International audience

International audience

International audience

In the next generation of wireless communication systems, Cell-Free massive Multiple-Input-Multiple-Output (CF-mMIMO) is emerging as a promising solution to address the challenges posed by traditional cellular networks, including interference management and resource allocation. The dynamic nature of CF-mMIMO requires efficient load management between the Access Points (APs) and the Central Processing Unit (CPU) to ensure optimal performance, reduced latency, and effective resource utilization. This paper investigates the load balancing between APs and CPUs in CF-mMIMO, focusing on the optimal distribution of computational resources for channel estimation. We explore the interaction between APs, which role is to provide connectivity to users, and the CPU that handles processing tasks, including channel estimation, data encoding/decoding, and user scheduling. We propose a Federated K-means framework (FedEst) that takes into account factors such as traffic load, user mobility, computational power, and network topology. Simulation results demonstrate that the proposed framework not only improves system throughput and efficiency but also enhances user experience in terms of latency and reliability. This work contributes to the development of more efficient, scalable, and robust cell-free network architectures capable of supporting the growing demands of next-generation wireless technologies.

As mobile data usage grows, wireless systems face increasing demands for ultra-low latency, high reliability, and massive connectivity. Traditional resource allocation methods struggle with scalability and the complexity of modern networks. While AI-based solutions offer potential, they often depend on labeled data and lack generalization across varied scenarios. To overcome these issues, we propose a supervised learning framework using pre-trained models and a multi-kernel neural network. This approach generalizes patterns from simple to complex scenarios and is applied to joint power control and user assignment in Reconfigurable intelligent surfaces (RIS)-based non-orthogonal multiple access (NOMA) systems. Simulation results demonstrate the effectiveness of our method, achieving scalable and efficient wireless network optimization.

Ultra-Reliable Low-Latency Communication is the Fifth Generation (5G) use case with the most stringent requirements for latency and reliability. In Beyond 5G and future 6G systems, there will be a need to support a large number of URLLC devices, giving rise to a new use case known as massive URLLC (mURLLC). Addressing these demands requires efficient resource sharing among multiple devices. Non-Orthogonal Multiple Access (NOMA) emerges as an efficient solution to enhance spectral efficiency by allowing simultaneous transmissions from multiple devices over shared resources. In this paper, we propose a novel joint sub-channel allocation and power control framework that integrates Irregular Repetition Slotted ALOHA (IRSA) with Grant-Free NOMA (GF-NOMA). The resource allocation problem is formulated as a multi-agent reinforcement learning task, where each device acts as a learning agent and the gNodeB (gNB) broadcasts global feedback to meet the stringent reliability and latency requirements. The framework introduces new Quality Scores (QS) that guide agents in selecting resources more efficiently. Extensive simulations demonstrate that the proposed framework significantly outperforms existing techniques in meeting the stringent mURLLC requirements.

Recently, cutting-edge techniques, such as Non-Orthogonal Multiple Access (NOMA), have been highlighted to enable wireless networks to handle massive access scenarios and further improve the spectral efficiency. Nevertheless, these advantages come at the price of privacy exposure since a received signal may contain information belonging to several users. Usual security mechanisms, such as upper-layer encryption and sophisticated authentications, are not well suited to low-capacity Internet of Things (IoT) devices. These technical challenges have spotlighted Physical Layer Security (PLS) as a key enabling technology since it takes advantage of the wireless communication characteristics to secure communications without adding complex encryption mechanisms at higher layers. In this paper, we propose a PLS approach based on a network coding technique in order to ensure secure NOMA-based downlink transmissions taking into account the Quality of Service (QoS) requirements of the users. By doing so, decoding successfully some of the transmitted packets by an eavesdropper does not reveal useful information about the data. In fact, we develop a sequence-based algorithm with the aim of ensuring the confidentiality by changing the users’ positions in the Successive Interference Cancellation (SIC) decoding process. Henceforth, using its corresponding sequence, the legitimate user becomes the only one able to decode the information sent by the Base Station (BS). We show that the eavesdropper decoding complexity increases exponentially with the sequence length making the task intractable for relatively long ones.

