The Medium Access Control protocol of Power Line Communication networks (defined in Homeplug and IEEE 1901 standards) has received relatively modest attention from the research community. As a consequence, there is only one analytical model that complies with the standardised MAC procedures and considers unsaturated conditions. We identify two important limitations of the existing analytical model: high computational expense and predicted results just prior to the predicted saturation point do not correspond to long-term network performance. In this work, we present a simplification of the previously defined analytical model of Homeplug MAC able to substantially reduce its complexity and demonstrate that the previous performance results just before predicted saturation correspond to a transitory phase. We determine that the causes of previous misprediction are common analytical assumptions and the potential occurrence of a transitory phase, that we show to be of extremely long duration under certain circumstances. We also provide techniques, both analytical and experimental, to correctly predict long-term behaviour and analyse the effect of specific Homeplug IEEE 1901 features on the magnitude of misprediction errors. Our simplified analysis is then used to model the priority resolution scheme defined in Homeplug IEEE 1901. We show that previous tendencies found in simulations and in an off-the-shelf testbed are correctly captured in our framework. We also show that resorting to the traffic differentiation provided by the different contention parameters alone solves the starvation faced by lower-access category traffic.
Two other analytic models of Homeplug exist in the literature ( @cite_25 and @cite_8 ). However, they model mechanisms that differ from the original standardised Homeplug procedure. In @cite_25 , the value of the contention window is fixed for all backoff stages, while in @cite_8 the backoff stage is incremented every time a new packet is overheard (thus having @math ). These new approaches obviously lead to a simplified analysis, as one of the dimensions of the Markov Chain is removed. However, the flexibility the standard provides by allowing to tune both the @math and @math is not captured by these frameworks.
@cite_25 @cite_8
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This paper analyzes the optimum constant contention window (CW) for the HomePlug 1.0 and AV CSMA CA MAC. A discrete time, homogenous Markov chain, with the state specified by both the backoff counter (BC) and deferral counter (DC), is used to model a single node contending for transmission. The structure of the Markov chain admits a generalized expression for the stationary state probability mass function (pmf) associated with each state. The recursively defined state pmfs can be analytically reduced to a single expression relating the probability p, of the node finding the medium idle, the maximum window size W, the maximum deferral counter size, and the number of nodes n. Optimizing the MAC efficiency provides a target value for p, which can be attained with the proper selection of W and. It is shown that an optimal contention window size can be chosen based on a linear relationship with the number of nodes. This study investigates replication of data in a novel streaming architecture consisting of ad-hoc networks of wireless devices. One application of these devices is home-to-home (H2O) entertainment systems where a device collaborates with others to provide each household with on-demand access to a large selection of audio and video clips. These devices are configured with a substantial amount of storage and may cache several clips for future use. A contribution of this study is a technique to compute the number of replicas for a clip based on the square-root of the product of bandwidth required to display clips and their frequency of access , i.e., where . We provide a proof to show this strategy is near optimal when the objective is to maximize the number of simultaneous displays in the system with string and grid (both symmetric and asymmetric) topologies. We say “near optimal” because values of less than 0.5 may be more optimum. In addition, we use analytical and simulation studies to demonstrate its superiority when compared with other alternatives. A second contribution is an analytical model to estimate the theoretical upper bound on the number of simultaneous displays supported by an arbitrary grid topology of H2O devices. This analytical model is useful during capacity planning because it estimates the capabilities of a H2O configuration by considering: the size of an underlying repository, the number of nodes in a H2O cloud, the representative grid topology for this cloud, and the expected available network bandwidth and storage capacity of each device. It shows that one may control the ratio of repository size to the storage capacity of participating nodes in order to enhance system performance. We validate this analytical model with a simulation study and quantify its tradeoffs. Link-layer fairness models that have been proposed for wireline and packet cellular networks cannot be generalized for shared channel wireless networks because of the unique characteristics of the wireless channel, such as location-dependent contention, inherent conflict between optimizing channel utilization and achieving fairness, and the absence of any centralized control. In this paper, we