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.

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.

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.

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In this paper, we develop an analytical framework to compute the Quality-of-Experience (QoE) metrics of video streaming in wireless networks. Our framework takes into account the system dynamics that arises due to the arrival and departure of flows. We also consider the possibility of users abandoning the system on account of poor QoE. Considering the coexistence of multiple services such as video streaming and elastic flows, we use a Markov chain based analysis to compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and bitrate switching. Our simulation results validate the accuracy of our model and describe the impact of scheduler at eNB on the QoE metrics.

Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.

This paper presents an online algorithm for mobile user speed estimation in 3GPP Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed method leverages on uplink (UL) sounding reference signal (SRS) power measurements performed at the base station, also known as eNodeB (eNB), and remains effective even under large sampling period. Extensive performance evaluation of the proposed algorithm is carried out using field traces from realistic environment. The online solution is proven highly efficient in terms of computational requirement, estimation delay, and accuracy. In particular, we show that the proposed algorithm can allow for the first speed estimation to be obtained after 10 seconds and with an average speed underestimation error of 14 kmph. After the first speed acquisition, subsequent speed estimations can be obtained much faster (e.g., each second) with limited implementation cost and still provide high accuracy.

The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users' QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization a hard task. This paper aims at taking a step further in order to address this limitation and meet users' profiles. Specifically, we propose a closed-loop control framework based on the users' (subjective) feedbacks to learn the QoE function and optimize it at the same time. Extensive simulation results show that the proposed scheme converges to a steady state, where the resulting QoE function noticeably improves the users' feedbacks.

By leveraging geolocation and contextual information for mobile users, the prediction of the future throughput becomes more and more feasible. Many approaches on contextaware content delivery have been explored to balance the operators' limited resources with users' requirements. However, the perfect knowledge of the future context cannot be easily performed in real world, which represents a hurdle for most context-aware approaches. In this paper, we address a contextaware delivery algorithm for adaptive video streaming (NEWCAST) that have already been explored in [1] under perfect knowledge of future capacity, to balance the user's perception of the video and the cost of network usage. In order to make NEWCAST more resistant to eventual throughput prediction errors and adapt it to short-term horizons, we propose 4 algorithms that efficiently reduce the number of stalls by at least 75%.

Security is crucial for information systems. In a company, security management is traditionally controlled via a centralized single-point. However, when we deal with multiple computer systems interconnected in a wide area networks (WAN), the use of a central authority for security management is completely meaningless. In this paper, we propose a distributed decision-making designed to thwart viruses in a WAN. A key aspect is whether owners of devices are willing to update their anti-virus in order to protect their computers or not to pay for an anti-virus update and take the risk to be contaminated. Given the fact that computers are interconnected via networks and the Internet, the risk of being infected does not only depend on each computer's strategy, but also on the strategies chosen by other computers in the network. This makes the virus protection problem much more challenging. To do so, we model the interaction between nodes as a non-cooperative game in which each node decides individually whether to update the anti-virus or not. The virus spread is assumed to follow a biologically inspired epidemic model in which the dynamic of sources that disseminate the virus evolves as function of the popularity of virus using the influence linear threshold model. We first provide a full characterization of the equilibria of the game and then we investigate the impact of the update cost. In particular, we study the performance of the strategies at the equilibrium in terms of the update cost and the network size on both the security management system and the anti-virus producers. These results give some helpful insights on how secure is decentralizing antivirus update decisions.

Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

Streaming services come with their own challenges and technical issues that still need to be addressed for satisfying the target quality of experience (QoE) of the end-users in mobile environments. In this paper, we explore the idea of combining users' context information with the packed prefetching process features to enhance users' QoE in heterogeneous networks. More specifically, we propose a scheduling mechanism for video streaming traffic, in which the access to the network resources is restricted to users with a signal-to-noise plus interference ratio (SINR) above a given threshold. This scheme benefits from the fact that, as users are in permanent motion, they may experience different SINR values during the same video streaming session offering the opportunity to only schedule users with good channel conditions. The proposed scheduling approach (subsequently referred to as context-aware mode switching (CAMS)) not only allows to achieve overall network spectral efficiency improvement, but also guarantees fairness and QoE among users. Our simulation results show that CAMS achieves almost 1 bit per second per Hertz gain compared to the conventional scheduler (without CAMS), and up to 87% improvement in the probability of no starvations when users move at 40 kmph.

