Stacked intelligent metasurfaces (SIMs) have recently emerged as an effective solution for next-generation wireless networks. A SIM comprises multiple metasurface layers that enable signal processing directly in the wave domain. Moreover, recent advances in flexible metamaterials have highlighted the potential of flexible intelligent metasurfaces (FIMs), which can be physically morphed to enhance communication performance. In this paper, we propose a stacked flexible intelligent metasurface (SFIM)-based communication system for the first time, where each metasurface layer is deformable to improve the system's performance. We first present the system model, including the transmit and receive signal models as well as the channel model, and then formulate an optimization problem to maximize the system sum rate under constraints on the transmit power budget, morphing distance, and the unit-modulus condition of the meta-atom responses. To solve this problem, we develop an alternating optimization framework based on the gradient projection method. Simulation results demonstrate that the proposed SFIM-based system achieves significant performance gains compared to its rigid SIM counterpart.

This paper investigates a near-field covert communication system enhanced by an absorptive reconfigurable intelligent surface (ARIS) and a fluid antenna system (FAS), enabling covert transmission to arbitrary receiver locations. By generating near-field spherical waves via large-scale antenna arrays at Alice and ARIS, covert transmission to Bob is enabled while evading detection by Willie. We jointly optimize Alice’s hybrid precoding, ARIS reflection coefficients, and Bob’s active port selection to maximize the worst-case covert transmission rate. We begin by evaluating ARIS’s suitability versus conventional RIS. We demonstrate the asymptotic orthogonality of near-field beam-focusing vectors in the 3D domain for uniform planar arrays, and characterize the beam-focusing behavior in cascaded ARIS-enabled covert transmissions. Additionally, we reveal the channel gain improvement owing to FAS over traditional antenna systems. To solve the coupled non-convex problem, we propose a low-complexity block coordinate descent algorithm. It incorporates Fibonacci search for hybrid precoding, three complexity-performance trade-off strategies for reflection coefficients optimization, and both exhaustive search and linear conic relaxation for active port selection. Finally, we recover precoding via an alternating minimization scheme. Numerical results show that (i) significant improvement of covert transmission is achieved only with both ARIS and FAS, when Bob and Willie are co-located; (ii) the proposed algorithm outperforms near-field and far-field beam alignment schemes without ARIS, as well as beam focusing of full-map zeroing with ARIS, when Bob and Willie share the same reception direction.

The growing demand for large artificial intelligence model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications. In particular, edge-device co-inference, which jointly executes LAIM inference across edge devices and servers, has emerged as a promising strategy for resource-efficient LAIM execution in wireless networks. In this paper, we investigate a pruning-aware LAIM co-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment. For analysis, we first prove that the LAIM output distortion is upper bounded by its parameter distortion. Then, we derive a lower bound on the parameter distortion via rate-distortion theory, analytically capturing the relationship between pruning ratio and co-inference performance. Next, based on the analytical results, we formulate an LAIM co-inference distortion bound minimization problem by jointly optimizing the pruning ratio, split point, transmit power, and computation frequency under system latency, energy, and available resource constraints. Moreover, we propose an efficient algorithm to tackle the considered highly non-convex problem. Finally, extensive experimental results demonstrate the effectiveness of the proposed design. In particular, model parameter distortion is shown to provide a reliable bound on output distortion. Also, the proposed joint design achieves superior performance in balancing trade-offs among inference performance, system latency, and energy consumption compared with various benchmark schemes.

Traditional covert ambient backscatter communication (AmBC) systems suffer from a fundamental throughput limitation governed by the square root law (SRL), restricting reliable covert transmission toO(√n) bits overnchannel uses. To overcome this limitation, we introduce a non-colluding replay node that retransmits ambient radio frequency (RF) signals with randomized power, significantly increasing channel uncertainty faced by an adversarial warden (Willie) while preserving compatibility with low-power AmBC architectures. Through rigorous theoretical analysis, we demonstrate that this approach enables linear scaling of covert throughput without necessitating power reduction or prior knowledge of ambient RF signal characteristics. Furthermore, it guarantees that Willie’s total detection error probability can be driven arbitrarily close to 1, specificallyPFA+PMD= 1 − ϵ for any ϵ > 0, simultaneously achieving an arbitrarily low decoding error probability at the legitimate receiver (Bob). Unlike conventional jamming-based solutions requiring stringent synchronization or complex multi-antenna configurations, our replay mechanism operates independently from covert communication participants, substantially simplifying the decoding architecture for the legitimate receiver and reducing synchronization overhead. By increasing the ambient signal power uncertainty, the proposed architecture provides a robust, scalable framework suitable for high-rate covert communication scenarios in IoT and privacy-sensitive applications, achieving an effective balance among covertness, energy efficiency, and system robustness.

Reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) is emerging as a key enabler for sixth-generation (6G) wireless systems, unifying communication, sensing, and control within a reconfigurable electromagnetic (EM) environment. This article presents a comprehensive review that integrates theoretical foundations, signal processing methodologies, and experimental implementations of RIS-ISAC. It first summarizes core principles of channel modeling, waveform design, and sensing parameter estimation across near-field, broadband, and multihop propagation regimes. Building on these foundations, the article surveys advances in hardware architectures, testbeds, and prototype demonstrations spanning sub-6 GHz to millimeter-wave and terahertz bands. Cross-cutting insights are drawn on system tradeoffs, performance bounds, and learning-aided adaptation. Finally, unified benchmarking metrics and open challenges are identified, covering synchronization, scalability, and standardization, to guide the evolution of RIS-ISAC from conceptual frameworks to practical 6G network deployments.

This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two nearly-passive reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmitter, and one in the immediate vicinity of the receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is converges to a first-order optimal point of the maximization problem.Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. A numerical performance analysis shows, in particular, that the considered architecture can provide significant energy efficiency gains compared to fully digital beamforming architectures.

This paper examines the number of communication modes, that is, the degrees of freedom (DoF), in a wireless line-of-sight channel comprising a small continuous linear intelligent antenna array in the near field of a large one. The framework allows for any orientations between the arrays and any positions in a two-dimensional space, assuming that the transmitting array is placed at the origin. Therefore, apart from the length of the two continuous arrays, four key parameters determine the DoF and are hence considered in the analysis: the Cartesian coordinates of the center of the receiving array and two angles that model the rotation of each array around its center. The paper starts with the calculation of thedeterministicDoF for a generic geometric setting, which extends beyond the widely studied paraxial case. Subsequently, a stochastic geometry framework is proposed to study thestatisticalDoF, as a first step towards the investigation of system-level performance in near field networks. Numerical results applied to millimeter wave networks reveal the large number of DoF provided by near-field communications and unveil key system-level insights. A comparison of the proposed method with the singular value decomposition-based method is illustrated to validate the proposed approach.

Reconfigurable intelligent surface (RIS) technology has gained widespread recognition for its ability to significantly enhance communication performance between base stations (BS) and users in blind spot areas. This paper investigates the energy efficiency (EE) of an RIS-assisted multi-cell communication system, incorporating a measurement-based empirical RIS power consumption model. Exploiting only statistical channel state information (CSI), we aim to maximize the overall EE of the system. An alternating optimization (AO) algorithm is proposed for joint optimization of the transmit beamforming vectors at the BSs and the RIS phase shift matrix. To circumvent the challenges arising from the discrete relationship between the power consumption of positive-intrinsic-negative (PIN) diode-based reflecting elements and their phase shifts, we introduce a continuous function to approximate the relationship. With this approximation, the complex circle manifold (CCM) technique is applied to optimize the continuous RIS phase shifts, which are then quantized within a discrete set. Then, to further reduce the complexity of the AO algorithm, a sub-optimal algorithm is proposed. In this algorithm, the statistical maximum ratio transmission beamforming is employed at each BS, which decouples the design of BS beamforming vectors from the design of BS transmit power and RIS phase shifts. Given the BS beamforming vectors, the BS transmit power and RIS phase shifts are then designed using the quadratic transformation and CCM techniques. Simulation results show that the proposed algorithm achieves near-optimal EE while offering guidance on PIN diode encoding to balance phase resolution and power consumption in practical RIS design.

This paper explores the application of Temporal Fusion Transformers (TFTs) to target recognition and multi-horizon tracking, with a particular emphasis on enhancing Direction of Arrival (DoA) estimation in complex signal environments. By leveraging the advanced temporal modeling capabilities and dynamic feature selection mechanisms inherent to the TFT architecture, we adapt and optimize the model for improved performance in antenna signal processing, leading to a more accurate and robust estimation framework. Our analysis underscores the architectural strengths of TFTs, especially their capacity to model long-range temporal dependencies and to dynamically prioritize sensor-derived features—capabilities that are essential for precise target tracking and recognition. We train and evaluate the model under diverse conditions, including multi-horizon forecasting and varying prediction latencies across models. The proposed method is benchmarked against state-of-the-art algorithms, including recurrent neural networks and standard transformer-based models. Extensive experimental results demonstrate that the TFT-based approach consistently outperforms existing techniques in forecasting accuracy, multi-target recognition, and computational complexity, highlighting its potential to advance the field of DoA estimation.