propose a general analytical framework that captures the unique characteristics of shared wireless channels and allows the modeling of a large class of system-wide fairness models via the specification of per-flow utility functions. We show that system-wide fairness can be achieved without explicit global coordination so long as each node executes a contention resolution algorithm that is designed to optimize its local utility function. We present a general mechanism for translating a given fairness model in our framework into a corresponding contention resolution algorithm . Using this translation, we derive the backoff algorithm for achieving proportional fairness in wireless shared channels, and compare the fairness properties of this algorithm with both the ideal proportional fairness objective, and state-of-the-art backoff-based contention resolution algorithms. We believe that the two aspects of the proposed framework, i.e. the ability to specify arbitrary fairness models via local utility functions, and the ability to automatically generate local contention resolution mechanisms in response to a given utility function, together provide the path for achieving flexible service differentiation in future shared channel wireless networks. In this paper, we focus on the Markov model of IEEE 802.11 distributed coordination function with power saving mode. The throughput evaluation is based on the model, with the comparison with the simulation result for validation. Furthermore, we notice that after each beacon transmission, there're more stations in contention than usual. Excessive contention results in high collision probability and low throughput. To solve this problem, we propose a downlink access scheme which can be used in both the basic access scheme and the access scheme with RTS CTS. This novel scheme enables AP to constrain the number of stations in contention to be an optimal value. Simulation results prove the performance improvement declared. The proposed scheme can be extended for multi-rate or multi-service WLANs. This paper proposes the analytical expressions for the IEEE 802.11 string-topology multi-hop networks using Markov-chain model. For achieving that, the proposed analysis procedure includes two proposals, which are: (i) Bianchi's Markov-chain model is modified for considering a relationship between the backoff timer and frame length, and (ii) the interferences, such as hidden node collision and carrier sensing, among network nodes are expressed by merging the proposed Markov-chain model and airtime expression. The analytical expressions are verified by comparisons with simulation results. This paper analyzes the worst-case performance of randomized backoff on simple multiple-access channels. Most previous analysis of backoff has assumed a statistical arrival model.For batched arrivals, in which all n packets arrive at time 0, we show the following tight high-probability bounds. Randomized binary exponential backoff has makespan Θ(nlgn), and more generally, for any constant r, r-exponential backoff has makespan Θ(nloglgr n). Quadratic backoff has makespan Θ((n lg n)3 2), and more generally, for r>1, r-polynomial backoff has makespan Θ((n lg n)1+1 r). Thus, for batched inputs, both exponential and polynomial backoff are highly sensitive to backoff constants. We exhibit a monotone superpolynomial subexponential backoff algorithm, called loglog-iterated backoff, that achieves makespan Θ(nlglg n lglglg n). We provide a matching lower bound showing that this strategy is optimal among all monotone backoff algorithms. Of independent interest is that this lower bound was proved with a delay sequence argument.In the adversarial-queuing model, we present the following stability and instability results for exponential backoff and loglog-iterated backoff. Given a (λ,T)-stream, in which at most n=λT packets arrive in any interval of size T, exponential backoff is stable for arrival rates of λ=O(1 lgn) and unstable for arrival rates of λ=Ω(lglgn lgn); loglog-iterated backoff is stable for arrival rates of λ=O(1 (lglgn )) and unstable for arrival rates of λ=Ω(1 lgn). Our instability results show that bursty input is close to being worst-case for exponential backoff and variants and that even small bursts can create instabilities in the channel. Performance evaluation of the 802.11 MAC protocol is classically based on the decoupling assumption, which hypothesizes that the backoff processes at different nodes are independent. This decoupling assumption results from mean field convergence and is generally true in transient regime in the asymptotic sense (when the number of wireless nodes tends to infinity), but, contrary to widespread belief, may not necessarily hold in stationary regime. The issue is often related with the existence and uniqueness of a solution to a fixed point equation; however, it was also recently shown that this condition is not sufficient; in contrast, a sufficient condition is a global stability property of the associated ordinary differential equation. In this paper, we give a simple condition that establishes the asymptotic validity of the decoupling assumption for the homogeneous case (all nodes have the same parameters). We also discuss the heterogeneous and the differentiated service cases and formulate a new ordinary differential equation. We show that the uniqueness of a solution to the associated fixed point equation is not sufficient; we