Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propagation environment. However, training such a model requires high-quality, local datasets at the deployment site, which are often difficult to collect. We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-user rate requirements. At equal energy consumption, zero-shot deployment of the proposed foundation model significantly outperforms zero forcing, and approaches weighted minimum mean squared error performance with 8× less complexity. To address model adaptation in data-scarce settings, we introduce a data augmentation method that finds training samples similar to the target distribution by computing the cosine similarity between the outputs of the pre-trained feature extractor. Our work enables the implementation of DL-based solutions in practice by addressing challenges of data availability and training complexity. Moreover, the ability to dynamically configure per-user rate requirements can be leveraged by higher level resource allocation and scheduling algorithms for greater control over energy efficiency, spectral efficiency and fairness.

This paper studies beam bandwidth partitioning problem in mobile millimeter-wave (mmWave) and multiple antennas networks. The main novelty is to flexibly optimize the beamforming bandwidth with the aim to minimize the energy consumption of the system while guaranteeing the data requirements of all mobile users. We formulate the problem as an integer nonlinear programming problem. To efficiently solve the problem, we design a deep reinforcement learning using the proximal policy optimization approach and train a deep neural network in an on-policy manner. Then, for comparison purposes, we develop low-complexity online iterative accurate solutions. We show that our approach achieves better performance compared to the iterative solutions and is able to achieve at least 4% less energy consumption and more than 12% energy efficiency gains.

Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect channel state information (CSI) is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.

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.

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.

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

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.

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.

This paper aims to study an energy-efficiency (EE) maximization hybrid precoding (HP) design for mmWave multi-user (MU) systems where the analog precoding (AP) matrix is realized by a number of switches and phase shifters so that a connection between an RF chain and a transmit antenna can be switched off for energy saving. By explicitly considering the effect of each connection on the required power of digital precoding (DP) and AP design process, we describe the total power consumption as a sparsity form of the AP matrix. Together with the novel sparsity-modulus constraints of AP matrix, these sparsity terms make our system EE maximization (SEEM) problem be non-convex and challenging to solve. To tackle the SEEM problem, we first transform it into a subtractive-form weighted sum rate and power (WSRP) problem. We then exploit an alternating minimization of the mean-squared error algorithm to solve the WSRP problem where the DP vectors and AP matrix are updated alternatively, and a compressed sensing-based re-weighted quadratic- form relaxation method is employed to deal with the sparsity parts and the sparsity-modulus constraints.

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.

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. Two two-stage solution approaches are proposed. In the first approach, a fully digital precoder (FDP) is optimized considering the computation sparsity constraint in the first stage. In the second approach, the sparsity constraint is only imposed in the second stage. To find the FDP, we employ the minimization of the weighted mean-squared error and the ℓ1-reweighted methods to tackle the non-convex objective function and sparsity constraints, respectively. In the second stage of each approach, we exploit an alternating weighted mean-squared error minimization algorithm to reconstruct HP's based on the FDP found in the first stage. Two novel analog precoding designs, namely semi-definite-relaxation-based and projected-gradient-descent-based, are then proposed to optimize the analog part of the obtained HP's. We also study the impacts of various system parameters on the system sum-rate and provide resource provisioning insights for HP systems. Numerical results show the superior performances of the proposed designs over joint SA and HP benchmark algorithms.

This paper studies hybrid precoding (HP) for mmWave multi-user OFDMA systems with sub-carrier allocation (SA) consideration. Constrained by a computation limit on the total number of data streams that can be processed, we aim to jointly optimize the SA and HP design to maximize the system sum-rate. This optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. We then propose two-stage solution approach to tackle the problem. In stage one, we optimize the fully digital precoding (FDP) considering the computation sparsity constraint. In the second stage, we exploit an alternating MMSE minimization algorithm to reconstruct the HP's based on the achieved FDP. A novel analog precoding design, namely “Projected-Gradient-Descent based”, is then proposed to optimize the analog part of the HP's.