Integrated sensing and communication (ISAC), near-field communication, and extremely large-scale antenna arrays are transformative and indispensable technologies that revolutionize 6G communications. This work proposes a monostatic ISAC architecture for near-field systems employing extremely large-scale uniform planar arrays (XL-UPAs). A theoretical analysis of multi-user interference and sensing-over-communication interference is provided for the considered near-field ISAC system, where the sensing and communication channels are modeled using a non-uniform spherical wave (NUSW) model. In addition to communication, the monostatic ISAC system must ensure simultaneous target detection with a constant probability of false alarm, independent of noise statistics. A grid-based two-dimensional constant false alarm rate (2D-CFAR) detection scheme is proposed to address the issue. Furthermore, an analytical expression is derived to quantify the achievable range resolution in near-field ISAC systems.

This work provides a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) communication system in the presence of phase errors. We first derive the exact expressions for the first and second moments of the maximum signal-to-noise ratio at the receiver. We then use these moments to obtain highly accurate and insightful expressions for the outage probability, average symbol error probability (SEP), and ergodic rate (ER) for various phase error distributions, leveraging the moment-matching approach. Furthermore, we present closed-form asymptotic expressions for these performance metrics. The derived expressions are then leveraged to gain important insights into the impact of phase errors on the scaling laws of diversity order, coding gain, and ER. The numerical results show that the presence of phase errors can significantly degrade the system’s diversity order and coding gain. Specifically, we show that, in the absence of phase errors, the diversity order scales linearly with the number of RIS elements, while the diversity order reduces to unity in the presence of uniformly distributed phase errors. Finally, the analytical findings are demonstrated through extensive simulation results.

This paper investigates uplink reconfigurable intelligent surface (RIS)-assisted massive random access (RA) in multiuser cellular networks with heterogeneous user distances. The goal is to enhance media access control (MAC)-layer throughput under practical signaling and channel constraints. Conventional RIS configurations using full-array probing incur excessive overhead, especially with large numbers of reflective elements (REs). To address this, we propose an overhead-aware, multi-phase uplink RA framework that integrates adaptive RIS subarray grouping with scalable multiuser scheduling for dynamic user activity. The access optimization is formulated as a sequential decision process, from which an optimal threshold-based policy with closed-form structure is derived. Building on this, an opportunistic RA algorithm is developed to jointly optimize user scheduling, subarray configuration, and access timing with low online complexity that scales with RIS resolution, subchannel count, and user density. Numerical simulations, supported by analytical results, demonstrate significant throughput gains over existing schemes—including full-array, fixed-grouping, and both orthogonal and non-orthogonal multiple access methods—across varying coherence times, RIS sizes, and user distributions. The robustness of the proposed strategy is further verified under imperfect CSI conditions, confirming its suitability for large-scale, dynamic cellular deployments. The proposed framework is further shown to remain effective under practical impairments such as spatial correlation and finite-resolution RIS phase control.

Over-the-air computation (AirComp) enables swift wireless data aggregation by leveraging the superposition property of multiple-access channels (MAC), making it essential for the seamless integration of communication and computing in future networks. Meanwhile, fluid antenna systems (FAS) offer dynamic spatial degrees of freedom (DoF) by reconfiguring antenna positions, thus enhancing adaptability under varying channel conditions. This paper investigates the integration of FAS into a communication and AirComp coexistence framework. We aim to jointly optimize the transceiver beamforming vectors and the antenna positioning vector (APV) to minimize the computation distortion while ensuring reliable cellular communication performance. To tackle this highly non-convex problem, we develop an efficient joint learning-optimization framework. Specifically, we propose a neural network (NN) framework with a dedicated surrogate loss function design to infer optimal APV based on multi-path channel conditions, while an alternating optimization (AO) method is developed to find a locally optimal solution of transceivers by iteratively optimizing each variables with the others being fixed. Besides, to provide analytical tractability and benchmark insight, the APV design problem is relaxed and transformed into a tractable quadratically constrained quadratic program (QCQP) by successive convex approximation (SCA) as a special case under line-of-sight (LoS) channels, which reveals the performance bounds and convergence properties of the system. Numerical results show that proposed method significantly improves the system performance compared with traditional fixed-position antenna (FPA) as well as various benchmark schemes with remarkable generalization capabilities across diverse channel conditions.

