Recently, vehicle platooning has demonstrated its potential for improving traffic efficiency and enhancing the driving experience. When the onboard sensors of follower vehicles are unavailable, the leader vehicle typically needs to maintain the safety of platooning system through vehicle-to-vehicle (V2V) communications. Increasing communication resource utilization, such as transmission power, can enhance communication accuracy and ensure platooning safety but induce more communication costs. Balancing communication costs and system safety poses challenges in this dynamic system. In this paper, we propose a joint optimization method for transmission power control and system stability maintenance based on deep reinforcement learning (DRL), which can minimize the total transmission power of the leader vehicle over a period of time while maintaining the stability of the platooning system. To achieve this, we implement two advanced DRL algorithms, namely Deep Q-Network (DQN) and Actor-Critic, to derive task-oriented power control strategies for platooning stability. Experimental results show that the proposed strategy outperforms the conventional power control strategy. The transmission power gradually increases as the follower vehicle approaches the boundary of the safe distance range, indicating that the proposed strategy can adaptively adjust the transmission power to meet the requirements of the task.

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general goal function$f(x;g)$for which the decision$x$has to be taken from a quantized version of the parameters$g$. This problem is relevant in many applications, e.g., for radio resource allocation (RA), high spectral efficiency communications, controlled systems, or data clustering in the smart grid. By resorting to high resolution (HR) analysis, it is shown how to design a quantizer that minimizes the gap between the minimum of$f$(which would be reached by knowing$g$perfectly) and what is effectively reached with a quantized$g$. The conducted formal analysis both provides quantization strategies in the HR regime and insights for the general regime and allows a practical algorithm to be designed. The analysis also allows one to provide some elements to the new and fundamental problem of the relationship between the goal function regularity properties and the hardness to quantize its parameters. The derived results are discussed and supported by a rich numerical performance analysis in which known RA goal functions are studied and allows one to exhibit very significant improvements by tailoring the quantization operation to the final task.

In this paper, we consider an orthogonal Multiple Access Multiple Relay Network (MAMRN), where several nodes cooperate to send their messages to a single destination. Frequency Division Multiplexing (FDM) is adopted for the sources and the relays, and the destination acts as the central node responsible for scheduling the cooperative retransmissions. Since FDM is used for orthogonality, different nodes are allocated at each sub-band of the transmission and retransmission phases. In this work, we present the system model of the considered orthogonal MAMRN when the FDM mechanism is adopted, while including the analytical derivations of the utility metrics (spectral efficiency and outage events). Then, two centralized node selection strategies are proposed. Moreover, we present the control information exchange process between the destination and the different nodes. The proposed strategies allocate for each sub-band the node that will transmit (or retransmit) with the goal of maximizing the spectral efficiency. Our numerical analysis considers both symmetric and asymmetric channel and source rate scenarios, and shows that the proposed algorithms outperform the algorithms used in the prior-art, and achieve a spectral efficiency that is close to the upper bound calculated by an exhaustive search approach while reducing the complexity and the overhead.

In this paper, we propose a novel selection strategy for the orthogonal Multiple Access Multiple Relay Networks (MAMRN). Rather than selecting a single relaying node to help one source node at a given retransmission time slot, we propose allocating one source to be helped by multiple relaying nodes. The idea is to exploit the multipath diversity of the different relaying nodes in order to optimize the spectral efficiency. We present the control exchange process in the novel selection strategy and we compare it to that of the prior art. In addition, we investigate the effect of equal gain combining on the performance, as well as the effect of the rates and the channel configuration. The numerical results show that the proposed strategy outperforms the prior art by exploiting the power budget available at each relaying node included in the system.

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general cost function f(x;g) for which the decision x has to be taken from quantized parameters g. Especially, we focus on the particular scenario where the decision space is a convex polyhedron with cost function being convex. Furthermore, we propose a new goal-oriented quantization algorithm by combining the procedure of iteratively expanding and reinstating decision set together with Jensen’s inequality. Proposed method could also be extended to some non-convex scenarios, namely, weakly convex cost function whose eigenvalues of Hessian matrix w.r.t decision x are lower-bounded. Numerical results show that proposed algorithm can considerably reduce the optimality loss (OL) compared to conventional approaches or the required number of quantization bits to achieve a certain relative optimality loss.