We study fast algorithms for the optimal power allocation in an OFDM-SDMA system when some users have minimum requirements for their downlink transmission rate. We first solve the unconstrained problem for which we propose a fast zero-finding technique that is guaranteed to find an optimal solution, and an approximate algorithm that has lower complexity but is not guaranteed to converge. For the rate-constrained problem, we propose two approximate algorithms. We present numerical results showing that the computation time for the iterative heuristic is about one order of magnitude faster than finding the exact solution with a numerical solver, and the non-iterative technique is an additional order of magnitude faster than the iterative heuristic. We also show that in most cases, the amount of infeasibility with the non-iterative technique is small enough that it can probably be used in practical systems.

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.

In this study, the authors analyse the impact of spatial correlation on the average bit error‐rate (BER) performance of amplify‐and‐forward cooperative relay networks employing Alamouti orthogonal space‐time block coding (OSTBC) over Rayleigh fading channels. Closed‐form expressions are derived for the moment generating function of the total signal‐to‐noise‐ratio (SNR). The authors then obtain closed‐form expressions for the average BER of the system as well as asymptotic expressions for the average BER at high SNRs. To quantify the effects of antenna correlation on the system performance, numerical results are provided and compared for different cases.

We provide a comprehensive delay and queueing analysis for two baseline medium access control protocols for multi-user cognitive radio networks with homogeneous users and channels and investigate the impact of different network parameters on the system performance. In addition to an accurate Markov chain, which follows the queue status of all users, several lower complexity queueing theory approximations are provided. Accuracy and performance of the proposed analytical approximations are verified with extensive simulations. It is observed that using an Aloha-type access to the control channel, a buffering MAC protocol, where in case of interruption the CR user waits for the primary user to vacate the channel before resuming the transmission, outperforms a switching MAC protocol, where the CR user vacates the channel in case of appearance of primary users and then compete again to gain access to a new channel. The reason is that the delay bottleneck for both protocols is the time required to successfully access the control channel, which occurs more frequently for the switching MAC protocol. It is thus shown that a clustering approach, where users are divided into clusters with a separate control channel per cluster, can significantly improve the performance by reducing the competitions over control channel.

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on, we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

Multi-antenna OFDMA-SDMA systems provide the required high spectral efficiency and flexibility to support the ever increasing data rates requirements of real-time multimedia applications in future wireless access systems. However, the resource allocation process becomes extremely complex because of the large number of degrees of freedom and the strict timing requirement of real-time traffic. In this paper, we propose heuristics to efficiently solve the zero-forcing OFDMA-SDMA resource allocation problem and provide, when feasible, guaranteed service to users with minimum rate requirements. The heuristics combine both rate-constrained power allocation and subcarrier reassignment algorithms. We compare the heuristics performance against an upper bound and other methods proposed in the literature and find that, although they have a slightly lower sum rate performance, they support a wider range of minimum rates while significantly reducing the computational complexity, making them suitable for usage in real-time systems.

In this study, we propose a queueing model to analyse the performance of an opportunistic spectrum access (OSA) system with service interruptions operating over heterogeneous channels in which the service transmission rate and the service interruption rate after the transmission is resumed are generally different than their value prior to the interruption. We first propose Markov chain models to analyse this system under memoryless service time and availability periods. On the basis of simplification assumptions, we also provide an analytical z ‐Transform analysis of the Markov models. The Markov model and approximations are validated with accurate system simulations. We also provide numerical results illustrating the non‐convex relations between the traffic metrics and system parameters and that the proposed models are essential for optimal OSA network planning and operation. We further analyse and discuss the OSA queueing model for general distribution of service time and availability periods. The analytical and simulation results indicate that for usual system parameters, the queue average occupancy is similar for different distributions of service time and availability periods and that the memoryless Markov models can be used to accurately predict the heterogeneous OSA system traffic performance.

