This paper investigates the sum spectral efficiency (SE) and total energy efficiency (EE) of the active reconfigurable intelligent surface (aRIS)-assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) over temporal and spatially correlated channels. Multiple aRISs are deployed between numerous access points (APs) and mobile users to enhance the signals. The active reflective elements (REs) amplify and vary the signal phase. The continuously evolving channel creates a situation, where the channel differs during training and data transmission. We characterize the joint impact of multi-user interference (MUI), pilot contamination (PC), channel aging (CA), and active RIS noise amplification (RNA). The desired signal is enhanced as the reflected signal amplitude and REs per aRIS increase. However, the PC, MUI, CA, and RNA also grow– constraining the SE. Also, the network power consumption increases. An alternating optimization framework based on the weighted minimum mean square error and fractional programming is proposed to optimize the transmit power and reflection coefficients (RCs) with the objective of maximizing the sum SE. It is demonstrated that the number of APs and REs can be reduced by deploying aRISs. The proposed power and RC optimization algorithms considerably improves the sum SE. The trade-off analysis between the total EE and sum SE shows that the envelope of the operating region of the CF mMIMO is expanded by deploying multiple aRISs.

To highlight the potential of RSMA and multicarrier (MC) techniques in vehicle-to-everything (V2X)communication, we propose and present a two-way communication MCRSMA V2X communication system that supports a large number of equipments (UEs) and promotes the dual function of sidelink and cellular communication. We employ$\mathbf{k}$-mean clustering for vehicle grouping and a suboptimal sub-carrier allocation with naive equal time and optimal precoder design for data transfer. To confirm the efficacy of the proposed system model, we compare the MC-RSMA solution against an MC-SDMA benchmark, using sum-rate as the performance metric. From the simulation results, the proposed system demonstrates significant improvements over the two multi-access technique benchmarks.

This paper presents a three-tier (3T) symbiotic radio device-to-device (D2D) routing Internet-of-Things network. The primary network (PN) consists of a base station routing data through multiple single antenna decode-and-forward sensor node(s) (DSNs) and to the destination sensor node. The secondary network (SN) consists of single-antenna active sensor tag nodes (ASN) transmitting sensed data to a multi-antenna reader using the backscatter (BC) technique. The tertiary network (TN) consists of single antenna semi-passive energy harvesting sensor tags (SSN) that transmit sensed data using BC to the same multi-antenna reader. The symbiotic nature exists where the ASNs and SSNs utilize the DSNs' radio frequency for data transfer. At the same time, the PN benefits from ASNs and SSNs assistive data transfer in terms of spatial diversity. In this work, we focus on maximizing the EE for the proposed 3T SRN D2D-routing by optimizing the DSN and ASN power allocation and the SSN reflection co-efficient. Based on the optimization problem, a minimum distance-based and a maximum channel gain-based D2D-routing algorithms are proposed. The proposed channel-gain-based 3T SRN D2D-routing algorithm is shown to be superior to the distance-based algorithm.

This paper analyzes the spectral efficiency (SE) of an active reconfigurable intelligent surface (RIS)-aided cell-free (CF) massive multiple-input multiple-out (mMIMO). The aggregated channel estimate is derived and the achievable SE is obtained in closed-form by assuming imperfect channel state information. It is shown that the sum SE increases with the number of access points (APs) and reflecting elements (REs). The noise amplification due to active REs escalates the pilot contamination and impairs the sum SE. The active RIS offers higher sum SE than the passive RIS in the extreme scenarios of completely blocked and completely unblocked direct AP-user links.

In this paper, we develop an optimization framework for the symbiotic operation of a primary multi-vehicle-to-everything (MV2X) communication network employing downlink communication and a secondary backscatter (BC) communication (BackCom) network of wireless-powered sensor equipped reflecting configurable surfaces (RISs), sharing the same spectrum. The secondary semi-passive RISs provide the multiple primary receivers with a spatial diversity gain and enable transmitting their own data to a designated IoT central system equipped with a BC reader. We develop an alternating optimization algorithm for resource allocation to maximize the sum-rate for mutually beneficial symbiotic radio operation. The presented simulation results demonstrate the superiority of the proposed optimization algorithm over conventional beamforming designs and fixed RIS reflection coefficient benchmarks.

Symbiotic radio networks (SRN) have gained traction for efficient spectrum usage in wireless communication Internet-of- Things networks (IoTNs) applications. Furthermore, the efficient use of energy resources under energy-efficient communication is rising in IoTNs to satisfy and attain the United Nations' sustainable development goals on sustainable and renewable energy usage. Hence, this work focuses on energy and spectrum efficient usage through renewable radio frequency (RF) energy harvesting (EH) backscatter (BC) communication (BackCom) and SRN between a secondary network sensor-equipped EH intelligent reflective surface BC and a primary network base station to multiple user equipment using multiple access communication. This work focuses on resource allocation optimization to maximize the system energy-efficiency quality-of-service for the SRN. The superiority of the proposed scheme over existing benchmark schemes is also presented in this work.

