Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FAenabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

In this paper, the performance of opportunistic user scheduling in reconfigurable intelligent surface (RIS)-aided multiuser communications is investigated by invoking 1-bit coding scheme. In particular, we derive new expressions for the outage probability (OP) and delay outage rate (DOR) by leveraging copula theory to model a two-fold interdependency stemming from the presence of discrete phase noise and a shared channel. We also provide simplified expressions for the OP and DOR using Clayton and Gumbel copulas. The newly derived expressions reveal the effects of different channel and RIS parameters simply. Finally, numerical results are provided to validate the derived exact closed-form expressions.

This study investigates the anti-jamming space-air-ground integrated network (SAGIN) scenario wherein a reconfigurable intelligent surface (RIS) is deployed on a fixed Unmanned Aerial Vehicle (UAV) to counteract malevolent jamming attacks. In contrast to existing research, in this paper, we consider that a Low Earth Orbit (LEO) satellite is sending the signal to the user on the ground in the presence of jamming from a Geostationary Equatorial Orbit (GEO) satellite side. We aim to maximize the signal-to-jamming plus noise ratio (SJNR) by optimizing the RIS beamforming and transmit power of the LEO satellite. Assuming the availability of global channel state information (CSI) at the RIS, we propose alternating optimization (AO) and semidefinite relaxation (SDR) techniques to address the complexity. Simulation results show that the optimization schemes lead to considerable performance improvements. The results also indicate that, given the high jamming power and the relatively small number of RIS elements, deploying the RIS on UAVs near the user is more effective in mitigating the impact of jamming interferers.

This paper addresses the problem of resource allocation in aerial intelligent reflecting surface (AIRS)-assisted wireless networks, where intelligent reflecting surfaces (IRS) are deployed on flying platforms. Specifically, we investigate the joint optimization of AIRS placement and phase shifts, along with base station subcarrier and power allocation to maximize the system sum rate. To address the problem's non-convexity, an alternating optimization framework is proposed. In this framework, we employ K-means clustering to determine optimal AIRS locations and decompose the problem into two interdependent subproblems: AIRS phase optimization on the one hand and base station subcarrier and power allocation on the other hand, each solved iteratively until convergence is reached. In addition, the successive convex approximation method is used to tackle the non-convexity of AIRS phase shift optimization. Numerical results demonstrate that the proposed approach outperforms benchmark schemes, highlighting the potential of AIRS to extend cellular coverage in challenging scenarios such as emergencies or regions with limited infrastructure.

Unmanned aerial vehicles (UAVs) face escalating cybersecurity threats, particularly from GPS spoofing and jamming attacks, which endanger flight safety and mission integrity. This paper introduces a novel UAV cybersecurity framework that integrates statistical extreme value meta-learning (EVML) with a dual-path classifier to detect and classify GPS attacks. The method overcomes the key limitations of some existing approaches, such as excessive training data needs, zero-day threat vulnerability, and disjointed detection/classification. In particular, the proposed framework includes two stages. The first one enables zero-day attack detection through few-shot meta-learning with prototype-based anomaly detection where support sets contain only benign flight data while query sets include both benign samples and synthetically generated attack patterns. Moreover, a prototypical OpenMax layer and extreme value theory are exploited to identify suspicious patterns in GPS telemetry data. The second stage utilizes a novel dual-path architecture that independently processes position-related and signal-related features to classify detected attacks as spoofing or jamming. Our results on real-world attack data demonstrates that the proposed method has exceptional performance with 97.33 % detection accuracy and 0% false alarm rate, which significantly outperform the state-of-the-art. Furthermore, attack classification achieves 82.33 % accuracy for spoofing and 94.31 % for jamming attacks, with an overall F1 score of 0.88.

Innovative holographic applications such as holographic concerts have recently emerged. They are expected to play an important role in the Metaverse. Hybrid Network Function Virtualization (NFV) / IN-Network Computing (INC) network infrastructures are needed to provision them as recently shown in the literature. INC is an emerging technology that aims to distribute the computational workload across the network by placing computational tasks on programmable devices (e.g., routers or switches). However, the integration of INC in existing infrastructures does face significant management and orchestration challenges. Although the ETSI Management and Orchestration (MANO) architectural framework designed for 5G facilitates application provisioning in networks that are NFV enabled, it lacks support for networks that are INC enabled. Therefore it is necessary to have a new MANO architecture in order to provision applications which have both NFV and INC components. This paper proposes a hybrid NFV-INC MANO architecture for provisioning holographic applications in hybrid NFV/INC environment. The proposed architecture is an extension of the ETSI NFV MANO. It will certainlyplay an important role in 6G since many applications foreseen for 6G will have the same stringent requirements as holographic applications. It is evaluated through a proof of concept prototype. The following tools were used for the prototype: Open Source MANO (OSM) and Mininet emulator.

In this paper, we address optimum design of uncrewed aerial vehicle (UAV)–based wireless networks with a focus on computation offloading in the presence of an active aerial attacker. Our design aims to minimize the maximum computation time among the tasks of ground users while satisfying the energy consumption requirements. To this end, we propose a joint optimization problem of partial computation offloading, ground user association, multiple UAVs trajectory control, computation resource, and sub-channel assignment. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach to iteratively solve the five sub-problems, namely, user-UAV association, user scheduling, partial offloading control and bit allocation over time slots, computation resource and sub-channel assignment, and UAV trajectory control until convergence. Moreover, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the system against jammer attacks. Additionally, we propose low-complexity algorithms to solve the involved sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the impact of an aerial attacker.

The increasing demand for ultra-low latency and high bandwidth in emerging applications, such as virtual reality gaming and telesurgery, is challenging current network infrastructures. In-Network Computing (INC) has emerged as a promising solution to these challenges by optimizing network performance, reducing congestion, and minimizing both latency and bandwidth usage. As 6G networks strive to deliver unprecedented connectivity and ultra-low latency, INC is poised to become an integral part of future network architectures. However, a significant gap exists in the literature concerning a comprehensive model for describing INC components, which is crucial for effective INC provisioning. To address this gap, we propose the In-Network Computing Ontology (INCO), a domain-independent, ontology-based model designed to describe and discover INC components in a centralized repository. Our model covers both the functional and non-functional specifications of INC components. Furthermore, we introduce a semantic matchmaking algorithm that uses the INCO model to automatically discover and select the most relevant INC components from the repository based on user requests. Experimental simulations validate our approach, demonstrating the effectiveness of the semantic matchmaking algorithm, particularly regarding response time and consistency.

In response to the challenges of spectrum scarcity and the exponential growth of the number of connected devices, this paper addresses the joint optimization problem of user-base station association, channel assignment and power allocation in a multi-band wireless network, where sub-6 GHz, millimeter wave, and terahertz frequency bands coexist. The problem is formulated as a mixed integer non-linear programming, a known NP-hard problem. Each user requests both a minimum data rate and a minimum reliability level defined by a signal-to-noise ratio. Considering the goal of optimizing the number of satisfied users, this paper proposes a multi-agent deep reinforcement learning solution. Simulation results convincingly demonstrate the effectiveness of our proposed algorithm and its ability to learn fast the best resource allocation solution.

In this paper, we study unmanned aerial vehicle (UAV) trajectory control and channel assignment for UAV-based wireless networks with wireless backhauling. Our design aims to maximize the sum rate achieved by ground users while satisfying their data demand where spectrum reuse and co-channel interference management are considered. To tackle the underlying non-convex mixed-integer nonlinear optimization problem, we use the alternating optimization approach where we iteratively optimize the channel assignment and UAV trajectory control until convergence. Particularly, we propose an efficient heuristic algorithm to solve the channel assignment sub-problem. Moreover, the successive convex approximation (SCA) is used to solve the non-convex UAV trajectory control sub-problem. Via extensive numerical studies, we illustrate the effectiveness of our proposed design considering different network settings.

In this paper, we study the computation offloading problem against an active attacker in space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, computation and resource allocation is performed. Our design aims to minimize the maximum computation time of individual tasks among ground users while satisfying energy consumption constraints. To tackle the underlying non-convex optimization problem, we use the alternating optimization approach to iteratively solve three sub-problems, namely, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and UAV trajectory control, until convergence. Furthermore, the successive convex approximation method is employed to solve the non-convex sub-problems and improve the resilience of the SAGIN against active attacks. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under the effect of an active attacker.

Reconfigurable intelligent surface (RIS) has received remarkable attention for its potential to improve the capacity and coverage of wireless communication networks. In this paper, we evaluate the performance of RIS-assisted communication systems in the presence of phase noise with the help of Student-T copula in two scenarios. The first one is the cascade link or RIS link and the second scenario involves direct link in conjunction with cascade link. In particular, we first analyze the probability density function and the cumulative distribution function of the signal-to-noise-ratio with/without direct link. Then, we investigate the outage probability and ergodic capacity of the RIS-assisted network by using Student- T copula function to characterize the non-linear dependency among the signal components. Furthermore, we reveal the relation between the Student- T copula dependency parameter and Pearson correlation coefficient. Finally, numerical results are presented confirm the validity of the analytical closed-form expressions.

Cloud and edge computing have proposed different functionalities to enable multiple applications requiring different communication, computing, and caching (3C) resources. The upcoming futuristic applications (e.g., metaverse, holographic, and haptic communication) impose further stringent requirements (e.g., ultra-low latency, ultra-high reliability) on the infrastructure. These requirements call for a paradigm shift in the infrastructure architecture where all resource components and owners collaborate from the cloud up to the edge, creating a cloud-to-edge continuum of integrated resources. Furthermore, we argue that artificial intelligence (AI) and collaborative-based decisions are promising techniques to efficiently manage the highly complex architecture that jointly leverages 3C in the continuum. This article presents a comprehensive survey of existing research, including AI and collaborative-based studies, targeting the effective and seamless provision of 3C resources and services in the cloud-to-edge continuum. Through an extensive analysis of driving use cases, the synergy between these three main services is scrutinized to highlight its crucial role in the next-generation network infrastructures (NGNI). Finally, a discussion on the opportunities and challenges brought by integrating 3C in NGNI from different perspectives, including architectural design as well as the regulatory and business aspects, are presented.

The emergence of a new class of enhanced multimedia services has pushed network operators to support innovative network services while meeting end-to-end Quality of Service requirements and maintaining profitability. Recent works have hailed Network Function Virtualization (NFV) as a cost-effective enabling technology for novel service delivery in 5G networks. Over time, the association between these NFV-based services and multi-domain networks has grown. As the market competition posed by third-party operators such as virtual operators and service providers has intensified, profitability has become a crucial factor in resource allocation issues. In this paper, we formulate the problem of multi-domain network slicing in dynamic market environments and investigate the effects of these variables on service placement. Due to the NP-hardness of the problem, we employ a node ranking-based algorithm to determine optimal slice entry points in order to maximize profits while meeting end-user QoS requirements. In terms of slice placement acceptance rate and profit growth, numerical results demonstrate the superior performance of our proposed solutions.

To cope with the spectrum scarcity of emerging mobile applications, using higher frequency bands (such as THz) becomes a necessity. Combining simultaneously higher and lower bands is then an important step for future cellular networks. Meanwhile, resource management plays a dominant effect on the system performance especially when different quality of service requirements are considered. In this paper, we formulate and investigate a joint optimization problem of resource allocation in integrated sub-6 GHz, mmWave and THz networks to maximize the system energy efficiency (EE). Therefore, we propose efficient centralized and distributed low-complexity greedy solutions. Also, we propose more efficient multi-agent reinforcement learning (MARL) solutions where users, modeled as agents, collaborate to learn and converge to the optimal user association that maximizes the EE. Simulation results show the EE provided by the proposed solutions and illustrate the superiority of the MARL-based solutions.

This paper considers an unmanned aerial vehicle (UAV)-assisted Hierarchical Federated Learning (HFL), where UAVs act as intermediate aggregators. We formulate an optimization problem that aims to find the best UAV placements, user-UAV associations, channel assignments, and user selection to minimize the time needed for HFL to achieve a target learning accuracy. We propose a two-phase approach to solve the problem. The first one deals with the UAV placement using the K-means algorithm. For the second phase, we propose a user association and selection algorithm that prioritizes selecting users who can make a time-efficient and significant contribution to the FL training process. Simulation results show the proposed algorithm’s efficiency and ability to reach the target learning accuracy faster than the considered benchmarks.

User-centric overlapped clustering, relying on base station (BS) cooperation, is a promising architecture for densely deployed BSs in millimeter-wave (mmWave) networks. In this architecture, a user can be served by a set of cooperating BSs which reduces the interference received from neighboring BSs. This paper studies the problem of maximizing the number of served users in a dense mmWave network while guaranteeing the quality of service (QoS) required by each UE, defined by a received signal quality. Since the formulated problem is NP-hard, two near-optimal solutions are proposed that perform clustering and resource allocation. The first is a heuristic algorithm that builds the clusters by greedily associating the user with as many BSs as needed. The second approach is a binary particle swarm optimization (PSO) algorithm adapted to our constrained problem. Simulations confirm that the proposed algorithms approach the optimal solution with substantially lower computational complexity.

In natural disasters and unforeseen incidents, such as floods, earthquakes and hurricanes, the traditional communication infrastructure may become unavailable to support the emergency tele-operations. Under such circumstances, deploying unmanned aerial vehicles (UAVs) as small flying base stations is seen as a promising solution to provide real-time data communication between physicians and remote robots in both up-link and down-link directions with strict transmission requirements. This paper studies the joint optimization problem of resource allocation and UAVs positioning in UAV-assisted wireless networks with the goal of minimizing the number of deployed UAVs. Since the formulated problem is a non-convex mixed-integer non-linear programming problem, efficient heuristic and genetic solutions are proposed. Simulation results show that the proposed heuristic algorithm approaches the genetic one with an important reduction in computational complexity.

Fog computing (FC) emerged as a new paradigm enabling the deployment of new Internet of Things (IoT) applications. Fog infrastructure is composed of heterogeneous nodes characterized by a complex distribution, mobility, and sporadic resource availability. Hence, resource coordination for continuous Quality-of-Service (QoS) satisfaction becomes challenging, and accurate resource tracking is needed for flawless servicing. In this context, we investigate and propose online resource allocation solutions. The main objective is to maximize the number of satisfied users within a predefined latency requirement. Hence, we model the FC environment as a Markov Decision Process, and then, we formulate the optimization problem. Due to the problem’s NP-hardness, we leverage the reinforcement learning (RL) tool to develop resource allocation schemes. First, a centralized method where a smart fog controller possesses a global awareness of the FC environment is proposed. Next, a more practical and collaborative solution is presented, where each RL-enabled agent manages a group of fog nodes and their resources in order to satisfy computing requests. Based on real-world mobility data sets, simulation results illustrate the high efficiency of the proposed solutions with a preference for the collaborative approach. The superiority of our proposed solutions over state-of-the-art methods is also illustrated.

Exploiting the advantages of both non-orthogonal multiple access technique and millimeter-wave communications requires joint efficient resource allocation techniques toward satisfying the stringent requirements of future mobile communication systems. This paper focuses on a multi-band (i.e., millimeter-wave band and sub-6 GHz band) wireless network where both orthogonal and non-orthogonal multiple access techniques coexist. A joint optimization of user association, transmit power allocation, sub-channel assignment, and multiple access technique selection is investigated to maximize the down-link sum-rate under a minimum rate requirement per user and power constraints. The problem is formulated as a non-convex mixed-integer optimization problem; then, it is proved to be$\mathcal {NP}$-hard. First, simple greedy and meta-heuristic solutions are proposed. Then, since model-based approaches have generally a high computational complexity, model-free centralized and distributed approaches based on deep reinforcement learning technique are proposed. The latter are based on multiple parallel deep neural networks to generate resource allocation solutions. The proposed approaches are evaluated and compared. Simulation results corroborate the high performance offered by the proposed solutions for stationary and mobile users. They also highlight the benefits of employing hybrid orthogonal and non-orthogonal multiple access scheme in multi-band systems in terms of down-link sum-rate and user fairness.

