In this paper, we propose a lightwave power transfer-enabled underwater optical integrated sensing and communication (O-ISAC) system, where an access point (AP) mounted on a seasurface ship transmits lightwave signals to two nodes, namely ($i$) a seabed sensor that harvests energy and transmits uplink information to the AP, and ($ii$) a sensing target whose position is estimated by the AP using an array of pinhole cameras. To capture practical deployment conditions, the ship attitude variation is modeled through its roll, pitch, and yaw angles, each following a Gaussian distribution under low-to-moderate sea states. Closed-form approximations are derived for the mean squared error (MSE) of target localization and the achievable uplink data rate. Analytical and simulation results demonstrate excellent agreement, validating the proposed models and derived expressions, while revealing the fundamental communication-sensing tradeoff in the O-ISAC system. The results further provide valuable design insights, including the optimal camera placement on the ship to minimize localization error, achieving a minimum MSE of $10^{-2}$ $\text{m}^2$ with multiple cameras under roll, pitch, and yaw angle variation of $10^{\circ}$, and the optimal harvest-use ratio of $0.55$ for the considered setup.

Stacked intelligent metasurfaces (SIM) have become a promising technology to improve the wave-domain signal processing and increase the wireless communication capacity. However, optimizing the phase configuration remains a significant challenge due to the discrete and highly combinatorial nature of the multi-layer architecture. To address this, we propose a quantum-inspired coordinated design framework for joint wave-based beamforming and power allocation in SIM-assisted multiuser systems. By leveraging a black-box second-order approximation, the discrete phase optimization is reformulated into a standard quadratic unconstrained binary optimization (QUBO) problem. Quantum-inspired discrete simulated bifurcation (dSB) solver is used to find the candidates effectively, and then a tabu-based local refinement strategy is applied to refine these candidates and reduce the deviation of the approximate solution. Concurrently, an iterative water-filling scheme is integrated to optimize power allocation, facilitating a synergy between global search and fine-grained control. Simulation results confirm that the proposed approach consistently outperforms classical benchmarks in terms of sum rate, convergence speed, and interference suppression. The framework exhibits strong scalability across varying system dimensions and channel realizations, validating its effectiveness in wave-domain communication scenarios.

We investigate a quantum integrated communication and computation (QICC) scheme for a single-mode bosonic multiple-access channel (MAC) with coherent-state signalling. By exploiting the natural superposition property of the quantum MAC, a common receiver simultaneously performs over-the-air computation (OAC) on the analogue symbols transmitted by one set of devices and decodes multiple-access data from another. The joint design of the transmit power control and the receive coefficient leads to a non-convex optimization problem that maximizes computation accuracy under a prescribed sum-rate communication constraint. To address this challenge, we develop a low-complexity alternating-optimization framework that incorporates: (i) closed-form linear minimum-mean square error updates for the receive coefficient, (ii) monotonicity properties of the quantum sum-rate constraint, and (iii) projected-gradient refinements for the communication powers. The proposed QICC scheme achieves an effective computation–communication trade-off with fast convergence and low computational complexity.

The emerging reconfigurable antenna (RA) array technology promises capacity enhancement through dynamic antenna positioning. Traditional approaches enforce half-wavelength or greater spacing among RA elements to avoid mutual coupling, limiting the solution space. Additionally, achieving sufficient spatial channel sampling requires numerous discrete RA positions (ports), while high-frequency scenarios with hybrid processing demand many physical RAs to maintain array gains. This leads to exponential growth in the solution space. In this work, we propose two techniques to address the former challenge: (1) surrounding a limited number of active RAs with passive ones terminated to tunable analog loads to \textit{exploit} mutual coupling and increase array gain, and (2) employing tunable loads on each RA in an all-active design to \textit{eliminate} mutual coupling in the analog domain. Both methods enable arbitrary RA spacing, unlocking the full solution space. Regarding the latter challenge, we develop greedy and meta-heuristic port selection algorithms, alongside low-complexity heuristic variants, that efficiently handle over $10^{20}$ array configurations. Furthermore, we optimize the loading values to maximize the sum-rate in a multiple-input single-output broadcast channel under transmission power constraints, assuming a heuristic linear precoder. In addition, we analyze performance degradation from quantized loads and propose corresponding robust designs. Numerical simulations reveal 20-56\% sum-rate gains over benchmarks and around 60\% performance recovery under quantization errors.

This paper presents a comprehensive analysis of the information-energy capacity region for simultaneous lightwave information and power transfer (SLIPT) systems over lognormal fading channels. Unlike conventional studies that primarily focus on additive white Gaussian noise channels, we study the complex impact of lognormal fading, which is prevalent in optical wireless communication systems such as underwater and atmospheric channels. By applying the Smith’s framework to these channels, we demonstrate that the optimal input distribution is discrete, characterized by a finite number of mass points. We further investigate the properties of these mass points, especially at the transition points, to reveal critical insights into the rate-power trade-off inherent in SLIPT systems. Additionally, we introduce a novel cooperative information-energy capacity learning framework, leveraging generative adversarial networks, to effectively estimate and optimize the information-energy capacity region under practical constraints. Numerical results validate our theoretical findings, illustrating the significant influence of channel fading on system performance. The insights and methodologies presented in this work provide a solid foundation for the design and optimization of future SLIPT systems operating in challenging environments.

Visible light communication (VLC) systems have emerged as a promising solution to radio frequency (RF) spectrum scarcity, providing wide, unlicensed bandwidth and enhanced security in RF-free environments. However, VLC systems are prone to significant signal degradation caused by receiver movement or rotation, which limits their applicability in dynamic or mobile scenarios. To address this challenge, we introduce the concept of liquid-based reconfigurable lens (LiqRL), an innovative reconfigurable optical architecture that leverages the adaptability and spatial diversity of liquid materials to enhance signal reception and mitigate the impact of blockage sensitivity in VLC networks. The key contribution of this work lies in the development of a comprehensive mathematical model to evaluate the performance of mobile user equipment (UE) in VLC environments with LiqRL-based orientation adjustable receivers (OAR). By employing stochastic geometry, we derive analytical and closed-form expressions for coverage probability and propose a novel distance-based orientation selection mechanism that optimizes spatial alignment for improved data transmission efficiency. We further account for temporal interference correlation caused by UE mobility and evaluate the probability of successful communication across different time slots. Our results show that the LiqRL-based OAR architecture improves network performance by approximately 15% compared to conventional VLC systems, demonstrating the potential of reconfigurable optical architectures for dynamic and mobile VLC scenarios.

This paper develops an algorithmic solution using Ising machines to solve large-scale higher-order binary optimization (HOBO) problems with inequality constraints for resource optimization in wireless communications systems. Quadratic unconstrained binary optimization (QUBO) aims to solve a special category of these problems widely encountered in engineering and science. To solve QUBO instances, specialized Ising machines have been designed, while sophisticated quantum annealing algorithm and quantum-inspired classical heuristics have been developed. However, the application of QUBO in wireless communications has limited practical interest mainly due to the complexity of resource optimization problems which are often characterized by high-order polynomial terms and strict inequality constraints. To overcome these bottlenecks and take advantage of recent advancements in Ising machines, in this paper, we propose an iterative algorithmic solution to solve HOBO problems, which is based on the augmented Lagrangian method to handle constraints. Specifically, Taylor expansion is employed to approximate higher-order polynomials to quadratic ones in the augmented Lagrangian function, which enables the solution of a single QUBO problem at each iteration without auxiliary variables. As an illustrative case study, we consider the problem of phase optimization in a simultaneous wireless information and power transfer system, where a reconfigurable intelligent surface with 1-bit phase resolution is used to facilitate information/energy transfer. Simulation results verify that the proposed algorithm achieves satisfactory performance and outperforms heuristic benchmark schemes.

In this study, we explore a terahertz (THz) wireless power transfer (WPT) system, aiming to determine the optimal transmit waveform that maximizes the rectification efficiency. Since traditional Schottky diodes may be inefficient for THz rectification, the receiver is assumed to be equipped with a resonant tunneling diode (RTD)-based rectifier. Such rectifiers are generally characterized by a non-monotonic energy harvesting (EH) behavior, necessitating the design of transmit waveforms that align with the receiver’s non-monotonic characteristics. To this end, we utilize a waveform-to-energy model and formulate an optimization problem to maximize the rectification efficiency under both average and peak power constraints. The derived closed-form optimal waveform is a pulsed tone whose adaptive duty cycle follows the rectifiers efficiency peaks via a simple, real-time implementable rule. Our results demonstrate consistent gains over multisine baselines across power regimes and clarify when peak/average power constraints change the optimal rectification efficiency.

Wireless power transfer (WPT) has emerged as a promising technology for prolonging the battery life of energy-constrained devices. Waveform optimization enhances energy harvesting efficiency, but depends on the applied energy harvesting model. Conventional models capturing rectifier nonlinearities unveiled multisines’ superiority over continuous waves, but exhibit limitations in supported waveforms and rectifier structures. A recently proposed waveform-to-energy harvesting model addresses these limitations and showed that pulsed radio frequency (RF) signals outperform multisines under ideal conditions. However, practical deployments face channel uncertainty. This paper presents a robust optimization framework for WPT systems under a bounded channel estimation error model. We develop an efficient algorithm maximizing worst-case harvested energy and propose single-and multi-frequency pulsed RF designs that maintain performance despite channel uncertainty. Numerical results demonstrate significant gains of the proposed designs over benchmarks, especially under severe channel uncertainty.

Stacked intelligent metasurface (SIM) as an advanced signal processing paradigm enables real-time processing of electromagnetic waves at the speed of light. Benefiting from this technology, the current paper studies the downlink transmission of a wireless network, where a SIM-deployed base station (BS) serves two disjoint sets of energy- and information-oriented terminals via simultaneous wireless information and power transfer (SWIPT). Toward optimizing the performance of this system, a resource allocation problem is formulated for characterizing the fundamental trade-off between the aggregate information rate and the overall harvested energy. By virtue of its tightly-coupled and non-convex nature, we equivalently transform this problem to a Markov decision process (MDP) form. Next, we train an asynchronous advantage actor critic (A3C) agent on the MDP-reformulated problem for optimizing the transmit power of the BS and the electromagnetic response of the SIM, in a joint fashion. Subsequently, by taking into account the mobility of terminals, we further enrich the adaptability of the trained A 3 C agent via meta-learning. It is numerically revealed that incorporating SIM leads to an approximate 30 % enhancement in the energy efficiency of existing SWIPT systems.

In this paper, we investigate a simultaneous wireless information and power transfer (SWIPT)-assisted vehicular network. By utilizing the concept of SWIPT technology, batteryoperated road-side sensors (RSSs) simultaneously receive control information and harvest energy from cellular base stations (BSs), followed by their communication with vehicles by utilizing the harvested energy. By leveraging stochastic geometry tools, we establish a tractable framework, where the load of BSs and RSSs are taken into account. The analytical expressions for the active probability and average harvested energy of RSSs, as well as the information decoding (ID) success probability of vehicles are derived. The optimal RSSs' density and time splitting factor that maximize ID success probability are illustrated. Additionally, the optimal sensor density within vehicular networks dynamically adjusts in response to varying traffic congestion levels. These results offer invaluable insights for vehicular network design, highlighting the need for adaptive strategies that seamlessly respond to evolving network conditions and traffic patterns.

Orthogonal time sequency multiplexing (OTSM) has arisen as a promising single-carrier waveform, providing reliable performance akin to orthogonal time frequency space (OTFS) while surpassing orthogonal frequency division multiplexing (OFDM) in high-mobility doubly spread channels (DSCs), yet with significantly lower complexity. To alleviate both time and frequency dispersions in DSCs, delay-Doppler alignment modulation (DDAM) has been recently proposed for the systems operating at millimeter-wave (mmWave) and higher frequency bands. Building on the promising combination of OTSM with DDAM, this paper presents for the first time, waveform optimization in mmWave massive multiple-input multiple-output (mMIMO) integrated sensing, communication, and wireless power transfer (ISCAP) systems. We propose an OTSM-DDAM-based ISCAP system and derive the system’s input-output relations in time, delay-time, and delay-sequency (DS) domains based on delay-Doppler (DD) bin alignment in the presence of fractional DD shifts and by incorporating transceiver hardware impairments. This facilitates the derivation of key performance metrics including bit error rate (BER), spectral efficiency (SE), Cramér-Rao bound (CRB) for sensing, and energy harvested via wireless power transfer (WPT). By harnessing the advantage actor-critic method combined with a greedy strategy, an on-policy deep reinforcement learning algorithm is introduced to solve a newly defined optimization problem. This approach optimizes the ISCAP waveform while simultaneously recognizing the environment, providing the base station with the path state information needed for practical DDAM implementation. The optimization problem jointly considers precoding, power loading, time and power splitting ratios, beam alignment, and receive combining with the goal of minimizing the transmitted power coupled with CRB while maintaining constraints on signal-to-interference-plus-noise ratio, BER, SE, CRB, harvested energy, and total power budget to ensure high-quality ISCAP services. Simulation results, based on both real-world and deep learning datasets, validate the effectiveness of the proposed scheme, demonstrating improvements in peak-to-average power ratio, SE, detection complexity, CRB, and BER in high-mobility DSCs.

The majority in existing energy harvesting (EH) models utilize complex analysis and often overlook the memory effects of the system’s low-pass filter (LPF). In this work, we aim to fill this gap and propose a simple yet effective approach to model the receiver’s output and capture the LPF’s memory. Specifically, we analyze two fundamental circuits: the half-wave rectifier (HWR) and the diplexer-based receiver (DBR). By using circuit analysis, we derive mathematical models for each receiver’s output, validated through circuit simulations. We then investigate these models in the context of integrated simultaneous wireless information and power transfer (SWIPT) receivers. For the HWR, we consider amplitude modulation and, using communication theory tools, we evaluate the performance in terms of bit error rate (BER) for both maximum likelihood (ML) and ML sequence detection (MLSD) schemes. Our results reveal the impact of memory-induced intersymbol interference on the BER and indicate that MLSD is necessary towards achieving higher data rates. For the DBR, we show that its LPF exhibits similar characteristics to the HWR yet is able to achieve better EH performance due to the lack of signal splitting. Finally, we explore the DBR’s band-pass filter output for information decoding, highlighting its capability to also use phase modulation. Clearly the DBR stands out as a more flexible receiver, while the HWR’s simple design makes it ideal for devices with limited resources.

The concept of fluid antennas (FAs) has emerged as a promising solution to enhance the spectral efficiency of wireless networks, achieved by introducing additional degrees of freedom, including reconfigurability and flexibility. In this paper, we investigate the use of index-modulated (IM) transmissions within the framework of FA systems, where an FA position is activated during each transmission interval. This approach is motivated by the common characteristics exhibited by FAs and IM transmissions, which entails the use of a single radio-frequency chain. From this perspective, we derive a closed-form expression for the bit error rate of IM-FAs considering spatial correlation, demonstrating superior performance compared to conventional IM systems. To enhance the performance of IM-FAs in correlated conditions, channel coding techniques are applied. We first analyze a set partition coding (SPC) scheme for IM-FAs to spatially separate the FA ports, and provide a tight performance bound over correlated channels. Furthermore, the spatial SPC scheme is extended to turbo-coded modulation where the performance is analyzed for low and high signal-to-noise ratios. Our results reveal that through the implementation of channel coding techniques designed for FAs and IM transmission, the performance of coded IM-FAs exhibits notable enhancements, particularly in high correlation scenarios.

In this paper, we study the fundamental limits of simultaneous semantic information and power transfer in wireless networks, where we consider both the point-to-point case as well as the Gaussian multiple access channel (MAC). Specifically, for the point-to-point case, we consider a three-party communication system, where a transmitter aims to simultaneously convey semantic information to an information receiver and energy to an energy harvesting receiver (ER). An achievable and a converse region in terms of information and energy rates are presented for both the discrete memoryless (DM) and Gaussian channel. For the DM channel, the achievable region is obtained by utilizing the asymptotic equipartition property and a converse region is obtained by using outer bounds on the semantic information rates. For the Gaussian channel, we characterize an achievable region by applying a power splitting technique between the information and the semantic context parts. A converse region is obtained that provides an estimate on the information-energy capacity while taking into account semantics. On the other hand, for the Gaussian MAC case, we consider an hybrid setup where a semantic transmitter and a conventional transmitter are employed subject to an energy harvesting constraint at the ER. Specifically, we characterize the semantic-bit information energy region, by providing an achievable and a converse region. Numerical results show that in both cases a higher performance can be achieved in terms of information and energy rates when considering a low semantic ambiguity code in comparison to the classical coding scheme (without semantic). Moreover, in the context of Gaussian MAC, it is shown that it is preferable to use semantic communications in scenarios with low signal-to-noise ratio (SNR), while conventional communications is more suitable at high SNRs.

This paper investigates the problem of transmit waveform design in the context of a chaotic signal-based self-sustainable reconfigurable intelligent surface (RIS)-aided system for simultaneous wireless information and power transfer (SWIPT). Specifically, we propose a differential chaos shift keying (DCSK)-based RIS-aided point-to-point set-up, where the RIS is partitioned into two non-overlapping surfaces. The elements of the first sub-surface perform energy harvesting (EH), which in turn, provide the required power to the other sub-surface operating in the information transfer (IT) mode. In this framework, by considering a generalized frequency-selective Nakagami-mfading scenario as well as the nonlinearities of the EH process, we derive closed-form analytical expressions for both the bit error rate (BER) at the receiver and the harvested power at the RIS. Our analysis demonstrates, that both these performance metrics depend on the parameters of the wireless channel, the transmit waveform design, and the number of reflecting elements at the RIS, which switch between the IT and EH modes, depending on the application requirements. Moreover, we show that, having more reflecting elements in the IT mode is not always beneficial and also, for a given acceptable BER, we derive a lower bound on the number of RIS elements that need to be operated in the EH mode. Furthermore, for a fixed RIS configuration, we investigate a trade-off between the achievable BER and the harvested power at the RIS and accordingly, we propose appropriate transmit waveform designs. Finally, our numerical results illustrate the importance of our intelligent DCSK-based waveform design on the considered framework.

