Supporting multiple partial computations efficiently at each of the workers is a keystone in distributed coded computing in order to speed up computations and to fully exploit the resources of heterogeneous workers in terms of communication, storage, or computation capabilities. Multivariate polynomial coding schemes have recently been shown to deliver faster results for distributed matrix-matrix multiplication compared to conventional univariate polynomial coding schemes by supporting multiple partial coded computations at each worker at reduced communication costs. In this work, we extend multivariate coding schemes to also support arbitrary matrix partitions. Generalized matrix partitions have been proved useful to trade-off between computation speed and communication costs in distributed (uni-variate) coded computing. We first formulate the computation latency-communication trade-off in terms of the computation complexity and communication overheads required by coded computing approaches as compared to a single server uncoded computing system. Then, we propose two novel multivariate coded computing schemes supporting arbitrary matrix partitions. The proposed schemes are shown to improve the studied trade-off as compared to univariate schemes.

This paper studies the mutual information for a general form of orthogonal M-ary frequency-shift keying modulations with non-coherent detection and Doppler frequency uncertainty at the receiver. The signal model includes as particular cases classical MFSK and special MFSK modulations, the later has been used in space communications for reporting spacecraft status and events during critical phases. The optimal code rates that minimize the energy-per-bit to noise power spectral density ratio required for reliable communications are studied. In addition, optimal and suboptimal metrics for soft-decoders are proposed for non-fading channels with or without average signal and noise power estimation and used to evaluate the performance of LDPC codes.

This paper studies the capacity of orthogonal M-ary frequency-shift keying modulations with non-coherent detection and Doppler frequency uncertainty at the receiver. The optimal code rates that minimize the energy-per-bit to noise power spectral density ratio required for reliable communications are studied. In addition, optimal and suboptimal metrics for soft-decoders are proposed for non-fading channels with or without average signal and noise power estimation and used to evaluate the performance of LDPC codes.

We consider the problem of secure distributed matrix multiplication (SDMM). Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we present a non-direct secure extension of the recently introduced bivariate polynomial codes. Bivariate polynomial codes have been shown to be able to further speed up distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them while reducing the upload communication cost and/or the workers’ storage’s capacity needs. We show that, especially for upload communication or storage constrained settings, the proposed approach reduces the average computation time of SDMM compared to its competitors in the literature.

We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an effective solution in distributed matrix multiplication, both providing privacy against the workers and boosting the computation speed by efficiently mitigating stragglers. In this work, we propose the use of recently-introduced bivariate polynomial codes to further speed up private distributed matrix multiplication by exploiting the partial work done by the stragglers rather than completely ignoring them. We show that the proposed approach reduces the average computation time of private distributed matrix multiplication compared to its competitors in the literature while improving the upload communication cost and the workers' storage efficiency.

Caching is a technique to reduce the communication load in peak hours by prefetching contents during off-peak hours. An information theoretic framework for coded caching was introduced by Maddah-Ali and Niesen in a recent work, where it was shown that significant improvement can be obtained compared to uncoded caching. Considerable efforts have been devoted to identify the precise information theoretic fundamental limits of the coded caching systems, however the difficulty of this task has also become clear. One of the reasons for this difficulty is that the original coded caching setting allows all possible multiple demand types during delivery, which in fact introduces tension in the coding strategy. In this paper, we seek to develop a better understanding of the fundamental limits of coded caching by investigating systems with certain demand type restrictions. We first consider the canonical three-user three-file system, and show that, contrary to popular beliefs, the worst demand type is not the one in which all three files are requested. Motivated by these findings, we focus on coded caching systems where every file must be requested by at least one user. A novel coding scheme is proposed, which can provide new operating points that are not covered by any previously known schemes.

Coded distributed computing is an effective framework to improve the speed of distributed computing systems by mitigating stragglers (temporarily slow workers). In essence, coded computing allows replacing the computation assigned to a straggling worker by that at a faster worker by assigning redundant computations. Coded computing techniques proposed so far are mostly based on univariate polynomial coding. These codes are not very effective if storage and computation capacity across workers are heterogeneous and lose completely the work done by the straggling workers. For the particular problem of distributed matrix-matrix multiplication, we show how bivariate polynomial coding addresses these two issues.

Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed matrix multiplication. Previous works employ univariate polynomials to encode matrix partitions. Such schemes greatly improve the speed of distributed computing systems by making the task completion time to depend only on the fastest workers. However, they completely ignore the work done by the slowest workers resulting in inefficient use of computing resources. In order to exploit the partial computations of the slower workers, we further decompose the overall matrix multiplication task into even smaller subtasks, and we propose bivariate polynomial codes. We show that these codes are a more natural choice to accommodate the additional decomposition of subtasks, and to exploit the heterogeneous storage and computation resources at workers. However, in contrast to univariate polynomial decoding, guarantying decodability with multivariate interpolation is much harder. We propose two bivariate polynomial coding schemes and study their decodability conditions. Our numerical results show that bivariate polynomial coding considerably reduces the computation time of distributed matrix multiplication.

We consider a cache network in which a single server is connected to multiple users via a shared error free link. The server has access to a database with N files of equal length F, and serves K users each with a cache memory of MF bits. A novel centralized coded caching scheme is proposed for scenarios with more users than files N ≤ K and cache capacities satisfying 1/K ≤ M ≤ N/K . The proposed scheme outperforms the best rate-memory region known in the literature if N ≤ K ≤ N2+1/2 .

For the caching problem, when the number of files is no larger than that of users, the best known rate-memory region is achieved by memory sharing between the rate-memory pairs obtained by three schemes: the scheme proposed by Yu et al., the scheme proposed by Gomez-Vilardebo and the scheme proposed by Tian-Chen. While the first two schemes operate on the binary field, the Tian-Chen scheme makes use of a finite field of order 2mwith m ≥ Klog2(N) in some situations, for a caching systems with K users and N files. The practical implications of this increase in the size of the field are equivalent to an increase, by a factor of m, in the number of subfile partitions required. We propose a novel caching scheme that approaches the rate-memory region achieved by the Tian-Chen scheme as the number of users in the system increases, which only requires a field of order 22.

For the caching problem, when the number of files is no larger than that of users, the best known rate-memory region is achieved by memory sharing between the rate-memory pairs obtained by three schemes: the scheme proposed by Yu et al., the scheme proposed by Gomez-Vilardebo, and the scheme proposed by Tian and Chen. While the first two schemes operate on the binary field, the Tian-Chen scheme makes use of a finite field of order 2 m with, in some situations, m ≥ K log 2 (N) for a caching systems with K users and N files. The practical implications of this increase in the size of the field are equivalent to an increase, by a factor of m, in the number of subfile partitions required. We propose a novel caching scheme that approaches the rate-memory region achieved by the Tian-Chen scheme as the number of users in the system increases, which only requires a field of order 2 2 .

We consider a cache network, in which a single server is connected to multiple users via a shared error free link. The server has access to a database with N files of equal length F, and serves K users each with a cache memory of MF bits. A novel centralized coded caching scheme is proposed for scenarios with more users than files N ≤ K and cache capacities satisfying (1/K) ≤ M ≤ (N/K). The proposed scheme outperforms the best rate-memory region known in the literature if N ≤ K ≤ ((N2+ 1)/2).

Demand-side energy management (EM) is studied from a privacy-cost trade-off perspective, considering time-of-use pricing and the presence of an energy storage unit. Privacy is measured as the variation of the power withdrawn from the grid from a fixed target value. Assuming non-causal knowledge of the household's aggregate power demand profile and the electricity prices at the energy management unit (EMU), the privacy-cost trade-off is formulated as a convex optimization problem, and a low-complexity backward water-filling algorithm is proposed to compute the optimal EM policy. The problem is studied also in the online setting assuming that the power demand profile is known to the EMU only causally, and the optimal EM policy is obtained numerically through dynamic programming (DP). Due to the high computational cost of DP, a low-complexity heuristic EM policy with a performance close to the optimal online solution is also proposed, exploiting the water-filling algorithm obtained in the offline setting. As an alternative, information theoretic leakage rate is also evaluated, and shown to follow a similar trend as the load variance, which supports the validity of the load variance as a measure of privacy. Finally, the privacy-cost trade-off, and the impact of the size of the storage unit on this trade-off are studied through numerical simulations using real smart meter data in both the offline and online settings.

