles
with different chip connectivities. To do this we consider the ensemble average mutual
information by averaging the mutual information over the connectivities (L̃), load factors,
and transmitted bits. This average is complicated, however it is possible to calculate the
dominant terms in the low and high PSD limits.
In the case of low noise (PSD → ∞) we find the asymptotically dominant terms
come first from the numerator
〈log2 exp−H(τ µ)〉
/ log(2) =
2 log(2)
, (12)
which is an average over the ground state energy, and also the logarithm of the denominator
which is
exp−H(τ µ)
ξi(bi − τi)
, (13)
where yµ has been decomposed into its bit ({bi}) and noise (ω) parts, and the averages are
now over the ensembles as well as yµ. The first part of (13) gives an energy contribution
cancelling (12). We call the remaining part the average over the chip entropy, by
Sparsely-spread CDMA - a statistical mechanics based analysis 8
comparison with (10) this determines the amount of information lost in decoding. The
chip entropy term contains an indicator function counting the ground states - the average
chip entropy is zero when τ µ = bµ is the only solution. For the case of BPSK however
there may be some degeneracy in ground states with two terms in the sum being non-zero
but cancelling one another. This degeneracy has a dependence on the distribution P (L̃)
for given L. Averaging over load factors and transmitted bits we find that in the zero
noise limit
I(τ , yµ)
ξi(bi − τi)
P (L̃)
, (14)
min(p,L̃−p)
L̃− p
P (L̃)
. (15)
By numerical evaluation of this function (see results section 4.2) we find that the optimal
ensemble is in fact the regular ensemble. This is because chip entropy, when averaged over
load factors and bits is a concave function in L̃, so that the information loss is minimised
when P (L̃) = δ
. This dependency on L̃ may be a peculiarity of the detector considered,
but many other aspects of the calculation may be generalised to give a similar result.
It is possible to consider the opposite limit σ20 → ∞ perturbatively. We found that
the leading four orders in 1/σ0 were identical for all code ensembles of the same mean chip
connectivity. We would anticipate the behaviour at non-extreme PSD to fall somewhere
between these two regimes and thus for the chip regular ensemble to be atleast as good as
the chip irregular ensembles.
We note here that another reason for considering the regular code optimal amongst
sparse random codes is to consider the field term when the Hamiltonian (11) is written
in canonical form with a set of couplings ({J〈ij〉}) and user specific external fields ({hi}).
In this representation the set of external fields are in expectation aligned with the sent
bit sequence, but subject to fluctuations for each code instance. The variance of these
fluctuations may be shown to be proportional to the excess chip connectivity over the
true chip connectivity [14], which amongst all ensembles is minimised by the regular chip
ensemble. The multi-user interference is larger in irregular codes and hence information
recovery is weaker as predicted in this section.†
3.2. Replica Method Outline
We determine the static properties of our model defined in section 2, including correlations
due to the full interaction structure, we use the replica method. From the expression of
the Hamiltonian (3) we may identify a free energy and partition function as:
f = − 1
lnZ Z = Trτ exp (−βH(τ )) .
† This argument is added since published version.
Sparsely-spread CDMA - a statistical mechanics based analysis 9
To progress we make use of the anticipated self-averaging properties of the system.
The assumption being that in the large system limit any two randomly selected instances
will, with high probability, have indistinguishable statistical properties. This assumption
has firm foundation in several related problems [15], and is furthermore intuitive after
some reflection. If this assumption is true then the statists of any particular instance can
be described completely by the free energy averaged over all instances of the disorder. We
are thus interested in the quantity
F = 〈f〉 = − lim
〈lnZ〉I , (16)
where the angled brackets represent the weighted averages over I (the instances). The
entropy density may be calculated from the free energy density by use of the relation
s = β(e− f) , (17)
where e is the energy density.
To determine the free energy we must average over disorder in (16), which is a difficult
problem except in special cases. This is why we make use of the replica identity
〈lnZ〉I = lim
〈Zn〉I . (18)
We can model the system now as one of interacting replicas, where Zn is decomposed as a
product of an integer number of partition functions with conditionally independent (given
the instance of the disorder) dynamical variables. The discreteness of replicas is essential
in the first part of the calculation, but a continuation to the real numbers is required
in taking n → 0+ – this is a notorious assumption, which rigorous mathematics can not
yet justify for the general case, in spite of the progress made in recent years [16, 17, 18].