This study analyses the impact of antenna correlation on orthogonal space–time block coding in amplify‐and‐forward cooperative relay networks in conjunction with different adaptive transmission techniques over Rayleigh fading channels. Closed‐form expressions are derived for the cumulative distribution function, and probability distribution function of the total signal‐to‐noise ratio. For each considered adaptive transmission technique, the authors derive closed‐form expressions for the average channel capacity, outage probability, average spectral efficiency and the average bit error rate of the system. Using numerical evaluations, the performances for different cases are compared in order to evaluate and quantify the effects of spatial correlation on the system performance.

Composite fading wireless channels possess both fast and slow fading dynamics. In such channels, the average bit error probability varies slowly over time, hence, it is feasible to harness a power control mechanism to effectively avoid the bit error outage (BEO). In this letter, we focus on a new adaptive power allocation scheme for M-ary phase shift keying ( M-PSK) signals. The proposed power control technique is designed to track the moments of the composite fading envelope. Analytical expressions are derived which provide the optimal transmit power (under long-term power constraint) and the minimum transmit power to cope with the BEO. Numerical results indicate that the transmit power can be potentially decreased depending on the fading gain observed in the coherent and diffuse components of the received signal.

Dynamic spectrum access (DSA) for secondary networks improves the spectrum utilization by finding spectrum opportunities and exploiting them efficiently. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, thus increasing the transmission throughput. However, in a multi-channel system, the problem becomes more complex since a sensing schedule, indicating to each user the channel that it must sense at different sensing moments, must be created to optimize system performance. In this paper, we first propose a general sensing strategy to schedule the users according to network parameters. We propose three sensing strategies, and within each one of them several solutions striking a balance between throughput performance, memory usage, and computational complexity are proposed. In addition, we show that the proposed sequential sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, the parallel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel sensing strategy achieves the best performance in all scenarios at the cost of extra complexity.

In this paper, we derive analytical expressions to compute the covariance matrix of the signals impinging on a reconfigurable antenna array. We consider a receiver equipped with a linear antenna array where each antenna element can be independently configured to create a directive radiation pattern toward a selected direction. In our derivation, we assume a multimodal truncated Laplacian distribution to model the power angular spectrum (PAS) of the signals arriving at the array. Using the derived expressions, we are able to evaluate the correlation between the received signals as a function of the signal spatial distribution, the antenna array topology, and the radiation pattern characteristics of each element in the array. We also study the capacity of a reconfigurable multiple-input multiple-output (RE-MIMO) system and illustrate its relation to radiation pattern configurations and characteristics. We demonstrate how the derived expressions can be used to efficiently choose the configuration for each reconfigurable antenna element in the array that leads to an optimal compromise between low receive correlation and high receive power in order to maximize the link capacity. Simulation results are provided to validate our analytical expressions.

Cognitive radio networks (CRNs) benefit from several features, such as decision-making, spectrum-awareness and reconfigurability, which enable them to perform spectrum migration in order to recover the link when the operating channel becomes occupied. The time spent for channel migration and recovery is a function of the order in which the channels are selected to be sensed. For optimal sensing order, the parameters which have mostly been considered in the literature are the availability and the quality of the channels. Another important parameter is the channel stability, which is the duration that a channel remains continuously available. We extend in this paper a single-slot model to propose novel decision making dynamic programming (DP) models where availability, quality and stability of the channels are taken into account. Considering the need for differentiation in cognitive radio network, we also propose a differentiated dynamic programming model considering different classes of traffic where the sensing order is determined based on an aggregated cost function. Simulation results show the superiority of decision-making based on DP models compared to other common schemes such as myopic, random or average-based.

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.

We propose a space-frequency (SF) block coding scheme for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system using antennas with reconfigurable radiation patterns. In this system, each element of the antenna array at the transmitter side is assumed to be reconfigurable so that it can independently change the physical characteristics of its radiation pattern. The proposed block code is full rate and benefits from spatial, frequency, and reconfigurable radiation pattern state diversity over frequency-selective fading channels. We provide simulation results to demonstrate the performance of the proposed block coding technique and make comparisons with that of the previous SF coding schemes in MIMO-OFDM systems. The results indicate that the proposed code achieves higher diversity and coding gain compared to other available SF codes.