One of the current challenges still facing artificial intelligence (AI) and machine learning (ML) based solutions is their performance in real life environments. In this work, we report and analyze the outcomes of the deployment of one of our previously proposed AI-based automatic tracking areas (TA) design algorithm in a real and live mobile network. The implemented automation is based on our solution in [1], and has been experimented in an area with accidental land morphology that often leads to aggressive overshooting, and other radio issues affecting many high traffic sites. The area under consideration includes one major city, surrounding suburbs and villages, and main road traffic sections. We first analyze the characteristics of the considered area of deployment, collect the required statistics in terms of handover attempts, mobility event measurement reports (MRs), total paging requests per source site, and the intersite distances ISD. We then build a real dataset and construct the needed similarity matrix to run our ML-based algorithm. Finally, we present and compare the results collected after the deployment of our proposed solution in the live network with the initially existing TA plans. The obtained reduction in overhead costs of both tracking area update and paging signaling exceeded the expectations and the MNO targets

Scalability and sustainability are the corner stones to unleash the potential of beyond fifth-generation (B5G) ultra-dense networks that are expected to handle massive and heterogeneous services. This implies that the transport of the underlying raw monitoring data should be minimized across the network, and urges to bring the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying users distributions, traffic profiles, and channel conditions. This makes the data collected across different points non independent and identically distributed (non-IID), which is challenging for FL tasks. To cope with this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This entropy is calculated using a clustering scheme based on a similarity matrix defined over both the features and the supervised output spaces, and is targeting classification as well as regression tasks. The FL aggregation server then uses the reported dataset entropies to devise i) an entropy-based federated averaging scheme, and ii) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to illustrate all these advantages.

Next-generation wireless networks are expected to be highly heterogeneous, multilayered, with embedded intelligence at both the core and edge of the network. In such a context, system-level performance evaluation will be very important to formulate relevant insights into tradeoffs that govern such a complex system. Over the past decade, SG has emerged as a powerful analytical tool to evaluate the system-level performance of wireless networks and capture their tendency toward heterogeneity. However, with the imminent onset of this crucial new decade, where global commercialization of fifth generation (5G) is expected to emerge and essential research questions related to beyond 5G (B5G) are intended to be identified, we are wondering about the role that a powerful tool, such as SG, should play. In this article, we first aim to track and summarize the novel SG models and techniques developed during the last decade in the evaluation of wireless networks. Next, we will outline how SG has been used to capture the properties of emerging RANs for 5G/B5G and quantify the benefits of key enabling technologies. Finally, we will discuss new horizons that will breathe new life into the use of SG in the foreseeable future, for instance, using SG to evaluate performance metrics in the visionary paradigm of molecular communications. Also, we will review how SG is envisioned to cooperate with machine learning that is seen as a crucial component in the race toward ubiquitous wireless intelligence. Another important insight is Grothendieck's toposes, which is considered as a powerful mathematical concept that can help to solve long-standing problems formulated in SG.

In this paper, we characterize, based on stochastic geometry, the uplink coverage probability with a unified power control scheme built upon realistic path loss models and user equipment (UE) constrained transmit power. To improve their uplink connectivity, active UEs are next assumed to move in a random direction without prior knowledge of their nearest base station location, namely the blind cell search (BCS) movement. A tractable expression of the uplink handoff rate is then derived and the induced uplink coverage probability following the BCS movement is evaluated. The results show different echoes of the uplink coverage probability depending on the serving UE profile (stationary or mobile) and the considered path loss model, which suggests new insights into the design of uplink system parameters.

Using millimeter wave (mmWave) bands in 5G self-organizing networks has a significant potential to provide high bandwidth. However, the major challenge lies in initial access beamforming where mmWave communications suffer from deafness problem that may cause a significant loss in the received power especially when narrow beams are adopted. This paper tackles the problem of beam alignment in mmWave 5G. The problem is formulated as a non-cooperative game between transmitter and receiver where each player tries to align its beamforming direction in a way to obtain maximum throughput. We first provide a full characterization of pure Nash equilibria. Then, we propose a gradient descent algorithm that allows users to learn their optimal beamwidth. Simulation results prove the performance of our beam alignment model since mmWave frequencies deliver narrow beams with high gain and significant capacity.

Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to guarantee a fast radio resource management in both uplink (UL) and downlink (DL), while tackling the corresponding propagation imbalance that may arise in blockage situations. In this context, we introduce a semi-blind UL/DL decoupling concept where, after its initial activation, the central processing unit (CPU) gathers measurements of the Rician K-factor-reflecting the line-of-sight (LOS) condition of the user equipment (UE)-as well as the DL reference signal receive power (RSRP) for both 2.6 GHz and 28 GHz frequency bands, and then train a non-linear support vector machine (SVM) algorithm. The CPU finally stops the measurements of mmWave definitely, and apply the trained SVM algorithm on the 2.6 GHz data to blindly predict the target frequencies and access points (APs) that can be independently used for the UL and DL. The accuracy score of the proposed classifier reaches 95% for few training samples.