In this paper, we consider a tunable liquid convex lens-assisted imaging receiver for indoor multiple-input multiple-output (MIMO) visible light communication (VLC) systems. In contrast to existing MIMO VLC receivers that rely on fixed optical lenses, the proposed receiver leverages the additional degrees of freedom offered by liquid lenses via adjusting both focal length and orientation angles of the lens. This capability facilitates the mitigation of spatial correlation between the channel gains, thereby enhancing the overall signal quality and leading to improved bit-error rate (BER) performance. We present an accurate channel model for the liquid lens-assisted VLC system by using three-dimensional geometry and geometric optics. To achieve optimal performance under practical conditions such as random receiver orientation and user mobility, optimization of both focal length and orientation angles of the lens are required. To this end, driven by the fact that channel models are mathematically complex, we present two optimization schemes including a blockwise machine learning (ML) architecture that includes convolution layers to extract spatial features from the received signal, long-short term memory layers to predict the user position and orientation, and fully connected layers to estimate the optimal lens parameters. Numerical results are presented to compare the performance of each scheme with conventional receivers. Results show that a significant BER improvement is achieved when liquid lenses and presented ML-based optimization approaches are used. Specifically, the BER can be improved from 6 × 10−2to 1.4 × 10−3at an average signal-to-noise ratio of 30 dB.

The co-design of fluid antenna (FA) technology and simultaneous wireless information and power transfer (SWIPT) can be jointly beneficial. Specifically, SWIPT facilitates both data and energy transfer to low-power devices, while FA technology introduces a new dimension for optimizing SWIPT performance through intelligent port selection. Thus, in this work, we develop an analytical framework by employing stochastic geometry and copula theory to evaluate FA-enabled users’ performance in SWIPT networks. All users utilize successive interference cancellation and two novel port selection schemes, namely information decoding-focused (IDf) and energy harvest-focused (EHf), to leverage FAs’ liquid dimension for enhanced data or energy transfer, by considering the counterposed effects of multi-user interference. We derive closed-form expressions for signal-to-interference ratio and received signal power under correlated Nakagami-$\kappa $fading by using Student’s t copula. The developed framework assesses SWIPT performance meta-distribution of the proposed schemes and facilitates the performance evaluation of two user location-based classifications i.e., cell-center (CC) and cell-edge (CE) users. Results reveal the beneficial synergy of FAs and SWIPT, with around 29% improvement for CC and 133% for CE users compared to conventional static SWIPT communications, and highlight that the EHf scheme proves more efficient for CE users, while the IDf scheme benefits CC users.

In this paper, we propose a novel simultaneous wireless information and power transfer (SWIPT) technique that employs orthogonal frequency-division multiplexing (OFDM)-based waveforms. By using deterministic symbols at the subcarriers with appropriate phase rotation, the proposed scheme embeds information into the position of the power peak of the OFDM-based waveform and is suitable for low-complexity SWIPT integrated receivers. Low-complexity decision rules are also investigated to extract information at the output of the rectification circuit. We study the performance of the proposed scheme in terms of average harvested power and symbol error probability for various modulation orders. The proposed technique transforms conventional OFDM transmitters to SWIPT devices without costly architectural modifications and is promising for practical applications.

Sixth generation networks will establish the terahertz (THz) era, introducing numerous potential applications for wireless power transfer (WPT). In this work, we focus on a fluid-antenna (FA)-aided WPT system which operates over the THz bands, featuring a non-monotonic diode-based rectifier. By taking into account the circuit's unconventional behavior, we first exploit a linear piece-wise function to approximate rectifier's input-output power relationship. Based on this, we provide an analytical framework in terms of the energy outage probability for three FA port selection (PS) schemes, namely (i) the input-based selection, (ii) the harvesting-based selection, and (iii) the random selection, each corresponding to different complexity and performance. Numerical results which validate our analysis, reveal a novel utilization of FAs, stemming from the alignment of the PS process with the non-monotonic harvesting characteristics.

A disruptive radio communication paradigm is proposed, where the dynamic channel reconfiguration capability of the emerging fluid antennas (FA) and hybrid reflecting intelligent surfaces (RIS) technologies is exploited to ensure favorable propagation and substantially boost multiple-input multiple-output capacity in a cost-effective and energy-efficient manner, offering fiber-like user experience improvement. The joint optimization of RIS scheduling and beamforming, precoding, and FAs’ positions is studied in a fully- or sub-connected active/passive RIS-aided multiple-input single-output broadcast channel with a FAs-equipped base station (BS), such that the energy efficiency is maximized subject to the BS’s and RIS’s power consumption constraints. An efficient iterative algorithm based on fractional programming techniques, the block coordinate ascent framework, big-M formulation, penalty-based optimization, and the gradient ascent method is developed to tackle this challenging mixed-integer nonlinear program. Numerical simulation results validate the proposed concept and unveil its significant performance gains over benchmarks.

Wireless power transfer has been proposed as a key technology for the foreseen machine type networks. A main challenge in the research community lies in acquiring a simple yet accurate model to capture the energy harvesting performance. In this work, we focus on a half-wave rectifier and based on circuit analysis we provide the actual output of the circuit which accounts for the memory introduced by the capacitor. The provided expressions are also validated through circuit simulations on ADS. Then, the half-wave rectifier is used as an integrated simultaneous wireless information and power transfer receiver where the circuit's output is used for decoding information based on amplitude modulation. We investigate the bit error rate performance based on two detection schemes: (i) symbol-by-symbol maximum likelihood (ML); and (ii) ML sequence detection (MLSD). We show that the symbol period is critical due to the intersymbol interference induced by circuit. Our results reveal that MLSD is necessary towards improving the error probability and achieving higher data rates.

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and copula theory (CT), in this paper, we assess the performance of fluid-based reconfigurable antenna (FA)-enabled user equipments (UEs) in the context of simultaneous wireless information and power transfer (SWIPT) networks. Particularly, by using CT tools, we initially derive a closed-form expression for the cumulative distribution function of the observed signal-to-interference ratio (SIR) under correlated Nakagami-μ fading channels by exploiting a well-investigated Archimedean copula, namely the Frank copula. According to the CT-based approach, a SG-based framework is presented to assess the SWIPT performance of FA-enabled UEs, that are equipped with a power splitting scheme to simultaneously extract information and harvest energy from the port with the strongest SIR. Our results reveal that FA-enabled SWIPT systems experience an improved information decoding performance of around 30% with a slight reduction in energy harvest performance of around 6% compared to conventional fixed-positioned antennas systems.

In this paper, we study the performance of fluid antenna (FA)-enabled user equipments (UEs) in the context of simultaneous wireless information and power transfer (SWIPT) networks. All UEs have successive interference cancellation (SIC) capabilities and employ a novel port selection (PS) scheme. In contrast to existing PS approaches, where the FA port with the highest signal-to-interference ratio (SIR) is selected, a UE communicates with its serving base station (BS) through the port that offers the minimum SIR. The proposed PS scheme leverages the additional degree of freedom offered by the FA technology to ensure the successful implementation of the SIC process, leading to an improved information decoding (ID) and energy harvest (EH) performance. By using stochastic geometry tools, analytical expressions for the ID and EH outage probability are derived. Our results illustrate that the employment of the proposed PS scheme leads to improved ID and EH performance of around 10% compared to conventional PS schemes.

In this paper, we consider an indoor visible light communication (VLC) system with multiple "white" light emitting diodes serving to form overlapping wireless communication cells. In order to maintain seamless connectivity to mobile users, a handover procedure should be implemented. In particular, practical conditions such as blockages due to obstacles inside the room environment and the mobility of users can affect direct VLC connectivity. The use of reconfigurable intelligent surfaces (RISs) in optical wireless systems allows to exploit non-direct connectivity links, thus providing efficient communication links. In this paper, we present a proactive handover mechanism that exploits the presence of a RIS, in order to redirect the communication links in case of blockages. The proposed approach has been implemented both in hard and soft modes and assessed in terms of achievable data rate and handover latency for a user walking in a given reference room at different user speeds and blockage conditions. Our presented results and comparisons with conventional handover methods (i.e., without RIS) are helpful in showing the superiority of the presented algorithm.

In this paper, we study the fundamental limits of simultaneous lightwave information and power transfer (SLIPT) systems over channels with path loss and lognormal fading conditions. We consider a system with a single transmitter transferring information to a photodiode-based receiver as well as transferring energy to a photovoltaic cell receiver. In particular, we study the information-energy capacity region and the optimal input distribution under (a) peak-power and average-power constraints at the transmitter, and (b) the minimum harvest energy at the energy harvesting receiver. To this end, an expression for the transition probability distribution function of the lognormal channel is derived. By extending Smith's framework and using Hermite polynomial bases, we prove that the optimal input distribution is discrete with a finite number of mass points. Information-energy capacity region for SLIPT over lognormal channel conditions is illustrated and compared with the case of additive white Gaussian noise channel.

In this paper, we present an experimental analysis of far-field wireless power transfer (WPT) using radio frequency (RF) signals. The dual capability of RF signals for power and information transfer drives the integration of WPT and communication systems. The objective is to experimentally validate different waveforms intended for WPT and compare their performance concerning separation distance, transmit power, and output voltage. The experimental analysis conducted involves single and superimposed chirp waveforms, utilizing real-time data for a comparative analysis with conventional fixed-frequency waveforms. Our experimental findings indicate that chirp waveforms significantly enhance the harvested energy, extending the operational range of WPT compared to well-established fixedfrequency waveforms.

Semantic communications offer a promising approach to decrease network congestion and improve reliability, leading to more sustainable and energy-efficient wireless networks. However, the design of semantic transceivers constrain their effectiveness. This paper introduces a novel multi-carrier system that combines both semantic and Shannon communications, with a focus on text transmission. We formulate an optimization problem that jointly selects the transmission method and allocates power to reduce the transmission delay. Despite the challenges of solving this non-convex problem, we employ alternating optimization techniques to address it and the closed-form solution of the power allocation is extracted. The simulation results verify that jointly selecting semantic and Shannon communications decreases the transmission delay compared to using only one of the schemes.

Wireless information and energy transfer (WIET) represents an emerging paradigm that employs controllable transmission of radio frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned sixth-generation (6G) use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective codesign of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions, such as resource allocation and waveform design, need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.

Reconfigurable antenna multiple-input multiple-output (MIMO) is a foundational technology for the continuing evolution of cellular systems, including upcoming 6G communication systems. In this paper, we address the problem of flexible/reconfigurable antenna configuration selection for point-to-point MIMO antenna systems by using physics-inspired heuristics. Firstly, we optimize the antenna configuration to maximize the signal-to-noise ratio (SNR) at the receiver by leveraging two basic heuristic solvers,i.e.,coherent Ising machines (CIMs), that mimic quantum mechanical dynamics, and quantum annealing (QA), where a real-world QA architecture is considered (D-Wave). A mathematical framework that converts the configuration selection problem into CIM- and QA- compatible unconstrained quadratic formulations is investigated. Numerical and experimental results show that the proposed designs outperform classical counterparts and achieve near-optimal performance (similar to exhaustive search with exponential complexity) while ensuring polynomial complexity. Moreover, we study the optimal antenna configuration that maximizes the end-to-end Shannon capacity. A simulated annealing (SA) heuristic which achieves near-optimal performance through appropriate parameterization is adopted. A modified version of the basic SA that exploits parallel tempering to avoid local maxima is also studied, which provides additional performance gains. Extended numerical studies show that the SA solutions outperform conventional heuristics (which are also developed for comparison purposes), while the employment of the SNR-based solutions is highly sub-optimal.

In this paper, we study an intelligent reflecting surface (IRS)-aided communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a bounded CSI uncertainty, while satisfying a minimum signal-to-noise ratio (SNR). In addition, we consider not only continuous but also discrete IRS phase shifts. First, we derive closed-form expressions of the worst-case SNRs, and then formulate the robust (discrete) optimization problems for each case. In the case of continuous phase shifts, we design a dynamic programming (DP) algorithm that is theoretically guaranteed to achieve the global maximum with polynomial complexity$O(L\,{\log L})$, where L is the number of IRS elements. In the case of discrete phase shifts, we develop a convex-relaxation-based method (CRBM) to obtain a feasible (sub-optimal) solution in polynomial time$O(L^{3.5})$, with a posteriori performance guarantee. Furthermore, numerical simulations provide useful insights and confirm the theoretical results. In particular, the proposed algorithms are several orders of magnitude faster than the exhaustive search when L is large, thus being highly scalable and suitable for practical applications. Moreover, both algorithms outperform a baseline scheme, namely, the activation of all IRS elements.

Semantic communications are considered a promising beyond-Shannon paradigm to reduce network traffic and increase reliability, thus making wireless networks more energy efficient, robust, and sustainable. However, the performance is limited by the efficiency of the semantic transceivers, i.e., the achievable “similarity” between the transmitted and received signals. Under strict similarity conditions, semantic transmission may not be applicable and Shannon communication is mandatory. In this paper, for the first time in the literature, we propose a multi-carrierHybrid Semantic-Shannoncommunication system where, without loss of generality, the case of text transmission is investigated. To this end, a joint semantic-Shannon transmission selection and power allocation optimization problem is formulated, aiming to minimize two transmission delay metrics widely used in the literature, subject to strict similarity thresholds. Despite their non-convexity, both problems are decomposed into a convex and a mixed linear integer programming problem by using alternating optimization, both of which can be solved optimally. Furthermore, to improve the performance of the proposed hybrid schemes, a novel association of text sentences to subcarriers is proposed based on the data size of the sentences and the channel gains of the subcarriers. We show that the proposed association is optimal in terms of transmission delay. Numerical simulations verify the effectiveness of the proposed hybrid semantic-Shannon communication scheme and the derived sentence-to-subcarrier association, and provide useful insights into the design parameters of such systems.

This paper proposes a chaotic waveform-based multi-antenna receiver design for simultaneous wireless information and power transfer (SWIPT). Particularly, we present a differential chaos shift keying (DCSK)-based SWIPT multiantenna receiver architecture, where each antenna switches between information transfer (IT) and energy harvesting (EH) modes depending on the receiver’s requirements. We take into account a generalized frequency-selective Nakagami-m fading model as well as the nonlinearities of the EH process to derive closed-form analytical expressions for the associated bit error rate (BER) and the harvested direct current (DC), respectively. We show that, both depend on the parameters of the transmitted waveform and the number of receiver antennas being utilized in the IT and EH mode. We investigate a trade-off in terms of the BER and energy transfer by introducing a novel achievable ‘success rate - harvested energy’ region. Moreover, we demonstrate that energy and information transfer are two conflicting tasks and hence, a single waveform cannot be simultaneously optimal for both IT and EH. Accordingly, we propose appropriate transmit waveform designs based on the application specific requirements of acceptable BER or harvested DC or both. Numerical results demonstrate the importance of chaotic waveform-based signal design and its impact on the proposed receiver architecture.

Reconfigurability is a desired characteristic of future communication networks. From a transceiver’s standpoint, this can be materialized through the implementation of fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the transceiver’s antennas. Due to the nature of liquids, FAs can practically take any size and shape, making them both flexible and reconfigurable. In this paper, we deal with the outage probability of FAs under general fading channels, where a port is scheduled based on selection combining. An analytical framework is provided for the performance with and without errors due to post-scheduling delays. We show that although FAs achieve maximum diversity, this cannot be realized in the presence of delays. Hence, a linear prediction scheme is proposed that overcomes delays and restores the lost diversity by predicting the next scheduled port. Moreover, we design space-time coded modulations that exploit the FA’s sequential operation with space-time rotations and code diversity. The derived expressions for the pairwise error probability and average word error rate give an accurate estimate of the performance. We illustrate that the proposed design attains maximum diversity, while keeping a low-complexity receiver, thereby confirming the feasibility of FAs.

In this paper, we present the superposition of chirp waveforms for simultaneous wireless information and power transfer (SWIPT) applications. Exploiting the chirp waveform characteristics enables us to superimpose multiple chirps, thereby allowing transmission of the same number of waveforms over less bandwidth. This enables us to perform subband selection when operating over set of orthogonal subbands. Furthermore, we consider a user equipped with a diplexer-based integrated receiver (DIR), which enables to extract radio frequency power and decode information from the same signal without splitting. Thereby, incorporating chirp superposition and subband selection, a transmission scheme is proposed to exploit both the diode’s nonlinearity and frequency diversity. We derive novel closed-form analytical expressions of the average harvested energy (HE) via transmission of superimposed chirp over selected subbands based on tools from order statistics. We also analyze the downlink information rate achieved at the user. Through our analytical and numerical results, for the considered system setup, we show that superimposed chirp-based SWIPT provides an improvement of 30% in average HE performance as compared to multisine waveforms consisting of a set of fixed-frequency cosine signals, improves the minimum level of HE in a multiuser network, and extends the operating range of energy transfer as compared to fixed-frequency waveforms. Furthermore, we illustrate that the inclusion of DIR at the receiver for SWIPT enlarges the energy-information transfer region when compared to the widely considered power splitting receiver.

In this paper, we investigate the use of index-modulated (IM) transmissions within the framework of fluid antenna (FA) systems, where an FA port is activated during each transmission interval. The adoption of this approach is motivated by the common physical characteristic exhibited by both FAs and IM transmissions, which entails the use of a single radio-frequency (RF) chain. From this perspective, we derive a closed-form expression for the bit error rate (BER) of IM-FA systems in the presence of spatial correlation. Our results demonstrate that IM-FAs outperform conventional IM systems. Since the FA ports are relatively close to each other, we focus on correlation mitigation techniques to improve performance. Specifically, we first investigate two port selection strategies, namely the maximum norm-based and the Euclidean distance-based selection schemes, assuming full channel knowledge. Then, we introduce the concept of spatial set partition coding for IM-FAs to spatially separate the FA ports. Numerical results demonstrate that the performance of IM-FAs is further improved in the case of high correlation whenever we apply the proposed correlation mitigation strategies.