Wireless communications is nowadays an important aspect of robotics. There are many applications in which a robot must move to a certain goal point while transmitting information through a wireless channel which depends on the particular trajectory chosen by the robot to reach the goal point. In this context, we develop a method to generate optimum trajectories which allow the robot to reach the goal point using little mechanical energy while transmitting as much data as possible. This is done by optimizing the trajectory (path and velocity profile) so that the robot consumes less energy while also offering good wireless channel conditions. In this article, we consider a realistic wireless channel model as well as a realistic dynamic model for the mobile robot (considered here to be a drone). Simulations results illustrate the merits of the proposed method.

Assuming that the number of possible decisions for a transmitter (e.g., the number of possible beam-forming vectors) has to be finite and is given, this paper investigates for the first time the problem of determining the best decision set when energy-efficiency maximization is pursued. We propose a framework to find a good (finite) decision set which induces a minimal performance loss w.r.t. to the continuous case. We exploit this framework for a scenario of energy-efficient MIMO communications in which transmit power and beamforming vectors have to be adapted jointly to the channel given under finite-rate feedback. To determine a good decision set we propose an algorithm which combines the approach of Invasive Weed Optimization (IWO) and an Evolutionary Algorithm (EA). We provide a numerical analysis which illustrates the benefits of our point of view. In particular, given a performance loss level, the feedback rate can by reduced by 2 when the transmit decision set has been designed properly by using our algorithm. The impact on energy-efficiency is also seen to be significant.

Cooperative schemes for energy efficient resource allocation usually require certain forms of communication (implicit or explicit) among the cooperating transmitters. These solutions are therefore impractical in the fast fading scenario where the channel gains change very quickly and there is no time for communication. Inspired by previous information theoretical results, in this paper we propose a heuristic resource allocation function which achieves cooperation without mutual communication among the transmitters. This function is a one shot decision function whose parameters are tuned offline for given channel statistics. It can be used online in a fast fading scenario as given the local channel state information (CSI), we can instantaneously find an energy efficient power allocation in a distributed manner. We compare the performance of the proposed function with other state of the art distributed power allocation schemes requiring communication. While some communicating schemes achieve better performances than the one proposed herein, it must be noted that they do so by using up timeslots for communication. By the virtue of not requiring communication, the proposed algorithm is superior for fast fading scenarios where communication might not be feasible as the coherence time of channels is only a few timeslots which will be used up for communication.

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loeve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantlv reduced.

In this paper, we propose a distributed implementation of the opportunistic interference alignment (OIA) technique developed in [1]. Therein, global channel state information (CSI) is assumed at the secondary transmitter that is, the knowledge of all the channel transfer matrices is required, which may be difficult or impossible to obtain in practice. Therefore, we propose to relax this assumption by only assuming local CSI at the transmitters and that the covariance of the received secondary signal is available at the secondary transmitter; this setup is quite similar to the one assumed for the MIMO iterative water-filling algorithm [2]. One of the key ingredients which allows the secondary transmitter to implement the OIA condition of [1] by only having this reduced knowledge is that the primary transmitter reveals information about its local channels to the secondary transmitter by embedding (for one time-slot typically, supposedly among many others) in its pre-processing matrix the information needed at the secondary. The proposed distributed implementation is proved to be effective analytically but simulations are also provided to prove that it seems to be robust against imperfect covariance feedback.

In this paper, we propose simple one-shot power control functions and assess their performance both through analytical and numerical results. The proposed functions only assume individual channel state information (CSI) at each transmitter and are based on channel inversion and more importantly on thresholding; a transmitter uses zero power if the channel gain is below a threshold. Although the idea of thresholding has been used for maximizing spectral efficiency, it has not been used for maximizing (the total network) energy-efficiency (EE), which is measured here in terms of sum-EE. More specifically, we prove the optimality of the proposed policy in asymptotic regimes such as the low and high interference scenarios. We also prove that the expected sum-energy is individually quasi-concave with respect to each of the thresholds; this allows us to provide a low-complexity algorithm which can be run offline to find good thresholds. Through numerical simulations, we show that the simple idea of thresholding provides very appreciable gains.