This work addresses the problem of secrecy energy efficiency (SEE) maximization in RIS-aided wireless networks. The use of active and nearly-passive RISs are compared and their trade-off in terms of SEE is analyzed. Considering both perfect and statistical channel state information, two SEE maximization algorithms are developed to optimize the transmit powers of the mobile users, the RIS reflection coefficients, and the base station receive filters. Numerical results quantify the trade-off between active and nearly-passive RISs in terms of SEE, with active RISs yielding worse SEE values as the static power consumed by each reflecting element increases.

Reconfigurable Intelligent Surfaces (RIS) enable dynamic electromagnetic control for 6G networks, but existing control schemes lack responsiveness to fast-varying network conditions, limiting their applicability for ultra-reliable low latency communications. This work address uplink delay minimization in multi-RIS scenarios with heterogeneous per-user latency and reliability demands. We propose Delay-Aware RIS Orchestrator (DARIO), an O-RAN-compliant framework that dynamically assigns RIS devices to users within short time windows, adapting to traffic fluctuations to meet per-user delay and reliability targets. DARIO relies on a novel Stochastic Network Calculus (SNC) model to analytically estimate the delay bound for each possible user–RIS assignment under specific traffic and service dynamics. These estimations are used by DARIO to formulate a Nonlinear Integer Program (NIP), for which an online heuristic provides near-optimal performance with low computational overhead. Extensive evaluations with simulations and real traffic traces show consistent delay reductions up to 95.7% under high load or RIS availability.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology to seamlessly charge low-power devices, but its low end-to-end power transfer efficiency remains a critical challenge. To address the latter, low-cost transmit/radiating architectures, e.g., based on reconfigurable intelligent surfaces (RISs), have shown great potential. Beyond diagonal (BD) RIS is a novel branch of RIS offering enhanced performance over traditional diagonal RIS (D-RIS) in wireless communications, but its potential gains in RF-WPT remain unexplored. Motivated by this, we analyze a BD-RIS-assisted single-antenna RF-WPT system to charge a single rectifier, and formulate a joint beamforming and multi-carrier waveform optimization problem aiming to maximize the harvested power. We propose two solutions relying on semi-definite programming for fully connected BD-RIS, a successive convex approximation (SCA)-based beamforming approach, and an efficient low-complexity iterative method relying on SCA. Numerical results show that the proposed algorithms converge and that adding transmit sub-carriers or RIS elements improves the harvesting performance. We show that the transmit power budget impacts the relative power allocation among different sub-carriers depending on the rectifier’s operating regime, while BD-RIS shapes the cascade channel differently for frequency-selective and flat scenarios. Finally, we verify by simulation that BD-RIS and D-RIS achieve the same performance under pure far-field line-of-sight conditions (in the absence of mutual coupling). Meanwhile, BD-RIS outperforms D-RIS as the non-line-of-sight components of the channel become dominant.

This paper investigates a secure aerial reconfigurable intelligent surface (A-RIS) communication system, where user mobility, imperfect channel state information (CSI), and RIS phase errors induced by unmanned aerial vehicle (UAV) jitter significantly degrade performance. To address these challenges, we formulate a joint optimization problem for UAV trajectory, base station (BS) we propose abeamforming, and A-RIS beamforming to maximize the minimum secrecy energy efficiency (SEE), subject to constraints on user secrecy rates and UAV energy efficiency. To solve this highly non-convex problem, we propose a novel reinforcement learning framework termed IA-CSORL based on the twin-twin-delayed deep deterministic policy gradient (TTD3) architecture, which incorporates two novel modules. Specifically, we develop the phase-aware relativistic adaptive descent (PRAD) algorithm is proposed, which embeds the learning process into a conformal Hamiltonian system. By integrating gradient-based phase error correction and adaptive momentum adjustment, PRAD effectively counteracts phase noise and stabilizes training. Furthermore, we design an environment-state interactive attention (ESIA) mechanism to dynamically fuse UAV positioning and environmental features, enhancing state representation and deployment accuracy. Numerical results demonstrate that IA-CSORL significantly outperforms existing RL baselines in terms of both robustness and convergence performance. Moreover, IA-CSORL achieves superior beamforming accuracy under phase errors and CSI imperfections and provides a better trade-off between sum secrecy rate (SSR) and SEE, with performance gains becoming more significant as the number of RIS elements increases.