Wireless communications have become essential to modern life. They allow user to stay connected to their applications while supporting their mobility requirements. Wi-Fi equipped devices are getting increasingly widespread, and high-bandwidth applications like video streaming are becoming more critical in many areas. Thus, many performance challenges and issues have emerged in the past decades. Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) is a Wi-Fi multiple access control method used to manage access to the channel and to mitigate collisions. However, it suffers from performance degradation when the density of Wi-Fi nodes or the offered load increase. The random behavior of CSMA/CA is one of the reasons behind the performance degradation. In this paper, we propose a Deep Reinforcement Learning (DRL) mechanism, Intelligent CSMA/CA (ICSMA/CA), to dynamically adapt the backoff duration of CSMA/CA algorithm in dense Wi-Fi environments. We train and evaluate our DRL model using the Network Simulator (NS-3) and Tensorflow. Results show an improvement in the capacity utilization of the channel and a reduction in the channel access delay

The evolution from 5G to the upcoming 6G has spotlighted the limitations of the available wireless spectrum, which has become more pronounced. Thereby, efficient spectrum allocation through the design of appropriate multiple access methods is a critical challenge in addressing the demands for massive connectivity, broadband services, and facilitating efficient sharing of communication resources among multiple users. To address this challenge, we propose, in this paper, a novel approach entitled the LoCoNOMA framework. This framework focuses on optimizing resource allocation in Grant-Free (GF) Non-Orthogonal Multiple Access (NOMA) systems, taking into account the limited capacity of IoT devices and enhancing the scalability of wireless communication. In our approach, IoT devices autonomously select sub-carrier and power-level for their transmissions. By adopting GF access, the complexity at the gNodeB (gNB) is significantly reduced, resulting in enhanced scalability. The gNB only broadcasts global feedback that coordinates and improves the overall performance of all devices. We assume that devices perform channel estimation, and no additional information about other devices or the gNB is required during the sub-carrier/power level selection process. The proposed framework exhibits impressive results shown through extensive simulations, highlighting its effectiveness.

Facing the exponential number of Internet of Things (IoT) devices and the scarcity of available resources, next-generation wireless networks have to meet very challenging performance targets in terms of providing massive access and ensuring higher spectral efficiency . In this vein, Non-Orthogonal Multiple Access (NOMA) has been widely recognized as one of the advantageous techniques to handle the proliferation of the IoT. Nevertheless, from a security standpoint, enabling a user to decode the signals of the other users, while using Successive Interference Cancellation (SIC), raises serious concerns regarding confidentiality and vulnerability to malicious attacks . Meanwhile, conventional security paradigms, such as upper-layer encryption and sophisticated authentication mechanisms , require high computational complexity and additional processing, which impose an overwhelming burden on energy-efficient IoT devices. Alternatively, Physical layer Security (PLS) has sparked a significant interest as a promising complement to cryptographic techniques . The key idea of PLS is to avail wireless communication properties to secure communications without adding complex encryption mechanisms at higher layers. In this paper, we propose a PLS approach based on a network coding technique to prevent eavesdroppers from decoding users’ information transmitted through a downlink-based NOMA system. This results in correlating the packets to be transmitted with each other, making the interception of a single packet useless. We demonstrate that the eavesdropper’s decoding complexity increases exponentially with the sequence length , making the task intractable for relatively long ones.

Massive machine-type communications (mMTC) represent a significant challenge in the fifth generation of wireless networks (5G) and become increasingly critical in the sixth generation (6G) due to the limited frequency spectrum. Addressing the demands of mMTC requires efficient resource sharing among multiple users. Integrating Grant-Free (GF) access with Non-Orthogonal Multiple Access (NOMA) is a promising strategy to improve spectral efficiency . However, it may cause additional interference and complexity at the gNodeB (gNB) side. To mitigate these issues, we propose a novel, low-complexity GF-NOMA framework for joint power and channel allocation, where devices autonomously select their sub-carriers and power levels in a fully distributed manner. Besides, the gNB’s role is limited to sending a global feedback for device coordination. The proposed technique has been validated analytically and through simulation, demonstrating superior performance compared to existing approaches, in particular for the massive access scenario.