Non-orthogonal multiple access (NOMA) is one of the fundamental techniques that have been actively investigated for the 5th generation (5G) of wireless communication systems, and will be adopted on a wider scale in the coming versions of beyond-5G networks. It presents the benefit of supporting a higher number of users; as compared to the conventional orthogonal multiple access (OMA) schemes, thanks to the successive interference cancellation (SIC) process. However, resources allocation optimization remains one of the main open research challenges in this area. In this paper, we are interested in the power allocation aspect in a downlink NOMA system. We first propose an efficient optimal power allocation strategy maximizing the system's average sum rate, under physical layer (PHY) quality of service (QoS) conditions. Then, we derive the necessary system conditions to guarantee optimal SIC-stable NOMA schemes. This provides an insight on the operational regions where the use of NOMA is of interest. Various numerical results are presented to illustrate the analysis.

In this paper, we address the issue of automatic tracking areas (TAs) planning in fifth generation (5G) ultra-dense networks (UDNs). By invoking handover (HO) attempts and measurement reports (MRs) statistics of a 4G live network, we first introduce a new kernel function mapping HO attempts, MRs and inter-site distances (ISDs) into the so-called similarity weight. The corresponding matrix is then fed to a self-tuning spectral clustering (STSC) algorithm to automatically define the TAs number and borders. After evaluating its performance in terms of the Q-metric as well as the silhouette score for various kernel parameters, we show that the clustering scheme yields a significant reduction of tracking area updates and average paging requests per TA; optimizing thereby network resources.

In this paper, we study the joint 3D placement of unmanned aerial vehicles (UAVs) and users association under bandwidth limitation and quality of service constraints. In order to allow to UAVs to dynamically change their 3D locations in a distributed fashion while maximizing the network's sum-rate, we break the underlying optimization into 3 subproblems where we separately solve the 2D UAVs positioning, the altitude optimization, and the UAVs-users association. First, given fixed 3D positions of UAVs, we propose a fully distributed matching based association that alleviates the bottlenecks of the bandwidth and guarantees the required quality of service. Next, to address the 2D positions of UAVs, we adopt a modified version of K-means algorithm, with a distributed implementation, where UAVs dynamically change their 2D positions in order to reach the barycenter of the served users cluster. In order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited-interferences, the UAVs altitudes game is a non-cooperative potential game where the players (UAVs) can maximize the limited-interference sum-rate by only optimizing a local utility function. Our simulation results show that, using the proposed approach, the network's sum rate of the studied scenario is improved by 200% as compared with the trivial case where the classical version of K-means is adopted and users are assigned, at each iteration, to the closest UAV.

We consider a 3D cellular network in which generalized shadowing and radio network planning and optimisation (RNPO) parameters (e.g., antenna height, antenna tilt/azimuth, power biaising...) are incorporated into the cell-selection model. Using tools from stochastic geometry (SG), we derive an equivalent 2D network in which no shadowing and RNPO parameters are considered. Next, we derive coverage probability for a tractable case-study network, and the regimes where coverage probability is maximized in addition to the interference-limited one are investigated. An intermediary result is a closed-form expression generator encompassing the Q -function based-expression in [1]. Numerical results confirm the accuracy of our approximations.

In this paper, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network's sum-rate. Our approach is referred to as 'Learn-As-You-Fly' (LAYF) algorithm. LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching-based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted. Finally, in order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited interference, the UAVs altitudes game is a potential game where UAVs can maximize the limited interference sum-rate by only optimizing a local utility function. Our simulation results show that the network's sum-rate is improved as compared to both a centralized suboptimal solution and a distributed approach that is based on closest UAVs association.

In this paper, we consider a multi-pair two-way full-duplex relaying system where each pair of users exchange information with the aid of an amplify-and-forward relay with massive number of antennas. In this work, we assume the direct link between a pair of users is non-negligible, and propose a transmission scheme that captures the joint benefits of relay and direct links. First, we derive the communication model and the sum spectral efficiency of the proposed system. Then, as a benchmark, two transmission modes are considered: i) the mode where the direct link is considered as interference (DLI mode) and ii) the device-to-device (D2D) mode where the relay is silent. Finally, using analytical evaluation, we compare the proposed transmission mode with the DLI and D2D modes, and study the impact of the transmit power on the system spectral efficiency. Analytical results show that the proposed transmission mode, the DLI mode, and the D2D mode can outperform each other depending on the direct link quality and the packet length; which is confirmed by the presented numerical results.

The promise of future 5G networks is to provide very high capacity and ultra-reliable communication. Performance analysis of wireless systems is a key towards insightful design of current and future wireless systems. In this paper, we investigate the uplink performance of a single cell multi-user massive multiple-input multiple-output (MIMO) system over inverse-Gamma shadowing channels. Assuming perfect knowledge of the channel state information (CSI), we derive closed-form expressions for outage probability, average achievable rate and bit error rate (BER). Additionally, we obtain an approximate expression of the signal-to-interference-plus-noise ratio with imperfect CSI, and we derive the corresponding outage probability expression. The obtained expressions are assessed through numerous simulations and numerical examples that confirm the accuracy of our analysis. Furthermore, the results give a clear view on the impact of various parameter settings (including fading severity, transmit power, and the number of base station antennas) on the system's performance.