However, we shall assume validity and since the methodology for the sparse structures is
well established [19, 20, 15] we omit our particular details. The final functional form for
the free energy is determlained from
〈Zn〉 =
dP (b,σ)dP̂ (b,σ)
exp{lnN +N(G1(n) +G2(n) +G3(n))} ; (19a)
G1(n) = ln
λ2α/2
P (b,σ)
λα(b− τα)
P (L̃)
; (19b)
G2(n) =
P (b,σ)P̂ (b,σ) ; (19c)
G3(n) = α ln
(−L)C̃
P̂ (b, τ )
P (C̃)
P0(b)
; (19d)
where N is a constant due to normalising the ensembles (6). This expression may be
evaluated at the saddle point to give an expression for the free energy. In the term (19d)
Sparsely-spread CDMA - a statistical mechanics based analysis 10
we account for the cases in which the marginalised probability distribution P0(b) and
assumed marginal probability distribution (described by H) are asymmetric. In the case
of maximal rate which we will consider, the b average is trivial and H = 0. Provided
that in addition the gain factor distribution is symmetric then it is possible to remove
the b dependence in the order parameters, since the symmetry P (b,σ) = P (−b,−σ) and
P̂ (b,σ) = P̂ (−b,−σ) leaves the free energy invariant.
3.3. Replica Symmetric Equations
The concise form for our equations is attained using the assumption of replica symmetry
(RS). This amounts to the assumption that the correlations amongst replicas are all
identical, and determined by a unique shared distribution. The validity of this assumption
may be self consistently tested (section 3.5). This assumption differs from that used
by Yoshida and Tanaka [2] where the correlations are described by only a handful of
parameters rather than a distribution once RS is assumed – this approach may therefore
miss some of the detailed structure although it is easier to handle numerically. The order
parameter in our case is given by
P (b, τ ) =
dπ(x)
(1 + bταx)
; (20a)
P̂ (b, τ ) = q̂
dπ̂(x̂)
(1 + bταx) ; (20b)
where q̂ is a variational normalisation constant and π, π̂ are normalised distributions on
the interval [−1, 1]. From here onwards we may consider the case in which the bit variables
τα and gain factors ξ are gauged to b (τb → τ , ξb → ξ).
Using Laplace’s method, this gives the following expression for the (RS) free energy
at the saddle point
FRS = −
Extrπ,bπ
G1,RS(L̃)(n) + G2,RS(n) + G3,RS(C̃)(n)
where
G1,RS(n)
= − L ln 2
[dπ(xl)]
ln Tr{τl=±1}χL̃(τ ; {ξ}, ω, {x})
Ω(ω),φ(ξ)
P (L̃)
; (22a)
χL̃(τ ; {ξ}, ω, {x}) = exp
(1− τl)ξl
(1 + τlxl) ; (22b)
G2,RS(n) = − L
dπ(xc)dπ̂(x̂c) ln(1 + xx̂c) ; (22c)
G3,RS(n) = α
[dπ̂(x̂c)] ln
(1 + x̂c) +
(1− x̂c)
P (C̃)
. (22d)
Sparsely-spread CDMA - a statistical mechanics based analysis 11
and the saddle point value for ŵ (= L) has been introduced. The averages over L̃ and C̃
encapsulate the differences amongst the ensembles.
Equation (22b) describes the interaction at a single chip in the factor graph (figure 1)
of connectivity L̃. The parameter ξl and variable τ are the gain factors, and reconstructed
bits respectively, both gauged to the transmitted bit, while ω is the instance of the chip
noise.
The order variational distributions {π, π̂} are chosen so as to extremise (21). The self
consistent equations attained by the saddle point method are:
π̂(x̂) =
[dπ(xl)]
Tr{τl=±1} τL̃+1 χ̄L̃(τ ; {ξ}, {x̂})
Tr{τl=±1} χ̄L̃(τ ; {ξ}, ω, {x})
{ξ},ω
P (L̃)
(23a)
χ̄L̃(τ ; {ξ}, ω, {x}) = exp
(1− τl)ξl
(1 + τlxl) (23b)
π(x) =
[dπ̂(x̂c)] δ
c=1(1 + x̂c)−
c=1(1− x̂c)
c=1(1 + x̂c) +
c=1(1− x̂c)
P (C̃)
. (23c)
The variables P (L̃) and P (C̃) are here the excess degree distributions of the particular
ensemble (6). For regularly constrained ensembles the chip and user excesses are L − 1
and C − 1 respectively. For Poissonian distributions the excess degree distribution is the
full degree distribution.