In this paper, we propose a new perspective for quantizing a signal and more specifically the channel state information (CSI). The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter. Roughly, the key idea is that the receiver sends the right amount of information to the transmitter so that the latter be able to take its (resource allocation) decision. More formally, the decision task of the transmitter is to maximize a utility function f(x;g) with respect to x (e.g., a power allocation vector) given the knowledge of a quantized version of the function parameters g. We exhibit a special case of an energy-efficient power control (PC) problem for which the optimal task oriented CSI quantizer (TOCQ) can be found analytically. For more general utility functions, we propose to use neural networks (NN) based learning. Simulations show that the compression rate obtained by adapting the feedback information rate to the function to be optimized may be significantly increased.

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destination can maximize a utility function which represents the task to be executed only knowing the quantized information. For example, for utility functions under the form u (x; g), x might represent a decision in terms of using some radio resources and g the system state which is only observed through its quantized version Q(g). Both in the case where the utility function is known and the case where it is only observed through its realizations, we provide solutions to determine such a quantizer. We show how this approach applies to energy-efficient power allocation. In particular, it is seen that quantizing the state very roughly is perfectly suited to sum-rate-type function maximization, whereas energy-efficiency metrics are more sensitive to imperfections.

This paper investigates the problem of coordinating several agents through their actions, focusing on an asymmetric observation structure with two agents. Specifically, one agent knows the past, present, and future realizations of a state that affects a common payoff function, while the other agent either knows the past realizations of nothing about the state. In both cases, the second agent is assumed to have strictly causal observations of the first agent's actions, which enables the two agents to coordinate. These scenarios are applied to distributed power control; the key idea is that a transmitter may embed information about the wireless channel state into its transmit power levels so that an observation of these levels, e.g., the signal-to-interference-plus-noise ratio, allows the other transmitter to coordinate its power levels. The main contributions of this paper are twofold. First, we provide a characterization of the set of feasible average payoffs when the agents repeatedly take long sequences of actions and the realizations of the system state are i.i.d.. Second, we exploit these results in the context of distributed power control and introduce the concept of coded power-control. We carry out an extensive numerical analysis of the benefits of coded power control over alternative power-control policies, and highlight a simple yet non-trivial example of a power control code.

In many resource allocation problems, optimal allocation strategies must be determined when only a quantized version of the relevant parameters are available, for instance, power allocation in wireless communications. The contribution of this work is threefold. First, the quantization problem is revisited and a framework which encompasses the classical problem of quantization is proposed. Instead of minimizing the distortion, the goal is to minimize the gap between the maximum of a general payoff function (which would be reached by knowing all parameters of the function) and what is effectively reached when only the quantized version of the parameters is available. Then, to determine such a quantizer, the well-known Lloyd-Max algorithm is generalized. At last, we show how this framework can be applied to the problem of power control in wireless communications; the obtained numerical results clearly show the potential of such a framework.

A novel technique is proposed, which enables each transmitter to acquire global channel state information (CSI) from the sole knowledge of individual received signal power measurements, which makes dedicated feedback or inter-transmitter signaling channels unnecessary. To make this possible, we resort to a completely new technique whose key idea is to exploit the transmit power levels as symbols to embed information and the observed interference as a communication channel the transmitters can use to exchange coordination information. Although the used technique allows any kind of low-rate information to be exchanged among the transmitters, the focus here is to exchange local CSI. The proposed procedure also comprises a phase which allows local CSI to be estimated. Once an estimate of global CSI is acquired by the transmitters, it can be used to optimize any utility function which depends on it. While algorithms, which use the same type of measurements, such as the iterative water-filling algorithm, implement the sequential best-response dynamics (BRD) applied to individual utilities, here, thanks to the availability of global CSI, the BRD can be applied to the sum-utility. Extensive numerical results show that significant gains can be obtained and, this, by requiring no additional online signaling.