Spectrum efficiency and users throughput are two major challenges for the 5th generation (5G) wireless mobile systems. Non-orthogonal multiple access (NOMA) and device-to-device (D2D) communications have been recognized as promising techniques to address these challenges, and improve the overall 5G networks performance. In this paper, adopting a game theoretical approach, we propose an analysis framework to help users with poor channel conditions increase their downlink transmission rates. Users (players) choosing between two strategies-namely, connect directly to a NOMA base station; or use the D2D link and rely on the other users to get the service-try to maximize their throughput (payoff) in a game-like context. We define the Nash equilibrium conditions for this game, and we simulate the behaviors of users based on the channel conditions of the different links. The results show a significant rate increase for the users with poor channels, especially when paired with users experiencing better link conditions.

In this contribution, we analyze the performance of a vehicle-to-X (V2X) cooperative two-hop relay system based on an appropriate channel model that can handle mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in individual relay links. Two typical scenarios are studied, namely, Rayleigh-to-Weibull and Weibull-to-Weibull, by deriving closed-form analytical expressions for the cumulative distribution function (CDF) of the logarithmic likelihood ratios(LLRs) at the ingress of the destination vehicle decoder. The obtained expressions are used to define a new metric for assessing the decoding gain in terms of bit error rate (BER). The accuracy of our derivations is demonstrated with Monte-Carlo simulations. For different system configurations and channel parameters, numerical results provide additional insight into the behavior of the network.

Closed-form analysis of composite fading-shadowing channels is quite involved, and the complexity is further increased when the performance investigation is considering diversity combining schemes. Recent works on mixture models offer a simple approach to performance analysis under several composite fading-shadowing channel distributions. In this work, we propose a unified approach based on the so-called mixture of Gaussians (MoG) model to derive closed-form expressions for the outage probability for both L-branch equal gain and maximal ratio combining receivers. The proposed expressions are accurate and circumvent the complexity of the classical approach. The presented analysis is corroborated by Monte Carlo simulations that assess the accuracy of our expressions in different system setups and channel fading conditions.

Many important research works focused in recent years on the analysis of massive multiple-input multiple-output (MIMO) systems as one of the key candidate technologies for future 5G networks. In uplink massive MIMO, the use of simple linear detection techniques at the receiver side is critical from the implementation complexity point-of-view, and contributes highly to the improvement of the transmission reliability. In this paper, the performance of linear detection techniques is investigated for an uplink scenario under a realistic correlated Rice fading channel model. We derive the achievable sum-rates for the most popular linear detectors, namely, maximum ratio combining (MRC), zero-forcing (ZF), and minimum mean square error (MMSE). The results confirm that ZF and MMSE outperform MRC. However, the two receive techniques involve matrix inversion computations, and the complexity increases with the number of antennas. To overcome this problem, we propose a low-complexity method based on the well-known Neumann series (NS), and we show quantitatively that the proposed approach yields an interesting trade-off between computational complexity and achievable performance.

In this paper, we investigate the performance of multihop communication schemes over millimeter wave Weibull-fading channels. We adopt the so-called detect-and-forward relaying strategy, with generalized M-quadrature amplitude modulations, as it offers an interesting performance-complexity tradeoff. We first provide closed-form and asymptotic expressions for the end-to-end bit error rate (BER) and energy efficiency (EE). Then, we use the obtained results to investigate both BER- and EE-optimal power allocation strategies in the adopted context. The obtained results are assessed through simulations, and the proposed analysis framework provides good insight into the design and optimization of multihop schemes as illustrated with various numerical examples.

In this paper, we analyze the outage performance of dual-hop full-duplex (FD) mmWave systems, where the relay receives an outdoor signal then retransmits it to an indoor receiver. Both transmitting nodes (i.e., the source and the relay) are equipped with massive MIMO setups. We exploit the directivity of the massive MIMO transmitters on both hops to assume self-interference free duplexing. First, we derive the end-to-end outage probability of the considered communication scheme under an infinite series closed form with a limited number of antennas. Then, to alleviate the computations and provide more insight, we propose a simplified approach based on the Gaussian approximation, and we re-derive the outage probability when the number of antennas is unboundedly large. In both cases, Monte Carlo simulation results are presented to assess the accuracy of the analysis, and confirm the tightness of the approximation.

In this paper, we propose a coding strategy designed to enhance the throughput of hybrid automatic repeat request (HARQ) transmissions over independent identically distributed block-fading channels with the channel state information unknown at the transmitter. We use a joint packet coding where the same channel block is logically shared among many packets. To reduce the encoding and decoding complexity, we use a two-layer coding where, first, packets are punctured, mixed, and then passed to the conventional channel encoder. We show how to optimize the puncturing rates on the basis of the empirical error-rate curves. We also discuss how the parameters of the practical turbo-codes may be modified to take advantage of the proposed HARQ scheme. Finally, simple and pragmatic rate adaptation strategies are developed. In numerical examples, our scheme is compared with the conventional incremental redundancy HARQ (IR-HARQ), which remains a strategy of choice in the region of small throughput; however, our scheme shows a notable gain of $1-2~\textrm {dB}$ in the region of high throughput, where IR-HARQ fails to provide any improvement.