Attracted by the advantages of Non-Orthogonal Multiple Access (NOMA) in accommodating multiple users within the same resources, this paper jointly addresses the resource allocation and power control problem for Machine Type Devices (MTDs) in a Hybrid NOMA system. Particularly, we model the problem using a Mean Field Game (MFG) framework underlying a Multi-Armed Bandit (MAB) approach. Firstly, the devices invoke the MAB tool to arrange themselves into multiple NOMA coalitions. Then, within each coalition, the MTDs apply the MFG approach to autonomously adjust their transmit power based on limited feedback received from the Base Station (BS). Simulation results are given to illustrate the equilibrium behavior of the proposed resource allocation algorithm and to underline its robustness compared to existing works in the literature.

This paper presents a load-aware network selection model intended to help users to determine whether or not to connect to a macro cell (MC) or a WiFi access point (AP) in non-cooperative user-centric networks. The problem is formulated as a game theoretic model in which users selfishly maximize their throughput. Unlike in most existing work, we do not assume that users have complete information about the other users’ dynamics, which makes it more realistic in a communication network with distributed users. Then, because the network selection decision depends crucially on truthful reporting of channel states by the users, we explore the idea of non-cooperative users sending signals that are likely to induce the scheduler to behave in a manner beneficial to them. We provide five procedures which consist of introducing hierarchy among the users reflecting their channel quality and dividing them into groups interfering with each other, but not within themselves. Having done this, we allow them to sequentially choose their preferred network. We also propose a solution to compel users to reveal the truthful signals to the macro eNodeB (MeNb) by designing an additional immunity parameter mainly meant to keep lying users from harming truthful users. Particularly noteworthy is the fact that the additional immunity parameter does not only decrease the gain of liars, but it further improves the overall system performance. We provide extensive system level simulation results comparing our procedures between themselves and with traditional schemes. It is shown that the proposed solutions outperform classical approaches in almost every respect.

Attracted by the advantages of Non-Orthogonal Multiple Access (NOMA) in accommodating multiple users within the same resource, this paper investigates the power allocation problem for Machine Type Devices (MTDs) in a Hybrid NOMA system. Particularly, we consider a densely deployed network in which the devices are divided into orthogonal coalitions. Firstly, the power control problem is modeled as a differential game. Then, we formulate the proposed game as a Mean Field Game (MFG) in order to handle massive Internet of Things (IoT) access scenarios. Furthermore, we design an iterative algorithm that paves the way for distributed control in which the devices can appropriately regulate their transmit power in response to brief information received from the Base Station (BS). The analysis of the proposed approach is conducted through coupled equations, namely the Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK). Our simulation results prove the convergence of the proposed power control strategy and spotlight the robustness of our formulated MFG.

The water-filling algorithm is well known for providing optimal data rates in time varying wireless communication networks. In this paper, a perfectly coordinated water-filling game is considered, in which each user transmits only on the assigned carrier. Contrary to conventional algorithms, the main goal of the proposed algorithm (FEAT) is to achieve near optimal performance, while satisfying fairness constraints among different users. The key idea within FEAT is to minimize the ratio between the utilities of the best and the worst users. To achieve this goal, we devise an algorithm such that, at each iteration (channel assignment), a channel is assigned to a user, while ensuring that it does not lose much more than other users in the system. In this paper, we show that FEAT outperforms most of the existing related algorithms in many aspects, especially in interference-limited systems. Indeed, with FEAT, we can ensure a low complexity near-optimal, and fair solution. It is shown that the balance between being nearly globally optimal and good from an individual point of view seems hard to sustain with a significant number of users, hence adding robustness to the proposed algorithm.

Smal1 cell densification is recognized as one of the most significant characteristics in the fifth-generation of communication systems (5G) and beyond. A substantial capacity boost can be achieved at a low cost by supplementing macro networks with numerous small cells to create ultra-dense heterogeneous networks, which can serve as the foundation for the next generation of services. In this paper, we investigate a model that accounts for the location and channel quality of an enhanced Mobile Broadband (eMBB) user as well as the locations, density, and energy levels of a large number of Internet of Things (IoT) devices. More specifically, the eMBB user is randomly distributed in the coverage area of the MBS, and given its channel gain, it adjusts its transmit power to achieve an acceptable Quality of Service (QoS). In contrast, the IoT devices are gathered around SBS and regulate their transmission power in accordance with their energy budget to minimize energy-efficient utility function. Due to the coupling, the Stackelberg-Nash differential game is initially used to model the power allocation problem, with the eMBB user playing the role of the leader and the IoT devices playing the role of the followers. Then, we use the mean-field approximation to construct a hierarchical mean-field game from which we can recover a set of equations that may be solved iteratively to provide the optimal power allocation strategies. Simulation results illustrate the optimal power allocation strategies and show the effectiveness of the proposed approach.