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

This paper investigates the use of cell-free unmanned aerial vehicles (UAVs)-assisted wireless networks and optimizes the number of deployed UAVs under quality of service and coverage constraints. The formulated problem tackles the user-UAVs association, UAVs placement, channel assignment and transmit power allocation while considering both access and backhaul networks. Since the problem is a non-convex and non-linear mixed-integer programming, low-complexity efficient greedy-based algorithmic solutions are proposed. The first one finds the UAVs' best positions and allocates resource whereas the second one guarantees the problem feasibility (i.e., all users can be satisfied) by removing the worst users. For comparison purposes, a meta-heuristic solution based on the Particle Swarm Optimization technique is proposed. Simulation results illustrate the efficiency of the proposed algorithms in terms of the number of deployed UAVs in cell-free wireless networks.

Microservices are a promising technology for future networks, and many research efforts have been devoted to optimally placing microservices in cloud data centers. However, microservices deployment in edge and in-network devices is more expensive than the cloud. Additionally, several works do not consider the main requirements of microservice architecture, such as service registry, failure detection, and each microservice's specific database. This paper investigates the problem of placing components (i.e. microservices and their corresponding databases) while considering physical nodes' failure and the distance to service registries. We propose a Components-aware Microservices Placement for In-Network Computing Cloud-Edge Continuum (CaMP-INC). We formulate an Integer Linear Programming (ILP) problem with the objective of cost minimization. Due to the problem's$\mathcal{NP}$-hardness, we propose a heuristic solution. Numerical results demonstrate that our proposed solution CaMP-INC reduces the total cost by 15.8% on average and has a superior performance in terms of latency minimization compared to benchmarks.

Even though combining multi-access techniques (i.e., orthogonal and non-orthogonal multiple access techniques) and multi-band communications (i.e., millimeter-wave and sub-6 GHz communications) is needed to satisfy the stringent requirements of emerging wireless applications, it triggers new resource management challenges. In this paper, we investigate the joint optimization problem of user association, transmit power allocation, and sub-channel assignment in a such network in order to maximize the down-link sum-rate. The problem is formulated mathematically as a mixed-integer non-linear programming problem. Due to the combinatorial and the non-convex characteristics, solving the problem demands further developed complex mathematical tools. To this end, simple but efficient centralized and fully-distributed greedy-based algorithms are proposed, then, genetic algorithms are presented. Simulation results show the efficiency of the proposed solutions and the effectiveness of using heterogeneous multi-access techniques in terms of down-link sum-rate.

Mobile edge computing (MEC) is an important technology for latency-sensitive applications. One of the biggest challenges in MEC is efficiently allocating resources under strict QoS requirements and resource constraints. The purpose of this paper is to study the joint problem of computation offloading and resource allocation in such networks. The problem is formulated as a mixed-integer non-convex optimization problem and is proved to be NP-hard. In order to solve it efficiently, we propose a multi-agent deep reinforcement learning solution based on actor-critic method. To reduce system latency, each agent aims to learn interactively the best offloading policy independently of other agents. The simulation results illustrate the performance and advantages of the proposed solution compared to benchmark solutions.

The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.

The presence of unwanted signals in the radio frequency (RF) spectrum, called RF interference (RFI), is a major drawback in wireless communication systems. The detection of REI has been dealt mainly with signal processing and supervising machine learning approaches. In this paper, we investigate two unsupervised machine learning alternatives for REI detection, the one-class support vector machine (SVM) and the support vector data description (SVDD) algorithm, which delimit the class boundaries of normal signals in high-dimensional space, and view REI contaminated signal as novelty, i.e., outsiders from unknown classes. Similar to the popular binary SVM classifier, these two algorithms can learn from a relatively small training set, and they use unsupervised training to learn from typically unbalanced RFI data sets without need for data augmentation techniques as in supervised training. The experimental results for detecting three types of RFI, using scaling features to a range as a standardization method, show that SVDD has a low computational complexity and an accuracy of 90.74 % versus 91.67 % for the One-class SVM.

This paper considers the transmission scheduling problem in a single-node energy harvesting (EH) wireless communication system, where the monitoring application requires regular status updates. The objective is to minimize the number of inter-delivery violations events over a time horizon in a wireless sensor network consisting of an EH sensor node providing status updates to a non-EH sink. The offline scheduling problem is formulated as an integer linear program and is solved optimally in polynomial time using a dynamic programming approach. Next, an efficient and low complexity heuristic algorithm is proposed for the online setting. Simulation results show the effectiveness of our proposed algorithms compared to baseline methods.

This paper investigates a user-centric clustering model for a hybrid network comprising both millimeter-wave (mmWave) and terahertz (THz) base stations (BSs). Based on the proposed model, a user can choose to be cooperatively served by multiple mmWave or multiple THz BSs depending on their link quality. Besides, to maximize the cooperation gains, the serving clusters are dynamically adjusted to user's channel conditions pertaining to the different properties of the mmWave and THz networks. Finally, we evaluate the coverage probability of the hybrid network by using stochastic geometric tools and validate the analysis through numerical simulations.

In this paper, we propose a collaborative smart solution for online traffic offloading among device-to-device (D2D) users underlying a cellular network. Specifically, we investigate the distributed pairing problem between requesting users and caching devices in their vicinity. Given that this problem is NP-hard, we propose a novel multi-agent reinforcement learning approach based on QMIX algorithm, where each requesting user is an agent capable of deciding to which cache device to pair, while respecting the quality-of-service of cellular users. Through simulations, we show the efficiency of the proposed algorithm in achieving D2D pairing. Finally, the impact of several parameters, such as the size of the network, size of files library, and communication requirements, is investigated.

The user-centric base station cooperation is a new approach that allows a mobile user to be connected to a set (cluster) of base stations instead of being associated with a single one. This approach is highly valuable in millimeter wave networks where the base stations are expected to be densely deployed. In this paper, we evaluate the performance, in terms of coverage probability, of user-centric millimeter wave networks with dynamic clustering. First, we propose a dynamic clustering model for base stations that will cooperate to serve a given user. Then, based on the proposed model, we investigate analytically the coverage probability performance of the considered user-centric network using stochastic geometry tools. Finally, numerical and simulation results are provided, showing that the proposed dynamic clustering model always outperforms static clustering and single base station selection schemes for given network parameters.

In this article, we investigate an energy harvesting (EH) wireless sensor network for the Internet of Things (IoT) where monitoring applications require a continuous update of sensing information. The considered system consists of independent EH sensor nodes equipped with capacitors and providing, through unreliable channels, status updates to a non EH sink. The distribution of the interdelivery time, i.e., the time elapsed between two successive and successful status update deliveries, is derived in the closed-form expression considering a random EH arrival process. Moreover, the interdelivery violation probability metric, defined as the probability to exceed a predetermined interdelivery threshold, is analyzed. Our analysis reveals that the violation probability is highly dependent on the size of the capacitor. Both analytical and simulation results demonstrate the existence of an optimal capacitor size that achieves the minimum violation probability. Moreover, our findings reveal an interesting tradeoff in the system design. On one hand, a small capacitor charges quickly and thus status updates are sent more frequently but with lower transmit power and thus a high error rate. On the other hand, a large capacitor increases the transmit power and boosts the successful data transmission probability, at the expense of a higher waiting time before filling the capacitor and transmitting sensed data.

In millimeter wave (mmWave) networks, base stations (BSs) are expected to be densely deployed in order to meet the demands of mobile users. A major challenge in dense mmWave networks is the interference experienced by the user from the neighboring BSs which limits the density of deployed BSs. A promising solution to this challenge is to adopt user-centric BS cooperation that allows a user to be connected to a set (cluster) of BSs instead of being associated with a single one. In this paper, we investigate the performance of user-centric mmWave networks with dynamic clustering. First, we propose a dynamic clustering model to enable the user-centric BS cooperation. Then, based on the proposed model, we analyze the coverage probability and average spectral efficiency (ASE) performances using stochastic geometry tools. We also propose a BS clustering optimization framework to achieve maximum ASE performance for given network configuration. Finally, numerical and simulation results are provided, showing that the proposed dynamic clustering schemes always outperform static clustering and single-BS selection schemes and yield an optimum system performance for given network parameters.

This paper investigates the throughput improvement of cell-free massive multiple-input multiple-output (MIMO) systems by non-orthogonal multiple access (NOMA) for future cellular networks under stochastic access point and user locations. In this context, the node locations are modeled with Poisson point processes. The time division duplexing mode is employed, and uplink channels are estimated locally using uplink pilots. Furthermore, unique pilot sequences are used between NOMA clusters, while pilot reuse occurs within each cluster to strike a balance between the training overhead and the number of clusters. Matched-filter-based precoding is utilized for downlink transmission. The aggregate received signal is analytically characterized by deriving the moment generating function and approximations via moment matching. Then, the asymptotic achievable rates of the NOMA users are derived, thereby quantifying the adverse impact of error propagation owing to imperfect successive interference cancellation. Special scenarios with prior downlink channel state information and log-distance power control are also considered. We show that NOMA greatly increases the achievable average rate, especially under low path loss exponents and dense networks, while user fairness may be boosted by the adoption of a log-distance transmit power control scheme with proper parameter selection (i.e. lower values for the power control parameter).

This article describes a design for a frequency synthesizer architecture based on a phase-locked loop (PLL) for avionic software-defined radio (SDR) applications at up to 9 GHz. Three basic architectural schemes: wide range voltage-controlled oscillator (VCO), single sideband (SSB) mixing, and multiple VCOs can be used to extend the frequency ranges. This article compares these schemes through quantitative evaluation to select the best synthesizer architecture to use according to the application specs. The chosen scheme is an optimized combination of a single VCO and a single SSB mixer. Using a quadrature VCO (QVCO) with a switched capacitor (SC) bank, the synthesizer provides a wide frequency band of operation ranging from 100 MHz to 9 GHz covering several avionic communication applications and several existing wireless standards’ frequency requirements. The proposed QVCO is able to generate in-phase and quadrature-phase signals spread into a frequency band between 6 and 9 GHz providing a tuning range of 40% at a center frequency of 7.5 GHz. The QVCO exhibits a phase noise of −107 dBc/Hz, at a 1-MHz offset frequency, while generating a 8-GHz carrier frequency. Its power consumption is of 3.4 mW. With a loop bandwidth of 120 kHz, the frequency synthesizer generates a phase noise, measured at 8 GHz, of −106.4 dBc/Hz at a 1-MHz frequency offset. The overall power consumption of the synthesizer to generate a 140-MHz carrier frequency is 26.57 mW from a 1.2-V supply. The frequency synthesizer is implemented in 0.13-$\mu \text{m}$CMOS technology and occupies an active area of$0.72 \times 0.72$mm2.

In this work, we deal with the violation probability of data update interval for a wireless sensor network with energy harvesting capabilities, in the context of monitoring applications requiring a continuous update of sensing information. Specifically, we consider a wireless sensor network consisting of independent energy harvesting sensor nodes providing status updates to a non energy harvesting sink. A sensor node generates a status update message when its battery becomes fully charged. The generated message is then transmitted without further energy management, i.e., using all the available harvested energy and according to a first-come-first-served access method. In this paper, we first derive the update interval distribution for a random energy arrival process. Then, the violation probability of the update interval is derived in closed-form for the one-node wireless sensor network. It is shown that it highly depends on the size of the battery. Furthermore, the update interval of a multi-node system is characterized. Obtained analytical and numerical results show that there exists an optimal battery size that minimizes the violation probability. Moreover, the design of the system introduces an interesting trade-off. On one hand, a small battery is charged quickly and thus updates are sent more frequently but with a high error rate. On the other hand, a larger battery increases the transmit power and boosts the successful data transmission probability but increases the time required before transmitting.

The next generations of cellular wireless systems promise an improved user experience with respect to the throughput, reliability, latency, and connectivity. To this end, multiple-input multiple-output (MIMO) and massive MIMO systems hold enormous potential. Moreover, due to heterogeneity and densification, user and base station locations are increasingly random. Thus, in this paper, we characterize the performance of a typical downlink user within a massive MIMO Voronoi cell when the base stations employ matched filter based precoding in the downlink. We consider a typical Voronoi cell where the base stations follow a Poisson point process (PPP), and also consider the users to be randomly distributed according to a PPP independent from the base stations. Furthermore, a wireless channel with log-distance path loss and Rayleigh fading is assumed. Using a novel framework to model the signal to noise ratio, the outage probability of a user is derived in closed-form while taking into account full and partial loading for the cell's base station due to the randomness of user numbers. Numerical results show that the outage probability is heavily dependent on the base station density, and that the performance decreases when the maximum number of users served by a base station increases.

Simultaneous wireless information and power transfer (SWIPT) is increasingly seen as a viable technique to power energy constrained user devices while transmitting data. SWIPT can be readily implemented within the recently proposed cell-free massive MIMO networks, where a large number of interconnected access points (APs) simultaneously serve users. This paper characterizes the coexistence and interplay between these two technologies by using tools from stochastic geometry. To this end, we consider a spatially random network, where the APs are modeled stochastically using a Poisson point process, and a time-switching protocol is used for the SWIPT operation at the users. A time-division-duplexing protocol is considered in which uplink pilots are used to obtain channel state information at the APs, while conjugate beamforming is performed in the downlink. Moreover, we consider blockages due to obstacles in the channel and the resulting line-of-sight and non-line-of-sight conditions affecting the fading and path loss. We derive the mean and variance of the harvested energy along with the average achievable rate in the downlink for an energy user. The tradeoff between the downlink data throughput and harvested energy is quantified, and thereby, we show that spatially-distributed APs in a cell-free arrangement can boost the energy-rate trade-off of SWIPT.

This paper studies the joint problem of user association and sub-channel assignment in a wireless network, where orthogonal (OMA) and non-orthogonal multiple access (NOMA) techniques co-exist. We also assume the co-existence of millimeter wave and sub-6 GHz communications. Inspired by the fact that traditional resource allocation methods may not be effective in such heterogeneous multiple access networks (HetNets), a joint optimization problem of user association and sub-channel assignment is formulated where the objective is to maximize the number of associated users. The considered problem is proved to be NP-hard, and therefore an efficient heuristic algorithm is proposed to solve it in a polynomial time. Simulation results corroborate the effectiveness of the proposed algorithm and show that combining OMA and NOMA techniques outperforms single OMA or NOMA technique.

In this paper, a new analytical framework model based on stochastic geometry for Device-To-Device (D2D) communication underlaying multi-cell massive Multi-Input-Multi-Output (MIMO) system is proposed. Assuming Maximum Ratio Transmission or Zero Forcing precoding scheme for cellular downlink transmission, the impact of RF mismatches and achievable rate of cellular user are analytically derived. The studied model assumes truncated Gaussian distribution to model RF mismatches, D2D interference, inter cell interference, and intra-cell interference. Accordingly, closed form expressions of lower-bound achievable data rate for cellular users is derived. Moreover, asymptotic performance analysis under the assumption of large number of antennas has been performed. Simulation results are found to coinside with the theoritical results and validated our model.

Radio frequency interference (RFI) is considered as anomalous disruptive parasite signal due to its harmful impact in wireless communication. That is why, RFI mitigation is indispensable to avoid this impact. Detecting and localizing the RFI are the first steps in RFI mitigation process. In this paper, we propose two approaches to detect and localize RFI using the supervised and unsupervised techniques of deep learning. First, our research investigates an object detection algorithm based on convolutionnal neural network as a supervised approach. This proposition is based on the object detection algorithm You Only Look Once v3 (YOLO-v3) trained on real-world data contaminated by multiples sources of RFI. Second, we propose the utilisation of Convolutionnal Autoencoder (CAE) as an unsupervised approach. Experimental results show that the RFI detection by YOLO-v3 is relatively fast and it has an excellent accurate detection rate of 94% and show that the average precision of the YOLO-V3 algorithm can achieve 89%. For CAE, the average precision achieves 78% and outperforms the supervised approach in certain cases.