Transmit power allocation techniques are very important to manage interference in small-cell networks. While available power allocation algorithms in the literature rely on a predefined routing protocol, we propose in this paper a power-efficient two-step algorithm that allows power allocation and routing to be performed jointly in femto-relay networks. First, we propose an interference-based partitioning method to cluster the femto-relays, then we adopt an iterative and distributed algorithm, inspired from game theory, for efficient transmit power allocation. We show that the corresponding power allocation game possesses a pure Nash equilibrium which is reached by the proposed algorithm within a number of iterations per femto-relay which can be as small as 1. Moreover, we show that our approach grants significant improvements in terms of power consumption, and permits the total consumed power to be divided by about 6 and 3 when respectively compared to the direct transmission and shortest path techniques.

A new problem of optimizing a wireless mobile terminal trajectory under a given communication constraint is introduced. The mobile or vehicle has to move from a given starting point to a target point while uploading/downloading a given amount of data; this contrasts with the classical mobile communications paradigm where the communication and motion aspects are assumed to be independent. To reach the two aforementioned objectives, the mobile has to move sufficiently close to the wireless base station, while accounting for the energy cost due to motion. This setup is formalized here and leads us to determining non-trivial trajectories for the mobile. Remarkably, a counterpart of the Snell-Descartes law for the light propagation is exhibited (see Prop. 2) for the optimal trajectory of the mobile when the latter crosses zones in which the available data rates are different.

International audience

This paper addresses a coordination problem between two agents (Agents 1 and 2) in the presence of a noisy communication channel which depends on an external system state {x0,t}. The channel takes as inputs both agents' actions, {x1,t} and {x2,t} and produces outputs that are observed strictly causally at Agent 2 but not at Agent 1. The system state is available either causally or non-causally at Agent 1 but unknown at Agent 2. Necessary and sufficient conditions on a joint distribution Q̅(x0, x1, x2) to be implementable asymptotically (i.e, when the number of taken actions grows large) are provided for both causal and non-causal state information at Agent 1. Since the coordination degree between the agents' actions, x1,tand x2,t, and the system state x0,tis measured in terms of an average payoff function, feasible payoffs are fully characterized by implementable joint distributions. In this sense, our results allow us e.g., to derive the performance of optimal power control policies on an interference channel and to assess the gain provided by non-causal knowledge of the system state at Agent 1. The derived proofs readily yield new results also for the problem of state-communication under a causality constraint at the decoder.

We consider a multi-agent scenario with K ≥ 2 agents that have partial information about some random nature state, and that take actions in a repeated manner. Each agent also has imperfect observations of the other agents' past actions and the nature state realization. Our goal is to characterize the set of asymptotically implementable distributions on the agents' actions and the nature state. We solve this problem for general K when all agents have only causal nature state information (NSI) and for K = 2 when: one agent has causal NSI and the other agent has non-causal NSI; or in some special cases when both agents have non-causal NSI.

The main contribution of this work is twofold. First, we apply, for the first time, a framework borrowed from economics to a problem in the smart grid namely, the design of signaling schemes between a consumer and an electricity aggregator when these have non-aligned objectives. The consumer's objective is to meet its need in terms of power and send a request (a message) to the aggregator which does not correspond, in general, to its actual need. The aggregator, which receives this request, not only wants to satisfy it but also wants to manage the cost induced by the residential electricity distribution network. Second, we establish connections between the exploited framework and the quantization problem. Although the model assumed for the payoff functions for the consumer and aggregator is quite simple, it allows one to extract insights of practical interest from the analysis conducted. This allows us to establish a direct connection with quantization, and more importantly, to open a much more general challenge for source and channel coding.