The steady expansion of the number of wireless devices and the ubiquity of the networks give rise to various interesting challenges for the future sixth generation (6G) of wireless communication systems. Particularly, the operators have to handle massive connectivity among Machine Type Devices (MTDs) and increasing demand for eMMB through limited spectrum resources. Non-Orthogonal Multiple Access (NOMA) has been spotlighted as an emerging technology to meet the above-mentioned challenges. In this paper, we consider a densely deployed network in which users are divided into NOMA coalitions. Firstly, we model the power allocation problem as a differential game. Then, we extend the formulated game using a Mean Field Game (MFG) theoretic framework by considering the effect of the collective behavior of devices. Furthermore, we derive a distributed power control algorithm that enables the users to appropriately regulate their transmit power according to brief information received from the BS. Indeed, the analysis of the proposed approach is governed by the two fundamental Hamilton- Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Numerical results are presented to analyze the equilibrium behaviors of the proposed power control algorithm and to demonstrate the effectiveness of the formulated MFG compared to existing works in the literature.

Non-Orthogonal Multiple Access (NOMA) is an interesting candidate to tackle the massive access challenges in Beyond 5G (B5G) systems. However, arranging Machine Type Devices (MTDs) into NOMA clusters and allocating resources to these clusters is a non-trivial task. In this paper, we consider a Hybrid NOMA system where every NOMA cluster is allocated an orthogonal sub-carrier and propose a game theoretical framework based on a bi-level game in order to achieve joint channel selection and power allocation for MTDs. Indeed, the proposed approach is composed of a non-cooperative power control game underlying a cooperative Hedonic game that enables MTDs to self-organize into coalitions. Furthermore, we propose two low-complexity algorithms that enable us to obtain a Nash-Stable partition where MTDs decide autonomously the appropriate Resource Block (RB) and the transmit power to use in order to deliver their packets. Our simulation results show that the proposed bi-level game allows the devices to achieve a high successful packet transmission rate while consuming less energy.

Network coding (NC) is one of the promising high-performance techniques for wireless sensor networks (WSNs). However, few works have focused on the concerns of multi-source networks using error correcting codes. When an intermediate node fails errors may occur and since NC combines packets from different sources, several packets can be affected. In this paper, we propose a modified-low rank parity check (M-LRPC) decoding algorithm for a scenario with multiple source nodes. Furthermore, we investigate the performance of the proposed coding technique in terms of success decoding rate. Then, we derive an analytical expression for the decoding probability of the proposed M-LRPC. Simulation results are conducted in order to validate our analytical findings. These results show that the proposed scheme significantly improves the decoding probability compared to Gabidulin codes.

In wireless sensor networks (WSNs), data produced by sensors are usually routed through several intermediate nodes to reach the sink Base Station (BS). In fact, since a WSN is usually composed of low-cost and limited capability sensors, their transmission range prevents the establishment of a reliable communication with the sink. When an intermediate node fails, errors may occur and the message is not delivered to the sink. The reliability of the system can be increased by using Network Coding (NC) techniques. In this paper, we consider the problem of data gathering in WSNs and we propose a novel error correction mechanism using Low Rank Parity Check code (LRPC), which is known to be good at correcting burst errors, as an outer code and a convolutional code as an inner code to correct sparse errors. Furthermore, we investigate the performance of the proposed system in terms of the packet error probability and the decoding complexity. We also propose a theoretical approximation of the decoding probability for LRPC codes in the case of network coding for binary and non-binary fields. We show, through Matlab simulations, that the proposed concatenated code outperforms the proposed coding schemes in the literature for data gathering in terms of decoding rate and complexity.