The knowledge of the future capacity variations in wireless networks using smartphones becomes more and more possible by exploiting the rich contextual information from smartphone sensors through mobile applications and services. It is entirely likely that such contextual information, which may include the traffic, mobility and radio conditions, could lead to a novel agile resource management not yet thought of. Inspired by the attractive features and potential advantages of this agile resource management, several approaches have been proposed during the last period. However, agile resource management also comes with its own challenges, and there are significant technical issues that still need to be addressed for successful rollout and operation of this technique. In this paper, we propose an approach (called NEWCAST) for anticipating throughput variation for mobile video streaming services. The solution of the optimization problem realizes a fundamental trade-off among critical metrics that impact the user's perceptual quality of the experience (QoE) and system utilization. Both simulated and real-world traces collected from [1] are carried out to evaluate the performance of NEWCAST. In particular, it is shown that NEWCAST provides the efficiency, computational complexity and robustness that the new 5G architectures require.

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.

Complementing traditional cellular networks with the option of integrated small cells and WiFi access points can be used to further boost the overall traffic capacity and service level. Small cells along with WiFi access points are projected to carry over 60 percent of all the global data traffic by 2015. With the integration of small cells on the radio access network levels, there is a focus on providing operators with more control over small cell selection while reducing the feedback burden. Altogether, these issues motivate the need for innovative distributed and autonomous association policies that operate on each user under the network operator's control, utilizing only partial information, yet achieving near-optimal solutions for the network. In this paper, we propose a load-aware network selection approach applied to automated dynamic offset in heterogeneous networks (HetNets). In particular, we investigate the properties of a hierarchical (Stackelberg) Bayesian game framework, in which the macro cell dynamically chooses the offset about the state of the channel in order to guide users to perform intelligent network selection decisions between macro cell and small cell networks. We derive analytically the utility related to the channel quality perceived by users to obtain the equilibria, and compare it to the fully centralized (optimal), the full channel state information and the non-cooperative (autonomous) models. Building upon these results, we effectively address the problem of how to intelligently configure a dynamic offset which optimizes network's global utility while users maximize their individual utilities. One of the technical contributions of the paper lies in obtaining explicit characterizations of the dynamic offset at the equilibrium and the related performances in terms of the price of anarchy. Interestingly, it turns out that the complexity of the algorithm for finding the dynamic offset of the Stackelberg model is O(n4) (where n is the number of users). It is shown that the proposed hierarchical mechanism keeps the price of anarchy almost equal to 1 even for a low number of users, and remains bounded above by the non-cooperative model.

The co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively.

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.

In this paper, we develop a hierarchical Bayesian game framework for automated dynamic offset selection. Users compete to maximize their throughput by picking the best locally serving radio access network (RAN) with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. In particular, we investigate the properties of a Stackelberg game, in which the base station is a player on its own. We derive analytically the utilities related to the channel quality perceived by users to obtain the equilibria. We study the Price of Anarchy (PoA) of such system, where the PoA is the ratio of the social welfare attained when a network planner chooses policies to maximize social welfare versus the social welfare attained in Nash/Stackeleberg equilibrium when users choose their policies strategically. We show by means of a Stackelberg formulation, how the operator, by sending appropriate information about the state of the channel, can configure a dynamic offset that optimizes its global utility while users maximize their individual utilities. The proposed hierarchical decision approach for wireless networks can reach a good trade-off between the global network performance at the equilibrium and the requested amount of signaling. Typically, it is shown that when the network goal is orthogonal to user's goal, this can lead the users to a misleading association problem.

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.

Our purpose in this paper is to characterize buffer starvations for streaming services. The buffer is modeled as a FIFO queue with exponential service time and Poisson arrivals. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches to obtain exact distribution of the number of buffer starvations, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit result. We extend this approach to the scenario with a constant playback rate using Tàkacs Ballot theorem. The recursive approach, though not offering an explicit result, allows us to obtain the distribution of starvations with non-independent and identically distributed (i.i.d.) arrival process in which an ON/OFF bursty arrival process is considered. We further compute the starvation probability as a function of the amount of prefetched packets for a large number of files via a fluid analysis. Among many potential applications of starvation analysis, we show how to apply it to optimize objective quality of experience (QoE) of media streaming, by exploiting the tradeoff between startup/rebuffering delay and starvations.