Aside from entropy, the other quantities of interest may be determined from the
probability distribution for the overlap of reconstructed and sent variables mk = 〈τk〉,
P (m) = lim
δmk,m
, (24)
[dπ̂(x̂c)] δ
c=1(1 + x̂c)−
c=1(1− x̂c)
c=1(1 + x̂c)+
c=1(1− x̂c)
P (C̃)
. (25)
We note finally that equivalent expressions to these found with the RS assumption
may be obtained by using the cavity method [6] with the assumption of a single pure state.
This approach is a probabilistic one and hence more intuitive on some levels.
3.4. Population Dynamics
Analysis of these equations is primarily constrained by the nature of equations (23a-
23c). No exact solutions are apparent, and perturbative regimes about the ferromagnetic
solution (which is only a solution for zero noise) are difficult to handle. Consequently
we use population dynamics [21] – representing the distributions {π(x), π̂(x̂)} by finite
populations (histograms) and iterating this distribution until convergence. It is hoped,
and observed, that each histogram captures sufficient detail to describe the continuous
Sparsely-spread CDMA - a statistical mechanics based analysis 12
function and the dynamics (described below) allow convergence towards a true solution
distribution with only small corrections due to finite size effects.
To solve the equations (23a,23c) with population dynamics finite histograms
constucted from M undirected cavity magnetisations are used. Histograms approximating
each function are formed
π(x) → W = {x1, . . . , xi, . . . , xM} , (27a)
π(x̂) → Ŵ = {x̂1, . . . , x̂a, . . . , x̂M} , (27b)
with M sufficiently large to provide good resolution in the desired performance measures.
The discrete minimisation dynamics of the histograms is derived from (23a-23c).
Histogram updates are undertaken alternately, with all magnetisation in the histogram
being updated sequentially. In the update of field xa the quenched parameters {L̃, ω, ξ}
are sampled, L̃ being the chip excess degree, and L̃ magnetisations are randomly chosen
from W , defining through (23a) the update
x̂a =
Tr{τl=±1} τL̃+1 χ̄L̃(τ ; {ξ}, ω, {x})
Tr{τl=±1} χ̄L̃(τ ; {ξ}, ω, , {x})
. (28)
The update of the other histogram follows dynamics in which C̃ is sampled, C̃ being
the user excess degree, along with C̃ randomly chosen magnetisations from Ŵ , defining
through (23c) the update
c=1(1 + x̂c)−
c=1(1− x̂c)
c=1(1 + x̂c) +
c=1(1− x̂c)
. (29)
There is a strong analogy between the population dynamics algorithm and that of
message passing on a particular instance of the graph. The iteration of the histograms
implicit in (28-29) is analogous to the propagation of a population of cavity magnetisations
between factor (a) and user (i) nodes, which may be written as the self consistent equations:
x̂a→i =
Tr{τl=±1}τi exp
l∈∂ari
(1− τl)ξal
l∈∂ari
(1 + τlxl→a) ; (30a)
xi→a =
c∈∂ira
(1 + x̂c→i)−
c∈∂ira
(1− x̂c→i)
; (30b)
where Nx,x̂ are the relevant normalisations, and the abbreviation ∂y indicates the set
of nodes connected to y. In population dynamics, the notion of a particular graph
with labelled edges is absent however, and the only the distribution of the two types
of magnetisations are relevant.
3.5. Stability Analysis
To test the stability of the obtained solutions we consider both the appearance of
non-negative entropy, and a stability parameter defined through a consideration of the
Sparsely-spread CDMA - a statistical mechanics based analysis 13
fluctuation dissipation theorem. The first criteria that the entropy be non-negative is
based on the fact that physically viable solutions in discrete systems must have non-
negative entropy so that any solution found not meeting this criteria must be based on
bad premises; replica symmetry is a likely source.