In this paper, we investigate multihop cooperative data exchange (CDE) using instantly decodable network coding (IDNC) in decentralized wireless nodes. In such model, we focus on how these wireless nodes can cooperate in limited transmission ranges without increasing the IDNC delay nor their energy consumption. For that purpose, we model the problem using a two stage game theory framework. We first model the problem using non-cooperative game theory where users jointly choose their desired transmission power selfishly in order to reduce their energy consumption and their IDNC delay. The optimal solution of this game allows the players in the next step to cooperate with each other through limited transmission ranges using cooperative game theory in partition form framework. Thereafter, a distributed multihop merge-and-split algorithm is defined to form coalitions where players maximize their utilities in terms of decoding delays and energy consumption. Indeed, the solution of the proposed framework determines the stable feasible partition for the wireless nodes with reduced interference and reasonable complexity. We demonstrate through simulations that the cooperation between nodes in the multihop cooperative scheme achieves a significant minimization of the energy consumption with respect to the most stable cooperative scheme in maximum transmission range without hurting the IDNC delay.

In this paper, we consider a Wireless Sensor Network (WSN) with Wireless Energy Transfer (WET) capability, and we focus on the data gathering in a multi-hop scenario. In fact, we assume that any wireless sensor that needs the help of relays to deliver the packet may transfer energy as a kind of reward or payment. We assume that only few sensors have direct reliable channel to the sink Base Station (BS), i.e. sensors that are close the BS. Hence, other sensors should relay on them to transmit their packets to the BS. We assume that the nodes of the WSN are selfish and aim to maximize their utility function, which mainly accounts for their energy consumption. We propose a multi-level multi-leader-follower Stackelberg game framework to analyse the competition between users. Particularly, we characterise the Stackelberg equilibrium (SE) for the three-hop WSN, and we propose a distributed algorithm to achieve the SE. Moreover, we show that the network lifetime is enhanced compared to other multi-hop data transmission in WSN.

In this paper, we investigate the cooperative data exchange (CDE) using instantly decodable network coding (IDNC) across the wireless nodes. We model the problem using the cooperative game theory in partition form. Unlike most of existing works concerning IDNC, we focus not only on the decoding delay, but also the consumed energy in order to increase the network lifetime. A distributed merge-and-split algorithm is proposed to form coalitions that maximize their utilities in terms of energy consumption and delay experienced by all the receivers. Indeed, the proposed algorithm enables the wireless nodes to self-organize into independent disjoint coalitions and the resulting clustered network structure is characterized through stability notion. Simulation results show that the cooperation between nodes not only reduces the energy consumption, but also the IDNC completion time. Note also that the proposed solution reduces the complexity of the CDE which makes the network more scalable and more reliable.

As the size of Wireless Sensor Networks continues to grow, the amount of data for processing and transmitting becomes enormous. In many practical cases, the wireless sensors are distributed across a physical field to monitor physical phenomena with high space-time correlation. Compressive Sensing is a promising technique to exploit this correlation in order to limit the number of transmission and therefore increase the lifetime of the network. In this paper, we are interested in mesh network topology where the sink node is not in the range of sensors and routing schemes must be applied. We propose a joint Space-Time Compressive Sensing by exploiting jointly inter-sensor and intra-sensor data dependency. Since the routing and the number of retransmission affect significantly the total energy consumption, we introduce the routing in our cost function in order to optimize the selection of transmitting sensors. The simulations show that this method outperforms the existing ones and confirm the validity of our approach.

In this paper, we propose a hierarchical game approach to model the energy efficiency maximization problem, where transmitters individually choose their channel assignment and power control. We conduct a thorough analysis of the existence, uniqueness, and characterization of the Stackelberg equilibrium. Interestingly, we formally show that a spectrum orthogonalization naturally occurs when users decide sequentially about their transmitting carriers and powers, delivering a binary channel assignment. Both analytical and simulation results are provided for assessing and improving the performances in terms of energy efficiency and spectrum utilization between the simultaneous-move game (with synchronous decision makers), the social welfare (in a centralized manner), and the proposed Stackelberg (hierarchical) game. For the first time, we provide tight closed-form bounds on the spectral efficiency of such a model, including correlation across carriers and users. We show that the spectrum orthogonalization capability induced by the proposed hierarchical game model enables the wireless network to achieve the spectral efficiency improvement while still enjoying a high energy efficiency.