This paper investigates the problem of finding optimal paths in single-source single-destination accumulative multi-hop networks. We consider a single source that communicates to a single destination assisted by several relays through multiple hops. At each hop, only one node transmits, while all the other nodes receive the transmitted signal, and store it after processing/decoding and mixing it with the signals received in previous hops. That is, we consider that terminals make use of advanced energy accumulation transmission/reception techniques, such as maximal ratio combining reception of repetition codes, or information accumulation with rateless codes. Accumulative techniques increase communication reliability, reduce energy consumption, and decrease latency. We investigate the properties that a routing metric must satisfy in these accumulative networks to guarantee that optimal paths can be computed with Dijkstra's algorithm. We model the problem of routing in accumulative multi-hop networks, as the problem of routing in a hypergraph. We show that optimality properties in a traditional multi-hop network (monotonicity and isotonicity) are no longer useful and derive a new set of sufficient conditions for optimality. We illustrate these results by studying the minimum energy routing problem in static accumulative multi-hop networks for different forwarding strategies at relays.

This paper considers the design of online transmission strategies for slotted energy harvesting point-to-point communication systems in wireless fading channels. Online transmission strategies decide the amount of energy allocated to each transmission slot based on the energy harvested amounts and channel gains observed in the current and previous time slots. Offline strategies, in contrast, assume non-causal knowledge of future energy arrivals and channel gains. We adopt a worst case design objective. For a given online policy, we are interested in computing its maximum rate gap that is defined as the difference between the offline and online rates, maximized over all possible energy arrivals and channel states. The competitive rate gap is then defined as the minimum maximum rate gap over all possible online strategies. Here, we obtain, within a constant, the maximum rate gap for the Myopic policy, which equally distributes the available energy over the remaining slots, and provide an upper and a lower bound on the competitive rate gap. Moreover, we propose a new online policy targeting the competitive rate gap. Numerical results show that the policy proposed performs close to the competitive rate gap lower bound in constant and arbitrarily varying channels, and obtains good performance with real energy harvesting traces.

Energy harvesting has emerged as an appealing technology to recharge battery powered devices. Recently, an extensive research has been conducted on the design of power allocation policies for energy harvesting devices. Most works have focused on offline policies that assume non-causal knowledge of the energy harvesting process. Only a few works have considered online policies with the more realistic assumption of only having past knowledge of the energy harvesting process; however, these works generally incur an additional assumption on the knowledge of the probability distribution of the harvested energy (e.g. Poisson distribution) leading to online algorithms that are rarely applicable with available energy harvesting technologies. This paper proposes three online power allocation algorithms capable of learning from the harvested energy in previous days and that perform, in average, as well as the best fixed offline strategy. The numerical results validate the performance of the proposed algorithms when energy is harvested through solar panels.

The fifth generation wireless networks must provide fast and reliable connectivity while coping with the ongoing traffic growth. It is of paramount importance that the required resources, such as energy and bandwidth, do not scale with traffic. While the aggregate network traffic is growing at an unprecedented rate, users tend to request the same popular contents at different time instants. Therefore, caching the most popular contents at the network edge is a promising solution to reduce the traffic and the energy consumption over the backhaul links. In this paper, two scenarios are considered, where caching is performed either at a small base station, or directly at the user terminals, which communicate using \ac{D2D} communications. In both scenarios, joint design of the transmission and caching policies is studied when the user demands are known in advance. This joint design offers two different caching gains, namely, the \textit{pre-downloading} and \textit{local caching gains}. It is shown that the finite cache capacity limits the attainable gains, and creates an inherent tradeoff between the two types of gains. In this context, a continuous time optimization problem is formulated to determine the optimal transmission and caching policies that minimize a generic cost function, such as energy, bandwidth, or throughput. The jointly optimal solution is obtained by demonstrating that caching files at a constant rate is optimal, which allows to reformulate the problem as a finite-dimensional convex program. The numerical results show that the proposed joint transmission and caching policy dramatically reduces the total cost, which is particularised to the total energy consumption at the \ac{MBS}, as well as to the total economical cost for the service provider, when users demand economical incentives for delivering content to other users over the D2D links.