Low Earth Orbit (LEO) satellite communications have emerged as a promising paradigm for achieving ubiquitous coverage, driving the evolution of space-ground integrated networks (SGINs). The cell-free (CF) architecture has attracted significant attention in SGINs as the terrestrial segment for its potential to enhance capacity and connectivity. However, deploying CF necessitates numerous access points (APs), resulting in a prohibitive cost. To this end, we propose reconfigurable intelligent surface (RIS)- and simultaneous transmitting and reflecting (STAR)-RIS-assisted CF systems for SGINs, where part of the APs is replaced with cost-efficient RISs and STAR-RISs. Non-orthogonal multiple access (NOMA) is incorporated to improve connectivity under limited spectrum. We formulate transmit power minimization problems for both RIS- and STAR-RIS-assisted CF-NOMA in SGINs, jointly optimizing the active beamforming vectors of the satellite and APs, as well as the discrete passive beamforming (DPB) vectors of RISs/STAR-RISs. For the RIS-assisted scenario, a semi-definite programming (SDP)-based method is proposed to optimize the active beamforming vectors, while an enhanced integer linear programming (ILP) method is proposed to obtain the optimal DPB of RISs. To reduce complexity, we develop a low-complexity penalty-based SDP (PB-SDP) algorithm that achieves near-optimal DPB solutions. For the STAR-RIS-assisted scheme, both independent and coupled DPB for transmission and reflection are optimized alone with the active beamforming vectors. Numerical results demonstrate that: 1) The proposed systems outperform cell-based systems and heuristic optimization algorithms in terms of transmit power consumption; 2) The proposed PB-SDP algorithm achieves near-optimal performance with reduced complexity; 3) It is shown that DBP with 3 quantization bits achieves performance comparable to continuous passive beamforming (CPB) in both RIS- and STAR-RIS-assisted systems; 4) Also, it is shown that beyond a certain number of APs, further increasing the APs yields only limited transmit power consumption gains under a fixed total number of antennas.

Integrated sensing and communication (ISAC) has emerged as a promising technology for sixth-generation (6G) communication networks. At the same time, ensuring the privacy of targets in ISAC is important in contexts where a malicious sensor is present. In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted ISAC system designed to protect a sensing region against an adversarial detector (AD), where the base station (BS) has imperfect knowledge of the AD’s location. The RIS consists of both reflecting and absorptive elements (the latter serving as sensing elements), which can be adaptively reconfigured to meet system requirements. Specifically, the system is designed to maximize the jamming power from the BS to the AD by jointly optimizing the transmit beamformer at the BS, the RIS phase-shift matrix, the receive beamformer at the RIS, and the allocation between reflecting and absorptive elements at the RIS while ensuring a minimum sensing signal-to-interference-plus-noise ratio (SINR) at sample points within the sensing region, as well as a minimum communication SINR for each user. To address this challenging optimization problem, we propose an alternating optimization framework combined with a successive convex approximation method tailored for each subproblem. Our results show that the proposed system model offers significant protection of the sensing area compared to the case where the target privacy is not considered. Simulations also confirm that the proposed adaptive RIS partitioning outperforms the fixed RIS partitioning approach.

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

We propose a framework to design integrated communication and computing (ICC) receivers capable of simultaneously detecting data symbols and performing over-the-air computing (AirComp) in a manner that: a) is systematically generalizable to any nomographic function, b) scales to a massive number of user equipments (UEs) and edge devices (EDs), c) supports the computation of multiple independent functions (streams), and d) operates in a multi-access fashion whereby each transmitter can choose to transmit either data symbols, computing signals or both. For the sake of illustration, we design the proposed multi-stream and multi-access method under an uplink setting, where multiple single-antenna UEs/EDs simultaneously transmit data and computing signals to a single multiple-antenna base station (BS)/access point (AP). Under the communication functionality, the receiver aims to detect all independent communication symbols while treating the computing streams as aggregate interference which it seeks to mitigate; and conversely, under the computing functionality, to minimize the distortion over the computing streams while minimizing their mutual interference as well as the interference due to data symbols. To that end, the design leverages the Gaussian belief propagation (GaBP) framework relying only on element-wise scalar operations coupled with closed-form combiners purposebuilt for the AirComp operation, which allows for its use in massive settings, as demonstrated by simulation results incorporating up to 200 antennas and 300 UEs/EDs. The efficacy of the proposed method under different loading conditions is also evaluated, with the performance of the scheme shown to approach fundamental limiting bounds in the under/fully loaded cases.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for an energy-efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FuZ) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FuZ-based MARL cooperative architecture effectively improves EE performance, offering a 85% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multi-user multiple-input multiple-output (MIMO) RIS-aided broad-cast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic.