In this work, we propose and optimize a new coding strategy designed to enhance the throughput of hybrid ARQ (HARQ) transmissions over i.i.d. block-fading channels where the channel state information (CSI) is unknown at the transmitter. Unlike in the conventional approach, where many blocks are exclusively assigned to carry the HARQ rounds of a single packet, we use a joint coding and the same channel block is logically shared among many packets. To reduce the complexity, we use a two-layer coding, where, first, packets are mixed on the binary level, and the resulting mixture is then passed for the conventional channel encoder. This allows us to decode packets on a block-by-block basis. Knowing the decoder error rate curves, the challenge is to find the optimal packets mixing that maximizes the total throughput. We show how to solve this problem using a dynamic programming (DP) approach, which we next apply to the off-the-shelf turbo-codes. We also discuss how the parameters of the practical turbo-encoder may be modified to take advantage of the proposed HARQ scheme. In numerical examples, our scheme is compared to the conventional incremental redundancy HARQ (IR-HARQ), and it yields a notable gain of 1 - 2dB in the region of high throughput, where IR-HARQ fails to provide any improvement.

To ensure reliable communications in randomly varying and error-prone channels, wireless systems use adaptive modulation and coding (AMC) as well as hybrid ARQ (HARQ). In order to elucidate their compatibility and interaction, we compare the throughput provided by AMC, HARQ, and their combination (AMC-HARQ) under two operational conditions: in slow- and fast block-fading channels. Considering both incremental redundancy HARQ and repetition redundancy HARQ, we optimize the rate-decision regions for AMC/HARQ and compare them in terms of attainable throughput. Under a fairly general model of the channel variation and the decoding functions, we conclude that: 1) adding HARQ on top of AMC may be counterproductive in the high average signal-to-noise ratio regime for fast fading channels and 2) HARQ is useful for slow fading channels, but it provides moderate throughput gains. We provide explanations for these results which allow us to propose paths to improve AMC-HARQ systems.

Modern wireless systems deal with the adverse and unpredictable channel conditions using two main transmission schemes considered as complimentary: adaptive modulation and coding (AMC) and hybrid ARQ (HARQ). In this work we use the effective capacity as the performance measure to evaluate different design options. We thus show how to calculate this performance measure under independent, identically distributed (i.i.d.) block-fading channel model considering AMC, HARQ and more importantly, a combination of thereof. Numerical results indicate that AMC alone outperforms the other schemes in the region of high average SNR; we provide an explanation for this observation and discuss possible paths for improvement.

This paper presents a future terminal antenna to enhance the performance of wireless container monitoring systems. It first introduces an optimized design that is experimentally validated for Wireless Personal Area Network (WPAN) technology taking into account the harsh environment of containers. The proposed antenna exhibits excellent performance next to the container in terms of return loss, gain, and bandwidth. Further, deterministic and stochastic path loss models for inter-container link budget calculations are used to illustrate the impact of the optimized antenna on path loss and attainable maximum ranges.

Wireless body area networks have raised considerable attention within the wireless personal area community. To optimize the network architecture and the energy consumption of on-body equipments, a good knowledge and understanding of the radio link and the propagation channel are required. This work addresses the influence of medium presence on the reflection loss of horn and textile antennas. In particular, the path loss in free space and under skin-equivalent phantom of horn-to-horn and textile-to-textile communications is presented. The obtained results show that the path loss differs considerably in the analyzed situations; with horn-to-horn links showing a path loss exponent between 1.13 and 2.35, while it is reaching 4.7 for textile-to-textile links. The obtained results provide additional input for the design of communications links in the context of future 5G and IoT eco-systems.

In this work, various channel coding schemes that can be used in hybrid automatic repeat request (HARQ) transmission protocols are investigated from an energy efficiency point of view. Conventional HARQ, where only one bit is used to inform the transmitter about the decoding success or failure, is compared to adaptive HARQ where the transmitter adapts either the length or the transmit power of the codewords using outdated channel state information (i.e., experienced by the receiver during the past transmissions). Describing the problems within a Markov decision process framework, we find optimal adaptation policies for both persistent (unlimited number of transmission) and truncated HARQ protocols. Numerical examples obtained in a Rayleigh block fading channel show that, in terms of energy efficiency, the adaptation of the codewords length provides notable gains over power adaptation and conventional HARQ.

Heterogeneous deployment of base stations with different transmit power levels helps increase the network efficiency. Yet, the allocation of powers to base stations should be optimal in order to minimize interferences and thus improve users through-put. In this paper, we present a game theoretical approach to maximize the overall throughput in heterogeneous networks with optimal power allocation. We formulate the allocation problem as a "potential game". This type of games is characterized by a potential function, and has a specific property: a pure Nash equilibrium solution always exists and it is, moreover, either a local or a global optimizer of the potential function. We use a fully distributed algorithm that requires minimal coordination between base stations to reach a Nash equilibrium, and we prove that it is necessarily a global maximum. The performance of the algorithm is evaluated through simulations.