Enabling coordination between products from different vendors is a key characteristic of the design philosophy behind future wireless communication networks. As an example, different devices may have different implementations, leading to different user experiences. A similar story emerges when devices running different physical and link layer protocols share frequencies in the same spectrum in order to maximize the system-wide spectral efficiency. In such situations, coordinating multiple interfering devices presents a significant challenge not only from an interworking perspective (as a result of reduced infrastructure), but also from an implementation point of view. The following question may then naturally arise: How to accommodate integrating such heterogeneous wireless devices seamlessly? One approach is to coordinate the spectrum in a centralized manner. However, the desired autonomous feature of future wireless systems makes the use of a central authority for spectrum management less appealing. Alternately, intelligent spectrum coordination have spurred great interest and excitement in the recent years. This paper presents a multi-level (hierarchical) power control game where users jointly choose their channel and power control selfishly in order to maximize their individual energy efficiency. By hierarchical, we mean that some users' decision priority is higher/lower than the others. We propose two simple and nearly-optimal algorithms that ensure complete spectrum coordination among users. Interestingly, it turns out that the complexity of the two proposed algorithms is, in the worst case, quadratic in the number of users, whereas the complexity of the optimal solution (obtained through exhaustive search) is N!. These results offer hope that such simple and accurate power control algorithms can be designed around competition, as hierarchical behavior does not only improve the mechanism's performance but also leads to simpler distributed power control algorithms.

Smooth and green future extension/scalability (e.g., from sparse to dense, from small-area dense to large-area dense, or from normal-dense to super-dense) is an important issue in heterogeneous networks. In this paper, we study energy efficiency of heterogeneous networks for both sparse and dense (two-tier and multi-tier) small cell deployments. We formulate the problem as a hierarchical (Stackelberg) game in which the macro cell is the leader whereas the small cell is the follower. Both players want to strategically decide on their power allocation policies in order to maximize the energy efficiency of their registered users. A backward induction method has been used to obtain a closed-form expression of the Stackelberg equilibrium. It is shown that the energy efficiency is maximized when only one sub-band is exploited for the players of the game depending on their channel fading gains. Simulation results are presented to show the effectiveness of the proposed scheme.

The intense throughput and stringent delay requirements of Internet multimedia applications has spurred the need for new transport protocols with flexible transmission control. Current TCP congestion control adopts an Additive Increase Multiplicative Decrease (AIMD) algorithm that linearly increases or exponentially decreases the congestion window based on transmission acknowledgements. In this paper, we propose an AIMD-based media-aware congestion control that determines the optimal congestion window updating policy for multimedia transmission. The media-aware congestion control is formulated as a Partially Observable Markov Decision Process (POMDP), which maximizes the long-term expected quality of the received multimedia data. Moreover, we propose a reinforcement learning algorithm in order to estimate the environment and adapt to the source and network variations on the fly. Simulation results show that the proposed approach can significantly improve the received video quality, particularly at high source rates, compared to conventional TCP.

In this paper, we propose an algorithmic perspective of the Stackelberg game model introduced in [1] applied to cognitive radio networks (CRN). Typically, we assume that individual users attempt to access to the wireless spectrum while maximizing their individual energy efficiency. Having looked at the main properties of the proposed energy efficient and in particular the one related to spectrum coordination, we address the problem of sensing. Then, we provide a deep algorithmic analysis on how primary and secondary users can reach such a spectrum coordination using an appropriate learning process. We validate our results through extensive simulations and compare the proposed algorithm to some typical scenarios including the non-cooperative case in [2] and the throughput-based-utility systems. Specifically it is shown that the proposed Stackelberg decision approach maximizes the energy efficiency while still optimizing the throughput at the equilibrium.

We consider a non-cooperative Opportunistic Spectrum Access (OSA) where Secondary Users (SUs) access opportunistically the spectrum licensed for Primary Users (PUs) in TV white spaces (TVWS). As sensing licensed channels is time and energy consuming, we consider a hierarchical Cognitive Radio (CR) architecture, where CR base stations sense a subset of the spectrum in order to locate some free frequencies. Thereafter, a SU that needs to communicate through TVWS sends a request to a CR base station for a free channel. We model the problem using a Partially Observable Stochastic Game (POSG), and we take into consideration the energy consumption of CR base stations and the Quality of Services (QoS) of SUs. Since solving POSG optimally may require a significant amount of time and computational complexity, we model the OSA problem using a game theoretical approach, and we propose a symmetric Nash equilibrium solution concept. Finally, we provide some simulations that validate our theoretical findings.