In cellular networks that exploit the millimeter wave bands, access points are expected to be densely deployed and to share limited radio resources. In such systems, the geometric distribution of the downlink signal-to-interference-plus-noise ratio (SINR) is affected by several random variables such as path loss, blockage, small scale channel fading, interferer density, antenna array orientation, and thermal noise. Therefore, using fixed modulation in such varying environments is inefficient as it degrades the spectral efficiency and/or increases the outage probability. To tackle this issue, the transmitter needs to adapt the modulation order to the link condition. In this paper, we introduce an adaptive modulation technique, which depends on the geometric distribution of the SINR, for millimeter wave cellular systems in order to enhance their performances in terms of average spectral efficiency. In this case, the transmitter adjusts the modulation type of the transmitted signal based on the receiver geometry and the link condition. First, we present an analytical model using geometry tools to compute the statistical distributions of the downlink SINR. Then, the performance of the adaptive modulation scheme is studied and evaluated in terms of the average spectral efficiency. Numerical results show that, while keeping the outage probability as minimum as possible, this geometry-based adaptive modulation can efficiently enhance the millimeter wave system average spectral efficiency.

This paper studies the problem of minimizing the age of information (AoI) by optimally associating users to energy harvesting access points (EH-APs) and scheduling their packets that have stringent deadlines constraints. With a single EH-AP, this problem is already shown to be NP-hard. First, we consider the single EH-AP scenario and study the fairness between packets. We show the existence of fairness-AoI tradeoff. Further, we improve the previously proposed algorithms by reducing the average age of information. Finally, the general problem is considered. We reduce the problem to a knapsack problem and propose a dynamic programming approach to solve it. We present simulation results and show the efficiency of the proposed solutions compared to the optimal and the state-of-the-art ones.

Non-orthogonal multiple access (NOMA) and millimeter wave communications are key technologies for the fifth-generation of cellular networks and beyond, and the coexistence of these two techniques is critical. This paper characterizes the system performance through the outage probability and achievable downlink rate for large-scale millimeter wave NOMA networks by using stochastic geometry. In order to reflect spatial randomness, we consider homogeneous Poisson point processes to model the base stations and user equipments. Moreover, blockages which affect the channel characteristics, power allocation based on the combined channel gain, and imperfections in the successive interference cancellation are considered. The aggregate co-channel interference at a user is characterized based on the moment generating function. Finally, the outage probability and downlink rate are derived for a two-user NOMA scenario under two user-base station association schemes: 1) closest base station association and 2) closest line-of-sight base station association. It is seen that using NOMA under millimeter wave channels increases the achievable downlink rate while keeping the performance impact on individual users low, and that the closest line-of-sight base station association scheme is comparatively advantageous. Moreover, a dense base station deployment generally improves the performance further.

This paper considers the problem of maximizing the number of scheduled users that request to download data with deadlines from an energy-harvesting base station. This problem is solved based on two frameworks: online computation and online learning. In the first framework, an optimal offline and a deterministic competitive algorithms are designed. In the online learning framework, the multi-armed bandit approach is used to design a learning algorithm based on the well-known exponential-weight algorithm for exploration and exploitation. We bound its regret and show that it grows sub-linearly with time. Finally, we supplement our theoretical results by simulations to illustrate the performance of the proposed algorithms.

Cell-free massive multiple-input multiple-output (MIMO) is a form of distributed massive MIMO aiming to provide massive access and improve spectral efficiency by inheriting favorable properties of traditional massive MIMO, while mitigating detrimental effects such as shadowing and spatially correlated fading. This paper investigates how the throughput of cell-free massive MIMO is affected by non-orthogonal multiple access (NOMA) for the next-generation cellular networks under stochastic access point (AP) and user locations. Thus, we consider homogeneous Poisson point processes (PPPs) to model node locations, while considering a Rayleigh channel with log-distance path loss. The time division duplexing (TDD) mode is employed and uplink channels are estimated autonomously/locally at each AP via uplink pilots sent by users. Moreover, while unique pilots are used between NOMA clusters, pilots are reused within each cluster in order to strike a balance between the training overhead and number of clusters. Matched filter based precoding is performed within the downlink based on the estimated channels. The aggregate signal received from all access points is characterized based on the moment generating function and approximated via moment matching. Thereby, the achievable rates for the users are derived, under the consideration of error propagation due to imperfect successive interference cancellation (SIC). It is shown that NOMA increases the overall rate under environments with low path loss exponents and networks with high access point densities, while careful power allocation can significantly improve user fairness.

Radio frequency interference (RFI) is a manifestation of several interference instances that are caused by either intentional or unintentional interferes. Unless efficiently mitigated, RFI can evoke several system performance losses in various radio frequency (RF) operating systems. As a preliminary step of mitigating RFI, a number of RFI detectors have been proposed to date. These existing RFI detectors assume Gaussian noise with certain power despite the fact that it is impossible to know parameters to infinite precision. In this respect, the impact of noise power uncertainty on the existing RFI detectors needs to be investigated. Toward this end, this paper showcases the existence of performance limits-in a power-based RFI detector- dubbed INR walls. Interference-to-noise ratio (INR) walls are the maximum INRs below which an RFI detector cannot satisfy the target error probabilities, regardless of the number of intercepted samples. Simulations validate the derived INR walls which implicate the existence of fundamental RFI detection limits.

5G networks are required to provide ultra reliable low latency communications while dealing with the permanent growth of data traffic. In Heterogeneous Networks (Hetnets) assisted with Device-to-Device (D2D) communications, traffic can be offloaded to small base stations or to devices in order to improve the transmission delays even with small caching. In this paper, we aim at reducing the average content delivery delay by optimizing the caching placement strategy in the context of D2D-assisted Hetnets. First, we analytically derive an upper bound on the average content delivery delay. Then, we formulate the problem of minimizing this upper bound through caching placement. The optimal solution is obtained for a single file, then used to propose a low-complex heuristic solution for multiple files. Numerical results illustrate the efficiency of our solution compared to other strategies.

This paper studies the problem of minimizing the age of information by scheduling packets with hard deadlines in wireless networks powered by energy harvesting base stations. The problem is shown to be NP-hard in its simplest form. We propose a general optimization framework to model the problem and further we provide an integer linear programming formulation. The integer program is useful to help solving the problem optimally using off-the-shelf solvers. To solve the problem in polynomial-time efficiently, we derive two greedy online (non-anticipative) algorithms-one being an improved version of the other. We present simulation results and show the efficiency of the proposed solutions compared to the optimal and the state-of-the-art ones.

In this paper, we study the downlink transmission in a multi-UAV enabled wireless communication system where different types of contents are requested by ground users. Particularly, in the system, we consider a number of UAVs each equipped with a cache that is refreshed during off-peak hour to store some contents to be requested by ground users. To harness their full potential, a novel cooperative communication for the cache-enabled UAVs is presented, and we formulate an optimization problem that jointly decides the UAV placement as well as the transmit beamforming to maximize the number of users admitted to the system. Since the formulated problem is a mixed-integer non-convex program, which is generally known to be NP-hard, we resort to an appealing framework developed from the conventional difference-of-convex (DC) programming. Numerical results reveal the superiority of employing cooperative UAVs over non-cooperative ones and offer some insights into how the flexible deployment of cooperative UAVs benefits the system performance.

By pushing computation to the mobile network edge, Multi-access Edge Computing (MEC) has been an enabler for the stringent latency and energy requirements of the new Internet of Things (IoT) services. On the other hand, ultra-dense heterogeneous networks with wireless backhaul have been proposed as a low-cost solution, allowing Network Operators (NOs) to extend the network capability, by deploying densified close-proximity small-cells and hence supporting a large number of low-latency low-energy IoT devices. In this paper, we study the problem of IoT task offloading in a MEC-enabled heterogeneous network, which to the best of our knowledge, is the first attempt to thoroughly explore the task offloading problem in a heterogeneous network with MEC support and wireless backhaul. We jointly optimize the offloading decision, transmission power, and the allocation of radio and computational resources, with the objective of minimizing the devices energy consumption, while respecting their latency deadline. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method for providing an approximate solution on the original problem. Through numerical analysis, we perform simulations based on multiple scenarios, and find out how NOs can respond to the requested load and help in minimizing the total devices energy consumption.

In Vehicular Ad hoc Networks (VANET), each node can communicate with the other ones in single-hop or multi-hop manner. VANET users require increasingly multimedia services (i.e video streaming) which now represents an important part of the data traffic between those users and the internet. Actually, VANET users tend to pay more attention to Broadband mobile wireless systems for having internet connection to watch video from the internet. Whereas, due to the limited bandwidth to the Internet, users may experience low video resolution and bad transmission quality. In addition, high mobility and dynamic network topology may create an unstable and unreliable communication among VANET users. Thus, guaranteeing quality of service in this kind of communication networks is known to be very challenging. With the objective of providing high-quality video transmission, we propose in this paper, a new multisegment cooperative forwarding strategy for video streaming for VANET users. The new transmission scheme takes into account the speed of the vehicle, its position (direction) and the distance to the access point. To evaluate the performance of the proposed scheme, extensive simulations are conducted using myEvalvidSVC, NS-2 and VanetMobiSim. Simulation results show that the proposed transmission scheme enables efficient and reliable video transmission over VANETs.

Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this article, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with integer linear programming. Due to its NP-hardness, we propose a low-complex particle-swarm-optimization-based metaheuristic and a greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior/mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources' availability models, conducted simulations demonstrate that the PSO-based algorithm achieves near-optimal results. Whereas, the greedy algorithm realizes only 10%-30% less success ratio than the optimal solution with negligible execution time.

Massive multiple-input multiple-output (MIMO) systems operating within the millimeter wave frequency range offer exciting opportunities for the future fifth-generation (5G) wireless networks. While the increased bandwidth and spectral efficiency are attractive, transitioning to millimeter massive MIMO presents significant challenges with respect to blockages, high attenuation, and channel estimation errors. To address these challenges, this paper evaluates the outage performance of a millimeter wave cellular network using massive MIMO under a stochastic set-up subject to pilot contamination and matched-filter precoding. We model the cellular users and base stations with Poisson point processes. Furthermore, we consider blockages from random objects, and employ different path loss and fading models for the line-of-sight (LOS) and non-line-of-sight scenarios. Moreover, both fixed power transmissions and path loss inversion-based power control are considered along with the sectored antenna patterns. Using stochastic geometry, we derive the moment generating function of the interference experienced by a typical cellular user and its outage probability. It is observed that the environments with different path loss exponents have varying behaviour for similar blockage sizes and densities. In addition, the ratio of the number of cellular users to that of base stations, and the antenna beamwidth are critical parameters affecting the outage performance.

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices' energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users' tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.

Massive multiple-input multiple-output (MIMO) enabled base stations employing non-orthogonal multiple access (NOMA) hold immense potential in increasing the spectral efficiency of future cellular networks. In this paper, we evaluate the achievable rate of N-user NOMA under a Poisson process of massive MIMO enabled base stations. We adopt a timedivision duplexing (TDD) mode, where the uplink pilots are reused among the different base stations, and within each NOMA cluster of each base station, while matched-filter based precoding is employed in the downlink. The achievable rate by a typical NOMA user is characterized by taking into account imperfect successive interference cancellation (SIC) and error propagation. To this end, the moment generating function of the interference from other base stations due to pilot contamination is derived along with the signal detection probability. It is shown that NOMA can significantly improve the rate performance under most system parameters, and that the rate performance can be increased further through denser networks. Moreover, we show that the individual user rates and fairness amongst users within a NOMA cluster are significantly impacted by the specific power allocation algorithm.

Non-orthogonal multiple access (NOMA) and millimeter wave communications are key technologies for the fifth generation (5G) of cellular networks in order to increase the spectral efficiency, ensure massive connectivity, and to provide large bandwidths to mobile internet services. However, the coexistence of these two techniques is critical, especially under increasingly random dense base station assignments. To this end, this paper characterizes the system coverage for users utilizing NOMA in a stochastic multi cell set-up under millimeter wave frequencies. We employ the Poisson point process (PPP) to model the base stations and user devices. Moreover, our system model considers the power allocation, imperfections in the successive interference cancellation (SIC), and blockages that affect the channel path loss and fading characteristics. The aggregate co-channel interference experienced at a user is characterized based on the moment generating function, and the outage probability is derived for a two user NOMA scenario. Analytical and numerical results show that NOMA is a feasible candidate technique to increase the spectral efficiency under millimeter wave channels if careful power allocation is implemented, with limited performance penalties for each user. It is also shown that the performance is further improved by utilizing a dense base station deployment.

The problem of user scheduling with an energy harvesting base station is considered. We study a wireless network where a set of users request to download data of certain sizes with hard deadline constraints from a base station powered exclusively by harvested energy. The objective is to maximize the number of scheduled users while respecting the deadline and energy constraints. To solve the problem, a polynomial-time algorithm is developed and proved to be optimal. In addition, when the users have common deadlines, a less complex and optimal algorithm is designed. Finally, we present simulation results to illustrate the impact of different parameters on the performance of the proposed algorithms.

This paper proposes a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA) to improve the performance of wireless backhaul two-tier heterogeneous networks. Our work's novelty lies in the formulation to solve for the NOMA decoding order along with the cooperation rule between downlink small cell transmissions. We first employ the cochannel time division duplexing (CoTDD) combined with spectrum partitioning to manage interference. Then, we propose an optimization problem which maximizes the total achievable rate by jointly designing the NOMA decoding order and small cell cooperation rule along with the beamformer at the macro base station and small cells. The formulated problem is a mixed integer non-convex one which is generally NP-hard. To attain the solution, we exploit the structure of difference of convex functions to rewrite the formulated binary variables and then to equivalently transform the optimization problem into more amenable form. Finally, we develop an iterative lowcomplexity algorithm based on successive convex approximation (SCA) principle, which is provable to eventually converge at a sub-optimal solution. Numerical results show that our proposed strategy outperforms the conventional designs in terms of total achievable rate.

As sub-6 GHz spectrum is becoming increasingly scarce, millimeter wave (mmWave) bands are considered as one of the key technologies for future cellular networks. Motivated by the rapid growth of the data rate demands and the number of wirelessly-connected devices, this paper considers a hybrid (sub-6 GHz and mmWave) heterogeneous network with a limited number of time-frequency resource blocks (RBs). To overcome the mmWave propagation problems and the need of frequent update of association due to mobility, a novel mobility-aware user-base station association strategy based on Markov chain is proposed. Simulation results validate the performance of the proposed strategy by reducing the need of frequent handovers between mmWave base stations in the network.

Wireless communication systems are irreversibly changing our lives. Today, wireless networks are extremely complex systems and they are evolving towards more complex ones because of the increasing diversity and heterogeneity of applications, devices, quality requirements and standards. At the same time, resources used by wireless communications are either naturally limited e.g., time, spectrum, or need to be optimally exploited e.g., energy, computation, infrastructure. Hence, traditional resource allocation approaches that are based on optimization and heuristic techniques start to show their limitations. Those approaches are often centrally-managed, reactive, and not adaptive. They also require a huge amount of control data exchange. Hence, there is a need for new approaches to provide adaptive, proactive and self-organized networking solutions. Thanks to the availability of increasingly powerful computing systems and of huge amount of data that can be efficiently exploited in wireless networks, we envision the employment of machine learning techniques in order to achieve intelligent, adaptive, resource-efficient and data-driven future wireless networks. This talk discusses how wireless network designers and operators can employ and adopt advanced machine learning techniques for adding predictive and adaptive intelligence to the system. The state of the art of using machine learning in wireless networks will be deeply discussed and some interesting issues for new research avenues will be identified.