The central result of this paper is the analysis of an optimization problem which allows one to assess the limiting performance of a team of two agents who coordinate their actions. One agent is fully informed about the past and future realizations of a random state which affects the common payoff of the agents whereas the other agent has no knowledge about the state. The informed agent can exchange his knowledge with the other agent only through his actions. This result is applied to the problem of distributed power allocation in a two-transmitter M-band interference channel, M ≥ 1, in which the transmitters (who are the agents) want to maximize the sum-rate under the single-user decoding assumption at the two receivers; in such a new setting, the random state is given by the global channel state and the sequence of power vectors used by the informed transmitter is a code which conveys information about the channel to the other transmitter.

In this paper, we introduce the general concept of coded power control (CPC) in a particular setting of the interference channel. Roughly, the idea of CPC consists in embedding information (about the channel state) into the transmit power levels themselves: in this new framework, provided the power levels of a given transmitter can be observed by other transmitters, a sequence of power levels of the former can therefore be used to coordinate the latter. To assess the limiting performance of CPC (and therefore the potential performance brought by this new approach), we derive, as a first step towards many extensions of the present work, a general result which not only concerns power control (PC) but also any scenario involving two decision-makers (DMs) which communicate through their actions and have the following information and decision structures. We assume that the DMs want to maximize the average of an arbitrarily chosen instantaneous payoff function which depends on the DMs' actions and the state realization. DM 1 is assumed to know the state non-causally (e.g., the channel state) which affects the common payoff while DM 2 has only a strictly causal knowledge of it. DM 1 can only use its own actions (e.g., power levels) to inform DM 2 about its best action in terms of payoff. Importantly, DM 2 can only monitor the actions of DM 1 imperfectly and DM 2 does not need to be observed by DM 1. The latter assumption leads us to exploiting Shannon-theoretic tools in order to generalize an existing theorem which provides the information constraint under which the average payoff is maximized. The derived result is then exploited to fully characterize the performance of good CPC policies for a given instance of the interference channel.

In this work, we propose a new energy efficiency metric which allows one to optimize the performance of a wireless system through a novel power control mechanism. The proposed metric possesses two important features. First, it considers the whole power of the terminal and not just the radiated power. Second, it can account for the limited buffer memory of transmitters which store arriving packets as a queue and transmit them with a success rate that is determined by the transmit power and channel conditions. Remarkably, this metric is shown to have attractive properties such as quasi-concavity with respect to the transmit power and a unique maximum, allowing to derive an optimal power control scheme. Based on analytical and numerical results, the influence of the packet arrival rate, the size of the queue, and the constraints in terms of quality of service are studied. Simulations show that the proposed cross-layer approach of power control may lead to significant gains in terms of transmit power compared to a physical layer approach of green communications.

We analyze the power allocation problem for orthogonal multiple access channels by means of a non-cooperative potential game in which each user distributes his power over the channels available to him. When the channels are static, we show that this game possesses a unique equilibrium; moreover, if the network's users follow a distributed learning scheme based on the replicator dynamics of evolutionary game theory, then they converge to equilibrium exponentially fast. On the other hand, if the channels fluctuate stochastically over time, the associated game still admits a unique equilibrium, but the learning process is not deterministic; just the same, by employing the theory of stochastic approximation, we find that users still converge to equilibrium. Our theoretical analysis hinges on a novel result which is of independent interest: in finite-player games which admit a (possibly nonlinear) convex potential, the replicator dynamics converge to an ε-neighborhood of an equilibrium in time O(\log(1/ε)).

This paper addresses the power control problem in wireless networks where transmitters choose their control policy freely and selfishly in order to maximize their individual energy-efficiency. In this framework, two new scenarios are studied in details: 1. a scenario where a fraction of the transmitters can observe the power levels of the other transmitters while the latter have no sensing capabilities; 2. a scenario where the observation structure is triangular, that is, the kthtransmitter can observe the k - 1thtransmitters (which corresponds to a multi-level hierarchical game). In both scenarios the equilibrium analysis (existence, uniqueness, determination, efficiency) is conducted. In scenario 1, it is proved that the game outcome Pareto dominates the one obtained when no transmitters can sense the others. Taking the sensing cost into account, a simple condition under which being a follower (namely a transmitter who senses) is better than a leader is provided. Interestingly, the existence of an optimum fraction of cognitive transmitters in terms of sum utility is proved in the case where the sensing cost is neglected. In scenario 2, it is proved analytically that knowing more leads to a better utility and the game outcome Pareto dominates the solution with no sensing. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in heterogeneous networks in terms of sensing capabilities.