The stability parameter λ is defined in connection with the cavity method for spin
glasses [22] and tests local stability of the solutions. It is equivalent to testing the local
stability of belief propagation equations as proposed in [23]. A necessary condition for
the stability of the RS solution is that the corresponding susceptibility does not diverge.
This condition ensures that fields are not strongly correlated. The spin glass susceptibility
when averaged over instances may be defined
〈τ0τd〉2c
, (31)
where d is the distance between two nodes in the factor graph, the inner average denotes
the connected correlation function between these nodes, Xd describes the typical number
of variables at distance d, and the outer average is over instances of the disorder (self-
averaging part). This quantity is not divergent provided that
λ = ln
〈τ0τd〉2c
is negative, since this indicates an asympoptically exponential decrease in the terms of (31)
and hence convergence of the sum. In the thermodynamic limit the connected correlation
function is dominated by a single direct path which may be decomposed as a chain of local
linear susceptibilities
〈τ0τd〉c ∝
(i,j)
∂xi→a
∂x̂b→i
∂x̂b→i
∂xj→b
, (33)
where (i,j) indicate the set of variables on the shortest path between nodes 0 and d in a
particular instance of the graph (30a).
This representation allows us to construct an estimation for λ numerically based
on principles similar to population dynamics [24] – the directedness and fixed structure
implicit in a particular problem is removed with the self-averaging assumption leaving a
functional description similar to (23a-23c), which may be iterated. In order to approximate
the stability parameter λ one introduces additional positive numbers in the population
dynamics histograms (27b,27a), xi → {xi, vi} and x̂a → {x̂a, v̂a} respectively. These new
values represent the relative sizes of perturbations in each magnetisation, and are updated
in parallel to (28,29) as
v̂a =
, (34)
and with similar assignments for the field update of W
. (35)
Sparsely-spread CDMA - a statistical mechanics based analysis 14
The partial derivatives are calculated from (28-29) and evaluated at the corresponding
values in the sampled population. If the final fixed point is stable against small
perturbations in the initial field then these values {v, v̂} must decay exponentially on
average. Renormalisation of {vi} and {v̂a} such that the mean is 1 after each update is
necessary. The numerical renormalisation constant for each population yields (dependent)
estimations of λ, which can be sampled at a suitable convergence time (end of the {W, Ŵ}
minimisation process).
Like population dynamics we expect behaviour to be sensitive to initialisation
conditions and finite size effects in some circumstances. In addition the estimation requires
good resolution in the histograms W and Ŵ .
4. Results
Results are presented here for the canonical case of Binary Phase Shift Keying (BPSK)
where ξl ∈ {1,−1} with equal probability. Furthermore, we assume an AWGN model for
the true noise ω (of variance σ20). For evaluation purposes we assume the channel noise
level is known precisely, so that β = 1, employing the Nishimori temperature [5]. This
guarantees that the RS solution is thermodynamically dominant. Furthermore the energy
takes a constant value at the Nishimori temperature and hence the entropy is affine to
the free energy. Where of interest we plot the comparable statistics for the Single User
Gaussian channel (SUG), and the densely spread ensemble, each with MPM detectors –
equivalent to maximum likelihood for individual bits.
For population dynamics two parallel populations (27a,27b) are initialised either
uniformly at random, or in the ferromagnetic state. These two populations are known
to converge towards the unique solution, where one exists, from opposite directions, and
so we can use their convergence as a criteria for halting the algorithm and testing for the
appearance of multiple solutions. In the case where they converge to different solutions
we can usually identify the solution converged to from the ferromagnetic initial state as
a good solution - in the sense that it reconstructs well, and that arrived at from random
initial state as a bad solution. In the equivalent belief propagation algorithm one cannot
choose initial conditions equivalent to ferromagnetic – knowing the exact solution would
of course makes the decoding redundant. We therefore expect the properties of the bad
solution to be those realisable by belief propagation (though clever algorithms may be
able to escape to the good solution under some circumstances). The stability variables
{v, v̂} were initialised independently each as the square of a value drawn from a gaussian
distribution – and tests indicated other reasonable distributions produced similar results.