In this paper, we study the fundamental limits of simultaneous semantic information and power transfer. In particular, a three-party communication system is considered, where an information transmitter aims to simultaneously convey semantic information to an information receiver (IR) and deliver energy to an energy harvesting receiver. An achievable and a converse region in terms of information and energy rates (in bits per channel use and energy-units per channel use, respectively) are presented for the discrete memoryless (DM) channel. The achievable region is obtained by using the asymptotic equipartition property (AEP) and a converse region is obtained by using outer bounds on the semantic information rates. In addition, we characterize an achievable region for the Gaussian case by using a power splitting technique between the information and the semantic context parts. A converse region is also obtained that provides an estimate of the information-energy capacity while taking into account semantics. Numerical results show a higher performance in terms of information and energy rates by considering a low semantic ambiguity code in comparison to the classical coding scheme (without semantic).

In this work, a low-complexity hybrid scheme is presented for a wireless network assisted by a reconfigurable intelligent surface (RIS), where channel estimation is required for only a subset of the elements. Specifically, in order to reduce the channel training overhead and boost the performance of the RIS-aided network, the RIS is partitioned in two sub-surfaces, which are sequentially activated to assist the communication. The elements of the first sub-surface align their phase shifts, based on the acquired channel state information (CSI) from a channel training period, whereas the elements of the second sub-surface randomly rotate the phase of the incident signals. The performance of the proposed scheme is investigated under the effect of imperfect CSI acquisition at the RIS. Analytical expressions for the outage probability are derived and useful insights on the optimal configuration of the RIS are provided. We show that, by optimizing the number of elements that need to be estimated, the proposed scheme provides significant performance gains and overcomes the limitations caused by the imperfect CSI acquisition.

In this work, we introduce novel hybrid reflecting intelligent surface (RIS) architectures where at least one reflecting sub-surface (RS) resembles a sub-connected RIS. Next, assuming a multiple-input single-output broadcasting system where the base station (BS) is aided by a hybrid RIS adopting either one of the proposed structures or the fully-connected active/passive design, we jointly optimize the transmit precoding and reflect beamforming schemes to maximize the energy efficiency subject to the power consumption constraints of the BS and any active RS. We develop efficient, low-complexity iterative algorithms based on the Fractional Programming, Block Coordinate Descent, Lagrange multipliers, and Majorization-Minimization methods to tackle these challenging non-convex optimization problems and obtain closed-form expressions of the optimal solutions. We also derive the necessary conditions for feasible allocation of RIS elements. Numerical evaluations unveil the performance gains of the proposed designs over benchmarks and provide insights.

In this work, we investigate a superimposed chirp waveform for wireless power transfer (WPT) applications. By taking into account the properties of chirp signals, a transmission scheme is proposed that incorporates chirp superposition and subband selection to exploit both diode’s nonlinearity and frequency diversity. Novel closed-form expressions of the average harvested energy (HE) at the receiver are derived by considering tools from order statistics. Through analytical and numerical results, we prove that superimposed chirp-based WPT improves the HE level of about 30%, and extends the operating range of energy transfer compared to conventional fixed-frequency waveforms.

In this paper, we study an intelligent reflecting surface (IRS) assisted communication system with single-antenna transmitter and receiver, under imperfect channel state information (CSI). More specifically, we deal with the robust selection of binary (on/off) states of the IRS elements in order to maximize the worst-case energy efficiency (EE), given a bounded CSI uncertainty, while satisfying a minimum signal-to-noise ratio (SNR). The IRS phase shifts are adjusted so as to maximize the ideal SNR (i.e., without CSI error), based only on the estimated channels. First, we derive a closed-form expression of the worst-case SNR, and then formulate the robust (discrete) optimization problem. Moreover, we design and analyze a dynamic programming (DP) algorithm that is theoretically guaranteed to achieve the global maximum with polynomial complexity O(L log L), where$L$is the number of IRS elements. Finally, numerical simulations confirm the theoretical results. In particular, the proposed algorithm shows identical performance with the exhaustive search, and significantly outperforms a baseline scheme, namely, the activation of all IRS elements.

Owing to the growing demand for ubiquitous connectivity, low earth orbit (LEO) satellite-based communication networks are envisioned as a key-enabling technology for the next-generation networks. However, the existing literature disregards the heterogeneous nature of the real-world LEO satellite networks. Motivated by this, in this paper, an analytical framework based on stochastic geometry is developed, aiming to assess the down-link coverage performance of the large-scale heterogeneous LEO satellite-based communication networks. Based on the proposed mathematical framework, we derive the analytical expressions for the coverage probability, by taking into account the existence of inter-cell interference. Our results show that the inter-cell interference and fading channels jeopardize the coverage performance. Moreover, increasing the transmit power can improve the coverage probability at the low signal-to-noise ratio regime. Finally, we demonstrate that a higher coverage probability is achieved by narrowing the beam and/or by lowering the altitude of the LEO satellites.

A desired characteristic of future communication networks is the notion of reconfigurability. For a wireless device, this can be realized through the employment of the so-called fluid antennas (FAs). An FA consists of a dielectric holder, in which a radiating liquid moves between pre-defined locations (called ports) that serve as the device's antennas. Therefore, due to the nature of liquids, an FA can essentially take any size and shape, making them both flexible and reconfigurable. In this paper, we study the outage probability of FAs where the scheduled port, based on selection combining, is subject to scheduling delays. An analytical framework is provided for the performance with and without estimation errors, as a result of post-scheduling delays. We show that even though FAs achieve maximum channel (spatial) diversity, this cannot be attained in the presence of delays.

The concept of reconfigurable fluid antennas (FAs) is a potential and promising solution to enhance the spectral efficiency of wireless communication networks. Despite their many advantages, FA-enabled communications require a significant amount of spectral resources in order to select the most desirable position of the radiating element from a large number of prescribed locations. In this paper, we present an analytical framework for the outage performance of large-scale FA-enabled communications under limited coherence interval scenario. Under this framework, we propose a novel sequential linear minimum mean-squared error-based channel estimation method, that is performed for a limited number of FA ports, followed by data reception from the port with the strongest estimated channel. A complete analytical framework in terms of the outage probability is developed by using tools from stochastic geometry. Our results reveal that the proposed technique can provide significant performance gains, especially for FAs with a large number of ports.

This paper studies the performance of a wireless powered backscatter communication network consisting of an exciter, several randomly distributed backscattering devices (BDs) and a receiver. We consider selection/scheduling schemes, where a BD is selected for information transmission. The proposed schemes are based on random, end-to-end (e2e) signal-to-noise ratio (SNR) and Euclidean distance. By using stochastic geometry tools, we derive analytical expressions for the outage probability of each selection scheme. Our results show that the scheme based on the e2e SNR achieves the best performance. We also present a cluster-based selection scheme and provide some simulation results. The derived analytical framework provides useful insights on the design of such networks.

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side; this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface.

The synergy of fluid-based reconfigurable antenna (FA) technology and full-duplex (FD) communications can be jointly beneficial, as FD can enhance the spectral efficiency of a point-to-point link, while the new degree of freedom offered by the FA technology can be exploited to handle the overall interference. Hence, in this paper, an analytical framework based on stochastic geometry is developed, aiming to assess both the outage and average sum-rate performance of large-scale FA-aided FD cellular networks. In contrast to existing studies, where perfect channel state information is assumed, the developed framework accurately captures the impact of channel estimation (CE) on the performance of the considered network deployments, as well as the existence of residual loop-interference (LI) at the FD transceivers. Particularly, we focus on a limited coherence interval scenario, where a novel sequential linear minimum-mean-squared-error-based CE method is performed for all FA ports and LI links, followed by data reception from the port with the strongest estimated channel. By using stochastic geometry tools, analytical expressions for the outage and the average sum-rate performance are derived. Our results reveal that FA-aided FD communications experience an improved average sum-rate performance of around 45% compared to conventional FD communications.

In this paper, we study the fundamental limits of simultaneous information and power transfer over a Rayleighfading channel, where the channel input is constrained to peak-power (PP) constraints that vary in each channel use by taking into account high-power amplifier (HPA) nonlinearities.In particular, a three-party communication system is considered, where a transmitter aims simultaneously conveying information to an information receiver and delivering energy to an energy harvesting receiver.For the special case of static PP constraints, we study the information-energy capacity region and the associated input distribution under: a) average-power and PP constraints at the transmitter, b) an HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver.By extending Smith's mathematical framework [1], we show that the optimal input distribution under those constraints is discrete with a finite number of mass points.We show that HPA significantly reduces the information energy capacity region.In addition, we derive a closed-form expression of the capacityachieving distribution for the low PP regime, where there is no trade-off between information and energy transfer.For the case with time-varying PP constraints, we characterize the optimal input distribution of this channel by using Shannon's coding scheme.Specifically, we numerically study a particular scenario for the time-varing PP constraints, where the PP constraint probabilistically is either zero or equal to a non-zero constant.

The concept of reconfigurable fluid antennas (FA) is a potential and promising solution to enhance the spectral efficiency of wireless communication networks. Despite their many advantages, FA-enabled communications have limitations as they require an enormous amount of spectral resources in order to select the most desirable position of the radiating element from a large number of prescribed locations. In this paper, we present an analytical framework for the outage performance of large-scale FA-enabled communications, where all user equipments (UEs) employ circular multi-FA array. In contrast to existing studies, which assume perfect channel state information, the developed framework accurately captures the channel estimation errors on the performance of the considered network deployments. In particular, we focus on the limited coherence interval scenario, where a novel sequential linear minimum mean-squared error (LMMSE)-based channel estimation method is performed for only a very small number of FA ports. Next, for the communication of each BS with its associated UE, a low-complexity port-selection technique is employed, where the port that provides the highest signal-to-interference-plus-noise-ratio is selected among the ports that are estimated to provide the strongest channel from each FA. By using stochastic geometry tools, we derive both analytical and closed-form expressions for the outage probability, highlighting the impact of channel estimation on the performance of FA-based UEs. Our results reveal the trade-off imposed between improving the network’s performance and reducing the channel estimation quality, indicating new insights for the design of FA-enabled communications.

Intelligent reflecting surfaces (IRSs) have emerged as a promising wireless technology for the dynamic configuration and control of electromagnetic waves, thus creating a smart (programmable) radio environment. In this context, we study a multi-IRS assisted two-way communication system consisting of two users that employ full-duplex (FD) technology. More specifically, we deal with the joint IRS location and size (i.e., the number of reflecting elements) optimization in order to minimize an upper bound of system outage probability under various constraints: minimum and maximum number of reflecting elements per IRS, maximum number of installed IRSs, maximum total number of reflecting elements (implicit bound on the signaling overhead) as well as maximum total IRS installation cost. First, the problem is formulated as a discrete optimization problem and, then, a theoretical proof of its NP-hardness is given. Moreover, we provide a lower bound on the optimum value by solving a linear-programming relaxation (LPR) problem. Subsequently, we design two polynomial-time algorithms, a deterministic greedy algorithm and a randomized approximation algorithm, based on the LPR solution. The former is a heuristic method that always computes a feasible solution for which (a posteriori) performance guarantee can be provided. The latter achieves an approximate solution, using randomized rounding, with provable (a priori) probabilistic guarantees on the performance. Furthermore, extensive numerical simulations demonstrate the superiority of the proposed algorithms compared to the baseline schemes. Finally, useful conclusions regarding the comparison between FD and conventional half-duplex (HD) systems are also drawn.

Buffer-aided relay networks provide more reliability and coverage in future wireless communications. Therefore, this paper investigates a buffer-aided cooperative relaying system with$K$relays and a direct link from the source to the destination, providing a general scenario different from other existing state-of-the-art techniques. In particular, we propose a novel link selection scheme, which adaptively coordinates the selection priorities of the direct and cooperative relay link according to the instantaneous buffer state. The performance of the proposed link selection scheme is analyzed, in terms of outage probability, average packet delay (APD) and diversity order by providing closed-form expressions. For asymptotic analysis, a theoretical framework is presented by dividing all buffer states into different sets, which verifies that the minimum buffer size is just two for achieving the full diversity order of$2K+1$. We also provide the relationship between the asymptotic APD and diversity order by adjusting predefined target queue lengths, which shows that the diversity order ranges from$K+1$to$2K+1$as the asymptotic APD ranges from 0 to$K$time slots per packet. Both theoretical and simulation results demonstrate that direct transmission significantly improves the outage and delay performance simultaneously.

The future sixth generation (6G) wireless communications are envisioned to bring forth the era of the Internet of Everything (IoE). This work investigates wireless power transfer (WPT) in the radiating near-field region, as a medium for charging the low-powered IoE devices. Specifically, we exploit the near- field channel model in order to create power beamfocusing at a predefined focal point. We consider a uniform planar array employing beamfocusing, and provide analytical expressions for the harvested power at the receiver located at (i) a fixed and (ii) a random location in the network. We present numerical results which validate our analysis and draw an insight overview for the near-field WPT under various design parameters while demonstrating the gains brought against far-field WPT.

In this paper, we present a non-coherent spatial modulation (SM) detection scheme appropriate for low-cost low-powered devices, where channel knowledge is restricted to the magnitude of the fading gains. We first derive a low-complexity energy detection metric for the multiple receive-antenna case based on the maximum-likelihood criterion. Next, we investigate a biased (non-negative) pulse amplitude modulation design and develop an accurate analytical framework for the symbol error rate at high signal-to-noise ratio. We compare the performance of the proposed scheme with that of the optimal/coherent maximum-likelihood receiver design. Numerical results show that the non-coherent SM outperforms the coherent SM technique for scenarios where non-negative one-dimensional constellations are employed.

― Simultaneous wireless information and power transfer (SWIPT) represents an enabling paradigm for future energysustainable networks. Multiple-input multiple-output (MIMO) technology enhances the performance of SWIPT systems via precoding. We typically rely on heuristic precoding designs in practice, due to their favorable performance-complexity balance, as opposed to their optimization-based counterparts. On the other hand, these designs provide limited flexibility. Furthermore, the standard precoding heuristics are SWIPT-agnostic. In this paper, we propose a novel MIMO precoding framework for SWIPT that is based on the notion of controllable residual interference to resolve the aforementioned issues. Specifically, we consider a multiple-input single-output (MISO) broadcast system for SWIPT with separate energy and information receivers. In this context, we formulate interference-constrained problems and apply a relaxation to obtain low-complexity solutions that admit closed-form expressions. We also extend our approach to hybrid precoding designs for the case where the base station adopts an analog/digital architecture due to particularly stringent cost and energy consumption constraints. Numerical simulations unveil the performance gains of the proposed precoding schemes over the solutions based on semi-definite relaxation (SDR) and heuristic baseline methods and provide valuable insights.

In this work, we propose an opportunistic beamforming strategy, which enables beam selection through random- rotations of an intelligent reflecting surface (IRS). To boost performance over a time slot, the proposed scheme splits the training period into multiple mini-slots. In each mini-slot, the access point generates different sets of orthonormal beamforming vectors and the IRS employs random-rotations. We provide an analytical framework for the sum-rate capacity and it is shown that a trade-off between the sum-rate capacity and the length of the training period exists due to the time constraint on the communication process. Based on this, we also derive the optimal number of the training mini-slots. The proposed low- complexity scheme outperforms conventional counterparts (single training slot) and approximates the performance of conventional beamforming (with channel state information) even for small number of users. Finally, by utilizing extreme value theory tools, we analyze the system’s performance under an asymptotic scenario, where the number of the users significantly increases.

Intelligent reflecting surfaces (IRSs) have emerged as a promising wireless technology for the dynamic configuration of electromagnetic waves. In this context, we study a multi-IRS assisted two-way communication system consisting of two users that employ full-duplex (FD) technology. More specifically, we deal with the joint IRS location and size (i.e., the number of reflecting elements) optimization in order to minimize an upper bound of system outage probability under various constraints. First, the problem is formulated as a discrete optimization problem and, then, a lower bound on the optimum value is computed by solving a linear-programming relaxation (LPR) problem. Subsequently, we design a polynomial-time greedy algorithm based on the LPR solution. The proposed algorithm always computes a feasible solution for which (a posteriori) performance guarantee can be provided. Finally, numerical simulations demonstrate the superiority of the greedy algorithm compared to a baseline scheme and provide useful comparisons between FD and conventional half-duplex (HD) systems.

In this paper, we evaluate the performance of a linear minimum mean square error (MMSE) receiver in the context of simultaneous wireless information and power transfer (SWIPT)-enabled cellular networks. In contract to the existing works, where a single-antenna SWIPT architecture is mainly considered, we focus on the SWIPT performance of the multi-antenna receiver architecture, based on the antenna switching (AS) and power splitting (PS) techniques. Aiming to further boost the network performance, we investigate a scenario where the receivers have the capability to employ a successive interference cancellation (SIC) scheme. By leveraging tools from stochastic geometry, we establish an analytical and tractable framework to evaluate the information decoding (ID) and the energy harvesting (EH) success probabilities of the considered network topologies. Our results reveal that the ID performance achieved by the MMSE receiver outperforms that of the conventional maximum ratio combining, leading to an enhanced EH performance, for a given ID reliability constraint. Moreover, by allocating an equal fraction of resources for ID and EH purpose, the PS scheme outperforms the AS in terms of both ID and EH success probabilities.

In this paper, we investigate the outage performance of fluid antenna (FA)-based user equipments (UEs) in the context of large-scale downlink cellular networks, where all UEs employ linear minimum mean-squared error (LMMSE) channel estimation method. In contrast to existing studies, which assume the existence of perfect channel state information, we develop a novel mathematical framework that accurately captures the channel estimation errors on the performance of the considered network deployment. Specifically, we focus on the limited coherence interval scenario, where a sequential LMMSE-based channel estimation procedure is performed for all FA ports, followed by data reception from the port with the strongest estimated channel. By using stochastic geometry tools, we derive both analytical and closed-form expressions for the achieved outage probability, highlighting the impact of channel estimation on the performance of FA-based UEs. Our results reveal the trade-off imposed between improving the network’s outage performance and reducing the channel estimation quality, indicating new insights for the design of FA-based wireless systems.