Radio frequency interference (RFI) is being common in both satellite and terrestrial communication systems. To detect it efficiently, this paper proposes, analyzes, and evaluates the utilization of eigenvalue-based blind RFI detector for single-input multiple-output systems. Simulations corroborate that the proposed detector performs as good as a generalized likelihood ratio test (GLRT) detector and a matched subspace detector, respectively, fed with the knowledge of the signal of interest (SOI) channel, and of the SOI and RFI channels even under sample starved settings. It is to be noted that such performance reveals the attractiveness of the eigenvalue-based RFI detector for real-time applications.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge implicating its sensitivity to CSI estimation error. In this paper, we present and evaluate the performance of a simple F-test based spectrum sensing technique that doesn't require the knowledge of the CSI for a multi-antenna cognitive radio. For this detector, exact and asymptotic analytical performance closed-form expressions are derived. Simulations assess the performance of the presented detector and validate the derived closed-form expressions.

This paper proposes a new transmission technique for heterogeneous networks with massive MIMO wireless back-haul with the objective of minimizing the power consumption cost. We assume that transmissions occur during two phases. During the first phase, the multi-antenna small-cell base stations (SBSs) receive signals from their associated users and from the macro-cell base station (MBS) thanks to MIMO spatial multiplexing. In the second phase, the SBSs transmit signals to the MBS and to the users. We study the problem of minimizing the sum SBS transmit power under minimum-rate constraints required at the users. We solve the formulated problem by deriving analytically the optimal time splitting parameter and the allocated transmit powers. Compared with the well-known reverse time division duplex (RTDD) with bandwidth splitting, considered as a benchmark, simulations show that the proposed transmission technique allows the SBSs to reduce considerably the power consumption.

In this paper, we propose a status monitoring strategy by an energy harvesting sensor node that i) harvests energy, ii) collects data, iii) estimates the channel state, and iv) decides whether to perform or defer data delivery to a sink. To comprehensively evaluate the performance of the proposed scheme, we consider the packet loss and the information age metrics and we analytically derive their statistics. Both mathematical analysis and simulations show that, despite the time and energy costs associated, estimating the channel state allows to intelligently manage the harvested energy by avoiding erroneous transmissions. Thereby, it significantly reduces the packet loss and the information age. We also asymptotically obtain the necessary conditions under which the proposed scheme performs strictly better than transmitting without estimating the channel state.

For various sources of intentional or unintentional interference, radio frequency interference (RFI) is increasingly affecting many radio frequency operating systems. To mitigate RFI efficiently, it should, first, be detected robustly. On the other hand, energy detector (ED) is a very popular signal detector applied in various research sub-fields. On the contrary, it has not been deployed, to the best of our knowledge, for the detection of RFI to date. Accordingly, this paper investigates ED in the context of RFI detection and provides a theory for it. Monte-Carlo simulations corroborate that ED outperforms kurtosis detector (KD) even under the scenario that KD intercepts the received signal for a much longer interval. Furthermore, the performance of ED is assessed using real-world RFI contaminated data.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. Proposed for a single-input multiple-output (SIMO) systems, the existing F-test requires the channel state information (CSI) as a prior knowledge. On the contrary, the CSI requirement renders a sensitivity to a CSI estimation error and multiple-input multiple-output (MIMO) systems guarantee better array gain, spatial diversity gain, spatial multiplexing gain, and interference reduction than SIMO systems. Accordingly, we present and evaluate the performance of a simple F-test based spectrum sensing technique that doesn't require the knowledge of the CSI for the MIMO cognitive radio networks. For this detector, exact and asymptotic analytical performance closed-form expressions are derived. Simulations assess the performance of the presented detector and validate the derived closed-form expressions.

Device-to-device (D2D) communications combined with Heterogeneous networks (Hetnets) has attracted growing interest. Indeed, Hetnets deploy small-cells within macro-cells in order to offload traffic and improve the overall network coverage and capacity. Whereas, D2D promotes the use of communications between users for content delivery without going through the small or macro bases stations. Hence, it reduces communication delays and improves the spectral efficiency. In this context, we aim in this paper at reducing the average transmission delay, defined as the average sum delays of contents transmission to satisfy users' requests in a macro-cell, by jointly optimizing caching placement and channel resource allocation, in cache-enabled Hetnet with D2D assistance. At first, a lower-bound expression of the average transmission delay is derived. Then, the optimization problem is formulated. Afterwards, we propose a sub-optimal random search algorithm and a low-complexity greedy algorithm that solve the problem. Finally, numerical results illustrate the performances of the proposed algorithms.

Device-to-Device (D2D) communication has gained interest as a promising technology for next generation wireless networks. D2D communication promotes the use of point-to-point communications between users without going through the base stations. In this paper, we aim at maximizing the sum rate of a D2D network, under the assumption of realistic time-varying channels and D2D interference. Specifically, we formulate channels as Finite-State Markov Channels (FSMC). With realistic FSMC, the complexity of the problem is high. Consequently, we propose the use of a centralized Deep Reinforcement Learning (DRL) transmission scheme for D2D communications, where transmission decisions are taken by one agent that has a global knowledge of the D2D network. We compare the DRL-based scheme with other transmission schemes. The results show that it outperforms other approaches in terms of achieved sum rate.

In this paper, we investigate the downlink transmissions in wireless backhaul (WB) networks when unmanned aerial vehicles (UAVs) are used as flying small cell base stations. We propose to employ the non-orthogonal multiple access (NOMA) on the WB transmissions and introduce a novel cooperative transmission scheme for the wireless access links. Then, we formulate an optimization problem which jointly determines the radio resource allocation at the macro cell base station (MBS) and UAVs along with the optimization of the decoding order of the NOMA process and the positions of the UAVs in space to maximize the sum achievable rate of all users. The formulated problem is a general mixed integer non-convex program, which is very difficult to solve optimally within a polynomial time. Therefore, we propose a framework based on the method of difference of convex program characterized by the Lipschitz continuity to transform and approximate the original problem into a series of convex approximate ones and develop a low-complexity algorithm to sequentially solve for each approximate problem until convergence. Numerical evaluation and analysis show that our achieved solution, under the proposed framework and developed algorithm, can outperform the other schemes which aim at either optimizing without using cooperative NOMA or do not optimize the UAVs' positions.

An F-test detector with a simple analytical false alarm threshold expression is considered an alternative to the blind detectors which exhibit complicated analytical expressions. However, the existing F-test requires the channel state information (CSI) as a prior knowledge and is known to be sensitive to CSI estimation errors. In this paper, we present and evaluate simple F-test-based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna cognitive radios. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived closed-form expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are robust against noise uncertainty.

In this paper, we consider a novel model of two-tier wireless backhaul (WB) small cell networks where each small cell access point is equipped with a buffer of finite storage. We employ a time-spectrum transmission accommodation scheme to separate the WB and access transmissions on top of developing a joint optimized transmit beamforming and power allocation algorithm. Unlike previous work, we propose a more advanced buffering protocol to study the small cell performance improvement by solving the offline and online optimization problems that maximize the total small cell access rate. Both kind of problems are generally non-convex and NP-hard. The offline scheme assumes the availability of channel state information (CSI) in current and future time slots and is used as a benchmark for online algorithms. To solve it, we develop a low-complexity algorithm which iteratively solves a sequence of lower bounded convex approximated problems until convergence. The formulated online problems must be solved in a slot-by-slot manner since transmitters only know the CSI of the current time slot. Then, we also develop low-complexity online algorithms to jointly design the transmit beamforming and power allocation. Our theoretical and numerical simulation results show that our proposed model with advanced buffering strategy outperforms the conventional designs in terms of small cell access rate for both offline and online algorithms.

As radio frequency interference (RFI) affects many systems operating radio frequencies, RFI detection is essential for excising such RFI efficiently. For this reason, here we investigate an energy-based RFI detector for wireless communication systems suffering from RFI. For this detector, its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated energy detector (ED) outperforms a kurtosis detector (KD)-even under the scenario that KD intercepts the received signal for a longer interval-and a generalized likelihood ratio test detector (GLRT). At last, the performance of ED is also assessed using real-world RFI contaminated data.

This paper studies the base station operation and association (BOA) problem for energy-efficient heterogeneous cellular networks. The objective is to find the set of base stations (BSs) to activate and to associate users to BSs under minimum rate requirements. BOA is formulated as a nonconvex programming problem. In order to solve it, we distinguish between two cases: 1) BOA with high-rate requirements (BOAH) and 2) BOA with low-rate requirements (BOAL). First, we show that finding feasible solutions for BOAH is NP-hard, and second, we reduce it to a BS operation problem (i.e., user association becomes straightforward). Based on this reduction, we develop a brute force algorithm and show that its complexity can be extremely reduced though it is still exponential. Hence, we propose a polynomial-time heuristic algorithm. As for BOAL, since BOA is extremely coupled, we relax the problem. Consequently, BOAL can be formulated as an integer linear program. Finding feasible solutions to it is shown to be NP-hard. To efficiently solve it, we propose a greedy-based algorithm. The proposed greedy algorithm is shown to admit a logarithmic approximation factor when it finds feasible solutions and a constant approximation factor otherwise. Finally, simulation results illustrate the performance of the proposed algorithms.

In this paper, we propose to re-engineer the wireless backhaul two-tier heterogeneous networks architecture by developing a novel cooperative transmission scheme based on non-orthogonal multiple access (NOMA). To effectively manage severe interference from the newly introduced backhaul communications, we employ the cochannel time division duplexing combined with spectrum partitioning between two considered tiers. This paper's novelty lies in the formulation to solve for the NOMA decoding order, which affects the rule of the cooperation between small cell transmissions. We propose two optimization problems of jointly designing the NOMA decoding order together with the transmit beamforming at the macro base station and power allocation at the small cells which maximize the total achievable rate and the number of satisfied users, respectively. The first and second formulated problems are both mixed integer non-convex and are generally NP-hard. To solve them, we first employ the difference of convex functions to present the formulated binary variables and then equivalently transform the optimization problems into more tractable forms. Finally, we develop an iterative low-complexity algorithm based on successive convex approximation technique and majorization minimization method, which is provable to eventually converge at a sub-optimal solution. Numerical results are extensively studied to corroborate that our proposed strategy outperforms the conventional designs in terms of total achievable rate and number of satisfied users.

This paper studies the base station operation and association problem for energy-efficient heterogeneous cellular networks. The objective is to find a set of base stations to activate and to associate users to base stations under minimum rate requirements. The problem is formulated as a nonconvex program and is shown to be NP-hard. Then, the problem is reduced to a base station operation problem where the association of users becomes straightforward. Based on this reduction, a brute force algorithm is developed and its complexity is discussed. To solve the problem efficiently in polynomial-time, an heuristic algorithm is proposed. Simulation results illustrate the performance of the proposed algorithm and compares it to the brute force algorithm and a benchmark algorithm.

This paper investigates the downlink of an energy efficient distributed large-scale MIMO system. The studied system is assumed to be made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy allows to compensate for the randomness and intermittency of the harvested energy. Hence, the problem of grid power consumption minimization under quality of service (QoS) constraints has to be solved. First, this paper solves the optimal offline version of the problem using linear programming. Next, an iterative link removal algorithm is proposed in order to ensure the feasibility of the problem. Finally, the optimal online energy management algorithm is also proposed to solve the same problem. Simulation results show the performance of the proposed algorithms. The proposed approach in this paper allows efficient use of non-renewable energy to compensate the variability of renewable energy in large-scale MIMO systems.

We consider the problem of associating users to base stations in heterogeneous cellular networks (HetNets). Given a set of users, base stations (BSs) and time-slots, the considered problem, called multi-slot user-BS association (MUBA), is to maximize the number of associated users to the BSs during the time-slots such that the signal to interference-plus-noise ratios (SINRs) of the users in each slot are above a certain threshold. First, we formulate MUBA as a 0-1 nonlinear optimization problem and then we transform it into a linear one. Next, MUBA is reduced to a link scheduling problem. Based on this reduction, an approximation algorithm for MUBA is designed and is shown to admit a constant approximation factor. Simulation results support our theoretical analysis and show that the proposed approximation algorithm gives tight-to-optimal performance.

In this work, we study a novel model of two-tier wireless backhaul small cell networks that considers buffering of finite storage size at each small cell access point. By employing a reverse time division duplexing (RTDD) interference management, we develop an online algorithm that jointly optimizes the transmit beamforming and power allocation. Unlike previous works, we propose a more advanced buffering protocol to improve the small cell performance by solving an online constrained optimization problem that maximizes both the total small cell access rate and backhaul rate. To deal with the non-convex property of the formulated problem, we invoke the framework of successive convex approximation to develop the online algorithm to iteratively solve a convex approximated problem and update corresponding parameters until convergence. Numerical results show that our proposed model with advanced buffering strategy outperforms the traditional designs in terms of small cell access rate.

In this paper, a fully integrated frequency synthesizer architecture, designed in 0.13 μm CMOS technology, for avionic software defined radio (SDR) applications is presented. The synthesizer provides a carrier frequency range from 100 MHz to 12 GHz covering the avionic communication applications and existing wireless standards. The switched capacitors voltage controlled oscillator (VCO) used realizes a wide tuning range from 8 GHz to 12 GHz. The VCO phase noise simulated at 12 GHz is −125 dBc/Hz at a 10 MHz offset frequency with a power consumption of 1.4 mW. The transient phase locked loop (PLL) response shows a settling time of 3.92 μs whereas the PLL loop bandwidth is of about 600 kHz. Furthermore, the synthesizer exhibits a phase noise, simulated at 12 GHz, of −104 dBc/Hz at a 1 MHz frequency offset with an overall power consumption of 14.88 mW, comparing favourable to other documented schemes, but with four time the frequency range.

Combining millimeter wave (mmWave) with sub-6 GHz communications is a promising solution for future heterogeneous cellular networks (HetNets) to improve coverage and capacity. This paper studies the user-base station association problem in HetNets with the existence of both sub-6 GHz and mmWave base stations (BSs) where each BS has a limited number of resource blocks (RBs). Motivated by the observation that traditional user-BS association methods may not be effective in such hybrid HetNet, an optimization problem is formulated in order to maximize the number of associated users and to ensure an efficient resource utilization by minimizing simultaneously the number of used RBs. Since the formulated problem is proved to be NP-hard, a heuristic algorithm is proposed. Simulation results show that the proposed algorithm approaches the optimal one with a significant reduction in computational complexity.

Radio frequency interference (RFI) is causing performance loss in microwave radiometry, radio astronomy, and satellite communications. As the number of interferers increases, the performance loss gets more severe and RFI excision becomes more difficult. In this regard, this paper introduces the multilinear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for single-input multiple-output (SIMO) systems suffering from MI-RFI. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm, which enhances MLSEP, is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibit a faster convergence to a zero excision error than MLSEP which, in turn converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller.

In this paper, a generalization of the satisfaction equilibrium (SE) for games in satisfaction form (SF) is presented. This new solution concept is referred to as the generalized satisfaction equilibrium (GSE). In games in SF, players choose their actions to satisfy an individual constraint that depends on the actions of all the others. At a GSE, players that are unsatisfied are unable to unilaterally deviate to be satisfied. The concept of GSE generalizes the SE in the sense that it allows mixed-strategy equilibria in which there exist players who are unable to satisfy their individual constraints. The pure-strategy GSE problem is closely related to the constraint satisfaction problem and finding a pure-strategy GSE is proven to be NP-hard. The existence of at least one GSE in mixed strategies is proven for the class of games in which the constraints are defined by a lower limit on the expected utility. A dynamics referred to as the satisfaction response is shown to converge to a GSE in certain classes of games. Finally, Bayesian games in SF and the corresponding Bayesian GSE are introduced. These results provide a theoretical framework for studying service-level provisioning problems in communications networks as shown by several examples.