Computer resources restrict the cases studied in detail to an intermediate PSD
regime, and small L. In particular, the problem at low PSD, is the Gaussian noise
average, which is poorly estimated, while at high PSD a majority of the histogram is
concentrated at magnetisations x, x̂ ≈ 1 not allowing sufficient resolution in the rest of
the histogram.
Several different measures are calculated from the converged order parameter,
Sparsely-spread CDMA - a statistical mechanics based analysis 15
indicating the performance of sparsely-spread CDMA. Using the converged histograms
for the fields we are able to determine the following quantities: free energy, energy and a
histogram for the probability distribution, from discretisations of the previously presented
equations (23a-23c). Using the probability distribution we are also able to approximate
the decoding bit error rate
dP (m)
1− sign(m)
; (36)
multi-user efficiency
MuE =
erfc−1(Pb)
; (37)
and mutual information between sent and reconstructed bits per chip, I(b; τ )/N (taking
a factorised form given the RS assumption)
MI = α
dP (m)
1 + τm
1 + τm
. (38)
The spectral efficiency is the capacity I(τ ;y) per chip, which is affine to the entropy (and
the free energy at the Nishimori temperature)
ν = α− s/ ln 2 . (39)
Negative entropy can be identified when the measured spectral efficiency exceeds the load,
and thermodynamic transition points correspond to points of coincident spectral efficiency.
Figure 2‡ demonstrates some general properties of the regular ensemble in which the
variable and factor degree connectivities are C : L = 3 : 3, respectively. Equations (23a-
23c) were iterated using population dynamics and the relevant properties were calculated
using the obtained solutions; the data presented is averaged over 100 runs and error-bars,
which are typically small, are omitted for brevity. Figure 2(a) shows the bit error rate
in regular and Poissonian codes, the inset focuses on the range where the sparse-regular
and dense cases crossover. The sparse codes demonstrate similar trends to the dense case
except the irregular code, which show weaker performance in general, and in particular at
high PSD. Detailed trends can be seen in figure 2(b) that shows the multiuser efficiency.
Codes with regular user connectivity show superior performance with respect to the dense
case at low PSD. Figure 2(c) shows similar trends in the spectral efficiency and mutual
information (shown in the inset); the effect of the disconnected (user) component is clear
in the fact that the irregular code fails to reach capacity at high noise levels. In general
it appears the chip connectivity distribution is not critical in changing the trends present,
unlike the user connectivity distribution. It was found in these cases (and all cases with
unique solutions for given PSD), that the algorithm converged to non-negative entropy
values and to a stability measure fluctuating about a value less than 0, as shown in
figure 2(d). These points would indicate the suitability of the RS assumption.
‡ This figure has been modified from the published version, the difference being that the Poissonian chip
connectivity codes have everywhere weaker performance than the dense and sparse regular code ensemble.
Sparsely-spread CDMA - a statistical mechanics based analysis 16
The outperformance of dense codes by sparse ensembles with regular user connectivity
in the low PSD regime is new to our knowledge, although Poissonian chip connectivity
is everywhere inferior to both the dense and regular sparse codes. The difference between
codes disappears rapidly with increasing (connection) density at fixed α (figure 3). This
is inline with our prediction of the regular code being a high performance ensemble in
preceeding sections.
Figure 3 indicates the effect of increasing density at fixed α in the case of the
regular code. As density is increased the statistics of the sparse codes approach that
of the dense channel in all ensembles tested. For the irregular ensemble performance
increases monotonically with density at all PSD. The rapid convergence to the dense
case performance was elsewhere observed for partly regular ensembles, and ensembles
based on a Gaussian prior input [2, 7]. At all densities for which single solutions were
found the RS assumption appeared validated in the stability parameter and entropy.
Figure 4 indicates the effect of channel load α on performance. We first explain results
for codes in which only a single solution was found (no solution coexistence). For small
values of the load a monotonic increase in the bit error rate, and capacity are observed as α
is increased with C constant, as shown in figures 4(a) and 4(b), respectively. This matches
the trend in the dense case, the dense code becoming superior in performance to the sparse
codes as PSD increases. We found that for all sparse ensembles there existed regimes with
α > 1.49 for which only a single stable solution existed, although the equivalent dense
systems are known to have two stable solutions in some range of PSD [3]. In all single
valued regimes we observed positive entropy, and a negative stability parameter. However,
in cases of large α many features became more pronounced close to the dense case solution
coexistence regime: notably the cusp in the stability parameter, gap between MI and ν
and the gradient in Pb.