We consider a non-cooperative Dynamic Spectrum Access (DSA) game where Secondary Users (SUs) access opportunistically the spectrum licensed for Primary Users (PUs). As SUs spend energy for sensing licensed channels, they may choose to be inactive during a given time slot in order to save energy. Then, there exists a tradeoff between large packet delay, partially due to collisions between SUs, and high-energy consumption spent for sensing the occupation of licensed channels. To overcome this problem, we take into account packet delay and energy consumption into our framework. Due to the partial spectrum sensing, we use a Partial Observable Stochastic Game (POSG) formalism, and we analyze the existence and some properties of the Nash equilibrium using a Linear Program (LP). We identify a paradox: when licensed channels are more occupied by PUs, this may improve the spectrum utilization by SUs. Based on this observation, we propose a Stackelberg formulation of our problem where the network manager may increase the occupation of licensed channels in order to improve the SUs' average throughput. We prove the existence of a Stackelberg equilibrium and we provide some simulations that validate our theoretical findings.

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. We consider Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy constraint as well as, the delay constraint, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We further consider that the secondary user (SU) may decide, at any moment, to use another dedicated way (3G) of communication in order to transmit its packets. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. In particular, numerical illustrations validate our theoretical findings. It is shown that optimal policy has a threshold structure.

Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Quality-centric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithms on a Microsoft Lync testbed. We observe that our algorithm improve significantly the QoE compared to standard AIMD congestion control mechanism.

The authors are interested in evaluating the performance of a cognitive radio network composed of secondary and primary mobiles, and look for optimising the decision process of the secondary mobiles when they have to choose between licensed or unlicensed channels. In fact, the system is composed of several channels where only one unlicensed channel is shared between all the secondary mobiles, when they decide to use this particular channel. As the secondary mobiles are equipped with cognitive radios, they are able to sense the licensed channels and use one of them if it is free. The authors consider first the global system and look for the optimal proportion of secondary mobiles that sense the licensed channels in order to optimise an average performance of the system. Second, the authors assume that each secondary mobile decides to sense or not the licensed channels and, are interested in an equilibrium situation as the secondary mobiles are in competition. After showing the existence and the uniqueness of equilibrium, the performance of this equilibrium is evaluated by looking at the price of the anarchy of the system.

Nowadays, multimedia applications and specifically streaming systems over wireless networks use the TCP transport protocol. Indeed, TCP can deal with practical issues such as firewalls and also deploys built-in retransmissions and congestion control mechanisms. We propose in this paper a Quality-centric Mean Opinion Score (MOS) based congestion control that determines an optimal congestion window updating policy for multimedia transmission. Unlike the standard congestion control algorithms, our approach defines a new Additive Increase Multiplicative Decrease (AIMD) algorithm given the multimedia application and the transmission characteristics. In order to get the optimal congestion policy in practice, the sender requires complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in wireless systems. Hence, we propose in this paper, a Partially Observable Markov Decision Process (POMDP) framework in order to determine an optimal congestion control policy which maximizes the long term expected Quality of Experience (QoE) of the receiver. Moreover, the computation of an optimal policy is usually time/process consuming and as wireless devices are capacity-limited, we consider optimal solutions based on temporal difference (TD-λ) online learning algorithms. Finally, we do some practical experiments of our algorithm on a Microsoft Lync testbed with unidirectional and bidirectional communications over a wireless network. We observe that for both scenarios, our algorithm improves significantly the QoE compared to standard AIMD congestion control mechanism.

In this paper, we are interested in evaluating the performance of a cognitive radio network. We look for optimizing the decision process of secondary mobiles between sensing or not primary's channels. We consider first the global system and look for the optimal proportion of secondary mobiles that sense the primary's channels. Second, we assume that each secondary mobile decides opportunistically to sense or not. In this case, the secondary mobiles are in competition. After showing the existence and uniqueness of the equilibrium, we evaluate the performance of this equilibrium by looking at the price of the anarchy.