This paper studies the joint design of downlink transmit beamforming and power allocation in two-tier wireless backhaul small cell heterogeneous networks. We consider reverse time division duplexing combined with equal spectrum splitting between two tiers for interference mitigation. We formulate a constrained optimization problem with the objective of maximizing the proposed access energy efficiency, defined by the ratio of the sum achievable rate at the users to the overall consumed power, where the power consumption model includes the adaptive decoding power. The formulated problem is non-convex and generally NP-hard. To solve it, we first apply the high-complexity branch-and-bound algorithm to find the global optimal solution. Then, we develop a lower complexity algorithm which iteratively solves the convex approximated problem until convergence. Compared with the conventional methods, this algorithm converges faster to a solution that is very close to the global optimal solution achieved by the branch-and-bound approach. Finally, we exploit the framework of the alternating direction method of multipliers on the convex approximated problem to develop a distributed algorithm. Numerical results are obtained to show the improvement of our proposed model with much better power conservation compared with the different design of fixed circuit power assignment.

Content-Centric Networking (CCN) has emerged as a future Internet communication model by replacing host addresses with named contents. Over the last years, caching at routers in CCN is attracting a lot of interest. Specially, caching of video traffic is emerging as a serious concern for CCN deployment due to the special characteristics of video streaming such as large content size and low latency requirements. In this paper, we propose a hierarchical parent-child caching strategy called H-CS in order to ensure low access latency and to reduce load on the network. Basically, each router is assigned a level-based caching indicator to guarantee higher probability content storage as closer as possible to the requesters. Performance evaluation demonstrates the effectiveness of the proposed H-CS scheme using ns-3 based NDN (named data netwok) simulator (ndnSIM). In fact, simulations show how H-CS functionality can be used to achieve efficient and reliable video dissemination including the support of delay tolerant delivery.

This paper considers the problem of maximizing energy efficiency on the downlink of two-tier wireless backhaul small cell heterogeneous networks, where an interference mitigation strategy that combines reverse time division duplexing and equally orthogonal spectrum splitting is proposed. By enabling the small cell access points with the capability of switching ON/OFF, we develop a joint design of transmit beamforming, power and small cell selection that maximizes the proposed weighted access energy efficiency metric. To better convey the total power consumption model, we assume the adaptive decoding power model at each small cell access point. The formulated problem is combinatorial and non-convex, which is NP-hard in general. Hence, to find a more realistic close-to-optimal feasible solution, we iteratively approximate the non-convex constraints in the formulated problem as second order cone ones based on the first order Taylor convex approximation and error-controlled second order cone approximation. The problem arrived at each iteration is a mixed integer second order cone programming, which can be solved optimally and efficiently by available dedicated solver to achieve the final result at convergence. Numerical results are studied to show the improvement of our proposed model compared to previous works.

We consider a model of two-tier heterogeneous cellular networks (HetNets), with wireless backhaul communication (WBC), consisting of a macrocell and a small cell tiers. A joint design of transmit beamforming, power allocation and bandwidth partitioning for both uplink (UL) and downlink (DL) transmissions is considered in this work. By proposing a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors, we formulate a constrained optimization problem with the objective of maximizing the total energy efficiency of the small cells considering both UL and DL. For this non-convex problem, we leverage the sequential parametric convex approximation (SPCA) method to develop an efficient iterative algorithm to find the local optimal solution. Numerical simulations corroborate the convergence of our proposed algorithm and their performance gains compared to the previous work.

The use of renewable energy at roadside units (RSUs) in vehicular ad hoc networks is a great alternative to the electric grid, since it lowers the carbon footprint, and the cost of deployment and maintenance. This paper describes a scheduler for serving vehicles by RSUs that use energy harvesting, with the aim to maximize the number of served vehicles. We start by defining an integer linear programming model for finding the optimal offline schedule. The model is shown to be NP-hard and hence we propose a greedy heuristic to solve it. We compare the optimal solution and near- optimal offline heuristic with an energy-efficient scheduler for RSUs. Our simulation results show that the proposed scheduler for energy harvesting RSUs can reduce the service delay of vehicles. It also provides good performance with respect to the percentage of served vehicles, in comparison to energy-efficient scheduler in grid-powered RSUs.

In this paper, we investigate the downlink of large-scale MIMO systems considering two practical constraints related to system power. More precisely, we consider a non-negligible circuit power consumption and we impose a per-antenna power constraint due to limitations on the linearity of the RF power amplifier. Hence, a sum-rate maximization problem considering the two constraints is formulated for conjugate beamforming and zero forcing beamforming. Next, we propose efficient greedy antenna selection and power allocation algorithms in order to heuristically solve the formulated problem with reasonable computational complexity. Simulation results show the efficiency of the proposed algorithms compared to random antenna selection and optimal brute force antenna selection.

The use of energy harvesting wireless sensor network (EH-WSN) is a rising wireless communication technology with a wide range of applications such as environment monitoring. Maximizing the number of samples collected by the sink from sensors is a key approach in order to minimize uncertainties for those applications. The considered system in this paper consists of an uplink scenario with EH sensors communicating with a non EH sink, equipped with multiple antennas, receiving data forwarded by the sensors. Using a zero-forcing (ZF) receiver, the data collector (i.e., sink) selects the largest possible set of transmitting sensor nodes to maximize the received quantity of information, while satisfying their signal-to-noise ratio quality of service (QoS) constraints. This paper presents efficient and simple EH node selection algorithms in EH-WSNs in order to maximize the number of selected sensors. The problem is formulated as an integer non linear program that can be optimally solved using an exhaustive search. Due to the prohibitive complexity of such a brute force approach, two low complexity and efficient heuristic algorithms are proposed to perform node selection decisions. Simulation results show the performance of the proposed algorithms and illustrate their adaptability and efficiency in the energy harvesting context.

We consider a multiantenna small-cell network where access-points serving multiple users on the downlink and uplink channels. The access point is powered by both renewable and non-renewable energy sources. Its objective is to jointly maximize the throughput of the downlink users and minimize the amount of energy drawn from the non-renewable energy source while guaranteeing the quality of service to both downlink and uplink users. The quality of service of uplink user is achieved if all the received packets are decoded in a given time. Further, we assume that the energy used for sampling and decoding the received packets is not negligible due to the access point small range of operation. With this perspective, the optimal transmit power allocation and received packet decoding policy are investigated in offline and online settings. The joint optimization problem is solved using the dual decomposition method to find the optimal policy. Numerical simulations are conducted to evaluate the performance of the proposed offline and online algorithms. Simulation results shown significant gains compared to the greedy method.

In this paper, we consider a multiantenna small-cell access-point serving multiple users on the downlink and uplink sides using the frequency division duplex scheme. The access point is powered by both renewable and non-renewable energy sources. The objective of this paper is to process the frequency division duplex frame by drawing the minimum amount of energy from the non-renewable energy source while guaranteeing the quality of service of downlink transmission and decoding all the received uplink packets. Assuming that the energy used for sampling and decoding the received packets is not negligible, the optimal transmit power allocation and received packet decoding policy is investigated first in an offline setting and then in an online setting. An iterative offline algorithm based on dual decomposition method is proposed to find the optimal policy. In the online setting, an optimal high-complexity solution using dynamic programming approach is developed. Then, a reduced complexity suboptimal online algorithm using dual decomposition is proposed. Finally, two more suboptimal and low-complexity online algorithms for maximizing the ratio of throughput and non-renewable energy, and fairness metric are further addressed. Numerical simulations evaluate the performance of the proposed offline and online algorithms and show the efficiency of our proposed algorithms.

This paper studies two-tier heterogeneous cellular networks, with wireless backhaul communication, consisting of macrocell and small cell tiers. A joint design of transmit beamforming, power allocation, and bandwidth partitioning for both uplink and downlink transmissions is considered. By assuming the reverse time division duplexing system, we propose a strategy to partition the bandwidth for two consecutive time slots by two separate partitioning factors. Under the proposed strategy, we formulate a constrained optimization problem with the objective of maximizing the sum rate of small cell uplink and downlink. For this non-convex problem, we leverage the sequential parametric convex approximation method to find the stationary point of the problem. In this method, a convex approximation of the problem is solved at each iteration. Furthermore, with appropriate transformations, we approximate the problem as second-order cone programming (SOCP) and propose a fast converging algorithm to attain the solution. We also evaluate the impact of imperfect channel state information by reformulating the optimization problem and applying the proposed algorithm to solve it. We conduct numerical simulations to show that the joint design of transmit beamforming, power allocation, and bandwidth partitioning leads to a better resource utilization and high spectral efficiency. Moreover, our results show that the proposed SOCP-based algorithm converges fast to a solution, which is shown to be closer to the global optimal solution achieved by the branch-and-bound algorithm compared with other works.

Abstract This paper considers uplink channel access in a zone of closed‐access small‐cells that is deployed in a macrocell service area. All small‐cell user equipments (SUEs) have access to a common orthogonal set of channels, leading to intercell interference. In addition, each channel forms a separate collision domain in each cell, thus can be successfully used only by one SUE of that cell. This paper proposes two non‐cooperative Bayesian games, G1 and G2, that are played among the SUEs. G1 assumes the availability of channel state information at the transmitters, while G2 assumes the availability of only the distribution of the channel state information. Each SUE can choose to transmit over one of the channels or not to transmit. The emphasis of the paper is on the set of symmetric threshold strategies where the Nash equilibrium is fully determined by a single parameter. The existence and uniqueness of pure Bayesian–Nash symmetric equilibrium of G1 in threshold strategies and mixed Bayesian–Nash symmetric equilibrium of G2 in uniformly distributed threshold strategies are proven. Numerical results corroborate the theoretical findings and benchmark against another decentralized scheme. Copyright © 2015 John Wiley & Sons, Ltd.

The availability of channel state information (CSI) at the transmitter plays a central role to provide high system performance in massive multiple-input multiple-output (MIMO) systems. In a frequency division duplexing (FDD) system, acquiring this information requires a prohibitive amount of feedback and a significant feedback energy, since it increases with the number of transmit antenna. In this paper, we address the issue of significant energy consumed to feedback all CSI to the base station (BS). To this end, we propose a novel feedback routing scheme based on transmit antenna selection for massive MIMO systems. The proposed scheme aims to jointly reduce the energy needed to feedback the CSI to the BS and the complexity of the transmit antenna selection. We formulate the problem of finding the feedback routing that minimizes the energy consumption as a least cost Hamiltonian path problem. To solve the formulated problem, we propose both an integer linear programming formulation to find the optimal solution and a heuristic dynamic programming algorithm to find a suboptimal solution with reasonable computational complexity. Computer simulations show that our scheme offers enormous reduction in feedback energy while ensuring low computational complexity.

Content Placement (CP) problem in Cloud based Content Delivery Networks (CCDNs) leverage resource elasticity to build cost effective CDNs that guarantee QoS. In this paper, we present our novel CP model, which optimally places content on surrogates in the cloud, to achieve (a) minimum cost of leasing storage and bandwidth resources for data coming into and going out of the cloud zones and regions, (b) guarantee Service Level Agreement (SLA), and (c) minimize degree of QoS violations. The CP problem is NP Hard, hence we design a unique push based heuristic, called Weighted Social Network Analysis (W SNA) for CCDN providers. W-SNA is based on Betweeness Centrality (BC) from SNA and prioritizes surrogates based on their relationship to the other vertices in the network graph. To achieve our unique objectives, we further prioritize surrogates based on weights derived from storage cost and content requests. We compare our heuristic to current state of the art Greedy Site (GS) and purely Social Network Analysis (SNA) heuristics, which are relevant to our work. We show that W-SNA outperforms GS and SNA in minimizing cost and QoS. Moreover, W-SNA guarantees SLA but also minimizes the degree of QoS violations. To the best of our knowledge, this is the first model and heuristic of its kind, which is timely and gives a fundamental pre allocation scheme for future online and dynamic resource provision for CCDNs.

In this paper, the user association problem under quality of service (QoS) requirements in a heterogeneous and small cells network (HetSNet) is considered. We have shown in a previous work that this problem is NP-hard and thus cannot be solved optimally in polynomial time unless P = NP. Therefore, new suboptimal algorithms are needed in order to solve it efficiently. Even though, it is very hard to implement the suboptimal algorithm in a centralized fashion because it needs a high amount of information exchange between the base stations and the users and it suffers from a huge computational complexity. Thus, in this paper, we model the problem of user association in HetSNets as a non-cooperative game and we propose a completely distributed algorithm inspired by the theory of learning to solve it. Specifically, we propose a modified win-stay-lose-shift learning model in order to converge to a near optimal user association. We evaluate by simulations the performance of the proposed algorithm and and we show that it is close to the performance of the computationally complex optimal centralized algorithm which assumes complete channel information knowledge.

We consider a point-to-point wireless link with automatic repeat request (ARQ) based packet transmission where both the transmitter and receiver nodes are energy harvesting (EHNs). Transmitter EHN has access to low-grade channel state information (CSI) as it is implicitly obtained from ARQ feedback. Furthermore, signal processing tasks such as sampling and decoding at the receiver EHN can be interrupted if there is insufficient energy in the battery that cause loss of packet and wastage of harvested energy both at the transmitter and receiver EHNs. We propose selective sampling (SS) scheme where only part of the transmitted packet is sampled and stored depending on the receiver nodes stored energy. SS information (SSI) is then fed back to the transmitter. Packet decoding is not performed until full packet is constructed. Hence, we modify the conventional ARQ messages, i.e., ACK/NAK by adding few more bits to carry additional SSI as well. Another objective is to find the optimal power allocation policy to adapt to the low-grade CSI and SSI available at the transmitter such that harvested energy can be utilized efficiently especially at the receiver. Furthermore, using a decision-theoretic framework, we propose greedy power allocation scheme to evaluate the performance of the proposed retransmission scheme. In numerical examples, we illustrate that our proposed scheme has lower average packet transmission time and packet drop probability (PDP) compared to the equal power allocation and greedy power allocation with conventional retransmission scheme.

This paper considers the problem of acquiring the channel state information (CSI) at the base station in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, acquiring the CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna at the base station. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a massive MIMO system in the broadcast channel. The proposed strategy has to reduce both the CSI feedback and the computational complexity, and also to improve the system sum-rate. Contrary to what is generally proposed in the literature, the decision in our strategy is performed in a distributed fashion at the users. Named Successive Removal for Antenna Selection, the strategy proposed in this work can be implemented with three proposed schemes, which aims to solve differently the tradeoff between the computational complexity and sum-rate performance. Computer simulations show that the proposed algorithms are able to achieve good performances while a significant reduction in both CSI feedback overhead and computational complexity is observed.

Heterogeneous and small cell networks (HetSNets) have emerged as a promising mean to significantly improve coverage and performance of next generation cellular networks. However, the high density of base stations in such networks accentuates the harmful impact of interference on network performance. This paper considers a network of multiple small cells where the base stations seek to maximize a common objective by forming clusters and allocating power to their users. We formulate the joint clustering and power allocation problem as a mixed integer optimization problem. We show that such problems can be optimally solved only by performing an exhaustive search over all the possible clustering decisions. Furthermore, it is shown that even if the clustering is established the power allocation problem remains NP-hard. Due to the high computational complexity of the optimal solution, we propose three heuristic algorithms which perform greedy clustering and iterative power allocation. Simulations show that the proposed algorithms, and especially the neighboring links first heuristic, provide a good computational complexity/performance tradeoff.