The impact of outdated channel state information (CSI) on the performance of variable‐rate adaptive M ‐ary quadrature amplitude modulation in amplify‐and‐forward (AF) opportunistic relaying systems over time‐variant Rayleigh fading channels is analysed. Two rate‐adaptive modulation techniques are considered. In the first scheme, the relay and the transmission rate are selected according to the CSI at the receiver side. In the second scheme, whereas selection of the relay is based on the receiver's CSI, the transmission rate is selected based on the predicted CSI at the transmitter after the feedback delay. The impact of imperfect CSI prediction on the system performance is also evaluated. For each scheme, analytical expressions are derived for the average spectral efficiency, outage probability and the average bit‐error rate under outdated CSI. Using numerical evaluations the performances of the considered rate‐adaptive modulation schemes are analysed to illustrate the impact of outdated CSI on AF opportunistic relaying systems.

In this paper, we introduce a queueing approach with server interruptions to analyze the performance of a cognitive radio (CR) link subject to recurrent failures and interruptions. This general model can be used to analyze the traffic metrics as a function of different recovery schemes, channel bandwidth variations and service differentiation. As a first step, we solve in this paper the queueing model without service variation and differentiation. Numerical results to illustrate the analysis accuracy are provided.

This paper discusses radiation pattern selection in multiple-input multiple-output (MIMO) wireless systems. Each receive antenna can be reconfigured to P distinct radiation patterns. We select radiation patterns in order to maximize instantaneous channel capacity. And we prove that for arbitrary correlation the maximum achievable diversity order of the pattern selection MIMO (PS-MIMO) system equals to the rank of the total correlation matrix of its full system so that the link reliability can be significantly improved. We propose a fast selection algorithm as the suboptimal solution for channel capacity maximization. For PS-MIMO systems over spatially correlated channels, we propose to use only a part of available radiation patterns based on the estimation of the correlation before performing the pattern search. Simulations are used to validate the theoretical results and illustrate the performances of proposed algorithms.

In this paper, for the purpose of service differentiation in opportunistic spectrum access (OSA) enabled networks, we take different priority queueing disciplines into account as decision variables in a dynamic optimization model. The objective is to optimize, after the departure of each packet, a weighted utility function of the performance of different classes of cognitive radio traffic by dynamically deciding the priority queueing discipline. A dynamic programming (DP) model, which takes into account the channel availability and channel handover recovery periods of the OSA network, is provided for two classes of traffic to solve this problem. Simulation results are presented.

This paper presents an evaluation of the impact of channel time variations on performance of multiuser selection diversity systems employing orthogonal space-time block coding (OSTBC) over MIMO Rayleigh fading channels. A rate-adaptive M-QAM modulation along with the maximum signal-to-noise ratio (SNR) based user selection diversity is considered. Analytical expressions are derived for the average spectral efficiency, average bit error rate (BER), and bit error outage (BEO) of the system. Using the results obtained from the analytical expressions as well as from Monte-Carlo simulations, the impact of channel time variations on the system performance is evaluated.

In this paper, we study the performance of a reconfigurable multiple-input multiple-output (RE-MIMO) system in spatially correlated frequency- selective fading channels. In particular, we propose to use a RE-MIMO system to overcome the inter symbol interference (ISI) problem caused by multipath propagation. In the proposed system, we use a 3-dimensional coding scheme to obtain the same diversity order as in MIMO-OFDM systems over frequency-selective channels, and achieving better bit error rate performance by using directive reconfigurable antennas. To demonstrate the superiority of the proposed system, we compare the performance of the RE-MIMO with MIMO-OFDM system over frequency-selective channels. Simulation results show that the coded RE-MIMO system outperforms the coded MIMO-OFDM systems for small angular spread. However, for larger angular spread, the performance of the RE-MIMO system degrades due to much stronger contribution of undesired multipath components.