In this paper, we present a closed-form performance analysis of multihop cooperative schemes in the context of the global effort towards the design of future 5G systems. We adopt the advocated Weibull channel fading model for its flexible ability to cover different channel conditions, and supported by many recent measurement campaigns in various emerging 5G scenarios. The analyzed scheme consists of multiple "Detect-and-Forward" relays with generalized high-order M-QAM transmissions. The end-to-end performance of the multihop communication is evaluated in terms of the outage probability, bit error rate, and ergodic capacity. For all these metrics, we present exact closed-form expressions, some in terms of the Meijer's G-function. Numerical results illustrate the analysis, and Monte-Carlo simulations assess the accuracy of our obtained results for different system and channel parameters.

In this work, we analyze hybrid ARQ (HARQ) transmission over the independent block fading channel. We consider two scenarios with respect to the message sent by the receiver via the feedback channel: i) “conventional”, one-bit feedback used to inform the transmitter about the decoding success/failure (ACK/NACK), and ii) the multi-bit feedback message, where on top of ACK/NACK, the transmitter is provided with additional information about the state of the receiver. To minimize the outage probability under long-term average and peak power constraints, we cast the problems into the dynamic programming (DP) framework and solve them for Nakagami-m fading channels. An approximate, closed-form solution for the high signal-to-noise ratio (SNR) regime is proposed using geometric programming (GP). The obtained results quantify the advantage of the multi-bit feedback over the conventional one-bit approach, and show that the power optimization can provide significant gains over conventional power-constant HARQ transmissions even in the presence of peak-power constraints.

In this work, we analyze hybrid ARQ (HARQ) protocols used over independent block fading channels. We assume that the transmitter is unaware of the channel state information (CSI) at the moment of transmission but knows the statistics of the channel. We consider two scenarios with respect to the feedback received by the transmitter: (i) “conventional”, one-bit feedback carrying the message about the decoding success/failure (ACK/NACK), and (ii) the multi-bit feedback scheme when, on top of ACK/NACK, the receiver feeds back information about the state of the decoder, which is equivalent to the CSI experienced by the receiver in the past (unsuccessful) transmissions. The feedback can then be used to allocate power across the HARQ transmission attempts (in the case of one-bit feedback) or adapt the power for each re-transmission as a function of multi-bit feedback. The objective in both cases is the minimization of the outage probability under peak and long term average power constraints. We solve the optimization problems using the dynamic programming framework. The obtained results quantify the advantage of the multi-bit feedback over the conventional approach and indicate that power optimization can provide significant gains over the conventional constant-power HARQ transmission even in the presence of peak-power constraints.

In this study, the outage performance of a multi‐relay cooperative network operating in a spectrum sharing environment is analysed. Focusing on the secondary communication process, a relay selection procedure is performed prior to the communication process, where the selected relay is the one maximising the instantaneous end‐to‐end signal‐to‐noise ratio (SNR) and, at the same time, satisfying the power restrictions imposed by the primary receivers. The secondary destination employs a selection combining strategy, selecting the best path (direct link or relaying link) with the highest instantaneous SNR. Two kinds of relaying protocols are considered: decode‐and‐forward and amplify‐and‐forward. For the former, an exact closed‐form expression for the outage probability (OP) is derived, whereas for the latter an accurate approximation for the OP is obtained. Then, an asymptotic analysis is carried out and reveals that the system diversity order is N + 1 for both relaying protocols, with N denoting the number of relays. Monte Carlo simulations assess the accuracy of the authors’ results.

Cooperative spectrum sharing systems (CSSSs) have recently received considerable attention from the wireless community due to their performance gains and spectrum utilization improvement when compared to traditional communication systems. Owing to this fact, we investigate the outage performance of CSSSs in the presence of multiple primary users (PUs). The secondary user (SU) network is composed by one source node, N decode-and-forward (DF) relays, and one destination. A best relay selection strategy is performed where the selected relay is that which maximizes the end-to-end signal-to-noise ratio (SNR) and, simultaneously, satisfies the interference constraint imposed by the MPU receivers. The communication between the SU source and SU destination is carried out through the help of one out of N DF relays and also via direct link. Afterwards, the SU destination selects the best path between the direct and the relaying path by using selection combining technique. A closed-form expression for the outage probability (OP) is derived, and an asymptotic analysis is carried out which reveals that the diversity order of the considered system equals N +1, showing that it is not affected neither by the number of PU receivers nor by the interference threshold. The presented analytical expressions are corroborated by means of Monte Carlo simulations and insightful discussions are provided.

In this work, we investigate the performance of cognitive multihop regenerative relaying systems in the “underlay” spectrum sharing scenario. The multiple relays perform “detect-and-forward” relaying strategy to convey a message with an order m quadrature amplitude modulation (m-QAM) from the source to the destination over independent but not necessarily identical Nakagami-m fading channels. We adopt a closed-form analysis framework based on univariate and bivariate Meijer G-functions to derive the end-to-end error performance (in terms of bit and symbol error rates), the outage probability, and the ergodic capacity. Various numerical examples are presented to illustrate the results with a large combination of system and fading parameters, and simulation results confirm the accuracy of our closed-form analysis.