In this paper, a partition-based scheme is investigated for multiple-input multiple-output (MIMO) fading channels assisted by an intelligent reflecting surface (IRS). In this scheme, the IRS elements are partitioned into sub-surfaces, which are periodically selected to assist the communication. Through the sequential activation of each sub-surface, an equivalent parallel channel in the time domain is produced. The proposed scheme has low implementation complexity and does not require knowledge of the channel state information. We analyze the performance of the proposed scheme in terms of outage probability and provide an expression for the diversity-multiplexing tradeoff. We show, through theoretical and numerical results, that the asymptotic performance of the considered network under the partition-based scheme can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, both maximum diversity and multiplexing gain can be achieved under certain conditions with respect to the number of elements in each sub-surface.

In this paper, we study the fundamental limits of a simultaneous wireless information and power transfer communication system, which is facilitated by an intelligent reflecting surface (IRS). Specifically, we propose a low-complexity optimization scheme that optimizes iteratively the input probability distribution at the transmitter side and the phase shift coefficient at the IRS to enhance the information-energy capacity region. By using alternating optimization and Lagrangian decomposition, analytical expressions for the optimal input distribution and the phase shifts are derived which decrease the computational complexity of the proposed scheme. A novel joint optimization scheme is also proposed inspired by the one in [1] for SWIPT systems. Numerical results show that the iterative optimization scheme almost achieves the same performance in comparison with the proposed joint optimization scheme while ensuring a reduced computation for high energy-harvesting thresholds.

Simultaneous wireless information and power transfer (SWIPT) is an emerging technology to connect and energize devices wirelessly, in the future Internet of Things (IoT) and wireless sensor networks (WSN). In this paper, we investigate two coherent detection schemes for integrated SWIPT receivers. At first, we examine a single input multiple output (SIMO) topology, with multiple rectennas at the receiver’s side, i.e. MRCA scheme. Targeting to reduce the decoding complexity, we further investigate a single input single output (SISO) topology, with one receive antenna connected to multiple rectifiers, i.e. MRCE scheme. A non-linear energy harvesting (EH) model, is considered for both schemes, by taking into account the sensitivity and saturation effects of rectifiers. With the use of maximum likelihood (ML) and soft decoding, energy detection is succeeded. An asymptotic analysis is conducted for both schemes, providing upper and lower bounds for EH and information decoding, respectively. Simulation and experimental results along with theoretical analysis, validate the enhanced performance of the proposed schemes.

In this paper, we investigate the performance of a linear minimum mean square error (MMSE) receiver in the context of simultaneous wireless information and power transfer (SWIPT)-enabled cellular networks. Specifically, the multi-antenna user equipments (UEs) are equipped with a linear MMSE receiver and employ either the time switching (TS), the power splitting (PS) or the antenna switching (AS) schemes to achieve the SWIPT capability, while a non-linear energy harvesting (EH) model is considered. The performance achieved by a linear MMSE receiver in the considered network deployment is evaluated in terms of multiple key performance metrics, e.g. information decoding (ID) and EH coverage probabilities, average spectral efficiency and average harvested energy. By leveraging tools from stochastic geometry, we establish an analytical and tractable framework to evaluate the aforementioned performance metrics, of which the analytical expressions are derived. Our results reveal that the ID performance achieved by the MMSE receiver outperforms that of the conventional maximum ratio combining, leading to an enhanced EH performance, for a given ID reliability constraint. Moreover, by using a linear MMSE receiver, PS scheme offers the best SWIPT performance compared to TS and AS schemes.

This paper studies the performance of a differential chaos shift keying (DCSK)-based wireless power transfer (WPT) setup in a frequency selective scenario. Particularly, by taking into account the nonlinearities of the energy harvesting (EH) process and a generalized frequency selective Nakagami-m fading channel, we derive closed-form analytical expressions for the harvested energy in terms of the transmitted waveform and channel parameters. A simplified closed-form expression for the harvested energy is also obtained for a scenario, where the delay spread is negligible in comparison to the transmit symbol duration. Nontrivial design insights are provided, where it is shown how the power delay profile of the channel as well as the parameters of the transmitted waveform affect the EH performance. Our results show that a frequency selective channel is comparatively more beneficial for WPT compared to a flat fading scenario. However, a significant delay spread negatively impacts the energy transfer.

Amplitude detection (AD) is a low-complexity nonlinear scheme for information retrieval, as it only considers the envelope of the received signal. This makes it ideal in applications with low-cost low-power devices such as the Internet of Things (IoT). We consider a basic Point-to-Point (P2P) setup and develop a unified analytical framework for the asymptotic symbol error rate (SER) performance of the maximum-likelihood (ML) decoder with$M$-ary amplitude-shift keying (ASK). The developed framework is general and can be applied for a large class of fading channels. The SER can be characterized by two parameters: the diversity order and the coding gain at a sufficiently high signal-to-noise regime. We derive closed-form expressions for the exact diversity order along with a lower and upper bound on the coding gain. We show that the diversity order is equal to$m$for Nakagami-$m$fading, whereas for the other considered models, it is equal to one. We also prove that among all possible binary modulations with equal power, the modulation with one symbol equal to zero [i.e., on–off keying (OOK)] achieves the minimum asymptotic SER. The mathematical framework developed for the P2P setup is extended for cooperative relay networks. Specifically, we consider a basic amplify-and-forward (AF) three-node relay topology that employs AD at both the relay and the destination. We analytically derive the ML decoder and study asymptotic bounds on the SER by showing that the proposed AF-AD relay setup can be transformed into an equivalent P2P-AD with respect to the ML decoder.

Simultaneous wireless information and power transfer (SWIPT) has long been proposed as a key solution for charging and communicating with low-cost and low-power devices. However, the employment of radio frequency (RF) signals for information/power transfer needs to comply with international health and safety regulations. In this article, we provide a complete framework for the design and analysis of far-field SWIPT under safety constraints. In particular, we deal with two RF exposure regulations, namely, the specific absorption rate (SAR) and the maximum permissible exposure (MPE). The state of the art regarding SAR and MPE is outlined together with a description as to how these can be modeled in the context of communication networks. We propose a deep learning approach for the design of robust beamforming subject to specific information, energy harvesting, and SAR constraints. Furthermore, we present a thorough analytical study for the performance of large-scale SWIPT systems, in terms of information and energy coverage under MPE constraints. This work provides insights with regards to the optimal SWIPT design and the potentials from the proper development of SWIPT systems under health and safety restrictions.

In this letter, we study the impact of the low-pass resistor-capacitor (RC) filter on radio frequency (RF) wireless power transfer (WPT). The RC filter influences both the RF bandwidth by removing the harmonics as well as the ripple voltage at the output of the rectifier. In particular, a large (small) RC time constant, reduces (increases) the ripple but decreases (enhances) the direct-current (DC) component. By following a Fourier series approach, we obtain closed-form expressions for the rectifier’s output voltage, the RC filter’s output as well as the DC voltage. Our analytical framework provides a complete characterization of the RC filter’s impact on the WPT performance. We show that this complete and tractable analytical framework is suitable for the proper design of the RC filter in WPT systems.

Rate splitting (RS) and wireless edge caching are essential means for meeting the quality of service requirements of future wireless networks. In this work, we focus on the cross-layer co-design of wireless edge caching schemes with sophisticated physical layer techniques, which facilitate non-orthogonal multiple access and interference mitigation. A flexible caching-aided RS (CRS) technique is proposed that operates in various modes that specify the cache placement at the receivers. We consider two caching policies: the intelligent coded caching (CC), as well as the well-known most popular content (MPC) policy. Both caching policies are integrated within the design parameters of RS in order to serve multiple cache-enabled receivers. The proposed technique is investigated from a system level perspective by taking into account spatial randomness. We consider a single cell network consisting of center and edge receivers and provide a comprehensive analytical framework for the evaluation of the proposed technique in terms of achieved rates. Specifically, we derive the rate achieved at each receiver under minimum rate constraints while incorporating the cache placement characteristics. Numerical results are presented which highlight the flexibility of the proposed technique and show how caching can be exploited in order to further boost the performance of RS.

This study considers the Joint Transmit/Reflect Beamforming and Power Splitting (JTRBPS) optimization problem in a spectrum underlay setting, such that the transmit sum-energy of the intelligent reflecting surface (IRS)-aided secondary transmitter (ST) is minimized subject to the quality-of-service requirements of the PS-simultaneous wireless information and power transfer (SWIPT) secondary receivers and the interference constraints of the primary receivers (PR). The interference at the PRs caused by the reception of IRS-reflected signals sent by the primary transmitter is taken into account. A coordinated channel state information (CSI) acquisition protocol is proposed. Next, assuming availability at the ST of perfect CSI for all direct and IRS-cascaded transmitter–receiver channels, two penalty-based iterative algorithms are developed: an alternating minimization algorithm that involves semi-definite relaxation in JTBPS design and successive convex approximation in RB optimization, and a block coordinate descent algorithm that employs the Riemannian conjugate gradient algorithm in RB updates. Finally, an outage-constrained robust design under imperfect CSI is devised. Numerical simulations highlight the performance gains of the proposed strategies over benchmarks, corroborate the benefits of using an IRS, and provide valuable insights.

In this paper, we study the co-design of full-duplex (FD) radio with joint communication and radar sensing (JCAS) techniques in millimeter-wave (mmWave) heterogeneous networks (HetNets). Spectral co-existence of radar and communication systems causes mutual interference between the two systems, compromising both the data exchange and sensing capabilities. Focusing on the detection performance, we propose a cooperative detection technique, which exploits the sensing information from multiple base stations (BSs), aiming at enhancing the probability of successfully detecting an object. Three combining rules are considered, namely theOR, theMajorityand theANDrule. In real-world network scenarios, the locations of the BSs are spatially correlated, exhibiting a repulsive behavior. Therefore, we model the spatial distribution of the BSs as a$\beta$-Ginibre point process ($\beta$-GPP), which can characterize the repulsion among the BSs. By using stochastic geometry tools, analytical expressions for the detection performance of$\beta$-GPP-based FD-JCAS systems are expressed for each of the considered combining rule. Furthermore, by considering temporal interference correlation, we evaluate the probability of successfully detecting an object over two different time slots. Our results demonstrate that our proposed technique can significantly improve the detection performance when compared to the conventional non-cooperative technique.

By utilizing the combination of two powerful tools i.e., stochastic geometry (SG) and evolutionary game theory (EGT), in this paper, we study the problem of mobile user (MU) mode selection in heterogeneous sub-6 GHz/millimeter wave (mmWave) cellular networks. Particularly, by using SG tools, we first propose an analytical framework to assess the performance of the considered networks in terms of average signal-to-interference-plus-noise (SINR) ratio, average rate, and mobility-induced time overhead, for scenarios with user mobility. According to the SG-based framework, an EGT-based approach is presented to solve the problem of access mode selection. Specifically, two EGT-based models are considered, where for each MU its utility function depends on the average SINR and the average rate, respectively, while the time overhead is considered as a penalty term. A distributed algorithm is proposed to reach the evolutionary equilibrium, where the existence and stability of the equilibrium is theoretically analyzed and proved. Moreover, we extend the formulation by considering information delay exchange and evaluate its impact on the convergence of the proposed algorithm. Our results reveal that the proposed technique can offer better spectral efficiency and connectivity in heterogeneous sub-6 GHz/mmWave cellular networks with mobility, compared with the conventional access mode selection techniques.

This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, when only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.

In this paper, we study the employment of conventional orthogonal frequency division multiplexing (OFDM) signals in simultaneous wireless information and power transfer (SWIPT) systems. Specifically, we investigate the impact of high peak-to-average power ratio (PAPR) on OFDM-based information/energy transfer under the nonlinearities and memory effects introduced by high power amplifier (HPA) at the transmitter. A closed-form expression of the symbol error rate (SER) as a function of the PAPR is derived for a Gaussian channel in this context. We notice that even under these realistic non-idealities, PAPR deteriorates the SER performance while facilitating energy transfer efficiency, as expected. To further enlarge the rate-energy (R-E) region, a predistortion scheme that allows the transmission of unclipped high PAPR OFDM signals is also proposed. Numerical simulations based on MATLAB and ADS software validate the impact of PAPR in SWIPT systems, regarding both the information detection and energy harvesting processes.

In this paper, we carry out a performance analysis of a full-duplex (FD) relaying system consisting of parallel hybrid radio frequency (RF)/free-space optical (FSO) communication links. The RF links are hampered by the residual self-interference (RSI), due to the FD relaying operation, along with the in-phase and quadrature-phase imbalance (IQI) effect, due to imperfections at the RF nodes' front-ends. The parallel FSO links, of the dual-hop configuration, are influenced by the joint effects of atmospheric turbulence and pointing errors. The performance of the dual-hop FD system with parallel hybrid RF/FSO links, operating under a hard-switching scheme, is evaluated in terms of the outage probability. Analytical closed-form expressions are derived for both RF and FSO subsystems as well as for the overall dual-hop hybrid system. The presented numerical results show the significant performance gains obtained by the exploitation of parallel RF/FSO links in an FD relaying channel under various operating conditions. Finally, the derived analytical results are verified by Monte Carlo simulations.

We consider an intelligent reflecting surface (IRS)-aided secondary multiple-input single-output (MISO) broadcast system for simultaneous wireless information and power transfer (SWIPT) in a spectrum underlay setup. The secondary transmitter (ST) regards the primary receivers (PR) as possible eavesdroppers. We propose an inter-system coordination protocol that enables acquisition at the ST of control information to facilitate interference management. We assume availability of imperfect channel state information (CSI) regarding the relevant direct and IRS-cascaded links at the ST. We aim at jointly optimizing the transmit precoding, artificial noise (AN) covariance, and reflect beamforming matrices, so that the transmit power of the ST is minimized subject to the quality-of-service (QoS) requirements of the information decoding and energy harvesting secondary receivers (IDR/EHR), the security and interference constraints of the PRs, and the unit-modulus constraints of the IRS phase shifts. We obtain convex approximations of the probabilistic constraints by employing Bernstein-type and first-order Taylor inequalities. We derive a robust outage-constrained design by developing an alternating minimization algorithm that makes use of the semi-definite relaxation (SDR) method and the penalty convex-concave procedure (CCP). Our design takes into account the additional interference incurred at the PRs by the IRS-reflected transmissions of the primary transmitter (PT) itself, which serves its users in an IRS-blind manner. Numerical simulation results reveal the performance gains of the proposed scheme over benchmark strategies and highlight the impact of the system parameters on the performance.

In this work, we investigate differential chaos shift keying (DCSK), a communication-based waveform, in the context of wireless power transfer (WPT). Particularly, we present a DCSK-based WPT architecture, that employs an analog correlator at the receiver in order to boost the energy harvesting (EH) performance. By taking into ac-count the nonlinearities of the EH process, we derive closed-form analytical expressions for the peak-to-average-power-ratio of the received signal as well as the harvested power. Nontrivial design in-sights are provided, where it is shown how the parameters of the transmitted waveform affects the EH performance. Furthermore, it is demonstrated that the employment of a correlator at the receiver achieves significant EH gains in DCSK-based WPT systems.

LoRa networks have been deployed all over the world and are a major enabling wireless technology for the Internet of Things (IoT). Massive connectivity applications such as smart metering, agriculture, and supply chain & logistics are most suitable for LoRa deployments due to their long range, low cost, and low power features. Meanwhile, energy harvesting technologies that extract energy from ambient sources have enabled the battery-less operation of many small wireless sensors. This paper studies the merger of these two technologies and mathematically models device and network performance using tools from stochastic geometry and Markov analysis. To that end, we derive the steady-state distribution of the capacitor voltage, the outage probability due to co-spreading factor interference at the LoRa gateway, and propose adaptive charging time schemes in order to mitigate energy outage events.

Intelligent reflecting surfaces (IRSs) have the potential of increasing the coverage and energy efficiency of future wireless networks. In this paper, we study the performance of an IRS-aided network in the presence of blockages from a large-scale point-of-view, where multiple IRSs are randomly distributed. We model the blockages and IRSs with a line segment process and take into account the fading channels from both the direct and indirect links. The performance of a receiver is studied based on specific association policies, i.e. which IRS(s) should be activated to reflect the signal to the receiver. Specifically, we consider the association with a random IRS, the closest IRS or with all available IRSs in the cell. A complete analytical framework in terms of the outage probability is developed using tools from stochastic geometry. We show that each association policy has its advantages, which heavily depend on the network’s parameters such as the cell-radius, the number of IRS elements and the blockage density.

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance.

In this paper, we study the fundamental limits of simultaneous information and power transfer over a Rayleigh fading channel in the presence of high-power amplifier (HPA) nonlinearity. In particular, a three-party communication system is considered, where a transmitter aims simultaneously conveying information to an information receiver and delivering energy to an energy harvester receiver. We study the information-energy capacity region and the associated input distribution under: i) average-power, peak-power (PP) constraints at the transmitter, b) HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver. By extending Smith’s mathematical framework [1], we show that the optimal input distribution under those constraints is discrete with a finite number of mass points. Moreover, we derive a closed-form expression of the capacity-achieving distribution for the low PP regime, where there is no trade-off between information and energy transfer. Finally, we show that HPA significantly reduces the information energy capacity region.

In this paper, we investigate a low complexity technique for simultaneous wireless information and power transfer (SWIPT) in the context of cellular networks, where the multiple-antenna user equipments (UEs) employ maximum ratio combining technique. In particular, our proposed technique allocates a subset of antennas for information decoding (ID), only when their post-combiner signal-to-interference ratio exceeds a certain threshold, while the remaining antennas are allocated for energy harvesting (EH). In contrast to conventional approaches, where an uncorrelated or a fully correlated interference is considered, we develop a realistic mathematical framework that accurately captures the interference correlation effects on the performance of the proposed technique. By using stochastic geometry tools, we derive analytical expressions for both the ID and EH success probability, as well as the joint ID and EH success probability. Our results demonstrate the impact of spatial interference correlation on both the ID and the EH success probability, and we establish the optimal threshold for the proposed antenna switching scheme, that maximizes the joint ID and EH success probability.