In this study, the authors provide an analytical framework to assess network-level quality of service (QoS) measures for differentiated, non-saturated traffic in infrastructure-mode 802.11 wireless local area networks using the distributed coordination function access mechanism. The authors build on a general analytical framework that takes into account the medium access control (MAC) access mechanism, the MAC layer packet buffer and the characteristics of the offered load to obtain the probability of a collision with M classes of traffic. The authors then derive analytical expressions for the throughput, the end-to-end packet delay and the packet delay outage probability for differentiated traffic. A case study of voice-over-IP (VoIP) traffic in 802.11b/g networks is used to validate the theoretical framework. The analytical results are in good agreement with the simulation results, showing that although the collision probability for packets transmitted at the access point (AP) is lower than for packets transmitted from the clients, the end-to-end delay in the downlink is much longer than that in the uplink because of the large queuing delay because of the multiplexing of several VoIP connections at the AP. The authors also compare the maximum number of VoIP connections that can be admitted in an 802.11 network while respecting their QoS constraints; these were computed with the proposed theoretical model, an ns-2 simulation model and other schemes previously proposed in the literature. The results indicate that their approach is more accurate over a wide range of parameter values, thus demonstrating the validity, the flexibility and the robustness of the proposed theoretical framework.

Cognitive radios (CR) are an efficient approach to deploy a secondary network in the vacant portions of the spectrum licensed to primary networks. However, when primary users return to a channel used by a CR, the later should vacate the channel and start a restoration process by searching and sensing other channels. The restoration process objective is to find the best channel in the shortest time. We propose in this paper a framework where the intrinsic features of learning and history-awareness of CRs are used to improve the restoration mechanism performance by providing a shorter restoration time or a restored channel with a higher quality. We study both distributed and centralized history-aware restoration schemes and show that it can provide significant improvements for both schemes. However, history-awareness should be carefully used in distributed algorithms since it can be detrimental in certain cases due to the increased users' contention for the best channels.

In this paper we consider an Interference Alignment (IA) approach for intercell interference coordination for the downlink of a MIMO multi-user cellular network. We determine the maximum degrees of freedom (DoF) of the network and give the feasibility proof of perfect IA. Furthermore, we consider the problem of approximate IA and the Mean-Squared Error (MSE) based optimization of the transmit and receive beamforming matrices with the orthogonality constraints and the per-antenna transmit power constraint. Iterative algorithms are presented to solve the corresponding MSE problems. Simulation results and convergence analysis of the algorithms are also discussed. The results indicate that it is feasible to significantly increase the network capacity using the proposed IA approach.

The impact of outdated channel state information (CSI) on the performance of variable-rate adaptive M-ary quadrature amplitude modulation (M-QAM) in an amplify-and-forward (AF) opportunistic relaying system over Rayleigh fading channels is analyzed. Closed-form expressions are derived for the average spectral efficiency, outage probability and the average bit error rate under outdated CSI. Using numerical evaluations, the performances of different cases are compared showing the impact of outdated CSI on the performance of the considered rate-adaptive modulation scheme in AF opportunistic relaying systems.

In this paper, we derive probability density function (PDF) and cumulative distribution function (CDF) expressions for the end-to-end signal-to-noise ratio (SNR) of an opportunistic relaying amplify-and-forward (AF) cooperative diversity system over independent and non-identically distributed (i.ni.d.) Rayleigh fading links where outdated channel state information (CSI) is used for relay selection. Based on these expressions, we derive the analytical capacity and outage probability expressions for four classical adaptive transmission techniques, namely, optimal power and rate adaptation (OPRA), constant power with optimal rate adaptation (ORA), channel inversion with fixed rate (CIFR) and truncated channel inversion with fixed rate (TIFR). Numerical evaluations results are presented showing the impact of relay selection with outdated CSI and i.ni.d. links on the performance of each adaptive transmission technique in AF opportunistic relaying systems.