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.

This paper studies a power control game over a collision channel. Each player has an energy state and balances energy conservation and transmission success. When opting for higher transmission power, the chances of a successful transmission in the presence of interference increases at the cost of a larger drop in energy. We study this dynamic game when restricting to simple non-dynamic strategies: a power level is chosen at start-up and maintained during the lifetime of the battery. A thorough analysis of the existence and characterization of the equilibria of this evolutionary Hawk-Dove game is conducted. Moreover, we study the stability of our results under various classes of evolutionary dynamics, including replicator dynamics and Brown-von Neumann-Nash (BNN) dynamics and identify various surprising paradoxes. Simulation results validate our theoretical claims.

We address a hierarchical game-theoretic model for studying power control in multi-carrier systems. The power control problem is modeled as a Stackelberg game where transmitters choose their control policy selfishly in order to maximize their individual energy efficiency. We establish the existence of the Stackelberg equilibrium and give its formal expression. Interestingly, we show that, under some assumptions, introducing a certain degree of hierarchy in a multi-carrier system results in a kind of coordination game where the primary and the secondary users have incentive to choose their transmitting carriers in such a way that they always transmit on orthogonal channels. Such an important result offers insights into how to design hierarchy in multi-carrier environments. Numerical results validate our theoretical claims.

This paper deals with the problem of joint sensing and medium accessing in cognitive radio networks. We design a non-cooperative two-step game to describe the Sense-Transmit-Wait paradigm in an opportunistic point of view. We give a full characterization of the Nash equilibria and analyze the optimal pricing policy, from the network owner view, for both centralized setting and decentralized setting. Next, we propose a combined learning algorithm that is fully distributed and allows the cognitive users to learn their optimal payoffs and their optimal strategies in both symmetric and asymmetric cases. The derived results are illustrated by numerical results and provide some insights on how to deploy cognitive radios in medium access cognitive radio networks in terms of sensing capabilities.

In this paper, we study a power control game over a collision channel. Each player has an energy state. When choosing a higher transmission power, the chance of a successful transmission (in the presence of other interference) increases at the cost of a larger decrease in the energy state of the battery. We study this dynamic game when restricting to simple non-dynamic strategies that consist of choosing a given power level that is maintained during the lifetime of the battery. We identify a surprising paradox in our Hawk-Dove game which we term the initial energy paradox.

Our purpose in this paper is to obtain the exact distribution of the number of buffer starvations within a sequence of N consecutive packet arrivals. The buffer is modeled as an M/M/1 queue. When the buffer is empty, the service restarts after a certain amount of packets are prefetched. With this goal, we propose two approaches, one of which is based on Ballot theorem, and the other uses recursive equations. The Ballot theorem approach gives an explicit solution, but at the cost of the high complexity order in certain circumstances. The recursive approach, though not offering an explicit result, needs fewer computations. We further propose a fluid analysis of starvation probability on the file level, given the distribution of file size and the traffic intensity. The starvation probabilities of this paper have many potential applications. We apply them to optimize the quality of experience (QoE) of media streaming service, by exploiting the tradeoff between the start-up delay and the starvation.

It has been known that the throughput of the 802.11 WLAN is much smaller than the nominal bit rate offered when attempting to connect to an access point. A user may discover the quality of service offered by an access point only after taking the decision of which of the access points to connect to. In fact, the actual throughput of a user is a function of not only his channel state but also of that of the other connected users. This could likely lead to congestion and overload conditions in the Access Point (AP) in question (which offers the best signal strength) and all users would lose. The information available to users attempting to connect to an AP is thus misleading. In this paper, we develop a Nash-Bayesian game framework where users compete to maximize their throughput by picking the best locally serving radio access network (RAN) with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. We derive analytically the utilities perceived by users to obtain the equilibria. In particular, it is shown that equilibria strongly depend on the channel quality indicator.