Delay constrained linear transmission (LT) strategies are considered for the transmission of composite Gaussian measurements over an additive white Gaussian noise fading channel under an average power constraint. If the channel state information (CSI) is known by both the encoder and decoder, the optimal LT scheme in terms of the average mean-square error distortion is characterized under a strict delay constraint, and a graphical interpretation of the optimal power allocation strategy is presented. Then, for general delay constraints, two LT strategies are proposed based on the solution to a particular multiple measurements-parallel channels scenario. It is shown that the distortion decreases as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions. If the CSI is known only by the decoder, the optimal LT strategy is derived under a strict delay constraint. The extension to general delay constraints is elusive. As a first step toward understanding the structure of the optimal scheme in this case, it is shown that for the multiple measurements-parallel channels scenario, any LT scheme that uses only a one-to-one linear mapping between measurements and channels is suboptimal in general.

Delay constrained linear transmission (LT) of a mixture of Gaussian measurements over an additive white Gaussian noise (AWGN) fading channel is considered. At each time slot (TS), the control center (CC) asks for the measurement of a particular system parameter from the sensor, which is capable of measuring multiple independent system parameters. The average mean-square error (MSE) distortion is studied for Gaussian parameters and a Gaussian fading channel under an average power constraint. The optimal LT scheme is characterized under a strict delay constraint, and a graphical interpretation for the power allocation strategy is presented. Then, two achievable LT strategies are proposed for general delay constraints. It is shown that the performance improves as the delay constraint is relaxed, and when the delay constraint is completely removed, both strategies achieve the optimal performance under certain matching conditions.

This paper investigates the problem of finding optimal paths in single-source single-destination accumulative multi-hop networks. We consider a single source that communicates to a single destination assisted by several relays through multiple-hops. At each hop, only one node transmits, while the rest of nodes receive the transmitted signal, and store it after processing/decoding and mixing with the signals received in previous hops. This is, we consider that terminals make use of advanced energy accumulation transmission/reception techniques such us maximal ratio combining reception of repetition codes, or information accumulation with rateless codes. Accumulative techniques increase communication reliability, reduce energy consumption, and decrease latency. We investigate the properties that a routing metric must satisfy in these accumulative networks to guarantee that optimal paths can be computed with Dijkstra's algorithm. We model the problem of routing in an accumulative multi-hop networks, as the problem of routing in a hypergraph. We show that optimality properties in traditional multi-hop network (monotonicity and isotonicity) are no longer valid and derive a new set of sufficient conditions for optimality.

Caching the most popular contents at Small Base Stations (SBSs) is envisioned as a promising solution to reduce both the load and energy consumption of the backhaul link connecting the SBSs to the core network. This paper considers a set of users whose demands are served by an SBS connected through a wireless backhaul link to a Macro Base Station (MBS). The SBS is capable of caching content in its limited cache memory. The transmission policy at the MBS and the caching policy at the SBS are jointly optimized in order to minimize the energy consumption in the backhaul link. The numerical results show significant improvements with respect to prior works.

Smart meters (SMs) measure and report users' energy consumption to the utility provider (UP) in almost real-time, providing a much more detailed depiction of the consumer's energy consumption compared to their analog counterparts. This increased rate of information flow to the UP, together with its many potential benefits, raise important concerns regarding user privacy. This paper investigates, from an information theoretic perspective, the privacy that can be achieved in a multiuser SM system in the presence of an alternative energy source (AES). To measure privacy, we use the mutual information rate between the users' real energy consumption profile and SM readings that are available to the UP. The objective is to characterize the privacy-power function, defined as the minimal information leakage rate that can be obtained with an average power-limited AES. We characterize the privacy-power function in a single letter form when the users' energy demands are assumed to be independent and identically distributed over time. Moreover, for binary and exponentially distributed energy demands, we provide an explicit characterization of the privacy-power function. For any discrete energy demands, we demonstrate that the privacy-power function can always be efficiently evaluated numerically. Finally, for continuous energy demands, we derive an explicit lower bound on the privacy-power function, which is tight for exponentially distributed loads.

A smart-meter (SM) measures and reports the energy consumption of a user at frequent time intervals, revealing critical private information about user's energy consumption behavior. In this paper, privacy in a SM system is studied in the presence of an alternative energy source (AES). The privacy-power function is introduced to study the trade-off between the achievable information theoretic privacy and the average power that can be provided by the AES. A single-letter information theoretic expression is provided for the privacy-power function, and its correspondence with the rate-distortion function is established. It is shown that the output alphabet can be restricted to be equal to the input alphabet without loss of optimality, which simplifies the numerical analysis significantly. Some numerical results are provided for various input alphabets and distributions.