MISO-OFDM systems enable the allocation of resources in the frequency and spatial domain to increase the data throughput of wireless systems. In this paper, we initially present a near-optimal solution to the MISO-OFDM Resource Allocation (RA) problem. This solution performs an exhaustive search over all possible user sets and then uses a dual-based approach to optimally assign power to the selected user sets. However, this algorithm requires computing the pseudo-inverse of all subchannel matrices. In order to reduce its computational complexity we derive an efficient heuristic RA algorithm. We first propose an approximation method that only computes the determinant of the squared subchannel matrices and selects the sets with maximum determinant per subcarrier. We then further reduce the computational cost by using a greedy search method which decreases the number of candidate users for each subcarrier. We present simulation results which show that the performance gap of the approximate method is under 0.6% with respect to the near-optimal solution, and under 1.4% when we use the greedy search method pre-selecting 4 sets. These results show that the proposed heuristic algorithms can achieve a performance very close to the optimal at a low computational cost even with a large number of users in the cell.

This paper investigates the downlink capacity of wideband high speed MIMO cellular networks for urban spatially correlated channels. Simulation results are presented illustrating the impact on the capacity of different channel models, including the 3GPP SCM model, the number of transmit/receive antennas and their spacing, and the available channel state information at the transmitter (CSIT). The results obtained with accurate modeling of the cellular network and propagation channel show that, similarly to point-to-point links, there is a linear capacity increase as a function of the number of degrees of freedom. The results also suggest that antenna spacing parameters have different effects depending on the mobile location in a cell and that user CSIT helps to improve the capacity when there is more transmit antennas than receive antennas but interference covariance information has little influence on the performances.

In this paper, we analyze a multiple-input multiple-output (MIMO) wireless system with dynamic radiation pattern diversity provided by composite right/left-handed (CRLH) leaky-wave antennas (LWA's). The CRLH-LWA is electronically reconfigurable with real time continuously direction-steerable radiation pattern and adjustable beamwidth. Analysis shows that these features match the clustered nature of indoor channels, and thus can provide high capacity gain by increasing received power. Furthermore, in MIMO systems the fully steerable direction provided by the CRLH-LWA is able to decorrelate the MIMO channel and improve the channel capacity. The results presented in this paper demonstrate that this is a highly promising approach for future high throughput reliable MIMO systems.

In this paper, we consider a class of wireless ad hoc networks, namely two-hop MIMO interference relay network, where L M-antenna source-destination pairs communicate via K relay nodes each equipped with N antennas. Based on recent advances on characterizing the limits of MIMO X channel and the concept of interference alignment, we treat this network as the cascade of two MIMO X networks and decompose it into L non- interfering parallel MIMO relay channels. The rationale behind this approach can be justified by the information theory of MIMO X networks which reveals that they provide higher degrees of freedom than the conventional MIMO interference channels. Signaling, interference alignment, relay selection and simulation results will be presented. Compared to the well-known distributed interference cancellation technique, this approach offers higher capacity for small to medium number of relay nodes at the cost of feeding back CSI from the relays to the source nodes and more advanced signaling.

In this paper we consider a two-hop MIMO parallel relay channel where a source-destination pair both equipped with M antennas, communicate via K N-antenna half-duplex amplify-and-forward relays (K,N ges M). Multi-user beamforming techniques are jointly applied at the source and destination to respectively multiplex and recover the data streams. In order to further exploit the multi-relay diversity, we propose new relay selection criteria based on semi-orthogonality among spatial eigenmode and antenna pairs of the relays. The proposed algorithms have very low complexity, decrease the amount of feedback, and consume less power across the relays compared to previously proposed MIMO relaying strategies. Furthermore, for small to medium number of relay nodes, the proposed algorithms' capacity outperform the other strategies and in the asymptote of large number of relay nodes (K rarr infin), the capacity of the proposed algorithms scales as M/2 log log K.

