Allocation de ressources multi-débit pour la radio ULB impulsionnelle

National audienceDans cet article, nous considérons un système de communication multi-utilisateurs mettant en oeuvre une couche physique ultra-large bande (ULB) impulsionnelle à répartition par code. Nous nous intéressons tout d'abord à l'expression de la variance de l'interférence d'accès multiple (IAM) lorsque les utilisateurs ont des durées symboles différentes grâce à un nombre de trames, $N_f$, variable selon les utilisateurs. Nous nous intéressons ensuite au problème de la maximisation du débit global en affectant un nombre de trames différent pour chaque utilisateur sous contrainte de qualité de service (QoS) hétérogène pour certaines classes d'utilisateurs. Nous proposons une heuristique à complexité linéaire avec le nombre d'utilisateurs pour l'allocation du nombre de trames et évaluons ses performances par rapport à deux algorithmes de références

Opportunistic Secondary Spectrum Sharing Protocols for Primary implementing an IR type Hybrid-ARQ Protocol

In this paper, we propose, analyze and compare three different methods for opportunistic spectrum sharing access when the primary users implements an Incremental Redundancy (IR) type Hybrid Automatic ReQuest (H-ARQ) protocol. The first method consists in allowing the secondary user to communicate only during the first primary transmission round of the IR H-ARQ protocol. In this scenario, if the the secondary receiver fails to decode its message after the first round, it realizes a successive interference cancellation in the subsequent primary HARQ rounds by listening to the primary user. The second method consists in realizing a perfect interference cancellation at the secondary receiver with causal channel state information. In this method, the secondary user communicates only when the secondary receiver succeeds in decoding the primary message.To improve throughput performance at the secondary, the secondary pair is also considering the use of an IR-HARQ protocol. In a third method, the secondary user communicates following the same rule as in the proposed second method, but implementing an Adaptive Modulation and Coding scheme instead of HARQ. In particular, we show that this last protocol with a small number of interfered slots allows to limit the loss in the primary throughput needed for the secondary user to transmit

Allocation conjointe de puissance et rendement d'un utilisateur cognitif exploitant les retransmissions d'un utilisateur primaire : le cas du canal en Z

National audienceDans cet article, nous considérons le problème de l’allocation conjointe de puissance et de rendement pour un utilisateur secondaire exploitant le protocole de retransmission d’un utilisateur primaire. Nous proposons un algorithme, basé sur les Processus de Markov Décisionnels (MDP), permettant de calculer une allocation optimale pour le problème de la maximisation du débit de l’utilisateur secondaire tout en garantissant un débit minimal pour l’utilisateur primaire

Performance Analysis of Uplink NOMA-Relevant Strategy Under Statistical Delay QoS Constraints

A new multiple access (MA) strategy, referred to as non orthogonal multiple access - Relevant (NOMA-R), allows selecting NOMA when this increases all individual rates, i.e., it is beneficial for both strong(er) and weak(er) individual users. This letter provides a performance analysis of the NOMA-R strategy in uplink networks with statistical delay constraints. Closed-form expressions of the effective capacity (EC) are provided in two-users networks, showing that the strong user always achieves a higher EC with NOMA-R. Regarding the network's sum EC, there are distinctive gains with NOMA-R, particularly under stringent delay constraints

Efficient spectrum scheduling and power management for opportunistic users

International audienceIn this paper, we study the centralized spectrum access and power management for several opportunistic users, secondary users (SUs), without hurting the primary users (PUs). The radio resource manager's objective is to minimize the overall power consumption of the opportunistic system over several orthogonal frequency bands under constraints on the minimum quality of service (QoS) and maximum peak and average interference to the PUs. Given the opposing nature of these constraints, we first study the problem of feasibility, and we provide sufficient conditions and necessary conditions for the existence of a solution. The main challenge lies in the non-convexity of this problem because of the discrete spectrum scheduling: one band can be allocated to at most one SU to avoid interference impairments. To overcome this issue, we use a Lagrangian relaxation technique, and we prove that the discrete solutions of the relaxed problem are the solutions to the initial problem. We propose a projected sub-gradient algorithm to compute the solution, when it exists. Assuming that the channels are drawn randomly from a continuous distribution, this algorithm converges to the optimal solution. We also study a specific symmetric system for which we provide the analytical solution. Our numerical results compare the energy-efficiency of the proposed algorithm with other spectrum allocation solutions and show the optimality of our approach