Smart meter (SM) measurements provide near realtime information on the electricity consumption of a user to the utility provider (UP). This data can be used to extract private information on the energy consumption patterns of the user. Assuming that the user has access to an alternative energy source (AES) in addition to the power grid, SM privacy problem is studied from an information theoretic perspective. The energy requirement of the user (input load) at each time instant can be satisfied either from the power grid (output load) or from the AES. It is assumed that the output load can be perfectly tracked by the UP, and the privacy is measured through the information leakage rate. For given average and peak power constraints on the AES, privacy-power function is defined, and its equivalence to the rate-distortion function with a difference distortion measure is shown. Focusing on continuous input loads, the privacy-power function is characterized when there is only peak power limitation on the AES, while the Shannon lower bound is provided for the general case. The bound is shown to be achievable for the exponential input distribution.

This paper investigates the minimum energy routing problem in cooperative multi-hop networks where a single source communicates to a single destination assisted by several relays that accumulate energy or information from retransmissions. This problem is known to be NP-complete if relays are required to decode the source message completely (allcast communications). We extend the problem formulation to also consider the following cases: i) allcast communications with limited accumulative capabilities at relays, ii) unicast communications where relays are only required to decode part of the source message, and iii) the cut-set upper bound. First, for each of these cases, we derive the minimum power-rate ratio for a given path, namely the path weight. We show that this path weight admits a useful duality property which, basically, allows us to compute the weight of a path either as a function of the weight of the intermediate paths between the source and the relays (forward) but also from the weights of the intermediate paths between the relays and the destination (backward). Based on this duality result, we present network scenarios for which we guarantee the optimality of efficient routing protocols based on Dijkstra's algorithm.

Physical-layer network coding over wireless networks can provide considerable throughput gains with respect to traditional cooperative relaying strategies at no loss of diversity gain. In this paper, a novel cooperation protocol is developed based on complex-field wireless network coding. Sources transmit efficiently information symbols linearly combined with symbols from other sources. Different from existing wireless network coding protocols, transmissions are not restricted to binary symbols, and do not have to be received simultaneously. In a network with N sources, the developed protocol can achieve throughput up to approximately 1/N symbols per source per channel use, as well as diversity of order N. To deal with decoding errors at sources, selective- and adaptive-forwarding protocols are also developed at no loss of diversity gain. Analytical results corroborated by simulated tests show considerable performance gains with respect to distributed space-time coding, and bit-level network coding protocols.

This Letter addresses the statistical modeling of dual-polarized MIMO-LMS fading channels. In the absence of accurate experimental results, a statistical model for the characterization of MIMO-LMS channels is proposed based on consolidation of available experimental results for SISO-LMS and MIMO wireless channels as well as on their extrapolation to the MIMO-LMS case of interest. Moreover, a step-by-step methodology for the simulation and time-series generation of the proposed MIMO-LMS channel model is provided, which is useful for the design and performance assessment of MIMO-LMS transmission systems. The proposed model incorporates the effects of all relevant critical channel aspects in a flexible and fully-parameterized way.

This paper addresses the energy efficiency analysis of the relay channel under additive white Gaussian noise. We consider the rate bounds given by decode and forward and the cut set bound and assume that resources are optimally allocated to maximize the spectral efficiency according to the channel information and the sum network energy. The low energy analysis tools are used to compute the maximum rate per energy (RPE) and the slope of the spectral efficiency as a function of the energy per bit. Using these metrics, the energy efficiency benefit of several capabilities at terminals is investigated. Specifically, we take into account: (i) the phase synchronization between transmitters, (ii) the full duplex capability at the relay and (iii) the channel access via superposition.

A multisource cooperative protocol is developed capable of achieving diversity order up to the number of cooperating users at a high throughput. In this design each source jointly encodes its own new information symbol with the information symbols received from other sources at past instants. Joint encoding is done using linear complex-field coefficients. Throughput analysis shows gains with respect to existing multi-source protocols and approaches the throughput of non-cooperative schemes. Diversity analysis shows that full spatial diversity is achievable. Simulations confirm the analytically established assessments.