This paper is concerned with the concept of equilibrium and quality of service (QoS) provisioning in self-configuring wireless networks with non-cooperative radio devices (RD). In contrast with the Nash equilibrium (NE), where RDs are interested in selfishly maximizing its QoS, we present a concept of equilibrium, named satisfaction equilibrium (SE), where RDs are interested only in guaranteing a minimum QoS. We provide the conditions for the existence and the uniqueness of the SE. Later, in order to provide an equilibrium selection framework for the SE, we introduce the concept of effort or cost of satisfaction, for instance, in terms of transmit power levels, constellation sizes, etc. Using the idea of effort, the set of efficient SE (ESE) is defined. At the ESE, transmitters satisfy their minimum QoS incurring in the lowest effort. We prove that contrary to the (generalized) NE, at least one ESE always exists whenever the network is able to simultaneously support the individual QoS requests. Finally, we provide a fully decentralized algorithm to allow self-configuring networks to converge to one of the SE relying only on local information.

In this paper, the capacity-achieving input covariance matrices for coherent block-fading correlated multiple input multiple output (MIMO) Rician channels are determined. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum input covariance matrix are available. Classically, both the eigenvectors and eigenvalues are computed numerically and the corresponding optimization algorithms remain computationally very demanding. In the asymptotic regime where the number of transmit and receive antennas converge to infinity at the same rate, new results related to the accuracy of the approximation of the average mutual information are provided. Based on the accuracy of this approximation, an attractive optimization algorithm is proposed and analyzed. This algorithm is shown to yield an effective way to compute the capacity achieving matrix for the average mutual information and numerical simulation results show that, even for a moderate number of transmit and receive antennas, the new approach provides the same results as direct maximization approaches of the average mutual information.

Decentralized multiple access channels where each transmitter wants to selfishly maximize this transmission energy-efficiency are considered. Transmitters are assumed to choose freely their power control policy and interact (through multiuser interference) several times. It is shown that the corresponding conflict of interest can have a predictable outcome, namely a finitely or discounted repeated game equilibrium. Remarkably, it is shown that this equilibrium is Pareto-efficient under reasonable sufficient conditions and the corresponding decentralized power control policies can be implemented under realistic information assumptions: only individual channel state information and a public signal are required to implement the equilibrium strategies. Explicit equilibrium conditions are derived in terms of minimum number of game stages or maximum discount factor. Both analytical and simulation results are provided to compare the performance of the proposed power control policies with those already existing and exploiting the same information assumptions namely, those derived for the one-shot and Stackelberg games.

In this paper, we address the problem of spectrum sharing where wireless (competitive) operators coexist in the same frequency band. First, we model this problem as a strategic non-cooperative game where operators simultaneously share the spectrum according to the Nash equilibrium (N.E). Given a set of channel realizations, several Nash equilibria exist which render the outcome of the game unpredictable. Second, the inter-operator spectrum sharing problem is reformulated as a hierarchical power allocation game, where one of the operators (i.e., primary) poses as a leader and the other operator (i.e., secondary) as a follower. Using backward induction, the Stackelberg equilibrium (S.E) is reached where the best response of the secondary operator is taken into account upon maximizing the primary operator's payoff. It turns out that the Stackelberg approach yields better payoffs for operators compared to the classical greedy water-filling approach. Furthermore, to reach Pareto-efflcient boundaries, the spectrum sharing problem is formulated as a repeated game, where players interact over a longer period of time and learning from each other's strategies. Numerical results provide a comparison between the non-cooperative, hierarchical and centralized approach.