4.1. Solution Coexistence Regimes
As in the case of dense CDMA [3], also here we observe a regime where two solutions, of
quite different performance, coexist. In order to investigate the regime where two solutions
coexist we investigated the states arrived at from random and ferromagnetic initial
conditions (giving bad and good solutions respectively). Separate heuristic convergence
criteria were found for the histograms, and these seemed to work well for the good
solution. For the bad solution we simply present results after a fixed number of histogram
updates (500) as all convergence criteria tested appeared either too stringent, to require
experimentally inaccessible timescales, or did not capture the asymptotic values for
important quantities like entropy. We believe 500 updates to be sufficiently conservative
to capture the properties of these solutions however.
Figure 4(a) shows the dependence of the bit error rate on the load, which is also
equivalent to L/C. There is a monotonic increase in bit error rate with the load and the
emergence and coexistence of two separate solutions above a certain point; in the case
of the 6 : 3 code the point above which the two solutions coexist is PSD = 10.23dB as
Sparsely-spread CDMA - a statistical mechanics based analysis 17
−10 −8 −6 −4 −2 0 2 4 6 8 10
Spectral Power Density [dB]
Irreg.
P. Reg.
Dense0 1 2 3 4 5 6 7 8
−10 −8 −6 −4 −2 0 2 4 6 8 10
Spectral Power Density [dB]
Irreg.
P. Reg.
Dense(b)
−10 −8 −6 −4 −2 0 2 4 6 8 10
Spectral Power Density [dB]
Irreg.
P. Reg.
Dense
2 2.5 3 3.5 4 4.5 5 5.5 6
−10 −8 −6 −4 −2 0 2 4 6 8 10
Spectral Power Density [dB]
Irreg.
P. Reg.
Reg. Type 1
Reg. Type 2
Figure 2. Performance of the sparse CDMA configuration of variable and factor degree
connectivities C : L = 3 : 3, respectively; all data presented on the basis of 100 runs, error
bars are omitted and are typically small in subfigures (a)-(c) the smoothness of the curves
being characteristic of this level (numerical accuracy was excellent only at intermediate
PSDs). (a) The bit error rate is limited by the disconnected component in the case of
irregular codes, otherwise trends match the dense case, lower bounded by the SUG. Inset
- the range where the sparse-regular and dense cases crossover.(b) Multiuser efficiency
indicates the regular user connectivity codes outperform the dense case below some PSD.
(c) The spectral efficiency [——] demonstrates similar trends, the entropy being positive.
The gap between the mutual information [· · · · · ·] and spectral efficiency (shown in the
inset) is everywhere small and especially so at small and large PSD, indicating little
information loss in the decoding process. (d) The two markers show the mean results
for the two different stability estimates in the algorithm for the regular code. There are
systematic errors at small PSD, and convergence is good only at intermediate PSD.
The lines represent the average of these quantities for each ensemble – all ensembles show
a cusp at some PSD, for 3 : 3 codes the various ensembles shows very similar trends,
indicating local stability everywhere.
Sparsely-spread CDMA - a statistical mechanics based analysis 18
−10 −8 −6 −4 −2 0 2 4 6 8 10
Spectral Power Density [dB]
Dense
0 1 2 3 4 5 6 7 8
Spectral Power Density [dB]
Dense
Figure 3. The effect of increasing density for the regular ensemble: (a) Multiuser
efficiency, (b) spectral efficiency [——] and mutual information [– – –]. Data presented on
the basis of 10 runs, error bars are omitted but of a size comparable with the smoothness
of the curves. The performance of sparse codes rapidly approaches that of the dense code
everywhere. The PSD threshold beyond which the dense code outperforms the sparse
code is fairly stable.
indicated by the vertical dotted line.
We use the regular code 6 : 3 to demonstrate the solution coexistence found above
some PSD in various codes. The onset of the bimodal distribution can be identified by
the divergence in the convergence time in the single solution regime (the time for the
ferromagnetic and random histograms to converge to a common distribution). The time
for this to occur, in a heuristically chosen statistic and accuracy, is plotted in figure 4(b).