This paper investigates the impact of usual practical constraints for the relay channel in the energy efficient regime. Specifically, we take into account constraints on the transmission/reception duplexing and on the synchronization between the source and the relay. New and already known bounds for the maximum rate-per-energy that depend on the constraints considered, are presented in a unified manner. Lower bounds are with decode-and-forward and upper-bounds with the cut-set bound. It is shown that while synchronism provides gains on the maximum RPE, duplexing does not. To see the gain by duplexing the slope of the spectral efficiency with respect the energy-per-bit is also derived.

This paper presents two lower bounds and one upper bound on the maximum Rate-Per-Energy (RPE) that can be achieved over the orthogonal, additive white Gaussian noise, multi-hop relay channel. The study of the maximum RPE for relay networks not only determines the energy efficiency of a communication system but can also define efficient energy allocation, relay selection, and routing solutions. For the three bounds studied here, these solutions have an attractive distributed structure. The optimal relaying strategy remains unknown, even for the most simple one-relay network and, thus, only bounds on the maximum RPE can be obtained. Here, lower bounds are obtained by considering relays that decode a previously transmitted message (regenerative) from just one of its received signals (non-accumulative). The first lower bound is obtained from the analysis of the traditional multi-hop network, where each relay is required to decode and retransmit the complete source message. Then, this lower bound is tightened, by considering relays that, instead of trying to retransmit the source message, facilitate the transmission between the previous relay and the destination. The destination decodes the source message by using every transmitted signal. Finally, an upper bound valid for any relaying strategy is derived by solving the max-flow min-cut bound.

In this paper, the multi-hop relay network with transmissions over orthogonal AWGN channels is optimized in terms of maximum rate-per-unit-energy. For such a scenario, the optimal regenerative-forwarding strategy is still unknown. Furthermore, among the known ones any outperforms, in all situations, the others. The results for four different cases and discussion on the energy allocation and routing requirements are provided. First, as a comparative example, a lower bound is obtained by considering the traditional multi-hop relaying network where all communication are point-to-point reliable. The solution for this scenario is shown to have good distributed properties. Then, an accumulative strategy which is known to achieve the capacity forallcastcommunication is solved. In this case, the solution requires exhaustive computation. To maintain the point-to-point distributed properties a unicast strategy is investigated, with relays not decoding the source message but a bin index transmitted by the previous relay. Finally, an upper- bound on the above regenerative techniques is derived and analyzed.

We consider a cooperative network where the communication between a single source-destination pair is assisted by the transmission of relays. In the considered cooperative model, once a node requires cooperation, it broadcasts a claim for cooperation message. Under high mobility conditions or in some special scenarios this is the natural procedure to discover the potential relays. However, when there is a large number of nodes that can potentially serve as a relay, a Multiple Relay Access Control (MRAC) problem turns up and the relay transmissions must be organized in an effective manner. We introduce this problematic and propose three different strategies to manage this severe competitive channel. In these strategies either the source, the destination or both trigger the cooperative process. The methodology employed makes the results and conclusions valid for a wide range of cooperative scenarios, and hence, useful for a wide range of applications.

In this paper repetition-based cooperative diversity technique has been taken into consideration. For this technique there exists a trade off between the gains achieved by diversity reception and the decrease in bandwidth efficiency. Both effects have a hard dependence on the number of cooperating terminals. Here an analysis based on the average bit error rate (BER) of multilevel signaling MQAM modulation is presented to characterize the optimal achievable end-to-end throughput. In this way it is shown that threshold decisions can be taken to find out the optimal choice for the number of relays and modulation size. Thanks to that, efficient protocols can be designed using this information.

It has been shown that the cooperation among nodes can improve the performance of a network under certain considerations. So far, research focus has been mainly put on cooperation and not on the coordination required to obtain this cooperation. In this paper we discuss how the MAC layer plays a key role in determining the effectiveness of a cooperative orthogonal multiple relay channel. We propose and analyze the performance of a novel MAC protocol based on the legacy IEEE 802.11 standard in a representative and general study case scenario. The main conclusion of the study is that a proper selection of the MAC protocol being used in a cooperative system is highly relevant in order to actually achieve the benefits of this kind of systems