exhibit one case where the fixed point equation has a unique solution but the decoupling assumption is not valid in the asymptotic sense in stationary regime. Correctly anticipating load characteristics of low voltage level is getting increased interest by distribution network operators. Energy disaggregation could be one of the potential approaches to exploit the massive amount of smart meter data to fulfill the task. Proper individual home appliance modeling is critical to the performance of NILM. In this paper, a hierarchical hidden Markov model (HHMM) framework to model home appliances is proposed. This model aims to provide better representation for those appliances that have multiple built-in modes with distinct power consumption profiles, such as washing machines and dishwashers. The dynamic Bayesian network representation of such an appliance model is built. A forward–backward algorithm, which is based on the framework of expectation maximization, is formalized for the HHMM fitting process. Tests on publically available data show that the HHMM and proposed algorithm can effectively handle the modeling of appliances with multiple functional modes, as well as better representing a general type of appliances. A disaggregation test also demonstrates that the fitted HHMM can be easily applied to a general inference solver to outperform conventional hidden Markov model in the estimation of energy disaggregation. This letter proposes an extension of the HomePlug-AV CSMA CA mathematical model. The existing models do not take into account the existence of different transmission rates between each transmitter-receiver pair. These differences are due to the modulation and coding used by the transmitter to overcome the channel limitations. The proposed model is evaluated by means of simulation in an in-building scenario. We study a fingerprinting game in which the number of colluders and the collusion channel are unknown. The encoder embeds fingerprints into a host sequence and provides the decoder with the capability to trace back pirated copies to the colluders. Fingerprinting capacity has recently been derived as the limit value of a sequence of maximin games with mutual information as their payoff functions. However, these games generally do not admit saddle-point solutions and are very hard to solve numerically. Here under the so-called Boneh-Shaw marking assumption, we reformulate the capacity as the value of a single two-person zero-sum game, and show that it is achieved by a saddle-point solution. If the maximal coalition size is k and the fingerprinting alphabet is binary, we show that capacity decays quadratically with k. Furthermore, we prove rigorously that the asymptotic capacity is 1 (k221n2) and we confirm our earlier conjecture that Tardos' choice of the arcsine distribution asymptotically maximizes the mutual information payoff function while the interleaving attack minimizes it. Along with the asymptotics, numerical solutions to the game for small k are also presented. We study the problem of contention resolution for different-sized jobs on a simple channel. When a job makes a run attempt, it learns only whether the attempt succeeded or failed. We first analyze binary exponential backoff, and show that it achieves a makespan of V2Θ(√logn) with high probability, where V is the total work of all n contending jobs. This bound is significantly larger than when jobs are constant sized. A variant of exponential backoff, however, achieves makespan O(V logV) with high probability. Finally, we introduce a new protocol, size-hashed backoff, specifically designed for jobs of multiple sizes that achieves makespan O(V log3logV). The error probability of the first two bounds is polynomially small in n and the latter is polynomially small in logV. In this paper, a unified analytical framework is established to study the stability, throughput, and delay performance of homogeneous buffered IEEE 802.11 networks with Distributed Coordination Function (DCF). Two steady-state operating points are characterized using the limiting probability of successful transmission of Head-of-Line (HOL) packets p given that the network is in unsaturated or saturated conditions. The analysis shows that a buffered IEEE 802.11 DCF network operates at the desired stable point p=pL if it is unsaturated. pL does not vary with backoff parameters, and a stable throughput can be always achieved at pL. If the network becomes saturated, in contrast, it operates at the undesired stable point p=pA, and a stable throughput can be achieved at pA if and only if the backoff parameters are properly selected. The stable regions of the backoff factor q and the initial backoff window size W are derived, and illustrated in cases of the basic access mechanism and the request-to-send clear-to-send (RTS CTS) mechanism. It is shown that the stable regions are significantly enlarged with the RTS CTS mechanism, indicating that networks in the RTS CTS mode are much more robust. Nevertheless, the delay analysis further reveals that lower access delay is incurred in the basic access mode for unsaturated networks. If the network becomes saturated, the delay performance deteriorates regardless of which mode is chosen. Both the first and the second moments of access delay at pA are sensitive to the backoff parameters, and shown to be effectively reduced by enlarging the initial backoff window size