Lattice codes are known to achieve capacity in the Gaussian point-to-point channel, achieving the same rates as independent, identically distributed (i.i.d.) random Gaussian codebooks. Lattice codes are also known to outperform random codes for certain channel models that are able to exploit their linearity. In this work, we show that lattice codes may be used to achieve the same performance as known i.i.d. Gaussian random coding techniques for the Gaussian relay channel, and show several examples of how this may be combined with the linearity of lattices codes in multi-source relay networks. In particular, we present a nested lattice list decoding technique, by which, lattice codes are shown to achieve the Decode-and-Forward (DF) rate of single source, single destination Gaussian relay channels with one or more relays. We next present two examples of how this DF scheme may be combined with the linearity of lattice codes to achieve new rate regions which for some channel conditions outperform analogous known Gaussian random coding techniques in multi-source relay channels. That is, we derive a new achievable rate region for the two-way relay channel with direct links and compare it to existing schemes, and derive another achievable rate region for the multiple access relay channel. We furthermore present a lattice Compress-and-Forward (CF) scheme for the Gaussian relay channel which exploits a lattice Wyner-Ziv binning scheme and achieves the same rate as the Cover-El Gamal CF rate evaluated for Gaussian random codes. These results suggest that structured lattice codes may be used to mimic, and sometimes outperform, random Gaussian codes in general Gaussian networks.
It has been conjectured that lattice codes are good for (almost) everything. As an additional bit of evidence for this claim, we offer a few results showing the utility of lattice codes for the AWGN relay channel. We show that the decode-and-forward rates of the relay channel can be achieved using lattice encoding and decoding. We present an encoding decoding technique that uses a doubly-nested lattice code. Encoding is accomplished using a combination of superposition encoding and block Markov encoding, while decoding is accomplished using a strategy reminiscent of Cover and El Gamal's list decoding. Our technique can be extended to a wide variety of relay topologies, including the half-duplex relay channel and the cooperative multiple-access channel. A relay channel consists of an input x_ l , a relay output y_ 1 , a channel output y , and a relay sender x_ 2 (whose transmission is allowed to depend on the past symbols y_ 1 . The dependence of the received symbols upon the inputs is given by p(y,y_ 1 |x_ 1 ,x_ 2 ) . The channel is assumed to be memoryless. In this paper the following capacity theorems are proved. 1)If y is a degraded form of y_ 1 , then C : = : !_ p(x_ 1 ,x_ 2 ) , I(X_ 1 ,X_ 2 ;Y), I(X_ 1 ; Y_ 1 |X_ 2 ) . 2)If y_ 1 is a degraded form of y , then C : = : !_ p(x_ 1 ) x_ 2 I(X_ 1 ;Y|x_ 2 ) . 3)If p(y,y_ 1 |x_ 1 ,x_ 2 ) is an arbitrary relay channel with feedback from (y,y_ 1 ) to both x_ 1 x_ 2 , then C : = : p(x_ 1 ,x_ 2 ) , I(X_ 1 ,X_ 2 ;Y),I ,(X_ 1 ;Y,Y_ 1 |X_ 2 ) . 4)For a general relay channel, C : : p(x_ 1 ,x_ 2 ) , I ,(X_ 1 , X_ 2 ;Y),I(X_ 1 ;Y,Y_ 1 |X_ 2 ) . Superposition block Markov encoding is used to show achievability of C , and converses are established. The capacities of the Gaussian relay channel and certain discrete relay channels are evaluated. Finally, an achievable lower bound to the capacity of the general relay channel is established. How much information can be carried over a wireless network with a multiplicity of nodes, and how should the nodes cooperate to transfer information? To study these questions, we formulate a model of wireless networks that particularly takes into account the distances between nodes, and the resulting attenuation of radio signals, and study a performance measure that weights information by the distance over which it is transported. Consider a network with the following features. I) n nodes located on a plane, with minimum separation distance spl rho sub min >0. II) A simplistic model of signal attenuation e sup - spl gamma spl rho spl rho sup spl delta over a distance spl rho , where spl gamma spl ges 0 is the absorption constant (usually positive, unless over a vacuum), and spl delta >0 is the path loss exponent. III) All receptions subject to additive Gaussian noise of variance spl sigma sup 2 . The performance measure we mainly, but not exclusively, study is the transport capacity C sub T :=sup spl Sigma on sub spl lscr =1 sup m R sub spl lscr spl middot spl rho sub spl lscr , where the supremum is taken over m, and vectors (R sub 1 ,R sub 2 ,...,R sub m ) of feasible rates for m source-destination pairs, and spl rho sub spl lscr is the distance between the spl lscr th source and its destination. It is the supremum distance-weighted sum of rates that the wireless network can deliver. We show that there is a dichotomy between the cases of relatively high and relatively low attenuation. When spl gamma >0 or spl delta >3, the relatively high attenuation case, the transport capacity is bounded by a constant multiple of the sum of the transmit powers of the nodes in the network. However, when spl gamma =0 and spl delta <3 2, the low-attenuation case, we show that there exist networks that can provide unbounded transport capacity for fixed total power, yielding zero energy priced communication. Examples show that nodes can profitably cooperate over large distances using coherence and multiuser estimation when the attenuation is low. These results are established by developing a coding scheme and an achievable rate for Gaussian multiple-relay channels, a result that may be of interest in its own right. 