User cooperation enables single antenna terminals to benefit from spatial diversity by partnering with other users to create a virtual transmit antenna array. A promising form of cooperation is called coded cooperation which integrates cooperation with channel coding, showing great performance gains. However, the optimal degree of cooperation between the users changes with the channel conditions and there is no known expressions indicating the required degree of cooperation for given channel conditions. Furthermore, coded cooperation has a fixed throughput which inefficiently use the degrees of freedom of the channel, especially at high SNR. This paper proposes an adaptive coded cooperation protocol based on incremental redundancy using an ARQ/FEC scheme with rate-compatible punctured convolutional codes (RCPC). By employing a ACK/NACK feedback channel from the partner and the destination, each user adapts the size of the frames transmitted in the first and second cooperation phases in order to maximize throughput and to minimize errors due to the channel. Simulation results illustrate the gains and flexibility of our protocol for both reciprocal and non-reciprocal channels.

In this paper, we evaluate the impact of the wireless channel and physical layer parameters on the performance of VoIP traffic in IEEE 802.11g networks. The wireless channel is modeled using a finite state Markov chain based on an adaptive modulation and coding scheme. The transition probabilities encompass the effects of the time-varying wireless channel, such as the Doppler frequency and the operating SNR, and the physical layer target packet error rate. This model is implemented inthe NS-2 tool and used to analyze the QoS (delay and packetdrop rate) of real-time multimedia traffic in realistic wireless channels. Numerical results showing the physical layer impact on the number of supported VoIP calls in an 802.11g network are presented. We also demonstrate that the admission region in the presence of mixed VoIP and data remains linear, even after considering the complex wireless channel effects.

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

A MIMO OFDM receiver with interference cancellation requires a channel estimation and matrix inversion on a sub-carrier basis. This is computationally intensive and can prevent a practical implementation. In this paper, we propose new interpolation algorithms for this type of receiver. To eliminate the need for a matrix inversion for each OFDM sub-carrier, the weight matrix is interpolated. Algorithms to determine the interference cancellation detection order are also introduced. The computation requirement of this receiver is greatly reduced while the performance degradation is acceptable. For example, for a normalized product of the rms delay spread by the interpolation factor below 6.25%, the degradation compared to the optimal solution is 2dB. The results also demonstrate that the impact of the sub-optimal interference cancellation detection order is non-significant compared to the coefficient matrix interpolation error and that using interference cancellation in a MIMO OFDM system with interpolation significantly improves the performance.

A new receiver structure able to deliver high data rates in a multiple-input multiple-output (MIMO) frequency selective wireless environment is proposed and investigated. The optimal solution for the finite length MIMO decision feedback equalizer (DFE) with cancellation is derived and used to illustrate the potential of this architecture for space-time communications. LMS and RLS adaptive algorithms are also presented for the MIMO architecture. The convergence and performance of these algorithms is confirmed through simulation results. The proposed adaptive solutions do not require channel identification, are less computationally intensive than the optimal solution and allow the proposed MIMO receiver to adapt to channel changes.

This paper reports on field measurements of point-to-point indoor high-speed (10 Mbit/s to 30 Mbit/s at 5 Mbaud) wireless communications realized using a flexible multilevel quadrature amplitude modulation (M-QAM) testbed that features real-time equalization and smart antenna-array technology. The results from an extensive set of measurements, 59262 trials in all, performed without cochannel interference under various receiver configurations and wireless environments are presented and analyzed. The results underscore the dramatic potential for a system that optimally combines equalization and a smart antenna array. For example, using only 10 mW of transmit power, the system delivered 30 Mitts at an uncoded bit error rate (BER) of 10/sup -3/ with 5% outage at a coverage radius of 20 in. For a lower data rate of 10 Mbitts, the coverage radius was increased to 32 in, the uncoded BER dropped below 10/sup -7/, and the outage improved to 1%. The field measurements indicate that a 4-tap feedforward-filter decision-feedback equalizer with eight feedback-filter taps is sufficient to mitigate the intersymbol interference for typical indoor environments. They also show a significant gain when using a smart antenna array. For example, when transmitting between rooms at a 2% outage probability, the signal-to-noise ratio (SNR) improves by 8.3 dB when using two antennas instead of one antenna. Doubling the number of antennas to four provided an additional SNR improvement of 5.2 dB. The paper also presents simulation results that confirm the performance trends observed from the field measurements.