In this paper, we address the issue of throughput-efficient half-duplex constrained relaying schemes for broadband uplink transmissions over multiple-input multiple-output (MIMO) channels. We introduce a low complexity signal-level cooperative spatial multiplexing (CM) architecture that allows for the shortening of the relaying phase without resorting to any symbol detection or re-mapping at the relay side. Half-duplex latency is thereby reduced, resulting in a significant throughput gain compared to amplify-and-forward (AF) relaying scheme. Surprisingly, we show that CM strategy becomes more powerful in boosting uplink throughput as the source approaches cell edge.

Multihop relaying is an efficient strategy to improve the connectivity and extend the coverage area of secondary networks in underlay cognitive systems. In this work, we provide a comprehensive performance study of cognitive multihop regenerative relaying systems in an underlay spectrum sharing scenario with the presence of multiple primary receivers. Both interference power and peak power constraints are taken into account. In our analysis, all the links are subject to independent, non-identically distributed Nakagami-m fading. We derive closed-form expressions for the outage probability, high-order amount of fading, bit error rate, symbol error rate, and ergodic capacity. Different scenarios are presented to illustrate the obtained results and Monte Carlo simulations confirm the accuracy of our analytical derivations.

This paper investigates a new constrained relay selection scheme for two-way relaying systems where two end terminals communicate simultaneously via a relay. The introduced technique is based on the maximization of the weighted sum rate of both users. To evaluate the performance of the proposed system, the outage probability is derived in a general case (where an arbitrary channel is considered), and then over independently but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. The analytical results are verified through simulations.

In this contribution, we present a new class of project-and-forward (PF) relays operating with broadband MIMO hybrid ARQ-aided cooperative systems. Their architecture enables to jointly perform the orthogonal projection over multiple ARQ transmissions, increasing thereby the relay diversity order and achieving an interesting power gain over amplify-and-forward (AF)-based relaying schemes.

We propose a simple relay-based adaptive cooperation scheme to improve the spectral efficiency of "Detect-and-Forward" (DetF) half-duplex relaying in fading channels. In a new common framework, we show that the proposed scheme offers considerable gains-in terms of the achievable information rates-compared to conventional DetF relaying schemes for both orthogonal and non-orthogonal source/relay transmissions. The analysis leads on to a general adaptive cooperation strategy based on the maximization of information rates at the destination which needs to observe only the average signal-to-noise ratios of the links.

In this paper, we analyze the coded performance of a cooperative system with multiple parallel relays using "Detect-and-Forward'' (DetF) strategy where each relay demodulates the overheard signal and forwards the detected binary words. The proposed method is based on the probabilistic characterization of the reliability metrics given under the form of L-values. First, we derive analytical expressions of the probability density functions (PDFs) of the L- values in the elementary two-hop DetF relay channel with different source-relay channel state information assumptions. Then, we apply the obtained expressions to derive tight approximations for the end-to-end coded bit error rate (BER) of a general cooperative scheme with multiple parallel relays. Simulation results demonstrate the accuracy of our derivations for different cooperation configurations and conditions.

To improve the spectral efficiency of Detect-and-Forward (DetF) half-duplex relaying in fading channels, we propose a cooperation scheme where the relay uses a modulation whose order is higher than the one at the source. In a new common framework, we show that the proposed scheme offers considerable gains-in terms of achievable information rates-compared to the conventional DetF relaying schemes for both orthogonal and non-orthogonal source/relay cooperation. This allows us to propose an adaptive cooperation scheme based on the maximization of the information rate at the destination which needs to observe only the average signal-to-noise ratios of direct and relaying links.

In this paper, we analyze the coded performance of a cooperative system with multiple parallel relays using “Detect-and-Forward” (DetF) strategy where each relay demodulates the overheard signal and forwards the detected binary words. The proposed method is based on the probabilistic characterization of the reliability metrics given under the form of L-values. First, we derive analytical expressions of the probability density functions (PDFs) of the L-values in the elementary two-hop DetF relay channel with different source-relay channel state information assumptions. Then, we apply the obtained expressions to calculate the theoretically achievable rates and compare them with the practical throughput of a simulated turbo-coded transmission. Next, we derive tight approximations for the end-to-end coded bit error rate (BER) of a general cooperative scheme with multiple parallel relays. Simulation results demonstrate the accuracy of our derivations for different cooperation configurations and conditions.

In this paper, we propose a Detect-and-Forward (DetF) relaying scheme for coded communications over fading channels based on the transmission of partial channel state information from the relay to the destination. In order to assist the detection at the destination, the relay transmits a quantized source-relay signal-to-noise ratio and the quantization criterion is based on the minimization of the estimated symbols transition probabilities at the relay. We show that the overhead necessary to communicate the quantized information between the relay and the destination is negligible for practical transmission rates and fading speeds. The efficiency of the proposed scheme is demonstrated through constrained capacity analysis along with throughput simulations based on practical coding schemes.