This paper investigates the downlink of massive multiple-input multiple-output (MIMO) systems that include a single cell Base Station (BS) equipped with large number of antennas serving multiple users. As the number of RF chains is getting large, the system model considered in this paper assumes a non negligible circuit power consumption. Hence, the aim of this work is to find the optimal balance between the power consumed by the RF chains and the transmitted power. First, assuming an equal power allocation among users, the optimal number of RF chains to be activated is analytically found. Then, for a given number of RF chains we derive analytically the optimal power allocation among users. Based on these analysis, we propose an iterative algorithm that computes jointly the optimal number of RF chains and the optimal power allocation vector. Simulations validate the analytical results and show the high performance provided by the proposed algorithm.

This paper studies the energy allocation for sensing and data transmitting in communication systems with energy harvesting sensor nodes. The investigated system consists of a point-to-point data transmission between two energy harvesting nodes that have limited batteries capacity and communicating over a wireless fading channel. The transmitter aims to optimize the throughput over slotted system and in an infinite horizon subject to time-varying conditions. These conditions include the energy harvested at both nodes, available energy at both nodes and the channel state. The energy allocation problem is formulated as a sequential decision problem. An optimal algorithm is given and a low-complexity suboptimal energy allocation algorithm is also proposed. Simulation results show the gain when the transmitter takes into account the state of energy at the receiver and that the proposed heuristic algorithm achieves near-optimal number of transmitted bits with lower complexity.

Video streaming (like YouTube) services and related applications become more and more widespread. Therefore, video streaming delivery over a mobile ad-hoc network (MANET) becomes a necessity as an important content delivery infrastructure between the user (content consumer) and the content storage node. Furthermore, user mobility impacts the quality of the delivered video and hence new concepts should be considered. Accordingly, the innovative concept on network coding (NC) emerges as a promising approach for improving the video transmission quality mainly in multicast environment. In this paper, we focus on Quality of Service (QoS) improvement for video streaming over MANET using random network coding. Basically, we consider video coded by H264/SVC codec that generates packets with different priorities and uses the IEEE 802.11e MAC for traffic differentiation. A successful transmission of high priority packets leads to enhance the video transmission quality. Accordingly, we propose a transmission scheme to protect high priority packets from being lost. Our approach, named Multicast Scalable Video Transmission using Classification-Scheduling Algorithms and Network Coding over MANET (and denoted MSVT_CSA_NC), adopts a cross layer solution between the H.264/SVC codec, the network and MAC layers. Moreover, our delivery mechanisms based on random network coding ensure high throughput and low network load over MANET. Simulation results confirm the substantial performance improvement brought by our approach.

It is largely accepted that the innovative technology of large-scale multiantenna systems (named Massive multiple input multiple output (MIMO) systems) will very probably be deployed in the fifth generation of mobile cellular networks. In order to render this technology feasible and efficient, many challenges have to be investigated before. In this paper, we consider the problem of antenna selection and user scheduling in Massive MIMO systems. Our objective is to maximize the sum of broadcasting data rates achieved by all the mobile users in one cell served by a massive MIMO transmitter. The optimal solution of this problem can be obtained through a highly complex exhaustive brute force search (BFS) over all possible combinations of antennas and users. This BFS solution cannot be implemented in practice even for small size systems because of its high computational complexity. Therefore, in this paper, we propose an algorithm that efficiently solves the problem of joint antenna selection and user scheduling. The proposed algorithm aims to maximize the achievable sum-rate and to benefit from both the spatial selectivity gain and multi-user diversity gain offered by the antenna selection and user scheduling, respectively. Compared with the optimal solution obtained by the highly complex BFS, the conducted performance evaluation and complexity analysis show that the proposed algorithm is able to achieve near-optimal performance with low computational complexity.

The downlink of a single cell base station (BS) equipped with large-scale multiple-input multiple-output (MIMO) system is investigated in this paper. As the number of antennas at the base station becomes large, the power consumed at the RF chains cannot be anymore neglected. So, a circuit power consumption model is introduced in this work. It involves that the maximal sum-rate is not obtained when activating all the available RF chains. Hence, the aim of this work is to find the optimal number of activated RF chains that maximizes the sum-rate. Computing the optimal number of activated RF chains must be accompanied by an adequate antenna selection strategy. First, we derive analytically the optimal number of RF chains to be activated so that the average sum-rate is maximized under received equal power. Then, we propose an efficient greedy algorithm to select the sub-optimal set of RF chains to be activated with regards to the system sum-rate. It allows finding the balance between the power consumed at the RF chains and the transmitted power. The performance of the proposed algorithm is compared with the optimal performance given by brute force search (BFS) antenna selection. Simulations allow to compare the performance given by greedy, optimal and random antenna selection algorithms.

Minimum-collisions assignment (MCA), in a wireless network, is the distribution of a finite resource set, such that the number of neighbor cells which receive common elements is minimized. In classical operator deployed networks, resources are assigned centrally. Heterogeneous networks contain user deployed cells, therefore centralized assignment is problematic. MCA includes orthogonal frequency bands, time slots, and physical cell identity (PCI) allocation. MCA is NP-complete, therefore a potential-game-theoretic model is proposed as a distributed solution. The players of the game are the cells, actions are the set of PCIs and the cost of a cell is the number of neighbor cells in collision. The price of anarchy and price of stability are derived. Moreover the paper adapts a randomized-distributed-synchronous-update algorithm, for the case, when the number of PCIs is higher than the maximum degree of the neighbor relations graph. It is proven that the algorithm converges to a optimal pure strategy Nash equilibrium in finite time and it is robust to node addition. Simulation results demonstrate that the algorithm is sub-linear in the size of the input graph, thus outperforms best response dynamics.

This paper addresses the problem of competition vs. cooperation in the downlink, between base stations (BSs), of a multiple input multiple output (MIMO) interference, heterogeneous wireless network (HetNet). This research presents a scenario where a macrocell base station (MBS) and a cochannel femtocell base station (FBS) each simultaneously serving their own user equipment (UE), has to choose to act as individual systems or to cooperate in coordinated multipoint transmission (CoMP). The paper employes both the theories of non-cooperative and cooperative games in a unified procedure to analyze the decision making process. The BSs of the competing system are assumed to operate at the maximum expected sum rate (MESR) correlated equilibrium (CE), which is compared against the value of CoMP to establish the stability of the coalition. It is proven that there exists a threshold geographical separation, dth, between the macrocell user equipment (MUE) and FBS, under which the region of coordination is non-empty. Theoretical results are verified through simulations.

Cognitive radio networks are emerging kind of wireless networks with cognitive radio nodes able to have dynamic spectrum access in order to make use more efficiently of the spectrum. The routing problem in such networks is quite complex due to the dynamic nature of the spectral environment where the availability of frequency bands for cognitive radio nodes is opportunistic. In this paper, we propose a robust and efficient routing solution in terms of throughput. Our proposition is a reactive routing protocol (named cognitive temporary ordering routing algorithm) CO-TORA based on the classic TORA protocol proposed for non cognitive wireless ad-hoc networks. We also implement CO-TORA in largely used NS-2 simulator. The utilization of CO-TORA protocol brings to the system many performance improvements that are evaluated by simulations and shown by comparing it with classical TORA.

Femtocell and WiFi play crucial roles in sustaining the continued growth in mobile traffic. Deploying Femtocells in WiFi hotspots would allow the access providers to provide more capacity for users and improve their quality of experience during mobility. Hence, the co-existence of Femtocell and WiFi carries critical importance for improving the total performance of the users and meeting the promised quality of service (QoS) satisfaction of Femtocell end users. In this paper, we propose and develop a framework allowing to make use of unlicensed band and to increase the total throughput of Femtocells while offloading the traffic of Femtocell users to unlicensed bands in case of severe interference with Macrocell. The channel access of both Femtocell and WiFi networks are analytically modeled and numerically verified. Moreover, the effects of WiFi channel access parameters on the performance of WiFi and Femtocell networks are investigated. Numerical evaluation of our proposed scheme show that by adequately tuning and giving priority, the throughput of small cells and utilization of unlicensed spectrum have been improved.

Wireless mesh networks (WMNs) have been developed to answer the needs of many wireless applications. A major limiting parameter of the performance of WMNs is the interference between the several communications that occur simultaneously in the same network. To address this limitation, an adequate scheduling algorithm has to be implemented. Hence, this paper focuses on the scheduling problem under the physical interference model. This problem is known to be NP-Hard problem. In this paper we develop and propose two efficient scheduling algorithms. We evaluate their performances by simulation in terms of spatial reuse and we compare their performance with known previously-proposed algorithms. We show that our proposed algorithms provide high performances with low complexity.

This paper considers the allocation of frequency and time resources in a heterogeneous network, in a self-organizing manner. The general problem is to assign a resource set, so as to minimize the number of pairs of adjacent base stations that obtain the same resource. This can be modeled by Minimum-Collisions Coloring (MCC) on an undirected graph, where the colors are the resources, the vertices are the wireless nodes and the edges represent interference relations between nodes. The MCC decision problem is NP-complete. This paper develops a game-theoretic model for the MCC problem. The players of this game are a set of colored agents, which in practice could be software robots. The game is proven to possess multiple pure-strategy Nash Equilibria (NEs). Then a swapping mechanism is developed to improve the NE performance and the resulting coloring is shown to be pairwise-Nash stable. Further refinement is proposed by making use of an external referee. All theoretical results are corroborated through simulations.

Cognitive radio (CR) systems allow unlicensed secondary users to transmit on the licensed frequency bands without degrading the transmissions of licensed primary users. Combining CR with other emerging techniques such as multi‐antenna relaying may bring many benefits for the secondary transmissions. In this study, the authors propose and investigate a new relay‐based cooperation scheme for a CR network to improve the secondary access to the licensed spectrum band without causing additional interference to the simultaneous primary transmission. The proposed scheme considers one multi‐antenna relay node that can assist either the primary or the secondary transmission using beam‐forming (BF). In the proposed new scheme, the BF weights are designed in the presence of imperfect channel state information (CSI). Simulation results show that the secondary's channel capacity is significantly improved and outperforms conventional transmission schemes. The results also reveal the impact of imperfect CSI on the primary outage performance and the efficiency of the proposed solution for minimising the interference due to imperfect CSI.

In this paper, we examine the problem of secondary access blocking in cognitive radio networks when secondary transmissions cause unacceptably high interference to primary transmissions. In general, the access of secondary users (SUs) to a licensed spectrum band is only allowed when this access does not alter the performance of primary users that can be defined by the primary QoS requirement. In this paper, we propose a cooperative scheme that allows SUs to increase their access to the spectrum band and access the spectrum even when the primary QoS is not satisfied. Using relay selection and a proper power allocation method, we show that the secondary outage performance can be significantly improved, whereas the primary outage performance is either not altered or slightly improved. Moreover, closed-form expressions of the primary and secondary outage probabilities are derived, and the achieved diversity order is calculated. Finally, analytical and simulation results illustrate the primary outage performance and secondary outage performance of the proposed scheme and show its advantages compared with conventional schemes.

ABSTRACT User cooperation has evolved as a popular coding technique in wireless relay networks (WRNs). Using the neighboring nodes as relays to establish a communication between a source and a destination achieves an increase of the diversity order. The relay nodes can be seen as a distributed multi‐antenna system, which can be exploited for transmit diversity by using distributed space–time block coding (STBC). In this paper, we investigate the bit error rate (BER) of multi‐hop WRNs employing distributed STBC at the relay nodes. We develop the general model of WRNs using distributed STBC, and we derive the pairwise error probability and an approximation of the BER. We examine the impact of several parameters, such as distributed STBC at the relays, the number of relays, the distances between the nodes, and the channel state information available at the receivers, on the BER performance of the multi‐hop WRN. The obtained results provide guidelines about the expected error performance and the design of channel estimation for these networks. Copyright © 2011 John Wiley & Sons, Ltd.

In full duplex relaying, the direct link and the decode and forward processing delay are not always negligible. The signal transmitted by the source thus interferes, at the destination, with the delayed signal retransmitted by the relay. This paper presents a novel full duplex transmission scheme based on distributed Alamouti encoding (denoted by FDAE) that eliminates the interference problem and combines efficiently each transmitted signal and its delayed copy at the destination for decode and forward relaying. The performances of FDAE are compared to the full duplex system with interference at the destination (denoted by FDI) and to the conventional half duplex relaying. The simulation results show the harmful effect of the interference problem on the end-to-end achievable data rate and on the bit error rate. They also show that our proposed scheme provides a highest end-to-end achievable data rate and lower bit error rate than FDI due to its ability to take advantage of full duplexing while eliminating interference.

In this paper, we propose a dynamic spectrum sharing protocol where a pair of secondary users and a pair of primary users bidirectionally communicate. A secondary relay equipped with multiple antennas is deployed to assist the secondary transmissions and improve the secondary access to the spectrum. We employ a new time division access so that no interference may exist between primary and secondary users. The proposed scheme is then compared to the axiomatic and simple scheme where the secondary users communicate with each other with the assistance of the relay in underlay mode. We study and compare the performances of the two schemes in terms of outage probability. An upper bound for the secondary outage probability of the proposed scheme is derived. Our simulation results prove that the proposed scheme significantly outperforms the underlay spectrum sharing while in both schemes, the primary outage probability is kept identical to the case where secondary users are absent.

In this paper, we propose a spectrum sharing mechanism with a two-phase two-way relaying protocol for an overlay cognitive network. The system comprises two primary users (PUs) and two secondary users (SUs). One of the SUs acts as a relay for the PUs and gains spectrum sharing as long as he respects outage probability constraints of the primary system. Moreover, we consider that the relaying node performs an optimal power allocation scheme that minimizes the outage performance of the secondary receiver. Closed form expressions for the outage probability are derived for the cases of Decode-and-Forward (DF), Amplify-and-Forward (AF), and adaptive relaying. Numerical simulations are presented to illustrate and compare the obtained results.

Secondary access to the licensed primary spectrum band at the same time as the primary nodes is generally conditioned on the satisfaction of a Quality-of-Service (QoS) requirement at the primary transmission (such as a Signal-to-Noise-Ratio -SNR- threshold or a primary outage probability threshold). Consequently, at low primary SNR that is below a cut-off value, secondary transmissions are totally blocked. In this paper, we propose a new cooperative scheme for cognitive radio networks, where secondary access to the primary spectrum band is granted whether or not the primary transmission satisfies its QoS requirement thanks to the utilization of secondary relay nodes. Using relay selection and proper power allocation at the secondary nodes, we show that the proposed scheme allows secondary access with low secondary outage performance without degrading the primary outage performance. We also compare the proposed scheme to other ones presented in the literature and we study the impact of the number of available relay nodes and the primary outage threshold value on the primary and secondary outage probabilities.

In this paper, we propose a spectrum sharing protocol where a pair of secondary users and a pair of primary users bidirectionally communicate. A secondary relay equipped with two antennas is deployed to ensure two-way relaying for primary users and thus they give the chance for secondary users to access the spectrum. To transmit its data, the secondary transmitter does not take into account the interference caused to primary receiver. It adjusts its transmit power so that the secondary required data rate is attained. In return, the relay uses zero forcing precoding to make two way relaying for primary users with no interference. We analytically derive the average outage probability and bit error probability of secondary transmissions. Analytical results are verified through simulations. Our proposed spectrum sharing scheme is proved to significantly outperform the conventional underlay scheme while respecting the performances requirement of the primary users.

In this paper we consider the problem of user assignment and power allocation in a small cell environment which is one of the most important problems in present wireless cellular network research. We consider a two-tier cellular network where randomly dispersed overlay femtocell base stations (FBSs) coexist with a macrocell. Our objective is to maximize the total number of users served by the FBSs while satisfying their signal to noise and interference (SINR) requirements. This problem is known to be NP-Hard and hence there is no known optimal solution to solve it in polynomial time. First we formulate the problem of maximization of allocated users under SINR constraints with constant transmit power as an integer programming problem. We provide two heuristic polynomial time algorithms. Then we propose a third algorithm for joint power and user allocation. We evaluate the complexity of the proposed algorithms and furthermore compare the results against the brute force optimal solution and a basic random user assignment through simulations. The results demonstrate the performance and the efficiency of the proposed algorithms. We see in the simulation that the best proposed heuristic for maximizing the number of assigned users is only 3% less than the optimal while reducing the power consumption below that of the optimal user assignment algorithm.