By a naive linear regression across 3 decades we found a power law exponent of 0.59 and
a transition point of PSD = 10.23dB, but cannot provide a goodness of fit measure to
this data. This would represent the point at which at least two stable solutions co-exist.
Beyond PSD ≈ 12dB only one stable solution is found from both random and
ferromagnetic initial conditions, corresponding statistically to a continuation of the good
solution. A solution which statistically resembles a continuation of the bad solution is
occasionally arrived at from both initial conditions, this solution had a positive stability
parameter and negative entropy – so is not a viable solution. Thus we predict a second
dynamical transition in the region of 12dB, as might be guessed by comparison with the
dense case and observation of the trend in the stability parameter (see figure 4(c)).
The stability results are presented in figure 4(c). Only two stable solutions were found
in the region beyond this critical point and upto 12dB, which we infer to be viable RS
solutions (where entropy is positive). The bad solution upto 12dB has a well resolved
negative value. The good solution has a negative value in its mean, but like other near
ferromagnetic solutions investigated results are very noisy due to numerical issues relating
to histogram resolution.
Both capacity and spectral efficiency monotonically increase with the load as shown in
figure 4(d). For the 6 : 3 code we see a separation of the two solutions at PSD = 10.23dB
Sparsely-spread CDMA - a statistical mechanics based analysis 19
−4 −2 0 2 4 6 8 10 12
Spectral Power Density [dB]
6:3 (Bad)
6:3 (Good)
 − PSD
Data Mean
Linear fit
Bounds
−4 −2 0 2 4 6 8 10 12
Spectral Power Density [dB]
6:3 (Bad)
6:3 (Good)
−4 −2 0 2 4 6 8 10 12
Spectral Power Density [dB]
6:3 (Bad)
6:3 (Good)
8   10  12  
Figure 4. The effect of channel load α on performance for the regular ensemble. Data
presented on the basis of 10 runs, error bars omitted but characterised by the smoothness
of curves. Dashed lines indicate the dense code analogues. The vertical dotted line
indicates the point beyond which 6 : 3 random and ferromagnetic initial conditions failed
to converge to the same solution, both dynamically stable solutions are shown beyond
this point. (a) There is a monotonic increase in bit error rate with the increasing load.
(b) Investigation of the 6 : 3 code (α = 2) indicates a divergence in convergence time as
PSD → 10.23dB with exponent 0.59 based on a simple linear regression of 15 points (each
point is the mean of 10 independent runs). Beyond this point different initial conditions
give rise to one of two solutions. (c) The stability parameter was found to be negative
for all convergent solutions, indicating the suitability of RS. Where the solution is near
ferromagnetic the stability measure becomes quickly very noisy (as shown for the 5 : 3
and 6 : 3 codes). (d) As load α is increased there is a monotonic increase in capacity.
The spectral efficiency for the ’bad’ solution exceeds 2 in a small interval (equivalent to
negative entropy), similar to the behaviour reported for the dense case.
Sparsely-spread CDMA - a statistical mechanics based analysis 20
(vertical dotted line.) The dashed lines correspond to a similar behaviour observed in the
dense case (the range of interest is magnified in the inset.) A cross over in the entropy of the
two distinct solutions, near PSD ≈ 11dB, is indicative of a second order phase transition.
As in the dense case, only the solution of smallest spectral efficiency is thermodynamically
relevant at a given PSD, although the other is likely to be important in decoding dynamics.
The trends in the sparse case follow the dense case qualitatively, with the good solution
having performance only slightly worse than the corresponding solution in the dense case
(and vice versa for the bad solution).
The entropy of the bad solution becomes negative in a small interval (spectral
efficiency exceeds 2) although no local instability is observed. The static and dynamic
properties of the histograms appear to be well resolved in this region. However, the
negative entropy indicates an instability towards either a type of solution not captured
within the RS assumption, or towards some metastable configuration. We will not
speculate further, the bad solution is in any case thermodynamically subdominant in
its low and negative entropy form.