Accurate acquisition of channel state information (CSI) is essential for fully harnessing the potential of stacked intelligent metasurfaces (SIMs) in communication systems. In this paper, we address the channel estimation (CE) problem in SIM-based multi-user (MU) millimeter-wave (mmWave) near-field communication systems. To address the severe path loss and blockage in mmWave communication systems, many meta-atoms are typically integrated into each layer of the SIM. Then, the number of radio frequency (RF) chains at the base station (BS) is fewer than that of meta-atoms per layer, resulting in an underdetermined problem. Additionally, the increase in the number of meta-atoms in each layer expands the SIM’s near-field region, leading to the user equipment (UEs) being mostly situated in this region, necessitating precise modeling of the channel under the spherical wavefront assumption. To address these issues, we introduce a compressed sensing (CS)-based CE protocol to tackle the underdetermined problem. In contrast to the traditional CS-based estimation framework, we investigate a polar-domain channel representation to tackle the severe energy spread effect of the classical angular-domain channel representation in near-field communication systems. Specifically, we design a novel polar-domain transform matrix for uniform planar arrays (UPAs), thereby transforming the CE problem into a sparse recovery task of the paths’ support set and complex gains. To overcome the limitations of the sparse Bayesian learning (SBL) framework in tackling high-dimensional dictionaries, we propose a low-complexity polar-domain SBL (LCPD-SBL) algorithm, which significantly reduces computational complexity without compromising estimation accuracy. Numerical simulation results demonstrate that the proposed polar-domain transform matrix yields a better estimation accuracy than traditional angular-domain approaches. Additionally, the proposed LCPD-SBL algorithm can be faster than existing SBL methods by up to 4× while sustaining the same estimation performance.

Reconfigurable intelligent surfaces (RISs) have demonstrated an unparalleled ability to reconfigure wireless environments by dynamically controlling the phase, amplitude, and polarization of impinging waves. However, as nearly passive reflective metasurfaces, RISs may not distinguish between desired and interference signals, which can lead to severe spectrum pollution and even affect performance negatively. In particular, in large-scale networks, the signal-to-interference-plus-noise ratio (SINR) at the receiving node can be degraded due to excessive interference reflected from the RIS. To overcome this fundamental limitation, we propose in this paper a trajectory prediction-based dynamical control algorithm (TPC) for anticipating RIS ON-OFF states sequence, integrating a long- short-term-memory (LSTM) scheme to predict user trajectories. In particular, through a codebook-based algorithm, the RIS controller adaptively coordinates the configuration of the RIS elements to maximize the received SINR. Our simulation results demonstrate the superiority of the proposed TPC method over various system settings.

This paper investigates channel-aware decision fusion empowered by massive MIMO systems and reconfigurable intelligent surfaces (RIS). By integrating both, we aim to improve goal-oriented (fusion) performance despite the unique propagation challenges introduced. Specifically, we investigate traditional favorable propagation properties in the context of RIS-aided Massive MIMO decision fusion. The above analysis is then leveraged (i) to design three sub-optimal simple fusion rules suited for the large-array regime and (ii) to devise an optimization criterion for RIS reflection coefficients based on long-term channel statistics. Simulation results confirm the appeal of the presented design.

In Reconfigurable Intelligent Surfaces (RIS), reflective elements (REs) are typically configured as a single array, but as RE numbers increase, this approach incurs high overhead for optimal configuration. Subarray grouping provides an effective tradeoff between performance and overhead. This paper studies RIS-aided massive random access (RA) at the Medium Access Control (MAC) layer in cellular networks to enhance throughput. We introduce an opportunistic scheduling scheme that integrates multi-round access requests, subarray grouping for efficient RIS link acquisition, and multi-user data transmission. To optimize access request timing, RIS estimation overhead and throughput, we propose a multi-user RA strategy using sequential decision optimization to maximize average system throughput. A lowcomplexity algorithm is also developed for practical implementation. Both theoretical analysis and numerical simulations demonstrate that the proposed strategy significantly outperforms the extremes of full-array grouping and element-wise grouping.