Cognitive radio (CR) systems allow unlicensed secondary users to transmit on the licensed frequency bands without degrading the licensed primary transmissions. Combining CR with other emerging transmission techniques, such as user cooperation may have many benefits on both the primary and secondary transmissions. In this study, the authors propose and investigate an adaptive relay‐based cooperation scheme for CR networks that improves the secondary outage performance, while respecting a primary outage probability threshold. The proposed adaptive scheme considers one multi‐antenna relay node that, by selecting the antenna(s) to use, can assist either the primary, the secondary or both transmissions simultaneously. Expressions of the conditional primary outage probability for Rayleigh fading channels are derived and used to investigate the associated power allocation problem. Simulation results show that both primary and secondary outage probabilities of the proposed scheme are significantly improved and outperform non‐cooperative and cooperative schemes given in the literature.

In this paper, we compare the performance in terms of symbol error probability, data rate and power consumption of the use of fixed transmit power (FTP) and adaptive transmit power (ATP) in underlay cognitive radio networks. The use of FTP alleviates the signaling requirements of underlay cognitive radio networks compared to the ATP. Nevertheless, the use of FTP influences the performances of the underlay cognitive radio networks. To study this influence, we consider three relay selection schemes using FTP: opportunistic decode and forward with FTP (O-DF with FTP), opportunistic amplify and forward with FTP (O-AF with FTP) and partial relay selection with FTP (PR with FTP). We compare the performances of these schemes in terms of symbol error probability, data rate and power consumption with three relay selection schemes using ATP: opportunistic decode and forward with ATP (O-DF with ATP), opportunistic amplify and forward with ATP (O-AF with ATP) and partial relay selection with ATP (PR with ATP). We provide exact and/or lower bound expressions of the symbol error probabilities of O-DF, O-AF and PR with FTP. The analytical study for the data rate and the power consumption is also provided. Our comparison study shows that FTP has a positive impact on the data rate and power consumption performance while it deteriorates the symbol error probability performance.

In this paper, we consider a secondary broadcast network where a multi-antenna transmitter broadcasts the same data toward a large number of secondary receivers (SRs) in the presence of a primary communication. Thanks to its multi-antenna capabilities, the secondary transmitter (ST) uses an orthogonal beamforming technique to broadcast its data while controlling the interference perceived by the primary receiver. We develop and investigate three broadcast transmission schemes. The first one is simple and operates in underlay mode where the ST broadcasts its data simultaneously as the primary transmission. The second scheme operates in overlay mode where ST helps the primary transmission by means of cooperative diversity transmission. SRs exploit a post-transmission interference cancellation techniques to cancel the interference caused by the primary transmission. The third scheme operates also in overlay mode and the secondary network exploits also the cooperative diversity technique. The metric used to evaluate the performance of secondary broadcast network is the rate of served SRs. We compare the performances of the three schemes by simulations. Also, analytical expressions of the outage probability for the first and second schemes are provided. Simulations along with analytical results proved that our two overlay proposed schemes ensure low secondary outage probability.

Achieving the goal of reliably delivering data to all nodes in broadcast wireless networks is very challenging since wireless channels may experience severe variations in signal strength and channel impairments. To mitigate this problem, one or several relays can be used as collaborators to forward the broadcasted signal to other nodes. In this paper, we propose and investigate several single relay selection schemes in broadcast wireless networks using either selective digital relaying or selective analog relaying. The key idea is to classify the nodes in the considered broadcast network into two sets. A set of "reliable" nodes, whose source-node signal-to-noise ratio exceeds a threshold value and a set of "unreliable" nodes gathering the remaining ones. Then, one node among "reliable" nodes is activated as a relay. We derive closed form expressions of the end-to-end bit error probabilities of some proposed single relay selection schemes for selective digital relaying. The data rate loss due to the cooperation is also studied. Analytical results along with simulations prove that compared to the direct transmission, the single relay selection schemes improve significantly the bit error probability performance of the broadcast network.

The cognitive radio technology allows the design of dynamic spectrum sharing techniques where unlicensed secondary users can use frequency bands owned by license primary holders. Thus, this emerging technology is regarded as the ideal candidate that can enhance the efficiency of spectrum usage for the next generation of wireless communication systems. In this paper, we consider the problem of spectrum sharing and user scheduling in a cognitive radio MIMO system. A secondary network made up of a multi-antenna base station and several secondary receivers share the same frequency bands owned by primary users.We study the scenario where the primary receivers do not allow any interference from the cognitive BS which serves its users in the broadcast channel.Using graph theory, we propose a novel algorithm that finds a near optimal spectrum sharing with the objective of approaching the maximum achievable sum rate of the secondary network. The spectrum sharing problem is formulated as a new vertex coloring problem. We show that this problem is NP-hard and then we design an efficient greedy algorithm using one out of four proposed selection criteria to solve the problem. We also formulate the coloring problem as a binary integer programming problem in order to find the optimal coloring solution. Through computer simulations, it is shown that the proposed algorithm is able to achieve near-optimal performances with very low computational complexity.

In this paper we develop an optimal solution for resource allocation between secondary users in a cognitive radio network (CRN). We assume a CRN that contains a set of primary users (PUs) coexisting with secondary users (SUs) in an underlay spectrum sharing paradigm. PUs use licensed bands of the spectrum while SUs try either to use unoccupied bands or coexist with PUs in the same band without harmfully affecting primary transmissions. We propose an algorithm based on the VCG (Vickrey-Clarke-Groves) model in a non cooperative game for spectrum allocation between secondary transmissions that guarantee a required minimum data rate for both PUs and SUs, assuming a fixed value of the bit error rate. It aims to find the optimal and fair assignment of secondary transmissions to spectrum bands that maximizes their sum data rate. Simulation results show that the proposed solution maximizes the sum data rate depending on the transmit power of primary transmissions and the data rate required for secondary transmissions. Using Jain's fairness index, we also show that our proposition is almost 98% fair.

In this paper, we investigate and evaluate the performance of incremental relaying and relay selection, when used in the context of cognitive radio networks. Assuming that a number of cognitive radio relay nodes N (N ≥ 2) are co-located with simultaneous primary and secondary transmissions, the “best” relays are chosen to assist the primary and/or the secondary transmission(s) (in case of decoding failure at the destination(s) using the direct link source-destination). The outage probability of both primary and secondary systems is investigated and the associated power allocation problem analyzed. Results show that incremental relaying allows to improve greatly the secondary outage probability with respect to a primary outage probability threshold, compared to the non-cooperative case. Moreover, they suggest that selecting at first the “best” relay to assist the primary transmission before the one that would assist the secondary transmission is more beneficial than choosing at first the “best” relay that would help the secondary transmission. Finally, by proposing an adequate transmit power allocation scheme, we bypass the secondary transmissions' blocking at low primary Signal-to-Noise-Ratio.

Combining cognitive radio technology with user cooperation could be advantageous to both primary and secondary transmissions. In this paper, we propose a first relaying scheme for cognitive radio networks (called “Adaptive relaying scheme 1”), where one relay node can assist the primary or the secondary transmission with the objective of improving the outage probability of the secondary transmission with respect to a primary outage probability threshold. Upper bound expressions of the secondary outage probability using the proposed scheme are derived over Rayleigh fading channels. Numerical and simulation results show that the secondary outage probability using the proposed scheme is lower than that of other relaying schemes. Then, we extend the proposed scheme to the case where the relay node has the ability to decode both the primary and secondary signals and also can assist simultaneously both transmissions. Simulations show the performance improvement that can be obtained due to this extension in terms of secondary outage probability.

The preliminary work on a new methodology to design low noise amplifiers (LNAs) for use in radiofrequency (RF) wireless systems is presented. The methodology aims to find the relevant design parameters faster than current analytical models and optimization procedures. To reach this goal, an artificial neural network (ANN) is used to learn the design task by being exposed to successful design examples. Our preliminary results, using a training set of two hundred design examples, show that a radial basis functions ANN can learn the provided designs perfectly, but a larger training set is required for definite conclusions regarding the prediction of component values for new designs.

We consider a point-to-multipoint cognitive network sharing the same frequency band with a primary network assuming a spectrum underlay model. We investigate the scenario where a cognitive base station equipped with multiple antennas attempts to serve secondary users through an antenna assignment scheme. We consider quality of service constraints for secondary users and an interference constraint for the primary receiver. Hence, the cognitive base station performs both antenna assignment and optimal power allocation for the selected secondary users. Due to the high computational complexity of this problem, we propose a heuristic algorithm that separates the two tasks and tries to maximize the number of served secondary users with respect to the system constraints. The antenna assignment phase is performed using an efficient selection criterion followed by an optimal power allocation as a second phase. We show that the proposed algorithm has a very low computational complexity compared to the brute force algorithm. Furthermore, simulation results show that the proposed heuristic algorithm is able to achieve performance very close to that of the optimal solution.

In this paper, we derive exact form expressions for the Bit Error Probability (BEP) and throughput of cooperative Direct Sequence-Code Division Multiple Access (DS-CDMA) systems using incremental selective relaying which combines selective relaying with incremental relaying protocols in the presence of multipath propagation. The derived results are valid for any multipath intensity profile of the channel and any path delays. They also consider the correlation of the multipath gains. Simulation results along with analytical studies of BEP and throughput prove that the combination of incremental relaying with selective relaying in cooperative DS-CDMA systems improve significantly the throughput performance and can achieve the maximum possible spatial diversity when it is required by destination.

In this paper, we investigate several relaying schemes for cooperative communications in Cognitive Radio Networks (CRNs) in order to improve the performances of secondary transmissions while respecting a certain Quality of Service (QoS) requirement at the primary transmissions. We propose relaying schemes where a number of relay nodes, randomly located, may help either the primary or the secondary transmission. By defining proper relay selection criteria and power allocation schemes, we illustrate the secondary outage probability performance while guaranteeing the primary QoS. Using simulations, we present the impact of different parameters, such as the QoS requirement, the chosen relay selection criteria, the number of available relays, the positions of the relays, etc., on the secondary transmission performance. The obtained results show the potential of the proposed relaying schemes, and provide guidelines about the expected secondary performance under the impact of several parameters.

In underlay cognitive radio networks, unlicensed secondary users are allowed to share the spectrum with licensed primary users when the interference induced on the primary transmission is limited. In this paper, we propose a new cooperative transmission scheme for cognitive radio networks where a relay node is able to help both the primary and secondary transmissions. We derive exact closed-form and upper bound expressions of the conditional primary and secondary outage probabilities over Rayleigh fading channels. Furthermore, we proposed a simple power allocation algorithm. Finally, using numerical evaluation and simulation results we show the potential of our cooperative transmission scheme in improving the secondary outage probability without harming the primary one.

In this paper, we evaluate the throughput and the packet total transmission delay for cooperative truncated Automatic Repeat Request (ARQ) using distributed space-time codes, where relays use either Decode-And-Forward (DF) or Amplify-and-Forward (AF) relaying. In this scheme, relays are involved in the retransmission only when the destination receives erroneously the packet. Both source and relays make use of an orthogonal space time block code. For the DF mode, relays are selected after a verification by the cyclic redundancy check (CRC) sequence. However, for the AF mode, all relays are involved in the retransmission. We show that the proposed design can significantly improve performances in terms of throughput and total transmission delay.

In this paper, we propose a new adaptive relaying protocol called Threshold-based Adaptive Decode-Amplify- Forward relaying protocol (T-ADAF). In our protocol, the relay compares the signal to noise ratio (SNR) of the received signal to the average SNR of the source-relay link. If the SNR of the received signal is greater than the average SNR of the source-relay link, then the relay performs the Amplify-Forward relaying protocol (AF). On the other hand, if the SNR of the received signal does not exceed the average SNR of the source-relay link, then the relay performs the Adaptive Decode-Forward relaying protocol (ADF). The performance of the proposed protocol is investigated and a closed form of its symbol error probability is derived in the presence of Rayleigh fading channels. Furthermore, a comparison with other relaying protocols such as AF, ADF and SNR-HDAF (SNR-based Hybrid Decode-Amplify-Forward) is made in order to evaluate the performance of our protocol and to show its benefits. We also investigate the T-ADAF protocol with multiple relays and we derived a closed form of its symbol error probability.

In this paper, we derive the end-to-end (e2e) Bit Error Probability (BEP) of cooperative Multi Carrier Code Division Multiple Access (MC-CDMA) systems using selective threshold digital relaying (STDR). In STDR, a set of potential relays whose received Signal-to-Noise Ratio (SNR) exceeds a threshold value γt, called reliable relays, is formed. Then, only the best relay among reliable relays is allowed to retransmit the received signal. We activate the relay with the largest SNR in relay-destination link. The derived BEP results are valid for any multipath intensity profile of the channel.

In today's wireless communication systems, the design of efficient packet scheduling algorithms at the MAC layer is proven to have significant impact on their overall performances. In the light of this fact, we propose and compare in this paper different scheduling techniques which aim at satisfying the users' requirements in terms of both rates and delays. The considered system is a downlink multi antenna code division multiple access (MIMO-CDMA) system which assumes both traffic arrival and users' mobility. First, the MIMO-CDMA system is modeled as a weighted graph. The weight of each vertex is then updated at each time slot according to a specified scheduling rule. Finally, we solve heuristically a graph coloring problem in order to find a near- optimal scheduling decision. We evaluate through simulations the performance of the proposed algorithms and show that a cross layer design taking the benefits of both MIMO and scheduling may be efficient to address the tradeoff between system capacity and users' quality of service requirements.

In this study, the performance of user selection diversity for rate-adaptive multiuser multiple-input multiple-output (MIMO) systems employing orthogonal space time block coding (OSTBC) is analysed and evaluated. An antenna selection scheme is used to overcome the drawback of channel hardening effects in multiuser MIMO systems. Closed-form expressions for the average spectral efficiency and outage probability of the system are derived. Using numerical evaluations the considered schemes are compared in terms of outage probability and spectral efficiency. The effects of antenna correlation at the receiver ends on the performance of the system are analysed and evaluated, indicating that spatial correlation may be beneficial for the spectral efficiency of the multiuser OSTBC MIMO systems employing user selection.

In multiuser multiple input multiple output (MIMO) systems, it is optimal to serve multiple users at the same time in order to achieve high data rates. However, the use of a transmit beamforming technique requires a well designed user selection scheme to obtain good performances. The optimal scheduling solution can only be obtained through a highly computationally complex exhaustive search. In addition, when employing a multiple access scheme, such as the code division (CDMA), the complexity of an optimal user selection becomes higher even for moderate number of users and antennas. In this context, this paper proposes a heuristic scheduling algorithm based on a tabu search approach for MIMO CDMA systems using ZFBF as a transmit technique. We use a graph theoretical approach to model the system as a weighted undirected graph. The problem of user selection is then formulated as a graph coloring problem. Numerical results show that the proposed algorithm outperforms the greedy scheduling scheme and achieves performances, in terms of system sum rate, very close to those of the highly complex optimal solution.

We investigate the packet-level scheduling for the downlink of multiple-input multiple-output (MIMO) multi-user systems using beamforming. We consider the traffic arrival process and different packet lengths. We tackle low-complex practical implementation that provides low average packet transmission delay and bit error rates (BER) to users. We propose a work-conserving scheduling scheme that considers different users guarantees (heterogeneous users). We implement and compare different MIMO schedulers at the packet level. Simulations show the low average packet transmission delay and bit error rate of our proposed scheduler.