This work proposes a new simultaneous wireless power transfer and modulation classification (SWPTMC) scheme appropriate for internet of things (IoT) applications. The problem of SWPTMC is investigated for various modulation formats, i.e., quadrature phase-shift-keying (QPSK), π/4-QPSK, offset QPSK (OQPSK), 16-pulse amplitude modulation (16-PAM), 16-quadrature amplitude modulation (16-QAM), and minimum shift keying (MSK). We propose three receiver architectures, i.e., an integrated receiver, a separate receiver with power splitting (PS), and a separate receiver with energy harvesting (EH)-based classification; all the architectures are studied under a non-linear model with a certain sensitivity and saturation level. Also, we derive the average harvested power over a Rayleigh fading channel for the different modulation formats. Two different approaches are used for the blind modulation classification (MC) algorithm: one for the intermediate frequency signal and the other for the baseband signal. Both the MC algorithms are based on the higher-order cumulants and cyclic cumulants of the received signal. The cyclic cumulants use the non-zero cycle frequency position, while the cumulants use threshold values for classifying modulation formats. Monte Carlo simulations are used to evaluate the performance of the proposed SWPTMC schemes. The results show that we can simultaneously harvest power without much affecting the classifier performance. Moreover, with an integrated receiver, we can simultaneously perform MC and harvest power without the requirement of PS circuit.

The rapid growth of the so-called Internet of Things is expected to significantly expand and support the deployment of resource-limited devices. Therefore, intelligent scheduling protocols and technologies such as wireless power transfer, are important for the efficient implementation of these massive low-powered networks. This paper studies the performance of a wireless powered communication network, where multiple batteryless devices harvest radio-frequency from a dedicated transmitter in order to communicate with a common information receiver (IR). We investigate several novel selection schemes, corresponding to different channel state information requirements and implementation complexities. In particular, each scheme schedules the$k$-th best device based on: a) the end-to-end (e2e) signal-to-noise ratio (SNR), b) the energy harvested at the devices, c) the uplink transmission to the IR, and d) the conventional/legacy max-min selection policy. We consider a non-linear energy harvesting (EH) model and derive analytical expressions for the outage probability of each selection scheme by using tools from high order statistics. Moreover, an asymptotic scenario in terms of the number of devices is considered and, by applying extreme value theory, the system’s performance is evaluated. We derive a complete analytical framework that provides useful insights for the design and realization of such networks.

The employment of intelligent reflecting surfaces (IRSs) is a potential and promising solution to increase the spectral and energy efficiency of wireless communication networks. Despite their many advantages, IRS-aided communications have limitations as they are subject to high propagation losses. To overcome this, the phase rotation (shift) at each element needs to be designed in such a way as to increase the channel gain at the destination. However, this increases the system's complexity as well as its power consumption. In this article, we present an analytical framework for the performance of random rotation-based IRS-aided communications. Under this framework, we propose four low-complexity and energy efficient schemes, based on a coding or a selection approach. Both of these approaches employ random phase rotations and require limited knowledge of channel state information. Specifically, the coding-based schemes use time-varying random phase rotations to produce an equivalent time-varying channel. On the other hand, the selection-based schemes select a partition of the IRS elements based on the received signal power at the destination. Analytical expressions for the achieved outage probability and energy efficiency of each scheme are derived. It is demonstrated that all schemes can provide significant performance gains as well as full diversity order.

In this paper, a cooperative protocol is investigated for a multi-hop network with buffers of finite size at the relay nodes. The protocol is based on the myopic decode-and-forward coding strategy, where each node of the network cooperates with a limited number of neighboring nodes for the transmission of the signals. Each relay stores in its buffer the messages that were successfully decoded, in order to forward them through the appropriate channel links. A complete theoretical framework is investigated that models the evolution of the buffers as a state Markov chain (MC). We analyze the performance of the proposed protocol in terms of outage probability and diversity gain by using the state transition matrix and the related steady state of the MC. Our results show that the proposed protocol outperforms the conventional multi-hop relaying scheme and the system's outage probability as well as the achieved diversity order depend on the degree of cooperation among neighboring nodes.

This paper studies the performance of a wireless powered communication network consisting of a finite number of batteryless devices that harvest radio frequency energy. We consider novel selection/scheduling schemes, where the k-th best device is selected for information transmission. The proposed schemes correspond to different complexity and are based on: a) the end-to-end (e2e) signal-to-noise ratio (SNR), b) the energy harvested at the devices, and c) the conventional channel-based max-min selection policy. By considering a non-linear energy harvesting (EH) model, we derive analytical expressions for the outage probability of each selection scheme by using high order statistics. We also consider an asymptotic scenario, where the number of devices increases and analyze the behavior of the system by applying extreme value theory. Due to the saturation effects of the non-linear EH model, the performance of all the proposed schemes converges to an error floor. Our results show that the scheme based on the e2e SNR achieves the best performance and the one based on the EH the worst. The derived analytical framework provides useful insights on the design of such networks.

On the brink of the internet of things (IoT) revolution, different low power wide area network (LPWAN) technologies are competing for market share and scalability. In this paper we leverage stochastic geometry tools to better understand how one should model the uplink wireless coverage in a multi-cell LoRa network while taking under consideration both the effects of co-spreading factor interference and the spatial diversity afforded by the LoRa network server. To that end, we propose a tractable model that captures these two system peculiarities and mathematically show that gateway densification has an overall positive effect on network coverage thus supporting the scalability of LoRa networks.

We study the information-energy capacity region (IE-CR) of an additive white Gaussian noise (AWGN) channel in the presence of high-power amplifier (HPA) nonlinearity. Specifically, we consider a three-node network consisting of one transmitter, one information receiver and one energy receiver and we study the capacity-achieving input distribution under i) average-power, peak-power constraints at the transmitter, b) HPA nonlinearity at the transmitter, and c) nonlinearity of the energy harvesting circuit at the energy receiver. We prove that the input distribution is discrete and finite and we derive closed form expressions for the special cases of maximizing the harvested energy and maximizing the information capacity. We show that HPA significantly reduces the achievable IE-CR; to compensate HPA nonlinearity, a predistortion technique is also discussed and evaluated in terms of IE-CR performance.

This paper investigates the hybrid precoding (HP) design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel setup where the terminals adopt the power splitting architecture. The problem of interest is the maximization of the signal-to-interference-plus-noise-ratio and the harvested power for all terminals under a total transmit power constraint. Our focus is on the derivation of frequency- and setup-agnostic low-complexity HP methods. Two baseline approaches for the determination of the analog precoder are considered. In the first one, the phases are computed via the singular value decomposition (SVD) of the channel matrix, while in the second they are selected randomly. Then, the baseband precoder is computed by applying semidefinite relaxation (SDR) to the problem under study. Alternatively, we combine the aforementioned analog precoders with a fixed zero-forcing baseband pre-coder, in order to further reduce the computational load. Another proposed strategy focuses on the minimization of the Euclidean distance between the optimal fully-digital precoder, which is obtained via SDR, and the hybrid one. To this end, an alternating minimization algorithm that employs Gaussian smoothing to convexify the problem and utilizes stochastic gradient descent to update the phases is introduced. The performance of the proposed HP methods is comparatively evaluated versus the one achieved by the optimal fully-digital precoder via numerical simulations. The simulation results indicate that the stochastic optimization approach presents a favorable performance-complexity trade-off as well as substantial power gains.

The employment of intelligent reflecting surfaces (IRSs) is a potential solution to increase the spectral and energy efficiency of wireless networks. The passive operation of their elements and the fact they can be deployed on any flat surface, makes them ideal for future wireless networks. However, the passive operation of their elements, incurs limitation on their capabilities. In this paper, we propose two low-complexity and energy efficient techniques for IRS-aided communications, namely, a coding-based and a selection-based scheme, both based on random phase rotations. In particular, the coding-based scheme uses time-varying random phase rotations to produce a time-varying channel. The selection-based scheme, selects and activates a partition of the IRS elements at each time slot based on the received signal power at the destination. Analytical expressions for both schemes are derived and the achieved performance gains are demonstrated. Furthermore, it is shown that both schemes provide full diversity order.

A vision of a digital and connected world is now a global strategy for 5G Internet of Things (IoT), targeting for high-speed communication services with more capacity, lower latency, increased reliability, and availability. In this article, we assess the added value of backscatter communication in 5G IoT technology, by studying spatial modulation (SM)-based techniques, applied over a traditional multiple antenna backscatter communication system. Particularly, with backscatter, we fulfill the need for wireless self-powered devices, as one of the main characteristics of 5G IoT. Furthermore, with the use of multiple antennas, we apply sophisticated techniques that enhance the overall efficiency of the backscatter communication system. Initially, we study generalized SM (GSM) and its special case of SM, exploiting the antenna index as an additional source of information. With this technique, enhanced performance in terms of symbol error rate (SER) and spectral efficiency, is provided. In addition, we expand GSM in the time domain, by applying the Alamouti coding scheme (GSMA) in two out of the multiple available antennas. In this way, we further enhance the performance and succeed transmit diversity. Finally, analytical expressions regarding the pairwise error probability are derived and presented, while a diversity analysis is carried out for the proposed techniques. The simulation results along with theoretical bounds are provided, validating the enhanced performance of our study.

Motivated by the demand of reliable and finite blocklength communications, we employ tools from information theory, stochastic processes and queueing theory, in order to provide a comprehensive framework regarding the analysis of a Time Division Multiple Access (TDMA) network with bursty traffic, in the finite blocklength regime. Specifically, we reexamine the stability conditions of a non-cooperative TDMA multiple access channel, evaluate the optimal throughput, and identify the optimal data packet size, k, for fixed codeword of blocklength, n. The evaluation is performed both numerically and via the proposed approximations, which result in closed form expressions and provide insight on how the optimal data size, k*, relates to the information metrics of channel capacity and channel dispersion in the finite blocklength regime. Then, we examine the stability conditions and the performance of the Multiple Access Relay Channel with TDMA scheduling, subject to finite blocklength constraints, by applying a cognitive cooperation protocol that assumes relaying is enabled when sources are idle. Finally, we propose the novel Batch-And-Forward (BAF) strategy, a mechanism that allows terminals to send batches of data packets instead on individual data packets, and evaluate the stability conditions and the optimal throughput. Numerical evaluation of the proposed strategy indicates that the performance of the cooperative network in the finite blocklength regime, in terms of throughput, can be significantly enhanced. Moreover, it reduces the requirement in control signals (metadata) [3], since, it avoids the unnecessary repetition of metadata (e.g. address of the source terminal and the destination). The BAF strategy is quite versatile, thus, it can be embedded in existing cooperative protocols, without imposing additional complexity on the overall scheme.

This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides the received signal into two parts: one for information decoding and the other for energy harvesting with a practical non-linear rectification model. The problem of interest is to maximize as much as possible the received signal-to-interference-plus-noise ratio (SINR) and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints by optimizing the transmit beamforming at the transmitter and the power splitting ratios at different receivers. The optimal beamforming and power splitting solutions are obtained with the aid of semidefinite programming and bisection search. Low-complexity fixed beamforming and hybrid beamforming techniques are also studied. Furthermore, we study the effect of imperfect channel information and radiation matrices, and design robust beamforming to guarantee the worst-case performance. Simulation results demonstrate that our proposed algorithms can effectively deal with the radio exposure constraints and significantly outperform the conventional transmission scheme with power backoff.

In this paper, we investigate the user association problem for mobile edge computation (MEC) offloading in non-orthogonal multiple access (NOMA) networks. According to NOMA, multiple users can access the MEC server to offload data simultaneously. However, resources are shared among the users which can potentially impact the required transmit power for offloading, thus increasing the total energy consumption. Aiming to minimize the overall energy consumption for all the users of the network, we formulate a problem where user association, optimal power allocation, data rate and offloaded data are jointly considered. More specifically, two coalition game algorithms are proposed and compared in an effort to efficiently reduce the total energy consumption. Simulation results show that both proposed algorithms can successfully reach a final state with low complexity, where the overall energy consumption is significantly reduced.

Ambient radio-frequency (RF) energy harvesting is a potential solution for charging low-powered devices, as a result of its cost effectiveness and pervasiveness. In this paper, we deal with devices which are underlaid in a wireless communication network and harvest energy from the ambient RF transmitted signals. The devices are equipped with an array of multiple rectifying antennas, combined either at the RF or the DC domain, to increase the harvesting efficiency. A large-scale analysis is undertaken, with the use of stochastic geometry. We take into account the non-linearities of the RF energy harvesting process and derive closed-form analytical expressions for the average harvested energy for both array architectures. Our results show the importance of considering non-linearities, mainly in terms of the network's density and the size of the rectifying antenna array but also for other network parameters.

Recent advances in information theory have provided a novel framework regarding finite blocklength analysis, which can be employed to address the current and future demands of communication networks. In this paper, we investigate the performance of the Time-Division Multiple-Access (TDMA) scheme with bursty traffic, subject to finite blocklength constraints. In contrast to previously reported work, where the analysis of such communication networks was performed under the infinite blocklength assumption, we develop a comprehensive framework that takes finite blocklength constraints into account. In particular, we employ the recent results in finite blocklength analysis, to prove necessary stability conditions for the overall system at the finite blocklength regime, and to identify the optimal trade-off between data length and channel blocklength. The later one is evaluated both numerically and via the proposed linear and quadratic approximations that result to closed form expressions.

This work provides a comprehensive framework regarding the analysis of the Multiple Access Relay Channel (MARC) with Time Division Multiple Access (TDMA) scheduling, subject to finite blocklength constraints. In particular, we examine the stability conditions and evaluate the maximum throughput, by applying a cognitive cooperation protocol that assumes relaying is enabled when sources are idle. Moreover, we propose the novel Batch-And-Forward (BAF) strategy, that can significantly enhance the performance of cooperative networks in the finite blocklength regime, as well as reduce the requirement in metadata. The BAF strategy is quite versatile, thus, it can be embedded in existing cooperative protocols, without imposing additional complexity on the overall scheme.

Characterising the fundamental energy efficiency (EE) limits of massive Multiple-Input-Multiple-Output (MIMO) systems is significant for the development of green wireless communications. This paper analyses the adaptation of optimal BS transmitted power according to channel conditions and users' Quality of Service (QoS) requirements. The optimal BS transmitted power has the potential to be utilised as an effective strategy to reduce BSs power consumption and consequently enhance the EE of the system. Thus, this paper conducts an analytical closed-form expression of optimal downlink BS transmitted power for massive MIMO systems with Zero Forcing (ZF) beamforming processing and perfect Channel State Information (CSI). User required data rate and maximum allowable outage probability are considered as users' QoS constraints. Both of theoretical and the simulation results, for several transmission modes, indicate that the average EE per BS is a unimodal function of the user required data rate that jointly increased up to the maximum point of EE, but further increases in the data rate can only come at a loss in average EE per BS. As the higher the data rate, the higher the circuit and transmit power consumption, due to the need for a larger number of BS antennas. The results indicate that system EE can be enhanced by reducing energy consumption through optimal BS transmitted power. This is achieved by adaptation to channel conditions and users' QoS requirements.

This paper studies the beneficial combination of wireless power transfer and millimeter-wave (mmWave) communications in wireless sensor networks. In particular, an mmWave wireless powered sensor network is considered, where the access point (AP) employs beamforming techniques to transfer energy to the sensors of a selected sector of the cell. The served sensors harvest and store energy from the received signal, and use it to power their uplink transmissions. We consider a random energy beamforming scheme but also propose several intelligent schemes that steer the beam to specific areas of the cell by considering the sensors' locations. This setup is investigated from a large-scale point-of-view where spatial randomness is considered with the aid of Poisson point processes. The performance of the network is described in terms of the energy outage probability and the beam outage probability. We show that, depending on the scenario, each of the considered energy beamforming schemes can provide significant gains to the network's performance. Finally, we study an event monitoring application of our theoretical framework, where the active sensors observe a random event in the network and the AP attempts to estimate it based on the received information; this scenario is evaluated in terms of the estimation's mean squared error.

We propose a new simultaneous wireless information and power transfer technique that embeds information bits in the tone index of multisine waveforms. By varying the number of subcarriers of the transmitted bandwidth-constrained multisine signal, the proposed scheme enables efficient radio-frequency energy harvesting and low-complexity information transmission. The receiver does not require channel estimation and employs a non-coherent maximum-likelihood detection at the envelope of the received signal. The performance of the proposed tone-index modulation is evaluated in terms of average error probability for a flat-fading channel, and we show that it outperforms its peak-to-average-power-ratio counterpart.

In this paper, we assess the effect of full-duplex (FD) radio in the context of millimeter-wave (mm-Wave) communications. Particularly, we propose an analytical framework, based on stochastic geometry, to evaluate the performance of heterogeneous FD-mm-Wave cellular networks for two user location-based classifications, namely cell-center users (CCUs) and cell-edge users (CEUs). Moreover, we evaluate the performance of the considered networks with successive interference cancellation (SIC) capabilities. Based on the proposed framework, analytical expressions for the coverage and sum-rate performance are derived. We investigate the impact of FD-mm-Wave communications on the network performance of CCUs/CEUs and quantify the associated performance gains under different network parameter settings. Our results demonstrate the beneficial combination of FD radio with heterogeneous mm-Wave cellular networks, since it increases the spectral efficiency but also alleviates the effects of the multi-user interference. Furthermore, we present the tradeoff between the coverage and sum-rate performance of heterogeneous FD-mm-Wave cellular networks for the considered user classifications. The results show that half-duplex mode is beneficial for the CEUs to achieve better network performance, as opposed to the CCUs for which FD mode is more efficient. Finally, we show the effectiveness of SIC on the network performance, with significant performance gains for the CEUs.