Accurate acquisition of channel state information is crucial for unlocking the potential of stacked intelligent metasurface (SIM)-assisted communication systems. This paper investigates the channel estimation (CE) problem in SIM-assisted multi-user (MU) millimeter-wave (mmWave) near-field communication systems. We aim to solve the underdetermined problem caused by the large-scale deployment of nearly-passive meta-atoms, where the number of antennas at the base station (BS) is smaller than the number of meta-atoms on the last layer of the SIM. Specifically, we first design a polar-domain transform matrix for uniform planar arrays (UPA) to convert the near-field channel into a polar-domain representation. Moreover, we leverage the channel sparsity in the polar domain and the sparse Bayesian learning (SBL) technique to recover the channel parameters. Additionally, a covariance-free expectation maximization (CoFEM) algorithm is introduced to reduce the computational complexity of the SBL. Numerical simulation results indicate that in SIM-assisted near-field mmWave communications, the algorithms based on the proposed polar-domain transform matrix outperform existing angular-domain approaches. Moreover, the CoFEM algorithm significantly reduces the computational complexity compared to SBL methods.

Due to the continuous traffic of ubiquitous cellular networks and the ultra low-power low-cost characteristics of backscatter communication, cellular backscatter communication is a crucial technology for passive Internet of Things. However, existing cellular backscatter systems suffer from the lack of downlink control ability, low-efficiency modulation, and unreliable demodulation for megabit-rate backscatter transmission. This paper proposes CellScatter to overcome such drawbacks. First, we design a synchronization-and-control module for a CellScatter tag, which exploits cellular reference signals to achieve accurate synchronization and reliable multi-tag control without extra time overhead. Then, we design a single-sideband high-order modulation module to focus the backscatter signal power to the desired band and improve the spectrum utilization efficiency. Furthermore, we establish an explicit model to represent the spectrum-expanded backscatter signal without requiring a high-speed analog-to-digital converter, and derive a closed-form solution for the user equipment to recover the data of the tag at low complexity. We prototype the CellScatter system and evaluate its performance via ambient 4G LTE and 5G NR signals. Experimental results show that CellScatter can achieve 2.24 Mbps backscatter communication within a range of 30 meters, 6.7× lower BER and 33% higher rate than state of the art solutions.

This paper proposes the orthogonal time frequency space-based code index modulation (OTFS-CIM) scheme, a novel wireless communication system that combines OTFS modulation, which enhances error performance in high-mobility Rayleigh channels, with CIM technique, which improves spectral and energy efficiency, within a single-input multiple-output (SIMO) architecture. The proposed system is evaluated through Monte Carlo simulations for various system parameters. Results show that increasing the modulation order degrades performance, while more receive antennas enhance it. Comparative analyses of error performance, throughput, spectral efficiency, and energy saving demonstrate that OTFS-CIM outperforms traditional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. Also, the proposed OTFS-CIM system outperforms benchmark systems in many performance metrics under high-mobility scenarios, making it a strong candidate for sixth generation (6G) and beyond.

This paper proposes a new orthogonal time frequency space (OTFS)-based index modulation system called OTFS-aided media-based modulation (MBM) scheme (OTFS-MBM), which is a promising technique for high-mobility wireless communication systems. The OTFS technique transforms information into the delay-Doppler domain, providing robustness against channel variations, while the MBM system utilizes controllable radio frequency (RF) mirrors to enhance spectral efficiency. The combination of these two techniques offers improved bit error rate (BER) performance compared to conventional OTFS and OTFS-based spatial modulation (OTFS-SM) systems. The proposed system is evaluated through Monte Carlo simulations over high-mobility Rayleigh channels for various system parameters. Comparative throughput, spectral efficiency, and energy efficiency analyses are presented, and it is shown that OTFS-MBM outperforms traditional OTFS and OTFS-SM techniques. The proposed OTFS-MBM scheme stands out as a viable solution for sixth generation (6G) and next-generation wireless networks, enabling reliable communication in dynamic wireless environments.

Stacked intelligent metasurfaces (SIMs), which are composed of multi-layer programmable metasurfaces, support beamforming in the wave domain, utilizing a limited number of radio frequency (RF) chains. This work investigates the application of SIMs in the downlink of cell-free massive multiple-input multiple-output systems with finite-capacity fronthaul links. Specifically, we address the joint optimization of fronthaul compression and hybrid digital and wave-domain beamforming. To tackle the resulting highly non-convex problem, an alternating optimization algorithm is proposed, which iteratively optimizes digital processing and wave beamforming variables. Numerical results demonstrate that the proposed method outperforms baseline schemes relying solely on digital or wave beamforming, achieving near fully-digital performance with a few RF chains, assuming a sufficiently high signal-to-noise ratio (SNR).