W. This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized @math -learning with compact state representation algorithm, which is named @math -learning. Moreover, a decentralized version of the @math -learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized @math -learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction. The performance analysis of optimal decode-and-forward (DF) relay selection for the full-duplex (FD) mode in underlay cognitive radio (CR) networks is studied, with the impact of critical parameters such as the residual self-interference, the distributions of the received signal-to-noise ratio (SNR), and the outage probability, being analyzed. Different from the conventional relay selection schemes with half-duplex (HD) mode, the FD relay suffers from the impact of residual self-interference due to its simultaneous transmission and reception using the same channel. Furthermore, the exact closed-form expressions for the outage probabilities of the proposed FD relay-selection scheme over both independent Nakagami- @math and Rayleigh fading channels are derived, with the validity of the proposed analysis being proven by using simulation. Numerical results show that, in the presence of multiple candidate relays, an optimum relay-selection solution in terms of outage probability can always be achievable within the SNR range of (10 dB, 15 dB). In much of the theoretical literature on wireless algorithms, issues of message dissemination are considered together with issues of contention management. This combination leads to complicated algorithms and analysis, and makes it difficult to extend the work to harder communication problems. In this paper, we present results of a current project aimed at simplifying such algorithms and analysis by decomposing the treatment into two levels, using abstract "MAC layer" specifications to encapsulate the contention management. We use two different abstract MAC layers: the basic one of [14, 15] and a new probabilistic layer. We first present a typical randomized contention-manageent algorithm for a standard graph-based radio network model We show that it implements both abstract MAC layers. We combine this algorithm with greedy algorithms for single-message and multi-message global broadcast and analyze the combination, using both abstract MAC layers as intermediate layers. Using the basic MAC layer, we prove a bound of O(D log(n ∈) log Δ) for the time to deliver a single message everywhere with probability 1 -- ∈, where D is the network diameter, n is the number of nodes, and Δ is the maximum node degree. Using the probabilistic layer, we prove a bound of O((D + log(n ∈)) log Δ), which matches the best previously-known bound for single-message broadcast over the physical network model. For multi-message broadcast, we obtain bounds of O((D + kΔ) log(n ∈) log Δ) using the basic layer and O((D + kΔ log(n ∈)) log Δ) using the probabilistic layer, for the time to deliver a message everywhere in the presence of at most k concurrent messages. We consider packet scheduling in a network providing differentiated services, where each packet is assigned a value. We study various queueing models for supporting QoS (Quality of Service). In the nonpreemptive model, packets accepted to the queue will be transmitted eventually and cannot be dropped. The FIFO preemptive model allows packets accepted to the queue to be preempted (dropped) prior to their departure, while ensuring that transmitted packets are sent in the order of arrival. In the bounded delay model, packets must be transmitted before a certain deadline, otherwise it is lost (while transmission ordering is allowed to be arbitrary). In all models the goal of the buffer policy is to maximize the total value of the accepted packets.Let α be the ratio between the maximal and minimal value. For the non-preemptive model we derive a Θ(log &alpha) competitive ratio, both exhibiting a buffer policy and a general lower bound. For the interesting case of two distinct values, we give an 2α--1 α competitive buffer policy, which exactly matches the lower bound. We also analyze a RED-like policy and derive its competitive ratio, which is approximately 2α--0.5 α for two values and Θ(log α) for multiple values. In addition we improve the previous known lower and upper bounds of the Fixed Partition and Flexible Partition policies.For the FIFO preemptive model, we improve the general lower bound and show a tight bound for the special case of queue size 2. We prove that the bounded delay model with uniform delay 2 is equivalent to a modified FIFO preemptive model with queue size 2. We then give improved upper and lower bounds on the 2-uniform bounded delay model. We also give lower bound for the 2-variable bounded delay model, which matches the previously known upper bound. In this paper we propose the following approach to the dimensioning of the radio part of the downlink in OFDMA networks. First, we use information theory to characterize the bit-rate in the channel from a base station to its mobile. It depends on the power and bandwidth allocated to this mobile. Then, we describe the resource (power and bandwidth) allocation problem and characterise feasible configurations of bit-rates of all users. As the key element, we propose some particular sufficient condition (in a multi-Erlang form) for a given configuration of bit-rates to be feasible. Finally, we consider an Erlang's loss model, in which streaming arrivals