In this paper, we investigate non-coherent detection schemes, for the integrated simultaneous wireless information and power transfer (SWIPT) receiver. Firstly, we study a symbol by symbol (SBS) detection, while optimization of the transmitted energy pulses, enhances the performance of the receiver, in terms of symbol error rate (SER). In addition, by exploiting the channel coherence time, over N transmitted energy pulses, we study sequential detection and introduce an integrated SEQ-MLSD decoder. With the use of sophisticated techniques such as Viterbi-type trellis-search algorithm and strategic-store strategy, we simplify the complexity of the sequential detection and overcome the error floor problem. Simulation results along with theoretical bounds are provided, validating the enhanced performance of our solution. The proposed sequential decoding scheme outperforms in terms of SER, the integrated SBS decoder and the conventional power-splitting SWIPT receiver, without degrading the energy harvested.

In this paper, we assess the effect of full-duplex (FD) communications in the context of millimeter-wave (mmWave) cellular networks. The FD radio can potentially double the spectral efficiency but its performance is compromised by the existence of loop- and multi-user interference compared to half-duplex radio. Using stochastic geometry tools, we propose an analytical framework to evaluate the performance of FD-mmWave cellular networks for two location-based user classifications, namely cell-center users (CCUs) and cell-edge users (CEUs). Based on the proposed framework, analytical expressions for the downlink and the uplink coverage performance are derived for each user classification. We evaluate the impact of the FD radio on the performance of CCUs/CEUs and quantify the associated performance gains under different network parameter settings. Our results demonstrate the significant impact of the user's location on the network performance and the beneficial combination of FD radio with mmWaves, which provides significant gains, as it alleviates the effects of multi-user interference.

Vehicular safety applications have much significance in preventing road accidents and fatalities. Among others, cellular networks have been under investigation for the procurement of these applications subject to stringent requirements for latency, transmission parameters, and successful delivery of messages. Earlier contributions have studied utilization of Long‐Term Evolution (LTE) under single cell, Friis radio, or simplified higher layer. In this paper, we study the utilization of LTE under multicell and multipath fading environment and introduce the use of adaptive awareness range. Then, we propose an algorithm that uses the concept of quality of service (QoS) class identifiers (QCIs) along with dynamic adaptive awareness range. Furthermore, we investigate the impact of background traffic on the proposed algorithm. Finally, we utilize medium access control (MAC) layer elements in order to fulfill vehicular application requirements through extensive system‐level simulations. The results show that, by using an awareness range of up to 250 m, the LTE system is capable of fulfilling the safety application requirements for up to 10 beacons/s with 150 vehicles in an area of 2 × 2 km2. The urban vehicular radio environment has a significant impact and decreases the probability for end‐to‐end delay to be ≤100 ms from 93%–97% to 76%–78% compared to the Friis radio environment. The proposed algorithm reduces the amount of vehicular application traffic from 21 Mbps to 13 Mbps, while improving the probability of end‐to‐end delay being ≤100 ms by 20%. Lastly, use of MAC layer control elements brings the processing of messages towards the edge of network increasing capacity of the system by about 50%.

Handover and mobility management are important and critical aspects of future ultra-dense cellular networks. This paper proposes an intelligent handover technique, which reduces the handover rate without significant performance losses. Specifically, we investigate a user-centric handover scheme that exploits base station (BS) cooperation to enable a dynamic handover skipping. By considering mobility awareness, the users skip some handover executions and remain connected with at least a single BS. In this way, we achieve a balance between BS cooperative transmissions and single BS transmission to reduce handover rate and consequently the associated signal overhead. Our results show that the proposed scheme outperforms conventional solutions in terms of the average user throughput, particularly for high velocities and ultra-dense networks.

Relay selection is a simple yet effective means to improve the reliability and coverage of wireless cooperative networks. However, it suffers from inefficient use of the available bandwidth resources. This paper introduces the use of content caching at relays in order to tackle this problem and improve the performance of relay selection. Three cache placement schemes are considered: one based on the most popular files, a uniform based caching and a hybrid scheme of the two. We analytically derive their outage performance as well as their diversity order and coding gain. Numerical results demonstrate the substantial performance gains of these schemes over the traditional optimal relay selection approach without caching capabilities.

In this paper, we study the performance of next-generation cellular networks in the context of a low-complexity base station (BS) selection scheme. In contrast to existing BS cooperation approaches, where multiple BSs jointly transmit to the user, by using our proposed low-complexity technique, a user communicates with the BS that provides the maximum signal-to-interference-plus-noise-ratio from a set formed according to a pre-selection policy. We consider three pre-selection policies based on: 1) the Euclidean distance; 2) the averaged received power; and 3) a random selection. Moreover, we consider the case where the users have the ability to employ the successive interference cancellation (SIC) scheme. Despite its high computational complexity, SIC can potentially decode and remove strong interfering signals from the aggregate received signal, which can significantly boost the user's performance. By using stochastic geometry tools, analytical expressions for the coverage performance are derived for each policy, by taking into account spatial randomness and blockage effects. Our proposed technique provides low computational and implementation complexity due to the two-level selection scheme. Furthermore, we show that our proposed scheme does not lose in diversity compared with existing cooperation techniques and that all policies can benefit by the employment of the SIC scheme.

International audience

In this paper, we consider two fundamental full-duplex (FD) architectures, two-node and three-node, in the context of cellular networks where the terminals employ directional antennas. The simultaneous transmission and reception of data in non-orthogonal channels makes FD radio a potential solution for the currently limited spectrum. However, its implementation generates high levels of interference either in the form of loopback interference (LI) from the output to the input antenna of a transceiver or in the form of co-channel interference in large-scale multicell networks due to the large number of active links. Using a stochastic geometry model, we investigate how directional antennas can control and mitigate the co-channel interference. Furthermore, we provide a model which characterizes the way directional antennas manage the LI in order to passively suppress it. Our results show that both architectures can benefit significantly by the employment of directional antennas. Finally, we consider the case where both architectures are employed in the network and derive the optimal values for the density fraction of each architecture, which maximize the success probability and the network throughput.

In this work, we address the relay selection problem for the wireless powered communication networks, where the relays harvest energy from the source radio frequency signals. A single source- destination pair is considered without a direct link. The connecting relay nodes are equipped with storage batteries of infinite size. We assume that the channel state information (CSI) on the source-relay link is available at the relay nodes. Depending on the availability of the CSI on the relay-destination link at the relay node, we propose different relay selection schemes and evaluate the outage probability. The availability of the CSI at the relay node on the relay- destination link considerably improves the performance due to additional flexibility in the relay selection mechanism. We numerically quantify the performance for the proposed schemes and compare the outage probability for fixed and equal number of wireless powered forwarding relays.

In this paper, we study the problem of base station (BS) cooperation in millimeter wave multi- tier heterogeneous cellular networks. In contrast to conventional approaches, where a number of BSs jointly transmit data to a user, we investigate a low-complexity technique that enables the selection of a single BS for transmission. Specifically, a single BS that provides the highest instantaneous signal-to-interference-plus- noise ratio is selected, among the strongest BSs from each tier. By using stochastic geometry tools, we derive closed-form expressions for the coverage probability and the diversity gain of the system by taking into account spatial randomness and blockage effects. Our results show that the proposed scheme achieves full diversity and is appropriate for networks with strict computation constraints. In addition, we study the case where users employ successive interference cancellation (SIC) to further boost the achieved performance; SIC allows the mitigation of strong interference terms from the received signal. The impact of SIC on the coverage probability of the system is studied and closed-form expressions are provided.

In the opportunistic beamforming (OBF) scheme, the access point (AP) acquires channel related feedback from the network's terminals and allocates the orthonormal beams to the users with the best link. This paper investigates the OBF scheme in a wireless powered communication network, where multi-antenna terminals with wireless power harvesting capabilities are randomly deployed around the AP. In this context, the terminals adapt the length of their feedback based on the power harvested with the use of rectenna arrays. We study two fundamental architectures for the combination of the rectenna elements, the direct-current combiner and the radio-frequency combiner, as well as a hybrid architecture of these two. By using higher order statistics and stochastic geometry, the beam outage probability is derived in closed form for all considered architectures.

In this paper, the fundamental limits of simultaneous information and energy transmission in the two-user Gaussian multiple access channel with feedback are fully characterized. A simple achievability scheme based on power-splitting and Ozarow's scheme is shown to be optimal. Finally, the maximum individual information rates and the information sum-capacity that are achievable given a minimum energy rate constraint of b energy-units per channel use at the input of the energy harvester are identified. An interesting conclusion is that for a fixed information transmission rate, feedback can at most double the energy transmission rate with respect to the case without feedback.

In this paper, we propose novel relay selection policies that aim at reducing the average delay by incorporating the buffer size of the relay nodes into the relay selection process. More specifically, we propose two delay-aware protocols that are based on the max - link relay selection protocol. First, a delay-aware only approach while it reduces the delays considerably it starves the buffers and increases the outage probability of the system. Towards this end, we propose a delay- and diversity-aware buffer-aided relay selection policy that aims at reducing the average delay considerably and at the same time maintaining good diversity. The protocols are analyzed by means of Markov Chains and expressions for the outage, throughput and delay are derived. The performance and use of our proposed algorithms is demonstrated via extensive simulations and comparisons.

Motivated by the effects of buildings/obstacles on the performance of high frequency cellular networks, this paper deals with the base station (BS) cooperation in heterogeneous cellular networks with blockages. Our main focus is a joint transmission scenario, where an ideal backhaul network allows a set of randomly located BSs belonging to different network tiers, to cooperate and jointly transmit data. By using concepts from random shape theory, we model the spatial randomness as well as the main characteristics of the blockages (e.g., size, orientation, etc). The outage probability performance of the system is analyzed for two low complexity transmission techniques with different channel state information requirements by using stochastic geometry tools. Our results show that the spatial diversity associated with the BS cooperation is an efficient technique to overcome the degradation effects of blockages and ensure connectivity.

In this paper, we propose a power-efficient approach for information and energy transfer in multiple-input single-output downlink systems. By means of data-aided precoding, we exploit the constructive part of interference for both information decoding and wireless power transfer. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information signal energy and electrical wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference, based on the constructive regions in the signal constellation. Although the resulting problem is nonconvex, several methods are developed for its solution. First, we derive necessary and sufficient conditions for the feasibility of the considered problem. Then we propose second-order cone programming and semi-definite programming algorithms with polynomial complexity that provide upper and lower bounds to the optimal solution and establish the asymptotic optimality of these algorithms when the modulation order and SINR threshold tend to infinity. A practical iterative algorithm is also proposed based on successive linear approximation of the nonconvex terms yielding excellent results. More complex algorithms are also proposed to provide tight upper and lower bounds for benchmarking purposes. Simulation results show significant power savings with the proposed data-aided precoding approach compared to the conventional precoding scheme.

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multiantenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.

This paper studies the problem of relay selection in wireless powered cooperative networks, where spatially random relays are equipped with two-state batteries. In contrast to conventional techniques, the relay nodes can either harvest energy from the source signal (in case of uncharged battery) or attempt to decode and forward it (in case of charged battery). Three relay selection schemes that correspond to different state information requirements and implementation complexities are proposed. The charging/discharging behavior of the battery is modeled as a two-state Markov chain and analytical expressions for the steady-state distribution and the outage probability performance are derived for each relay selection scheme. We prove that energy storage significantly affects the performance of the system and results in a zeroth diversity gain at high signal-to-noise ratios.

We consider a wireless system with a full-duplex (FD) access point (AP) that transmits to a scheduled user in the downlink (DL) channel, while receiving data from an user in the uplink (UL) channel at the same time on the same frequency. In this system, loopback interference (LI) at the AP and inter user interference between the uplink (UL) user and downlink (DL) user can cause performance degradation. In order to characterize the effects of LI and inter user interference, we derive closed-form expressions for the outage probability and achievable sum rate of the system. In addition an asymptotic analysis that reveals insights into the system behavior and performance degradation is presented. Our results indicate that under certain conditions, FD transmissions yield performance gains over half-duplex (HD) mode of operation.

In this paper we propose a power-efficient transfer of information and energy, where we exploit the constructive part of wireless interference as a source of green useful signal power. Rather than suppressing interference as in conventional schemes, we take advantage of constructive interference among users, inherent in the downlink, as a source of both useful information and wireless energy. Specifically, we propose a new precoding design that minimizes the transmit power while guaranteeing the quality of service (QoS) and energy harvesting constraints for generic phase shift keying modulated signals. The QoS constraints are modified to accommodate constructive interference. We derive a sub-optimal solution and a local optimum solution to the precoding optimization problem. The proposed precoding reduces the transmit power compared to conventional schemes, by adapting the constraints to accommodate constructive interference as a source of useful signal power. Our simulation results show significant power savings with the proposed data-aided precoding compared to the conventional precoding.

We consider a dual-hop full-duplex (FD) relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture. Both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, FD relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode has the highest throughput. However, compared to the delay tolerant transmission mode, the throughput gap is negligible. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay tolerant transmission mode depends only on the statistics of the channel, hence, is attractive for practical implementation.

The implementation of full-duplex (FD) radio in wireless communications is a potential approach for achieving higher spectral efficiency. A possible application is its employment in the next generation of cellular networks. However, the performance of large-scale FD multiuser networks is an area mostly unexplored. Most of the related work focuses on the performance analysis of small-scale networks or on loop interference cancellation schemes. In this paper, we derive the outage probability performance of large-scale FD cellular networks in the context of two architectures: two-node and three-node. We show how the performance is affected with respect to the model's parameters and provide a comparison between the two architectures.

This paper deals with the problem of relay selection in wireless powered cooperative networks where spatially random relays are equipped with energy storage devices, e.g., batteries. In contrast to conventional techniques and in order to reduce complexity, the relay nodes can either harvest energy from the source signal (in case of uncharged battery) or attempt to decode and forward it (in case of charged battery). Several relay selection schemes that correspond to different state information requirements and implementation complexities are proposed. The charging/discharging behavior of the battery is modeled as a two-state Markov chain and analytical expressions for the steady-state distribution and the outage probability performance are derived for each relay selection scheme. We prove that energy storage significantly affects the performance of the system and results in a zeroth diversity gain at high signal-to-noise ratios; the convergence floors depend on the steady-state distribution of the battery and are derived in closed form by using appropriate approximations. The proposed relay selection schemes are generalized to a large-scale network with multiple access points (APs), where relays assist the closest AP and suffer from multiuser interference.

This letter deals with the opportunistic beamforming (OBF) scheme for multi-antenna downlink with spatial randomness. In contrast to conventional OBF, the terminals return only 1-bit feedback, which is powered by wireless power transfer through a rectenna array. We study two fundamental topologies for the combination of the rectenna elements; the direct-current combiner and the radio-frequency combiner. The beam outage probability is derived in closed form for both combination schemes, by using high order statistics and stochastic geometry.

In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensure fairness for the downlink users under i) instantaneous channel state information (CSI) at the transmitter, and ii) average CSI. Although the formulated problems are non-convex, we have developed low-complexity polynomial algorithms that yield the optimal solution in both cases considered.

A full-duplex (FD) multiple antenna access point (AP) communicating with single antenna half-duplex (HD) spatially random users to support simultaneous uplink (UL)/downlink (DL) transmissions is investigated. Since FD nodes are inherently constrained by the loopback interference (LI), we study precoding schemes for the AP based on maximum ratio combining (MRC)/maximal ratio transmission (MRT), zero-forcing, and the optimal scheme for UL and DL sum rate maximization using tools from stochastic geometry. In order to shed insights into the systems performance, simple expressions for single antenna/perfect LI cancellation/negligible internode interference cases are also presented. We show that FD precoding at AP improves the UL/DL sum rate and hence a doubling of the performance of the HD mode is achievable. In particular, our results show that these impressive performance gains remain substantially intact even if the LI cancellation is imperfect. Furthermore, relative performance gap between FD and HD modes increases as the number of transmit/receive antennas becomes large, while with the MRC/MRT scheme, increasing the receive antenna number at FD AP, is more beneficial in terms of sum rate than increasing the transmit antenna number.

In this paper, we present a relay selection scheme which combines the spectral efficiency of successive opportunistic relaying with the robustness of single-link relay selection. More specifically, we propose a scheme that minimizes the total energy expenditure per time slot under an inter-relay interference cancellation scheme. The new relay selection policy is analyzed in terms of outage probability and diversity by modeling the evolution of relay buffers as a Markov Chain. We construct the state transition matrix of the Markov Chain and obtain its stationary distribution, which in turn, yields the outage probability. The proposed scheme outperforms relevant state-of-the-art relay selection schemes in terms of throughput, diversity, energy efficiency and average delay, as demonstrated via representative numerical examples.

In this paper, we study the problem of resource allocation for a multiuser orthogonal frequency-division multiple access (OFDMA) downlink with eavesdropping. The considered setup consists of a base station, several users, and a single eavesdropper that intends to wiretap the transmitted message within each OFDMA subchannel. By taking into consideration the existence of the eavesdropper, the base station aims to assign subchannels and allocate the available power in order to optimize the max-min fairness criterion over the users' secrecy rate. The considered problem is a mixed integer nonlinear program. For a fixed subchannel assignment, the optimal power allocation is obtained by developing an algorithm of polynomial computational complexity. In the general case, the problem is investigated from two different perspectives due to its combinatorial nature. In the first, the number of users is equal or higher than the number of subchannels, whereas in the second, the number of users is less than the number of subchannels. In the first case, we provide the optimal solution in polynomial time by transforming the original problem into an assignment one for which there are polynomial time algorithms. In the second case, the secrecy rate formula is linearly approximated and the problem is transformed to a mixed integer linear program, which is solved by a branch-and-bound algorithm. Moreover, optimality is discussed for two particular cases where the available power tends to infinity and zero, respectively. Based on the resulting insights, three heuristic schemes of polynomial complexity are proposed, offering a better balance between performance and complexity. Simulation results demonstrate that each one of these schemes achieves its highest performance at a different power regime of the system.