In this work, we propose and analyze coded modulation (CM) schemes suitable for mapping rearrangement (MaRe) in Hybrid ARQ (HARQ). Two known MaRe schemes for 16-ary quadrature amplitude modulation (16-QAM) are first studied. We demonstrate that the performance of the studied CM receiver is intrinsically limited by the MaRe design and we propose iterative detection to improve the performance. Further, we propose to change the design of the mapping to take into account the property of the CM receiver and we obtain well appreciable gains over other detection schemes and known mapping designs.

Abstract In this paper, we propose a method for evaluation of the bit error rate (BER) for packet combining based on constellation rearrangement (CoRe). Such mapping diversity scheme, adopted in the high speed downlink packet access (HSDPA), uses Gray‐mapped constellations and is based on suboptimal accumulation of the reliability metrics generated in each of the transmissions. We present an exact model for the logarithmic likelihood ratios (LLR) obtained by means of the so‐called max‐log approximation, and we show that their conditional probability density functions (pdf) are piecewise Gaussian. We then present the derivation of the uncoded BER and illustrate it with simulation results that confirm our formulation. Finally, we propose simplifications which significantly reduce the complexity of the evaluation method and provide results with a very good accuracy; an extension to transmissions over faded channel is also presented. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Hybrid ARQ with packet combining for high‐order modulations (such as 16‐QAM) may be significantly enhanced if the bits‐to‐symbols mappings are appropriately changed throughout the transmissions. In this paper, we analyze the relationship between such mapping diversity and channel coding. We calculate the capacity of the popular bit‐interleaved‐coded modulation (BICM) to draw qualitative and approximate quantitative conclusions that are valid for strong codes approaching the capacity limits. We conclude that the choice/design of the appropriate mapping depends on the targeted spectral efficiency and we demonstrate that certain forms of mapping diversity may be counterproductive. We also show that iterative demapping may be successfully applied to significantly reduce (by more than 1 dB) the gap between the BICM and coded modulations (CM) capacities. The analysis is illustrated with results obtained when the mapping diversity is combined with practical turbo codes. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract We present closed‐form expressions for the bit error rate (BER) of rectangular quadrature amplitude modulation (QAM) in Nakagami‐mfading channels. The presented formulas, which are valid for arbitrary bits‐to‐symbols mapping, thus may be used when non‐Gray mapping is employed, are particularly useful in the low signal‐to‐noise ratio (SNR) range and/or for small values of the parameterm. The advantage of the proposed expressions over the known bounding techniques is illustrated through numerical simulations. Copyright © 2006 John Wiley & Sons, Ltd.

Hybrid ARQ with packet combining for high-order modulations (such as 16-QAM) may be significantly enhanced if the bits-to-symbols mappings are appropriately changed throughout the transmissions. In this paper, we analyze the relationship between such mapping rearrangement (or mapping diversity) and channel coding. We calculate the capacity of the popular bit-interleaved coded modulation (BICM) to draw qualitative and approximate quantitative conclusions that are valid for strong codes approaching the capacity limits. We conclude that the choice/design of the appropriate mapping depends on the targeted spectral efficiency and we demonstrate that certain forms of mapping rearrangement may be counterproductive. We also show that iterative demapping may be successfully applied to significantly reduce the gap between the BICM and coded modulations capacities. The analysis presented is illustrated with the results obtained when the mapping rearrangement is combined with practical turbo codes.

In this paper, we propose a method to evaluate analytically the bit error rate (BER) for packet combining with Gray mapping and constellation rearrangement (CoRe) between retransmissions - a mapping diversity scheme adopted in the high speed downlink packet access (HSDPA) standard. We first develop the model for the logarithmic likelihood ratios (LLR) and show that their conditional probability density functions (pdf) are piecewise Gaussian. We then present the derivation of the BER and illustrate it with simulation results that validate our formulation.

Bit-interleaved coded modulation (BICM) is a coded-modulation scheme where the output of the channel encoder and the input of the modulator are separated by a bit- level interleaver. From the decoder's point of view, the modulator, the transmission channel, and the demodulator (calculating bits' reliability metrics) become a memoryless BICM channel with binary inputs and real outputs. The BICM channel's outputs (reliability metrics) are known to be Gaussian for binary-or quaternary phase shift keying but no expression for their probability density function (PDF) is known for higher-order modulation. We fill this gap presenting closed-form expressions for PDF of reliability metrics in BICM based on M-PSK with gray mapping. Such probabilistic description of BICM channel is necessary to calculate the channel's information-theoretic parameters such as capacity, or cut-off rate but it is also a tool required to analyze the performance of coded transmission.

The use of generalized logarithmic likelihood ratio (GLLR), as a metric for soft-input decoding, was shown to provide gains over the conventional logarithmic likelihood ratio (LLR). However, computational complexity of the GLLR is greater than in the case of LLR, so the choice of GLLRs must be justified by a notable performance improvement. In this paper, we investigate the judiciousness of the adoption of GLLRs or LLRs for soft decoding analyzing the performance of both metrics in two different scenarios. Metrics expressions are derived and channel estimation errors are considered in analysis and simulation. Numerical results show that GLLRs outperform LLRs but, in general, the achieved improvement is relatively small. Therefore, application of GLLR metrics requires cautious analysis.