A dynamic reservation time division multiple access (DR-TDMA) control protocol that extends the capabilities of asynchronous transfer mode (ATM) networks over the wireless channel is proposed in this paper. DR-TDMA combines the advantages of distributed access and centralized control for transporting constant bit rate (CBR), variable bit rate (VBR), and available bit rate (ABR) traffic efficiently over a wireless channel. The contention slots access for reservation requests is governed by the framed pseudo-Bayesian priority (FPBP) Aloha protocol that provides different access priorities to the control packets in order to improve the quality-of-service (QoS) offered to time sensitive connections. DR-TDMA also features a novel integrated resource allocation algorithm that efficiently schedules terminals' reserved access to the wireless ATM channel by considering their requested bandwidth and QoS. Integration of CBR, voice, VBR, data, and control traffic over the wireless ATM channel using the proposed DR-TDMA protocol is considered in this paper. Simulation results are presented to show that the protocol respects the required QoS of each traffic category while providing a highly efficient utilization of approximately 96% for the wireless ATM channel.

This paper reports on the development of a flexible 5-MBd equalized M-QAM testbed for high-speed wireless data communications. The unit operates in real time and was field tested in typical indoor environments. A total of 3600 independent experiments were conducted using the testbed where one of 4-, 16-, 64-QAM constellations were transmitted, and the performance as a function of adaptive equalization and antenna selection diversity was studied. The experimental results presented here help put previous simulation and analytical results into perspective and demonstrate some of the performance bounds associated with a practical implementation. The field trial results show that with only 10-mW of transmit power, reliable 10-Mbit/s data communication can take place in between rooms with a coverage radius of 17 m, and better than 15% outage at an uncoded bit error rate (BER) of 10/sup -3/. The addition of two-branch antenna selection diversity to the system would allow 10-Mbit/s transmission at better than 3% outage and 20-Mbit/s transmission at 10% outage in the same environment. Moreover, 30-Mbit/s data transmission is feasible when both the transmitter and receiver are located within the same room, albeit at higher outage levels. In general, the results demonstrate the tremendous impact that adaptive equalization can have on the achievable performance of indoor links. Average SNR was improved anywhere from 8 dB to 12 dB depending on the particular environment and data rate. The impact on the outage performance, however, was much more dramatic.

This paper proposes data and voice scheduling algorithms for the dynamic reservation time division multiple access (DR-TDMA) MAC protocol for wireless ATM networks. Featuring a novel framed pseudo-Bayesian priority ALOHA algorithm that provides access priority to voice control packets, DR-TDMA improves the quality-of-service offered to voice connections and increases the maximum throughput for integrated voice and data traffic. The DR-TDMA protocol can also be extended to integrate constant bit rate (CBR) and variable bit rate (VBR) traffic in general. Simulation results show that the DR-TDMA protocol can achieve throughput in the range of 96% while maintaining reasonable quality of service for data traffic and a voice loss rate lower than 1%.

In reservation MAC protocols, before obtaining a contention free access to the channel, a mobile must wait for its request packet to be successfully sent to the base station. A pseudo-Bayesian Aloha algorithm with multiple priorities is proposed in this paper to reduce the waiting time of delay sensitive request packets in a multimedia environment. Packets are transmitted in each slot according to a transmission probability based on the channel history and a priority parameter assigned to their priority class. An adaptation of the slotted protocol to the framed environment is also described. Simulation results are presented and show that the protocol offers a significant delay improvement for high priority packet with both Poisson and self-similar traffic while low priority packets only experience slight performance degradation.