We introduce a new channel, which consists of an interference channel (IC) in parallel with an interference relay channel (IRC), to analyze the interaction between two selfish and cognitive transmitters who compete for a relay implementing the amplify-and-forward protocol. It is shown that whatever the relay location there is always an equilibrium in the resource allocation game where the users selfishly share their power between the IC and IRC. The uniqueness and determination of this equilibrium is analyzed for two cases: the relay amplification gain is fixed; the IRC direct links are negligible. We show how to exploit this analysis to optimally locate the relay either in terms of individual rate or system sum-rate. Simulations are provided and show, in particular, how the users' selfish behavior leads to sharing the space in regions where the relay is used by only one user or not used at all.

In this paper, we consider the multiple input multiple output (MIMO) quasi static channel. Our objective is to study the power allocation (over the transmit antennas) problem where not only the performance with respect to (w.r.t.) the transmission reliability but also the cost in terms of the consumed power is accounted for. We first review the existing results w.r.t energy efficiency functions (benefit per cost) which focus mainly on the single input single output (SISO) case and then propose several extensions to the MIMO case. Then, we introduce a new energy efficiency metric based on the outage probability. We conjecture that there is a non-trivial solution to the proposed optimization problem. Several special cases are thoroughly analyzed and simulation results will be provided to sustain the conducted analysis.

A game theoretic approach is used to derive the optimal decentralized power allocation (PA) in fast fading multiple access channels where the transmitters and receiver are equipped with multiple antennas. The players (the mobile terminals) are free to choose their PA in order to maximize their individual transmission rates (in particular they can ignore some specified centralized policies). A simple coordination mechanism between users is introduced. The nature and influence of this mechanism is studied in detail. The coordination signal indicates to the users the order in which the receiver applies successive interference cancellation and the frequency at which this order is used. Two different games are investigated: the users can either adapt their temporal PA to their decoding rank at the receiver or optimize their spatial PA between their transmit antennas. For both games a thorough analysis of the existence, uniqueness and sum-rate efficiency of the network Nash equilibrium is conducted. Analytical and simulation results are provided to assess the gap between the decentralized network performance and its equivalent virtual multiple input multiple output system, which is shown to be zero in some cases and relatively small in general.

In this work, we introduce hierarchy in wireless networks that can be modeled by a decentralized multiple access channel and for which energy-efficiency is the main performance index. In these networks users are free to choose their power control strategy to selfishly maximize their energy-efficiency. Specifically, we introduce hierarchy in two different ways: 1. Assuming single-user decoding at the receiver, we investigate a Stackelberg formulation of the game where one user is the leader whereas the other users are assumed to be able to react to the leader's decisions; 2. Assuming neither leader nor followers among the users, we introduce hierarchy by assuming successive interference cancellation at the receiver. It is shown that introducing a certain degree of hierarchy in non-cooperative power control games not only improves the individual energy efficiency of all the users but can also be a way of insuring the existence of a non-saturated equilibrium and reaching a desired trade-off between the global network performance at the equilibrium and the requested amount of signaling. In this respect, the way of measuring the global performance of an energy-efficient network is shown to be a critical issue.

The proposed relaying scheme, which is an optimized scalar quantize-and-forward (QF) protocol, has at least three attractive features: 1. it is simple; 2. it exploits the signal-to-noise ratios (SNR) of the source-relay and relay-destination channels; 3. it can be seen as a digital alternative of the conventional (analog) amplify-and-forward (AF) in a digital relay transceiver. The presented QF protocol is optimized in terms of end-to-end distortion, extending the idea of joint source-channel coding. Using this cooperation protocol over orthogonal relay channels, it is shown that the quantization noise introduced by the relay can significantly degrade the receiver performance if the latter uses a maximum ratio combiner (MRC) to combine the two signals from the source and relay. In order for the receiver to compensate for this effect, we propose a maximum likelihood detector (MLD), which is optimum for the QF protocol.