Minima du critère Module Constant pour un canal AR

Dans cette contribution on s'intéresse à la caractérisation des minima du critère Module Constant (MC) pour un canal Auto Régressif (AR) particulier. Le résultat principal montre la colinéarité stricte entre l'ensemble des minima du critère MC (locaux et globaux) et les solutions de Wiener de même retard. En montrant que les solutions sous-optimales ont pour origines un effet comparable à une sous-modélisation de l'égaliseur, nous pouvons ainsi unifier des résultats a priori contradictoires qui permettent d'expliquer la présence de minima locaux. Ce résultat intègre comme cas particulier l'exemple bien connu de minimum local décrit par Ding et al [1]

Interference Mitigation via Pricing in Time-Varying Cognitive Radio Systems

International audienceDespite the lure of a considerable increase in spectrum usage efficiency, the practical implementation of cognitive radio (CR) systems is being obstructed by the need for efficient and reliable protection mechanisms that can safeguard the quality of service (QoS) requirements of licensed users. This need becomes particularly apparent in dynamic wireless networks where channel conditions may vary unpredictably – thus making the task of guaranteeing the primary users (PUs)' minimum quality of service requirements an even more challenging task. In this paper, we consider a pricing mechanism that penalizes the secondary users (SUs) for the interference they inflict on the network's PUs and then compensates the PUs accordingly. Drawing on tools from online optimization, we propose an exponential learning power allocation policy that is provably capable of adapting quickly and efficiently to the system's variability, relying only on strictly causal channel state information (CSI). If the transmission horizon T is known in advance by the SUs, we prove that the proposed algorithm reaches a " no-regret " state within O(T −1/2) iterations; otherwise, if the horizon is not known in advance, the algorithm still reaches a no-regret state within O(T −1/2 log T) iterations. Moreover, our numerical results show that the interference created by the SUs can be mitigated effectively by properly tuning the parameters of the pricing mechanism

Interference Mitigation via Pricing in Time-Varying Cognitive Radio Systems

International audienceDespite the lure of a considerable increase in spectrum usage efficiency, the practical implementation of cognitive radio (CR) systems is being obstructed by the need for efficient and reliable protection mechanisms that can safeguard the quality of service (QoS) requirements of licensed users. This need becomes particularly apparent in dynamic wireless networks where channel conditions may vary unpredictably – thus making the task of guaranteeing the primary users (PUs)' minimum quality of service requirements an even more challenging task. In this paper, we consider a pricing mechanism that penalizes the secondary users (SUs) for the interference they inflict on the network's PUs and then compensates the PUs accordingly. Drawing on tools from online optimization, we propose an exponential learning power allocation policy that is provably capable of adapting quickly and efficiently to the system's variability, relying only on strictly causal channel state information (CSI). If the transmission horizon T is known in advance by the SUs, we prove that the proposed algorithm reaches a " no-regret " state within O(T −1/2) iterations; otherwise, if the horizon is not known in advance, the algorithm still reaches a no-regret state within O(T −1/2 log T) iterations. Moreover, our numerical results show that the interference created by the SUs can be mitigated effectively by properly tuning the parameters of the pricing mechanism

Online Power Allocation for Opportunistic Radio Access in Dynamic OFDM Networks

International audienceUser mobility has become a key attribute in the design of optimal resource allocation policies for future wireless networks. This has become increasingly apparent in cognitive radio (CR) systems where the licensed, primary users (PUs) of the network must be protected from harmful interference by the network's opportunistic, secondary users (SUs): here, unpre-dictability due to mobility requires the implementation of safety net mechanisms that are provably capable of adapting to changes in the users' wireless environment. In this context, we propose a distributed learning algorithm that allows SUs to adjust their power allocation profile (over the available frequency carriers) " on the fly " , relying only on strictly causal channel state information. To account for the interference caused to the network's PUs, we incorporate a penalty function in the rate-driven objectives of the SUs, and we show that the proposed scheme matches asymptoti-cally the performance of the best fixed power allocation policy in hindsight. Specifically, in a system with S orthogonal subcarriers and transmission horizon T , this performance gap (known as the algorithm's average regret) is bounded from above as O(T −1 log S). We also validate our theoretical analysis with numerical simulations which confirm that the network's SUs rapidly achieve a " no-regret " state under realistic wireless cellular conditions. Moreover, by finetuning the choice of penalty function, the interference induced by the SUs can be kept at a sufficiently low level, thus guaranteeing the PUs' requirements