In this paper, we theoretically investigate a new technique for simultaneous information and power transfer (SWIPT) in multiple-input multiple-output (MIMO) point-to-point with radio frequency energy harvesting capabilities. The proposed technique exploits the spatial decomposition of the MIMO channel and uses the eigenchannels either to convey information or to transfer energy. In order to generalize our study, we consider channel estimation error in the decomposition process and the interference between the eigenchannels. An optimization problem that minimizes the total transmitted power subject to maximum power per eigenchannel, information and energy constraints is formulated as a mixed-integer nonlinear program and solved to optimality using mixed-integer second-order cone programming. A near-optimal mixed-integer linear programming solution is also developed with robust computational performance. A polynomial complexity algorithm is further proposed for the optimal solution of the problem when no maximum power per eigenchannel constraints are imposed. In addition, a low polynomial complexity algorithm is developed for the power allocation problem with a given eigenchannel assignment, as well as a low-complexity heuristic for solving the eigenchannel assignment problem.

Energy harvesting has been recognized as a key technique for improving the performance of energy constrained networks. In this paper, the application of wireless information and power transfer, an emerging concept of energy harvesting, to wireless cooperative networks is considered. Particularly the focus is on a cooperative network in which a source communicates with a destination with the help of relays and the relays are randomly deployed according to homogeneous Poisson point process. According to the available channel state information, three different strategies to use the available relays are studied, and their impact on the outage probability and diversity gain is characterized by applying stochastic geometry. Particularly the developed analytical results demonstrate that the use of energy harvesting relays does not degrade the achievable diversity gain of cooperative protocols, but has a deteriorating effect on the outage performance. Simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

In this paper, we investigate a new technique for simultaneous wireless information and energy transfer in multiple-input multiple-output relay channels. The proposed technique exploits the array configuration at the relay node and uses the antenna elements either for conventional decoding or for rectifying (rectennas). In order to keep the complexity low, a dynamic antenna switching between decoding/rectifying is proposed based on the principles of the generalized selection combiner (GSC); the L strongest paths are allocated for decoding while the remaining channel paths for rectifying (and vice versa). The optimal L as well as the allocation strategy that minimizes the outage probability are investigated via theoretical and numerical results. In addition, two performance bounds that provide the optimal performance without the limitation of GSC are proposed by solving a linear programming and a binary knapsack problem, respectively.

In this work, we study a cooperative network with multiple full-duplex buffer-aided relays. A hybrid cooperative relaying policy is proposed that employs power adaptation and consists of two alternative schemes: (i) full-duplex transmission through the relay which requires the least total power expenditure and loop interference is mitigated through power adaptation; (ii) buffer-aided max - link selection with power adaptation, when full-duplexity is not feasible. Aiming to reduce the overhead of channel state information (CSI) acquisition and processing, we propose a suboptimal distributed method for relay selection, for which the network performance is not degraded significantly. We show that power adaptation offers reduced overhead of CSI acquisition. Numerical results and comparisons with other state-of-the-art relaying schemes are provided and performance evaluation in terms of throughput, power minimization and switching rate, show the benefits of the proposed hybrid scheme.

In this paper, we study a simple X-relay configuration where the shared relay operates in full-duplex (FD) mode. The relay node may have limited spatial degrees of freedom, and as a result, it may not be able to handle both the loop interference and the multiuser interference. Hence, a decision on the precoding scheme is necessitated. It is often the case that the relay does not have the option of real-time switching between different precoding schemes, either due to hardware limitations of the relay or increased complexity of the problem. Hence, we investigate a “static” precoding decision where the relay node decides on its precoding scheme based only on statistical knowledge of the channel conditions. To perform this decision, the behavior of the system is formulated as a Markov chain and the outage probability of the system is derived in a closed-form with the precoding decision as a parameter. The outage probability is minimized by optimally choosing the precoding scheme, using easily verifiable conditions on the statistical knowledge of the channel conditions. Simulations validate the investigated scheme.

This paper deals with the problem of sum rate maximization for a multiuser orthogonal frequency-division multiple access channel with secrecy rate constraints. We consider the case of a multiple-antenna base station (BS) and several single-antenna downlink receivers; a single secure user, a single eavesdropper and several normal users (without secrecy requirements). The eavesdropper intends to wiretap the message of the secure user and the BS aims to protect its transmission by appropriately scheduling normal users and enforcing spatial multiplexing between them and the secure user. A frequency (subchannel) and power allocation problem that aims to maximize the sum rate of the normal users, while a secrecy rate constraint is ensured for the secure user, is formulated. The resulting resource allocation problem is non-convex. Based on the dual problem and some well-defined transformations, we provide an iterative resource allocation algorithm with linear complexity with respect to the number of normal users and subchannels. In addition, two low-complexity solutions that are based on the decoupling of the subchannel and the power allocation subproblems, are investigated. Numerical results are provided to illustrate the performance of all the proposed solutions.

In this paper, we study the problem of resource allocation for a multiuser orthogonal frequency-division multiple access (OFDMA) downlink with eavesdropping. The considered setup consists of a base station, several users and a single eavesdropper that indents to wiretap the transmitted message within each OFDMA subchannel. By taking into consideration the existence of the eavesdropper, the base station aims to assign subchannels and allocate the available power in order to optimize the max-min fairness criterion over the users' secrecy rate. The investigated problem is hard to be solved because of its combinatorial and nonlinear nature. Thus, optimality is discussed for two particular cases where the available power tends to infinity and zero, respectively. The optimal solution is obtained by formulating a mixed integer linear problem in the first case and a series of linear sum assignment problems in the second. In addition, two low-complexity solutions are presented which are based on decoupling the subchannel and the power allocation subproblems. Numerical results are provided to illustrate the performance of the presented solutions.

This paper studies the beamforming design of a multiple-input single-output system with eavesdropping, when a quantized channel feedback is available. We consider two basic cases a) the beamforming vector belongs to the quantization codebook and b) the beamforming design is not limited to the quantization codebook. For the first case, we design two beamforming schemes that incorporate a feedback channel from the eavesdropper, which provides the direction of its physical channel or the direction of its null-space. For the case where the codebook constraint is relaxed, we design an optimal beamformer that requires feedback from both receivers as well as a scheme that transmits towards the quantized null-space of the eavesdropper's channel. The ergodic secrecy rate is derived in closed-form and we show that all schemes suffer from a secrecy rate floor for a constant-size feedback. Simplified asymptotic expressions are proposed and the scaling laws of the feedback, which ensure constant secrecy losses, are provided.

Energy harvesting (EH) from ambient radio-frequency (RF) electromagnetic waves is an efficient solution for fully autonomous and sustainable communication networks. Most of the related works presented in the literature are based on specific (and small-scale) network structures, which although give useful insights on the potential benefits of the RF-EH technology, cannot characterize the performance of general networks. In this paper, we adopt a large-scale approach of the RF-EH technology and we characterize the performance of a network with random number of transmitter-receiver pairs by using stochastic-geometry tools. Specifically, we analyze the outage probability performance and the average harvested energy, when receivers employ power splitting (PS) technique for "simultaneous" information and energy transfer. A non-cooperative scheme, where information/energy are conveyed only via direct links, is firstly considered and the outage performance of the system as well as the average harvested energy are derived in closed form in function of the power splitting. For this protocol, an interesting optimization problem which minimizes the transmitted power under outage probability and harvesting constraints, is formulated and solved in closed form. In addition, we study a cooperative protocol where sources' transmissions are supported by a random number of potential relays that are randomly distributed into the network. In this case, information/energy can be received at each destination via two independent and orthogonal paths (in case of relaying). We characterize both performance metrics, when a selection combining scheme is applied at the receivers and a single relay is randomly selected for cooperative diversity.

In this paper, we investigate a low-complexity technique for simultaneous wireless information and energy transfer in multiple-input multiple-output relay channels. The proposed technique exploits the array configuration at the relay node and uses the antenna elements either for conventional decoding or for rectifying (rectennas). In order to keep the complexity low, a dynamic antenna switching between decoding/rectifying is proposed based on the principles of the generalized selection combiner (GSC); the L strongest paths are allocated for decoding while the remaining channel paths for rectifying (and vice versa). The optimal L as well as the allocation strategy that minimizes the outage probability are investigated via theoretical and numerical results. In addition, two performance bounds that provide the optimal performance without the limitation of GSC are proposed by solving a linear programming and a binary knapsack problem, respectively. The proposed technique is extended to scenarios with multi-user interference, where a zero-forcing receiver is used at the relay node; closed-forms expressions for the outage probability are also derived.

We consider a dual-hop full-duplex relaying system, where the energy constrained relay node is powered by radio frequency signals from the source using the time-switching architecture, both the amplify-and-forward and decode-and-forward relaying protocols are studied. Specifically, we provide an analytical characterization of the achievable throughput of three different communication modes, namely, instantaneous transmission, delay-constrained transmission, and delay tolerant transmission. In addition, the optimal time split is studied for different transmission modes. Our results reveal that, when the time split is optimized, the full-duplex relaying could substantially boost the system throughput compared to the conventional half-duplex relaying architecture for all three transmission modes. In addition, it is shown that the instantaneous transmission mode attains the highest throughput. However, compared to the delay-constrained transmission mode, the throughput gap is rather small. Unlike the instantaneous time split optimization which requires instantaneous channel state information, the optimal time split in the delay-constrained transmission mode depends only on the statistics of the channel, hence, is suitable for practical implementations.

In this paper, the application of wireless information and power transfer to cooperative networks is investigated, where the relays in the network are randomly located and based on the decode-forward strategy. For the scenario with one source-destination pair, three different strategies for using the available relays are studied, and their impact on the outage probability and diversity gain is characterized by applying stochastic geometry. By using the assumptions that the path loss exponent is two and that the relay-destination distances are much larger than the source-relay distances, closed form analytical results can be developed to demonstrate that the use of energy harvesting relays can achieve the same diversity gain as the case with conventional self-powered relays. For the scenario with multiple sources, the relays can be viewed as a type of scarce resource, where the sources compete with each other to get help from the relays. Such a competition is modeled as a coalition formation game, and two distributed game theoretic algorithms are developed based on different payoff functions. Simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

In this paper, we deal with the deployment of full-duplex relaying in amplify-and-forward (AF) cooperative networks with multiple-antenna terminals. In contrast to previous studies, which focus on the spatial mitigation of the loopback interference (LI) at the relay node, a joint precoding/decoding design that maximizes the end-to-end (e2e) performance is investigated. The proposed precoding incorporates rank-1 zero-forcing (ZF) LI suppression at the relay node and is derived in closed-form by solving appropriate optimization problems. In order to further reduce system complexity, the antenna selection (AS) problem for full-duplex AF cooperative systems is discussed. We investigate different AS schemes to select a single transmit antenna at both the source and the relay, as well as a single receive antenna at both the relay and the destination. To facilitate comparison, exact outage probability expressions and asymptotic approximations of the proposed AS schemes are provided. In order to overcome zero-diversity effects associated with the AS operation, a simple power allocation scheme at the relay node is also investigated and its optimal value is analytically derived. Numerical and simulation results show that the joint ZF-based precoding significantly improves e2e performance, while AS schemes are efficient solutions for scenarios with strict computational constraints.

We consider a multiuser multiple-input single-output interference channel where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) for information decoding and b) for battery charging. The minimum required power that supports both the QoS and the RF-EH constraints is formulated as an optimization problem that incorporates the transmitted power and the beamforming design at each transmitter as well as the power splitting ratio at each receiver. We consider both the cases of fixed beamforming and when the beamforming design is incorporated into the optimization problem. For fixed beamforming we study three standard beamforming schemes, the zero-forcing (ZF), the regularized zero-forcing (RZF) and the maximum ratio transmission (MRT); a hybrid scheme, MRT-ZF, comprised of a linear combination of MRT and ZF beamforming is also examined. The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming are developed for MRT, RZF and MRT-ZF beamforming to solve the problem. In addition, the joint-optimization of beamforming and power allocation is studied using semidefinite programming (SDP) with the aid of rank relaxation.

We consider the problem of antenna selection (AS) in full duplex amplify-and-forward relaying. We assume a basic relay system where the source, the relay and the destination, all equipped with multiple antennas. For this full-duplex relay system, we investigate different AS schemes to select a single transmit antenna at both the source and the relay, respectively, as well as a single receive antenna at both the relay and the destination, respectively. To facilitate comparison, exact outage probability expressions and asymptotic approximations of these AS schemes are derived. In order to eliminate the zero-diversity behavior associated with the full-duplex operation, we also propose a simple power allocation (PA) scheme at the relay. In addition, the optimal value of the PA parameter is analytically derived for the considered AS schemes.

This paper deals with a multiple-input single-output (MISO) network where the receivers are characterized by both quality-of-service (QoS) and radio-frequency (RF) energy harvesting (EH) constraints. We consider the power splitting RF-EH technique where each receiver divides the received signal into two parts a) the first part for information decoding and b) the second part for battery charging. The minimum required energy that supports both the QoS and the RF-EH constraints at each receiver is formulated by an optimization problem and is discussed for two standard beamforming designs, the zero-forcing (ZF) and the maximum ratio transmission (MRT). The optimal solution for ZF beamforming is derived in closed-form, while optimization algorithms based on second-order cone programming (SOCP) and Linear Programming (LP) are developed for MRT beamforming to solve the problem. Numerical results indicate that MRT significantly outperforms ZF in terms of transmitted power, as the associated cross-interference becomes beneficial from an EH standpoint, while ZF always ensures the existence of a solution for the optimization problem considered.

In this paper we consider a simple cooperative network consisting of a source, a destination and a cluster of decode-and-forward relays characterized by the half-duplex constraint. At each time-slot the source and (possibly) one of the relays transmit a packet to another relay and the destination, respectively. When the source and a relay transmit simultaneously, inter-relay interference is introduced at the receiving relay. In this work, with the aid of buffers at the relays, we mitigate the detrimental effect of inter-relay interference through either interference cancellation or mitigation. More specifically, we propose the min-power opportunistic relaying protocol that minimizes the total energy expenditure per time slot under an inter-relay interference cancellation scheme. The min-power relay-pair selection scheme, apart from minimizing the energy expenditure, also provides better throughput and lower outage probability than existing works in the literature. The performance of the proposed scheme is demonstrated via illustrative examples and simulations in terms of outage probability and average throughput.

Harvest-use (HU) is an energy harvesting (EH) architecture where the received energy cannot be stored and immediately must be consumed in order to maintain operability. Due to its current limited application interest, this architecture has not yet been examined in the literature and its deployment to communication system is an open problem. This paper deals with the application of HU architecture to communication systems and investigates cooperative protocols where the relay node has HU capabilities. We show that HU relaying introduces a trade-off between EH time and relaying (data communication) time; this trade-off is discussed for two fundamental relaying policies a) Amplify-and-forward (AF) with half-duplex (HD) relaying and b) AF with full-duplex (FD) relaying. The optimal time split is formulated as an optimization problem and an approximation is given in a closed form. Numerical results show that FD outperforms HD and is introduced as an efficient relaying policy for HU cooperative systems.

We employ orthogonal random beamforming (ORBF) for the worst-case multi-user downlink scenario where each user is wiretapped by one eavesdropper. Two opportunistic scheduling techniques that ensure confidentiality by exploiting multi-user diversity are investigated; the first technique (optimal) requires limited feedback of the effective signal-to-interference ratio (SIR) from all the users and the eavesdroppers while the second technique (suboptimal) incorporates SIR knowledge from only the legitimate users. By using extreme value theory, we derive the achievable SIR-based secrecy sum-rate and the associated scaling laws for both scheduling techniques.

We study the diversity unfairness associated with the conventional Tomlinson–Harashima precoding (THP) in multiuser multiple-input multiple-output downlink transmission. A single-retransmission scheme that combines two THP signals at each user with a complementary multi-user suppression order is investigated. For a system with$M$antennas at the transmitter and a single antenna at each user, the proposed scheme provides a diversity order$M+1$for all users and ensures diversity fairness. We study two retransmission policies, where the users either consider only the current received codeword or combine both codewords for decoding. An asymptotic analysis of the outage probability for both THP retransmission schemes is provided. In addition, a power allocation policy that minimizes the outage probability and accommodates the same coding gain at each user is discussed by formulating a geometric optimization problem.

This paper studies the cooperation between a primary system and a cognitive system in a cellular network where the cognitive base station (CBS) relays the primary signal using amplify-and-forward or decode-and-forward protocols, and in return it can transmit its own cognitive signal. While the commonly used half-duplex (HD) assumption may render the cooperation less efficient due to the two orthogonal channel phases employed, we propose that the CBS can work in a full-duplex (FD) mode to improve the system rate region. The problem of interest is to find the achievable primary-cognitive rate region by studying the cognitive rate maximization problem. For both modes, we explicitly consider the CBS transmit imperfections, which lead to the residual self-interference associated with the FD operation mode. We propose closed-form solutions or efficient algorithms to solve the problem when the related residual interference power is non-scalable or scalable with the transmit power. Furthermore, we propose a simple hybrid scheme to select the HD or FD mode based on zero-forcing criterion, and provide insights on the impact of system parameters. Numerical results illustrate significant performance improvement by using the FD mode and the hybrid scheme.

In this paper, a relay selection policy is proposed that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain. The suggested scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and the relay selection decision is based on the strongest available link. Hence the switching occurs dynamically between relay reception and transmission. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

This paper focuses on the relay selection problem in amplify-and-forward (AF) cooperative communication with full-duplex (FD) operation. Different relay selection schemes assuming the availability of different instantaneous information are studied. We consider an optimal relay selection that maximizes the instantaneous FD channel capacity and requires global channel state information (CSI) as well as several sub-optimal relay selection policies that utilize partial CSI knowledge such as a) source-relay and relay-destination links b) loop interference c) source-relay links and loop interference. To facilitate comparison, exact outage probability expressions and asymptotic approximations of these policies that show a zero diversity order are derived. In addition, an optimal relay selection that incorporates an hybrid relaying strategy, which dynamically switches between FD and half-duplexing relaying according to the instantaneous CSI, is also investigated.