We present two interference alignment techniques such that an opportunistic point-to-point multiple input multiple output (MIMO) link can reuse, without generating any additional interference, the same frequency band of a similar pre-existing primary link. In this scenario, we exploit the fact that under power constraints, although each radio maximizes independently its rate by water-filling on their channel transfer matrix singular values, frequently, not all of them are used. Therefore, by aligning the interference of the opportunistic radio it is possible to transmit at a significant rate while insuring zero-interference on the pre-existing link. We propose a linear pre-coder for a perfect interference alignment and a power allocation scheme which maximizes the individual data rate of the secondary link. Our numerical results show that significant data rates are achieved even for a reduced number of antennas.

This paper studies high SNR approximations of the ergodic mutual information of block fading MIMO correlated Rician channels. The exact expression of the mutual information of such channels is quite complicated, and difficult to use to obtain convenient high SNR approximations. In this paper, it is replaced by an accurate large system approximant obtained in the case where the number of transmit and receive antennas t and r converge to +infin at the same rate. The large system approximant is studied at high SNR, and it is shown that 3 different behaviours are possible depending on r/t, the rank of the line of sight component and the Rician factor. The accuracy of the high SNR approximant is shown to be connected to the support of the deterministic large system approximant of the eigenvalue distribution of the Gram matrix of the channel. This allows to infer that the approximant is accurate for realistic values of r and t if r and t or if r npar t, the line of sight component is invertible and the Rician factor is greater than a certain threshold.

We determine the capacity-achieving input covariance matrices for coherent block-fading correlated MIMO Rician channels. In contrast with the Rayleigh and uncorrelated Rician cases, no closed-form expressions for the eigenvectors of the optimum input covariance matrix are available. Both the eigenvectors and eigenvalues have to be evaluated by using numerical techniques. As the corresponding optimization algorithms are not very attractive, we evaluate the limit of the average mutual information when the number of transmit and receive antennas converge to +infin at the same rate. We propose an attractive optimization algorithm of the large system approximant, and establish some convergence results. Numerical simulation results show that, even for a quite moderate number of transmit and receive antennas, the new approach provides the same results than direct maximization approaches of the average mutual information, while being much more computationally attractive.

The main purpose of this paper is to evaluate the benefits of receive cooperation in a broadcast situation where two receivers want to decode the same message. Additionally the cooperation channel is assumed to be orthogonal to the downlink channels. In the case where the cooperation channel is unidirectional, the channel capacity is determined and the performance loss induced by orthogonalizing the cooperation channel is evaluated. In the bidirectional and orthogonal case lower and upper bounds for the capacity are provided. More specifically two decoding schemes are compared. Coding scheme 1: the two receivers use estimate-and-forward. Coding scheme 2: each receiver uses, in a complementary manner, estimate-and-forward a certain fraction of the time and decode-and-forward for the rest of the time. For realistic levels of the cooperation powers the performance of the second scheme is shown to be close to the considered upper bound

In this paper we investigate the performance of block-fading multiple input multiple output (MIMO) Rician correlated channels in the case where the number of transmit and receive antennas converge to infinity at the same rate. In this context we show that the expressions of classical performance indices such as the average mutual information or the output SINR of MMSE receivers converge to deterministic expressions. The analysis determines the parameter of interest and gives insight of the effect of the channel distribution on the performance metrics.

We provide a theoretical framework of de-noising for UMTS TDD-like mobile radio communication systems. Based on the Bayesian approach, we show how to de-noise channel estimates provided by the training-based estimation procedure. The proposed schemes allow not only for eliminating major drawbacks of hard thresholding but also for a low complexity implementation.

We study the performance of a semi-blind subspace channel estimate in the particular context of the uplink of the UMTS time division duplexing (TDD) mode. The TDD mode of the third generation system UMTS is a multiuser DS-CDMA scheme of maximal spreading factor N=16. In the uplink, each slot corresponds to 2560 chips and the channel estimation is classically achieved by using a 512 training chips midamble. In this well defined context, we study the improvements (in terms of bit error rate) provided by a semi-blind channel estimation technique introduced in the context of the single user system of Gorokhov and Loubaton (see Proc. ICASSP, p.3905-3908, 1997) and studied in detail by Buchoux, Moulines, Cappe and Gorokhov (see SPAWC, 1999).