whose admission would lead to the violation of this sufficient condition are blocked and lost. In this model, the blocking probabilities can be calculated using Kaufman-Roberts algorithm. We propose it to evaluate the minimal density of base stations assuring acceptable blocking probabilities for a streaming traffic of a given load per surface unit. We validate this approach by comparison of the blocking probabilities to these simulated in the similar model in which the admission control is based on the original feasibility property (instead of its sufficient condition). Our sufficient bit-rate feasibility condition can also be used to dimension the network with respect to the elastic traffic. Two widely studied models of multiple-node repair in distributed storage systems are centralized repair and cooperative repair. The centralized model assumes that all the failed nodes are recreated in one location, while the cooperative one stipulates that the failed nodes may communicate but are distinct, and the amount of data exchanged between them is included in the repair bandwidth. We show that cooperative model is stronger than the centralized one, in the sense that MDS codes with optimal repair bandwidth under the former model have the same property under the latter one. In this paper we present explicit constructions of MDS codes with optimal cooperative repair for all possible parameters. More precisely, given any @math such that @math we construct explicit @math MDS codes over the field @math of size @math that can optimally repair any @math erasures from any @math helper nodes. The repair scheme of our codes involves two rounds of communication. In the first round, each failed node downloads information from the helper nodes, and in the second one, each failed node downloads additional information from the other failed nodes. This shows that two rounds of communication in cooperative repair always suffice, and that the proposed codes achieve the optimal repair bandwidth using the smallest possible number of rounds. Evaluation of tracking algorithms in the absence of ground truth is a challenging problem. There exist a variety of approaches for this problem, ranging from formal model validation techniques to heuristics that look for mismatches between track properties and the observed data. However, few of these methods scale up to the task of visual tracking, where the models are usually nonlinear and complex and typically lie in a high-dimensional space. Further, scenarios that cause track failures and or poor tracking performance are also quite diverse for the visual tracking problem. In this paper, we propose an online performance evaluation strategy for tracking systems based on particle filters using a time-reversed Markov chain. The key intuition of our proposed methodology relies on the time-reversible nature of physical motion exhibited by most objects, which in turn should be possessed by a good tracker. In the presence of tracking failures due to occlusion, low SNR, or modeling errors, this reversible nature of the tracker is violated. We use this property for detection of track failures. To evaluate the performance of the tracker at time instant t, we use the posterior of the tracking algorithm to initialize a time-reversed Markov chain. We compute the posterior density of track parameters at the starting time t = 0 by filtering back in time to the initial time instant. The distance between the posterior density of the time-reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is observed that when the data are generated by the underlying models, the decision statistic takes a low value. We provide a thorough experimental analysis of the evaluation methodology. Specifically, we demonstrate the effectiveness of our approach for tackling common challenges such as occlusion, pose, and illumination changes and provide the Receiver Operating Characteristic (ROC) curves. Finally, we also show the applicability of the core ideas of the paper to other tracking algorithms such as the Kanade-Lucas-Tomasi (KLT) feature tracker and the mean-shift tracker. This paper provides proofs of the rate stability, Harris recurrence, and e-optimality of carrier sense multiple access (CSMA) algorithms where the random access (or backoff) parameter of each node is adjusted dynamically. These algorithms require only local information and they are easy to implement. The setup is a network of wireless nodes with a fixed conflict graph that identifies pairs of nodes whose simultaneous transmissions conflict. The paper studies two algorithms. The first algorithm schedules transmissions to keep up with given arrival rates of packets. The second algorithm controls the arrivals in addition to the scheduling and attempts to maximize the sum of the utilities, in terms of the rates, of the packet flows at different nodes. For the first algorithm, the paper proves rate stability for strictly feasible arrival rates and also Harris recurrence of the queues. For the second algorithm, the paper proves the e-optimality in terms of the utilities of the allocated rates. Both algorithms are iterative and we study two versions of each of them. In the first version, both operate with strictly local information but have relatively weaker performance guarantees; under the second version, both provide stronger performance guarantees by utilizing the additional information of the number of nodes in the network.