The error performance, in terms of Bit Error Rate (BER), of multi-stage (multi-hop) Wireless Relay Networks (WRNs) with distributed STBC at the relay stages is presented. One relay stage is defined by a set of relays located at the same distance from the source node where the distance is measured by the number of hops. We develop the multi-stage WRN model for Amplify-and-Forward (AF) and Decode-and-Forward (DF) procedures at the relaying nodes. The system's performance with imperfect Channel State Information (iCSI) at the receivers is also examined. Simulation results show that the tolerated error on the channel estimation increases when iCSI occurs at the channels between nodes that are far from the source node rather than close to it. This result gives good guidelines about the design of CSI knowledge at the receivers in such a way to reduce delay time and increase data rate.

A performance analysis for cooperative diversity systems with best relay selection over Rayleigh fading channels is presented. The authors obtain analytical expressions for the probability density function (PDF), cumulative density function (CDF) and the moment generating function (MGF) of end-to-end signal-to-noise ratio (SNR) of the system under study for independent and non-identically distributed (i.ni.d.) fading links. Using these expressions the authors derive lower bound closed-form expressions for the average symbol error rate (SER), the outage probability, and an upper bound closed-form expression for the average channel capacity. Using numerical evaluation of the mathematical expressions, system performances of different cases are evaluated and compared for both non-identically and identically distributed links showing the impact of the relay selection in cooperative communication systems.

Abstract In this paper, we present a finite‐signal‐to‐noise ratio (finite‐SNR) framework to establish tight bounds on the diversity‐multiplexing tradeoff of a multiple input multiple output (MIMO) system. We focus on a more realistic propagation environment where MIMO channel fading coefficients are correlated and where SNR values are finite. The impact of spatial correlation on the fundamental diversity‐multiplexing tradeoff is investigated. We present tight lower bounds on the outage probability of both spatially uncorrelated and correlated MIMO channels. Using these lower bounds, accurate finite‐SNR estimates of the diversity‐multiplexing tradeoff are derived. These estimates allow to gain insight on the impact of spatial correlation on the diversity‐multiplexing tradeoff at finite‐SNR. As expected, the diversity‐multiplexing tradeoff is severely degraded as the spatial correlation increases. For example, a MIMO system operating at a spectral efficiency ofRbps/Hz and at an SNR of 5 dB in a moderately correlated channel, achieves a better diversity gain than a system operating at the same spectral efficiency and at an SNR of 10 dB in a highly correlated channel, when the multiplexing gainris greater than 0.8. Another interesting point is that provided that the spatial correlation channel matrix is of full rank, the maximum diversity gain is not affected by the spatial correlation. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we present a performance analysis for the user scheduling for the multiuser MRC MIMO systems exploiting the user and antenna diversities. We consider two scheduling schemes including absolute SNR-based scheduling and normalized SNR-based scheduling schemes. We propose the utilization of an antenna selection scheme to overcome the drawback of channel hardening in multiuser MIMO systems and to improve the system performance. We derive new closed-form expressions for the average bit error rate of the presented schemes for two scenarios: heterogeneous (independent nonidentically distributed SNRs-i.n.i.d.) and homogeneous (independent identically distributed SNRs- i.i.d.) cases. Using mathematical analysis and numerical simulations, we compare the presented schemes.

Multi hop wireless mesh networks presents a promising solution to extent the coverage and increase the number of clients sharing the same broadband connection. Introducing the MIMO technology at the physical layer of those networks allows to improve the performance and support a higher number of clients. However, in such systems the absence of an efficient and fair medium access strategy can lead to severe unfairness between the clients and to poor system performances. In this paper, we propose a fair scheduling algorithm that improves considerably the bandwidth utilization while the fairness is guaranteed. We evaluate our proposed algorithm by simulation in order to show the maximum throughput that can be obtained while the different nodes of the MIMO-based wireless mesh networks are served fairly.

It has been shown that cooperative communication techniques have a great potential to increase the diversity in wireless relay networks and hence improve the Bit Error Rate (BER). When exploiting many users as relay nodes, a multi-antenna network called virtual-MIMO (Multiple Input Multiple Output) is set up. This special technique helps to solve the problem of transmission error occurrences when sending information through a low quality radio channel. Consequently, the transmission gets a better reliability and higher transmission rate. In this work, we focus on the distributed Space-Time-Block- Coding (STBC) with Amplify-and-Forward (AF) and Decode-and-Forward (DF) relays, for various network configurations and channel knowledge conditions. We investigate and evaluate the performance - in term of BER - of a cooperative communication system using multiple relays equipped with multiple antennas when DSTBC coding is employed at the relays with AF (or DF) relaying. Also, we examine the behavior of these cooperative communication techniques when the Channel State Information (CSI) available at the receivers is imperfect.

A performance analysis for cooperative diversity system with best relay selection over Rayleigh fading channels is presented. We obtain analytical expressions for the probability density function (PDF), cumulative density function (CDF), and the moment generating function (MGF) of end-to-end SNR of the system under study. Using these expressions we derive closed-form expressions for the average symbol error rate (SER), the outage probability and the average end-to-end SNR gain obtained form relay selection. Using numerical simulations and calculation of the mathematical expressions, the performances of different cases are evaluated and compared to show the significant advantages of the relay selection in a cooperative communication.

A performance analysis for the average channel capacity and average bit error rate (BER) of user scheduling schemes for multiuser MIMO systems exploiting the multiuser and antenna diversities over non-identically distributed Nakagami-m fading channels is presented. We consider different scheduling schemes including absolute SNR-based scheduling and normalized SNR-based scheduling schemes for both heterogeneous and homogeneous wireless networks. We derive expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-noise-ratio (SNR) in non-identical Nakagami-m MIMO channels for each scheduling scheme. These expressions are used to obtain analytical expressions for the average channel capacity and average bit error rate (BER) of the system under study. Finally, the performances of different schemes are evaluated and compared.

We propose efficient scheduling algorithms for down-link MIMO-CDMA systems using zero forcing beamforming to achieve high system throughput with low computational complexity. Based on a graph theoretical approach, we propose to represent the system as a graph and to formulate the scheduling problem as the maximum weight k-colorable subgraph problem. The proposed algorithms make use of two heuristic solutions to find the scheduled users in each time slot in an acceptable polynomial time. We evaluate the efficiency of the proposed schedulers and the results demonstrates that it can achieve near-optimal performance with very low complexity compared to the optimal exhaustive search.

In this paper, we propose a new switching scheme to improve the short-term delay and the packet jitter for real-time traffic. Even though, the proposed scheme is general and can be used in different points of the network, we propose to use it in MPLS ingress nodes. In our plan, we manage the delay to improve the QoS provisioning in a flexible manner, since traditional mechanisms such as leaky bucket can not have such kind of flexibility. Also, after using the scheme in MPLS ingress node the impact of short-time scale burstiness of the traffic will be decreased. The performance analysis shows that high quality of service provisioning for the real-time traffic will be achieved.

In this paper, we present a performance analysis of user scheduling for multiuser MIMO systems exploiting the multiuser and antenna diversities while maintaining the fairness among the users. We consider different scheduling schemes including absolute throughput-based scheduling, absolute SNR-based scheduling, and normalized SNR-based scheduling schemes. We also propose the utilization of an antenna selection scheme to overcome the drawback of channel hardening in multiuser MIMO systems. This also improves the system performance and reduces the system complexity. We derive closed-form expressions for the average spectral efficiency of the system under study for each scheduling scheme. Using the results obtained from the closed-form expressions, we compare the presented schemes and show their significant advantages in terms of fairness of scheduling and spectral efficiency.

In this paper, we present a multiuser scheduling technique for the MIMO-OFDM system over multipath frequency-selective fading channels to exploit the multiuser, space and frequency diversities. Two scenarios including full-feedback and limited-feedback channel information have been considered. A discrete-rate adaptive modulation is employed to increase the spectral efficiency of the system. Performance analysis and numerical simulation are conducted to evaluate the average bit error rate (BER) and average spectral efficiency (ASE) and to show the significant advantages of the proposed scheme. It is also shown that when the number of active users is moderately high and is above 30, even 10% feedback load is sufficient to get the benefits of the proposed scheme.

In this paper, we present a performance analysis of the user scheduling for the multiuser MIMO systems exploiting the multiuser and antenna diversities while maintaining the fairness among the users. We present different scheduling schemes including absolute throughput-based scheduling, normalized throughput-based scheduling, absolute SNR-based scheduling, and normalized SNR-based scheduling schemes. We use an antenna selection scheme to overcome the drawback of channel hardening in multiuser MIMO systems. This also improves the system performance and reduces the system complexity. Using mathematical analysis and numerical simulations, we compare the presented schemes and show their significant advantages.

In this paper, we present a multiuser scheduling technique for MIMO-OFDM system over multipath frequency- selective fading channels to exploit the multiuser, space and frequency diversities. A continuous-rate adaptive modulation is employed to increase the spectral efficiency of the system. A proportional fair scheduler is also considered to maintain the fairness among the users while exploiting the multiuser diversity. We also use a scheme to reduce the required feedback channel information. Using mathematical analysis and numerical simulations, the significant advantages of the proposed scheme have been shown. It is also shown that when the number of active users is moderately high and is above 30, even 10% feedback load is sufficient to get the benefits of the proposed scheme.

Packet scheduling at the data link layer may impact significantly the overall performance of a wireless system using multiple antennas. In this paper, we propose a novel packet scheduling scheme based on proportional fairness that considers the traffic arrival process with different packet lengths for the downlink of multiple-input multiple-output (MIMO) multi-user systems. We also provide analysis for the fairness of the new scheme in terms of time and service allocation. The scheduler, referred to as clock-time proportional fairness (C-T PF), performs at the packet level and can provide low average packet transmission delay as well as time and service fairness to users. It is work conserving and it can also take into consideration different users guarantees (heterogeneous users). We investigate an ideal service fair scheduler called C-T max-min for MIMO systems as well. We compare the performance of C-T PF with other MIMO schedulers. For the time and service fairness comparison of MIMO schedulers, we also propose time and service indexes. Simulations that consider the traffic characteristics and the mobility of users show the low average packet transmission delay and demonstrate the time and service fairness capabilities of C-T PF.

The impact of spatial correlation on the performance limits of multielement antenna (MEA) channels is analyzed in terms of the diversity-multiplexing tradeoff (DMT) at finite signal-to-noise ratio (SNR) values. A lower bound on the outage probability is first derived. Using this bound accurate finite-SNR estimate of the DMT is then derived. This estimate allows to gain insight on the impact of spatial correlation on the DMT at finite SNR. As expected, the DMT is severely degraded as the spatial correlation increases. Moreover, using asymptotic analysis, we show that our framework encompasses well-known results concerning the asymptotic behavior of the DMT.

Transmission power control (TPC) in a Mobile Ad hoc network (MANET) environment reduces the total energy consumed in packet delivery and/or enhances network throughput by increasing the channel's spatial reuse. In this paper, a distributed correlative power control scheme using prediction filters (Kalman or extended Kalman) is proposed. The prediction filter is used to estimate the forthcoming interference. Both the transmitter and receiver in MANET environment make use of predicted interference to assign correlative power values to their associated ensued packets to guarantee the success of the IEEE 802.11 four-way handshaking communication (RTS/CTS/DATA/ACK). Simulation results for different topologies are used to demonstrate the significant throughput and energy gains that can be obtained by the proposed power control scheme.

It is widely accepted that Ad Hoc networks are at the leading edge of the research in the domain of wireless networking. These networks are not supported by infrastructure to connect the mobile hosts, thereby they have to be self configured, self organized and the resources have to be allocated in a distributed manner. The medium access control (MAC) layer is seen as the bottleneck for the throughput in wireless Ad hoc networks. Hence, we propose in this work a new Multichannel MAC protocol. The proposed protocol can be based on Code Division Multiple Access (CDMA) or Frequency Division Multiple Access (FDMA). A channel can be represented by one spreading code in CDMA systems or by one frequency band in FDMA case. In our analysis and simulations, we assume that the protocol is based on CDMA technique. We consider one channel for control packets and multiple channels for transmitting data information. We propose that the reservation of a data cannel is done implicitly using the common channel. We show through computer simulations that our proposition of Multichannel MAC protocol improves significantly the communication performance in wireless Ad Hoc networks, even when the introduced overhead is considered.

We present tight upper bounds on the channel mutual information of spatially correlated and uncorrelated multielement antenna (MEA) channels. Using these upper bounds, accurate lower bounds on the outage probability are derived. Similarly, tight upper bounds on the outage rate are obtained. Interestingly, these bounds are even tighter as the spatial correlation increases. Simulation results show that, in a highly correlated channel, the worst gap between our outage probability lower bounds and the exact values (given by simulation) is about 0.2 and 0.3 dBs, respectively for 2 times 2 and 3 times 3 MEA systems. This tightness suggests using the derived lower bounds on the outage probability in order to characterize the performance limits of MEA in terms of the finite-SNR diversity-multiplexing tradeoff in correlated and uncorrelated spatial fading channels.

Higher throughput gains and prolonged life time can be achieved for mobile ad hoc networks (MANET) with nodes equipped with directional antennas. The employment of directional antennas can enhance the spatial reuse by allowing concurrent communications to occur within the same vicinity. Another advantage of directional antennas is the higher gain resulted from its directivity, which can be utilized to reduce the transmission power during a directional transmission. In order to maximize the throughput and energy gains from directional antennas, we propose in this paper a transmission power control scheme for directional medium access protocol (MAC) protocols. The proposed scheme can be integrated to any directional MAC protocol that adopts a single channel for transmission and reception of IEEE 802.11 frames. The proposed power control scheme exploits the temporal directional transmission power correlations that exist between the IEEE 802.11 frames (RTS/CTS/DATA/ACK) for successful communication. Simulation results for different topologies are used to demonstrate the significant throughput and energy gains that can be obtained under the investigated scheme.

This paper investigates link layer data units (frames) transmission strategies for MIMO wireless systems using spatial multiplexing. A new effective transmission strategy is proposed in this paper in order to decrease the frame error rate by making use of the multi-channel transmission characteristics provided in MIMO systems. The main idea is to select, in the context of a V-BLAST transmitter, between transmitting each frame, where a frame corresponds to an error correcting code word, from one antenna or from multiple antennas according to the channel state. Limited binary feedback information allows the transmitter to select the appropriate frame transmission policy. Analytical studies and simulations provided in this paper determine the optimal selection criterion and highlight the gains obtained by the proposed transmission strategy. This paper confirms that always transmitting each frame from multiple antennas gives quasi-optimal performances

This paper investigates the effect of circuit switched transmissions over General Packet Radio Service (GPRS) performance. GPRS is an additional GSM service, which provides packet switched data services over GSM network and a packet access to data networks. Radio resources, available in a GSM cell, are shared dynamically between GPRS and GSM circuit switched services and the physical channels associated to GPRS are called Packet Data Channels (PDCH). In this paper, we propose new procedures of physical channels assignment to mobile stations in order to integrate voice transmissions (circuit switching) and data transmissions (packet switching) over GSM network. However, these procedures can be used for diversified types of TDMA-based wireless networks. The proposed protocols are described and their performance is evaluated. Besides, the performance of GPRS system is evaluated in the case of a fixed number of PDCHs. The effect of voice activities on GPRS performance is analyzed and evaluated considering the best effort performance parameters and World Wide Web (WWW) application.

We propose and present a new access persistence control mechanism for packet cellular networks, such as General Packet Radio Service (GPRS) system, in order to improve the MAC layer performance. The MAC layer in GPRS make use of slotted ALOHA protocol combined with a specific retransmission mechanism to establish the physical connection. The main object of the new mechanism is to avoid the mixture of retransmitted access request and the new ones. The primary profit is the decrease of the transmission attempts number needed to successfully transmit an access request. Thus, it decreases the delay of transmission of one access request and the access requests blocking percent.