Distributing Radio Resource Management (RRM) in heterogeneous wireless networks is an important research and development axis that aims at reducing network complexity. In this context, RRM decision making can be delegated to mobiles by incorporating cognitive capabilities into mobile handsets, resulting in the reduction of signalling and processing burden. This may however result in inefficiencies (such as those known as the "tragedy of commons") that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized network architectures and protocols keep being considered and being compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal-centric RRM schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. At some system's states, the network manager may impose his decisions on the network users. In other states the mobiles may take autonomous actions in reaction to information sent by the network. In order to improve the performance of the non-cooperative scenario, we investigate the properties of an alternative solution concept named Stackelberg game, in which the network tries to control the users' behavior by broadcasting appropriate information, expected to maximize its utility, while individual users maximize their own utility. We derive analytically the utilities related to the Quality of Service (QoS) perceived by mobile users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE system that serve streaming and elastic flows.

Motivated by the fact that when selfish users choose their policies independently without any coordination mechanism, Nash equilibria could result in a network collapse, we develop in this paper a hierarchical distributed learning framework for decision-making in heterogeneous cognitive networks. We introduce the Nash-Stackelberg fuzzy Q-learning, with the network as leader that aims at maximizing its utility (revenue) and the mobiles as followers that have their individual objectives (maximizing their QoS). We validate our results through extensive simulations of the algorithm in a practical setting of a geographical area covered by a global HSDPA and 3G LTE system that serves both streaming and elastic traffic.

The area of networking games has had a growing impact on wireless networks. This reflects the recognition in the important scaling advantages that the service providers can benefit from by increasing the autonomy of mobiles in decision making. This may however result in inefficiencies that are inherent to equilibria in non-cooperative games. Due to the concern for efficiency, centralized protocols keep being considered and compared to decentralized ones. From the point of view of the network architecture, this implies the co-existence of network-centric and terminal centric radio resource management schemes. Instead of taking part within the debate among the supporters of each solution, we propose in this paper hybrid schemes where the wireless users are assisted in their decisions by the network that broadcasts aggregated load information. We derive the utilities related to the Quality of Service (QoS) perceived by the users and develop a Bayesian framework to obtain the equilibria. Numerical results illustrate the advantages of using our hybrid game framework in an association problem in a network composed of HSDPA and 3G LTE systems.

We consider the downlink of a cognitive radio network consisting of multiple secondary transmitter and receiver communicating simultaneously in the presence of one primary user. The key idea within this paper is to combine multi-user diversity gains with spectral sharing techniques through inter-system coordination, in order to maximize the secondary user sum rate while maintaining a guaranteed quality of service (QoS) to a primary user. We first present a distributed power allocation algorithm that maximizes the capacity of the cognitive ratio network. The algorithm is simple to implement, since a secondary user can decide to either transmit data or stay silent over the channel coherence time depending on a specified threshold without affecting the primary users QoS. Then, we analyze performance of such an algorithm in terms of number of cognitive users able to transmit while minimizing interference to guarantee QoS for the primary user. Simulation results carried out based on a realistic network setting showed promising results.

The authors investigate the idea of using cognitive radio to reuse locally unused spectrum to increase the total system capacity. The authors consider a multiband/wideband system in which the primary and cognitive users wish to communicate to different receivers, subject to mutual interference and assume that each user knows only his/her channel and the unused spectrum through adequate sensing. The basic idea under the proposed scheme is based on the notion of spectrum pooling. The idea is quite simple; a cognitive radio will listen to the channel and, if sensed idle, will transmit during the voids. It turns out that, although its simplicity, the proposed scheme showed very interesting features with respect to the spectral efficiency and the maximum number of possible pairwise cognitive communications. We impose the constraint that users successively transmit over available bands through selfish water filling. For the first time, our study has quantified the asymptotic (with respect to the band) achievable gain of using spectrum pooling in terms of spectral efficiency compared with classical radio systems. The authors then derive the total spectral efficiency as well as the maximum number of possible pairwise communications of such a spectrum-pooling system.

In this contribution , we investigate the idea of usingcognitiveradioto reuse locally unused spectrum to increase the total system capacity. We consider a multiband/wideband system in which the primary and cognitive users wish to communicate to different receivers, subject to mutual interference and assume that each user knows only his channel and the unused spectrum throughperfectsensing. Under this scheme, a cognitive radio will listen to the channel and, if sensed idle, will transmit during the voids. We impose the constraint that users successively transmit over available bands through proper water filling. Within this setting, we derive the total spectral efficiency of the cognitive radio system as well as the spectral efficiency gains and prove that we can improve the overall system spectral efficiency by considering cognitive communications in the system.