A novel cooperative transmission protocol is proposed for multiple access scenarios, where multiple users communicate with a common destination with the assistant of multiple half-duplex relays. The proposed alternative relaying decode-and-forward (ARDF) protocol can achieve the full diversity gain by applying superposition coding at each transmitter, which also utilizes the linear zero-forcing detection at each relay to combat a serious inter-relay interference. Different to the previous uplink protocols, the proposed protocol can exploit the cooperation involving both the relays and sources, which guarantees the proposed scheme to approach the optimal multiple-input singleoutput upper bound even with general inter-relay interference. Analytical and numerical results have been provided to demonstrate the performance of the proposed protocol.

A successful distributed power control algorithm requires only local measurements for updating the power level of a transmitting node, so that eventually all transmitters meet their QoS requirements. Nevertheless, the problem arises when the QoS requirements cannot be achieved for all the users in the network. In this paper, a distributed algorithm for wireless ad hoc networks which is contention-based and makes use of a back off mechanism is proposed. This algorithm aims to eliminate overhead communication, improve fairness, allow nodes to operate asynchronously while establishing some performance level. The performance of the algorithm is evaluated via simulations.

In this paper we study distributed transmission scheduling via power control in wireless ad hoc networks with multiple channels. The target for each node is to manage to be admitted into a channel from the available channels in the network. The aim of this work is twofold: (a) to determine how a wireless node, based on its limited information, will decide which channel to access and (b), to propose a distributed algorithm for each wireless node with which once the channel is chosen a decision is made whether to stay in the channel or not. Here, we propose an algorithm that, if adopted by all the nodes in the network, it converges to a solution that admits most of the wireless nodes in the network, based on limited information only. Simulations in MATLAB justify the good performance of the algorithm.

In this letter, we investigate the effects of network-layer cooperation in a wireless three-node network with energy-harvesting nodes and bursty data traffic. By modelling energy harvesting in each node as a queue (buffer) that stores the received energy, we study the interaction between data and energy queues when only knowledge of the arrival rates is available. The maximum stable throughput (in packets/slot) of the source as well as the required transmitted power for both a non-cooperative and an orthogonal decode-and-forward cooperative schemes are derived in closed-form. We prove that cooperation achieves a higher maximum stable throughout than direct link for scenarios with poor energy arrival rates.

It is the purpose of the note to point out that the Cumulative Distribution Function (CDF) (Eq. (23)) in Appendix A in the paper "Relay Selection for Secure Cooperative Networks with Jamming" by Krikidis et al. (IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5003-5011, Oct. 2009) is not the exact expression but an approximation. We provide the exact solution of the CDF in two forms: one using Beta and hypergeometric functions and the second exploiting a recurrence relationship.

In this paper, we study the relay selection problem for a finite buffer-aided decode-and-forward cooperative wireless network. A relay selection policy that fully exploits the flexibility offered by the buffering ability of the relay nodes in order to maximize the achieved diversity gain is investigated. This new scheme incorporates the instantaneous strength of the wireless links as well as the status of the finite relay buffers and adapts the relay selection decision on the strongest available link by dynamically switching between relay reception and transmission. In order to analyse the new relay selection policy in terms of outage probability and diversity gain, a theoretical framework that models the evolution of the relay buffers as a Markov chain (MC) is introduced. The construction of the state transition matrix and the related steady state of the MC are studied and their impact on the derivation of the outage probability is investigated. We show that the proposed relay selection scheme significantly outperforms conventional relay selection policies for all cases and ensures a diversity gain equal to two times the number of relays for large buffer sizes.

This paper focuses on the relay selection problem in amplify-and-forward (AF) cooperative communication with full-duplex (FD) operation. Different relay selection schemes assuming the availability of different instantaneous information are studied. We consider optimal relay selection that maximizes the instantaneous FD channel capacity and requires global channel state information (CSI) as well as several sub-optimal relay selection policies that utilize partial CSI knowledge such as a) source-relay and relay-destination links b) loop interference c) source-relay links and loop interference. To facilitate comparison, exact outage probability expressions and asymptotic approximations of these policies that show a zero diversity order are derived. In addition, an optimal relay selection procedure that incorporates a hybrid relaying strategy, which dynamically switches between FD and half-duplex relaying according to the instantaneous CSI, is also investigated.

In this paper, we study full-duplex (FD) operation from a diversity perspective and investigate several protocols that extract diversity gains over a block fading channel. The investigated approach introduces a block-by-block transmission and requires data codewords that span several independent realizations of channel fading. This fundamental approach ensures a diversity gain at least equal to one (i.e., it does not suffer from error floors) and is employed to different relaying strategies without loop interference cancellation (LIC) and with imperfect LIC: a) Amplify-and-Forward (AF) without LIC, b) AF with imperfect LIC and c) Decode-and-Forward with imperfect LIC. The proposed protocols are analyzed from a diversity-multiplexing tradeoff (DMT) standpoint and practical universal codes that achieve the DMT of the proposed schemes are also presented. We show that AF without LIC ensures a full time diversity independently on the statistics of the loop interference, and it is introduced as a general FD-based relaying scheme. In addition, we demonstrate that imperfect LIC is not efficient for cases with strong residual loop interference and limits the diversity gain to one.

This paper studies the combined problem of base station location and optimal power allocation, in order to optimize the energy efficiency of a cellular wireless network. Recent work has suggested that moving from a network of a small number of high power macrocells to a larger number of smaller microcells may improve the energy efficiency of the network. This paper investigates techniques to optimize the number of base stations and their locations, in order to minimize energy consumption. An important contribution of the paper is that it takes into account non-uniform user distributions across the coverage area, which is likely to be encountered in practice. The problem is solved using approaches from optimization theory that deal with the facility location problem. Stochastic programming techniques are used to deal with the expected user distributions. An example scenario is presented to illustrate how the technique works and the potential performance gains that can be achieved.

In this paper, we investigate some cross-layer cooperative strategies for cognitive Time-Division Multiple-Access relay channels with bursty arrivals. The proposed schemes adopt an advanced physical (PHY) layer cooperation and an “intelligent” cognitive network-layer cooperation in order to improve the stable throughput region of the system. In contrast to previously reported work, where relaying is only enabled on periods of source silence, here, we incorporate a Dynamic Decode-and-Forward (DDF) policy which allows relaying assistance also during the source transmission. The enhancement of cognitive relaying with DDF provides more cooperative opportunities which results in faster emptying of the user queues and higher stable throughput compared to the conventional approaches. In addition to this PHY-layer relaying, the cognitive cooperation is supported by an adaptive/non-adaptive superposition scheme which allows the relay node to simultaneously forward packets from different users. We demonstrate that superposition can further increase the transmission opportunities and significantly improve the stable throughput region. The proposed schemes are studied from a networking perspective and their advantages are shown through both theoretical results and computer simulations.

While various cooperative protocols have been developed for the simple scenario with one source-destination pair, most of them still suffer a significant loss compared with the optimal multiple-input single-output (MISO) upper bound. The diversity-multiplexing tradeoff will be used as the criterion for performance evaluation. In this paper, we propose two new half-duplex decode-forward cooperative transmission protocols, whose performance can approach the optimal MISO bound, and achieve a better diversity-multiplexing tradeoff when compared with existing cooperative protocols, particularly for large multiplexing gains. Firstly, a simple protocol of cooperative transmission is devised by combining opportunistic strategies with non-orthogonal transmission. When the number of relays is large, the proposed opportunistic decode-forward cooperative protocol can approach the optimal MISO upper bound. Due to the inter-relay interference constraint, each relay can only be used once, which limits the achievable diversity gain. Such an observation motivates our second transmission protocol which can further push the performance of cooperative transmission close to the optimal upper bound. Secondly, a relaying protocol is proposed for a four-node network where two multiple-antenna relays alternately forward messages to the destination when they can successfully cancel the inter-relay interference using the zero forcing method. Monte-Carlo simulation has also been provided to demonstrate the performance of both protocols and comparable ones.

This study deals with opportunistic relay selection in cooperative networks with secrecy constraints, where an eavesdropper node tries to overhead the source message. Previously reported relay selection techniques are optimised for non-eavesdropper environments and cannot ensure security. Two new opportunistic relay selection techniques, which incorporate the quality of the relay-eavesdropper links and take into account secrecy rate issues, are investigated. The first scheme assumes an instantaneous knowledge of the eavesdropper channels and maximises the achievable secrecy rate. The second one assumes an average knowledge of the eavesdropper channels and is a suboptimal selection solution appropriate for practical applications. Both schemes are analysed in terms of secrecy outage probability and their enhancements against conventional opportunistic selection policies are validated via numerical and theoretical results.

This letter analyzes the impact, from a network-layer perspective, of having a single cognitive radio transmitter-receiver pair share the spectrum with multiple primary users wishing to communicate to a single receiver in a multi-access channel (MAC). In contrast to previous work which assumes a time division multi-access strategy, here, we assume the set of primary users simultaneously access the channel to deliver their packets to a common destination. We derive the symmetric stable throughput regions, consisting of maximal arrival rates for primary and secondary (or cognitive radio) users under two investigated protocols. The first protocol is a conventional MAC scheme where the primary and secondary nodes operate independenly. The second protocol corresponds to a multi-access relay channel (MARC) which exploits user cooperation between primary and secondary nodes. We prove that cooperation is beneficial in the considered MARC as it enables higher throughputs for both primary and secondary users.

In this paper, we deal with the performance of nonorthogonal Amplify-and-Forward protocols over block-fading channels (BFNAF), where the source retransmits the same data during cooperation in order to increase spatial diversity. Despite the additional diversity degree that is offered by the channel, channel inversion amplification schemes are not always able to increase the diversity gain of the system due to the high correlation that can result in the two simultaneous transmissions. It is proven that this diversity loss is related to a poor source-relay link that via the relay amplification process affects the third available diversity branch corresponding to the second source transmission. In order to resolve this problem, we integrate a fixed gain amplification factor in the BFNAF scheme which efficiently uses the additional diversity degree of the channel and recovers the diversity loss associated with channel inversion schemes. This new BFNAF scheme offers spatial diversity benefits with high reliability and is an appropriate solution for Amplify-and- Forward scenarios in which the source-relay link is not stronger than the relay-destination link. The diversity analysis is based on some well-defined capacity bounds which follow the diversity order of the true capacity and enable theoretical derivations. The enhancements of the proposed schemes are verified through both theoretical results and computer simulations.

Two driving goals for cognitive radio (CR) techniques are maximizing spectrum utilization and avoiding interference to primary users. In this paper, we deal with the CR concept for legacy primary links optimized for non-interference (singe user) environments. In this type of network, primary destinations are not able to deal with possible interference and a missed cognitive detection significantly reduces the system performance. The enhancement of the primary network with cooperative diversity in addition to the well-known diversity gain for the primary link improves the sensing ability of the system and protects the primary user from possible interference. Both Amplify-Forward and Decode-Forward (DF) cooperative schemes are studied for the problem under consideration and a new DF policy which introduces a cognitive relay behavior is investigated. The proposed technique provides CR benefits without complicated network modifications and seems to be an attractive solution for future legacy networks with flexibility limitations.

This paper proposes a simple architecture for half-duplex cooperative systems which use amplify-and-forward (AF) as a relay strategy and one-dimensional modulations for source messages. The proposed solution uses the two orthogonal channels of quadrature modulation in order to allow a node to behave simultaneously as a source and a relay. We prove that the new scheme has similar performance to the conventional AF approach, and avoids error propagation problems of previously reported AF superposition schemes. The proposed technique is suitable for applications with low spectral efficiencies and practical adaptive systems where real modulations are implemented based on a quadrature modulation core.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput.

In this paper, an amplify-and-forward (AF) cooperative strategy in interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on a consideration of the interference term, two new selection criteria suitable for different system set-ups are proposed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented.

Using multiple antennas in coexisting radio systems can cancel or control the co-channel interference (CCI), hence improve the overall spectrum efficiency. However, one of the drawbacks of such techniques is the hardware complexity. Antenna selection technology may reduce such costs while partly keeping the advantages of multiple antennas. In this paper, we focus on the downlink of a linear precoding multiple input single output (MISO) cognitive radio (CR) system and apply antenna selection techniques in the transmitter side of the secondary system. We discuss the optimal, maximum norm, and our proposed subset optimal selection strategy, which has a lower computational complexity and reduces feedback information compared to the optimal method. The simulation results show that our proposed methods achieve near optimal performance in terms of SNR.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.

This paper deals with relay selection in cooperative networks with secrecy constraints. The proposed scheme enables an opportunistic selection of two relay nodes to increase security against eavesdroppers. The first relay operates as a conventional mode and assists a source to deliver its data to a destination via a decode-and-forward strategy. The second relay is used in order to create intentional interference at the eavesdropper nodes. The proposed selection technique jointly protects the primary destination against interference and eavesdropping and jams the reception of the eavesdropper. The new approach is analyzed for different complexity requirements based on instantaneous and average knowledge of the eavesdropper channels. In addition an investigation of an hybrid security scheme which switches between jamming and non-jamming protection is discussed in the paper. It is proven that an appropriate application of these two modes further improves security. The enhancements of the proposed selection techniques are demonstrated analytically and with simulation results.

In this paper, an amplify-and-forward (AF) cooperative strategy for interference limited networks is considered. In contrast to previously reported work, where the effect of interference is ignored, the effect of multi-user interference in AF schemes is analyzed. It is shown that the interference changes the statistical description of the conventional AF protocol and a statistical expression is subsequently derived. Asymptotic analysis of the expression shows that interference limits the diversity gain of the system and the related channel capacity is bounded by a stationary point. In addition, it is proven that previously proposed relay selection criteria for multi-relay scenarios become inefficient in the presence of interference. Based on consideration of the interference term, two extensions to the conventional max-min selection scheme suitable for different system setups are proposed. The extensions investigated are appropriate for legacy architectures with limitations on their flexibility where the max-min operation is pre-designed. A theoretical framework for selecting when to apply the proposed selection criteria is also presented. The algorithm investigated is based on some welldefined capacity approximations and incorporates the outage probabilities averaged over the fading statistics. Analytical results and simulation studies reveal enhancements of the proposed algorithm.

In this paper, we deal with the amplify-and-forward (AF) cooperative strategy in slot-based block-fading environments. In contrast with previous schemes which assume a constant channel during the cooperative frame (several slots), here, we relax this constraint and assume a classical quasi-static block-fading channel (constant for one slot). This additional degree of freedom modifies the behavior of the conventional non-orthogonal (NAF) schemes and generates a new block-fading NAF (BFNAF) protocol where the source can usefully retransmit the same data during the cooperative slot. This new protocol is interesting at low spectral efficiencies where diversity against fading is more important. Another issue which is discussed throughout the paper is the optimal power allocation of the investigated schemes. The proposed power allocation strategy uses as an optimization criterion well-defined asymptotic expressions of the outage probabilities, averaged over the fading statistics.

In this paper, a superposition-coded concurrent decode-and-forward (DF) relaying protocol is presented. A specific scenario, where the inter-relay channel is sufficiently strong, is considered. Assuming perfect source-relay transmissions, the proposed scheme further improves the diversity performance of previously proposed repetition-coded concurrent DF relaying, in which the advantage of the inter-relay interference is not fully extracted.

This paper deals with a cross-layer approach for cooperative diversity networks which use a combination of Amplify-and-Forward (AF) and Decode-and-Forward (DF) as a relaying strategy. Based on a well-selected ad-hoc configuration, the proposed approach combines the AF diversity concept with a simultaneous optimization of Physical, Network and Multiple Access Control layers. The considered optimization problem requires an appropriate distribution of three roles among the network nodes which are the diversity-relays (AF concept), the intermediate-router (DF and routing) and the destination (scheduling). The proposed role assignment is based on the instantaneous channel conditions between the links and jointly supports performance optimization and a long-term fairness concept. In order to minimize the required complexity, a partial and quantized channel feedback is also proposed. The proposed cross-layer solution is compared with conventional approaches by computer simulations and theoretical studies, and we show that it achieves an efficient performance-complexity trade-off.

Automatic repeat request (ARQ) retransmission in user cooperative networks, which use amplify-and-forward as a relaying strategy is dealt with. In contrast to the conventional environments, where the source responds in a retransmission demand, in cooperative systems this requirement can also be satisfied by a relay node. In addition, if a relay node can be selected according to the instantaneous channel conditions, the source is not always the optimal responder. The problem under consideration here is to find the retransmission combination which optimises the performance under a given delay quality-of-service constraint. It will be shown that the optimal combination depends on the number of available relays, the total number of retransmissions and the average signal-to-noise ratio. We provide an analytical framework for the definition of the optimal combination in function of these three system parameters. Since the practical ad hoc networks are not centralised and do not have an external control, a distributed truncated ARQ protocol is further proposed to apply the decided retransmission combination.

In this paper a reconfigurable implementation for the data detection in high data rate direct sequence code division multiple access (DS-CDMA) connections is presented. Due to some well defined real time system parameters, traditional implementations of this detector which deal with the mean operational case are not optimal. They consume a lot of power in the favorable operational cases and they loose a diversity gain in the worst cases. Thanks to reconfigurability, a detector can adapt its configuration to each operational condition. Reconfigurability can perform jointly performance and computational power optimization. Implementation issues have shown that the traditional DSPs provide a high degree of flexibility but they are inefficient for the high rate processing constraints involved to DS-CDMA detection with low spreading factors (SF). A reconfigurable hardware implementation is proposed and analyzed which besides its performance capabilities provides a minimum area overhead.

Recent advances in the reconfigurability concept have now made it possible to design blocks of the transceiver chain which can change its functionality in real time. Reconfigurability in general can provide performance improvements and reduction of the battery power consumption. In this paper we focus on the well-known Rake principle and we propose a reconfigurable receiver structure, able to support the required processing for a RAKE combination and a one-stage inter-path interference canceller (IC). This approach allows a more efficient use of the constraint calculation power of the reception block and improves the system performance. Through theoretical analysis and simulation, we also investigate a controller which can supervise, at run-time, switching between the two possible configurations.