Our hypothesis is therefore that the trends in the sparse ensembles match those in
the dense ensembles within the coexistence region and RS continues to be valid for each
of two distinct positive entropy solutions. The coexistence region for the sparse codes is
however smaller than in the corresponding dense ensembles. Since our histogram updates
mirror the properties of a belief propagation algorithm on a random graph we can suspect
that the bad solution may have implications for the performance of belief propagation
decoding in the coexistence region, and that convergence problems will appear near this
region. In the user regular codes investigated the bad solution of the sparse ensemble
outperforms the bad solution of the dense ensemble, and vice-versa for the good solution.
Thus regardless of whether sparse decoding performance is good or bad, the dynamical
transition point for the dense ensemble would corresponds to a PSD beyond which dense
CDMA outperforms sparse CDMA at a particular load.
4.2. Spectral Efficiency Lower Bound Numerical Results
Finally we present figure 5, which shows the the mutual information between a single
chip and transmitted bits for sparse ensembles of differing chip connectivity in the infinite
PSD (zero noise) limit (15). This shows that in expectation a chip drawn from the
regular ensemble contains more information on the transmitted bits than a chip drawn
from any other ensemble (including the Poissonian ensemble). The difference between the
regular and Poissonian ensembles becomes relatively smaller as L increases. This appears
consistent with the replica method results found at high PSD, although regular chip
connectivity under performed by comparison with Poisson distributed chip connectivity
in the low PSD regime, which was not anticipated by the single chip approximation.
Sparsely-spread CDMA - a statistical mechanics based analysis 21
0 5 10 15 20 25
Mean Chip Connectivity, L
Poissonian
Figure 5. A PSD → ∞ limit to the expected mutual information between a single chip,
and the transmitted bits. Mutual Information is highest for regular chip connectivities,
with the Poissonian chip connectivity result also shown, the discrepancy becoming
relatively small as L increases. The inset shows the mutual information/bit decoded
(〈I(τ ; yµ)〉 /L) on a log-log plot to demonstrate an asymptotic power law behaviour and
show more detail in the cases of small L.
5. Concluding Remarks
Our results demonstrate the feasibility of sparse regular codes for use in CDMA. At
moderate PSD it seems the performance of sparse regular codes may be very good. With
the replica symmetric assumption apparently valid at practical PSD it is likely that fast
algorithms based on belief propagation may be very successful in achieving the theoretical
results. Furthermore for lower density sparse codes the problem of the coexistence regime,
which limits the performance of practical decoding methods, seems to be less pervasive
than for dense ensembles in the over saturated regime.
A direct evaluation of the properties of belief propagation may prove similar results
to those shown here. In the absence of replica symmetry breaking states it is normally
true that belief propagation performs very well. However, to make best use of the channel
resources it may be preferable to implement high load regimes in cases of high PSD, and
so overcoming the algorithmic problems arising from the solution coexistence is a challenge
of practical importance in this case.
Other practical issues in implementation are certainly significant. Similar to the case
of dense CDMA there are considerable problems relating to multipath, fading and power
control, in fact it is known that these effects are more disruptive for the sparse codes,
especially regular codes. However, certain situations such as broadcasting (one to many)
channels and downlink CDMA, where synchronisation can be assumed, may be practical
points for future implementation. There are practical advantages of the sparse case over
dense and orthogonal codes in some regimes. The sparse CDMA method is likely to be
particularly useful in frequency-hopping and time-hopping code division multiple access
(FH and TH -CDMA) applications where the effect of these practical limitations is less
Sparsely-spread CDMA - a statistical mechanics based analysis 22
emphasised.
Extensions based on our method to cases without power control or synchronisation
have been attempted and are quite difficult. A consideration of priors on the inputs, in
particular the effects when sparse CDMA is combined with some encoding method may
also be interesting.
Acknowledgments
Support from EVERGROW, IP No. 1935 in FP6 of the EU is gratefully acknowledged.
DS would like to thank Ido Kanter for helpful discussions.
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	Background
	The model
	Code Ensembles
	Methodology
	Spectral Efficiency Lower Bound
	Replica Method Outline
	Replica Symmetric Equations
	Population Dynamics
	Stability Analysis
	Results
	Solution Coexistence Regimes
	Spectral Efficiency Lower Bound Numerical Results
	Concluding Remarks