Composed of an array of low-cost radiating elements, flexible intelligent metasurfaces (FIMs) can adaptively morph their surface shapes by adjusting the positions of elements along the direction perpendicular to the surface. This adaptive morphing, not only enhances wireless channel conditions, but also significantly curtails power consumption. This paper conducts an adaptive performance analysis of a wireless system, in which a FIM-equipped base station (BS) leverages the presence of a reconfigurable intelligent surface (RIS) to facilitate downlink transmission. To rigorously evaluate the system’s efficiency, we formulate an optimization problem centered on resource allocation, with the primary objective of maximizing the network achievable data rate. This maximization is subject to multiple constraints, especially on stringent quality-of-service (QoS) requirements of users and the BS finite power budget. Due to the highly intricate interdependencies among optimization variables and the inherent non-convexity of the problem, we strategically reformulate it as a Markov decision process (MDP), encapsulating its dynamic characteristics. To derive an optimal solution, we train a deep deterministic policy gradient (DDPG) agent, relying on MDP, which simultaneously optimizes the decision variables: the BS transmit beamforming, the morphology of the FIM and the reflection coefficient matrix of the RIS. Furthermore, to accommodate the real-world challenges imposed by user mobility, we enhance the generalization of the DDPG model through the integration of meta-learning technique, thereby significantly improving its adaptability to system variations. Numerical evaluations affirm that incorporating an RIS yields a pronounced improvement in achievable network data rate, particularly when the BS transmit power budget is maintained within a moderate operational range.

In this paper, we propose a new scheme for uplink transmission of low bit rate local data in wireless systems assisted by reconfigurable intelligent surface (RIS) arrays. With an incoming user signal that has a plane polarization (horizontal or vertical), the RIS array dynamically changes the reflected signal polarization and maps the local data on the polarization state. The base station employs two antennas, one with horizontal polarization and the other with vertical polarization, and detection of the RIS data is performed using a simple power comparator. Our results show that a bit error rate (BER) floor appears when the local data is transmitted at the user symbol rate, but the BER floor vanishes when the local data rate is reduced. The proposed technique thus turns out to be particularly suitable for transmission of low bit rate local data, and it features strong robustness to imperfections of the signal polarization.

Radio frequency (RF) wireless power transfer (WPT) is promising for promoting sustainability in future wireless systems, but its low end-to-end power transfer efficiency is a critical challenge. For this, reconfigurable intelligent surfaces (RISs) can be leveraged to enhance efficiency by providing nearly passive beamforming gains. Beyond diagonal (BD) RIS is a new RIS variant offering greater performance benefits than traditional diagonal RIS (D-RIS), though its potential for RF-WPT remains unexplored. Motivated by this, we consider a single-input single-output BD-RIS-aided RF-WPT system and we formulate a joint beamforming and waveform optimization problem aiming to maximize the harvested power at the receiver. We propose an optimization framework relying on successive convex approximation, alternating optimization, and semi-definite relaxation. Numerical results show that increasing the number of transmit sub-carriers or RIS elements improves the harvested power. We verify by simulation that BD-RIS leads to the same performance as D-RIS under far-field line-of-sight conditions (in the absence of mutual coupling), while it outper-forms D-RIS as the non-line-of-sight components dominate.

Reconfigurable intelligent surface (RIS) technology has gained wide recognition for its ability of enhancing the performance of wireless communication systems. This paper investigates the energy efficiency (EE) of a discrete phase shift RIS-assisted multi-cell communication system. Assuming that only statistical channel state information is available, we maximize the EE under a practical measured-based RIS power consumption model. Approximating the discrete measured-based RIS power consumption model by a continuous differentiable function, the problem is addressed by jointly optimizing the transmit beamforming at the base stations and the RIS phase shift matrix, by using fractional programming and complex circle manifold techniques. Simulation results demonstrate that the proposed algorithm can effectively achieve near-optimal EE performance and low RIS power consumption.

In integrated sensing and communication (ISAC) systems, reconfigurable intelligent surface (RIS) is often able to provide a virtual line-of-sight path to overcome blockage and introduce new degrees of freedom to enhance both communications and sensing performances. In this paper, we explore the synergistic integration of massive multiple-input-multiple-output (MIMO) ISAC and RIS technologies. We first consider the uplink training phase and estimate the channels between the base station and communication users, by using the minimum mean square error method. Then, we derive the closed-form expressions for the downlink communications rate and the Cramér-Rao lower bound for the estimation of the azimuth and elevation angles associated with the sensing target. Extensive simulations verify that the derived analytical results match well with Monte Carlo simulation results. Furthermore, communication and sensing performances are significantly improved owing to the deployment of the RIS. Our analysis lay an important foundation for optimizing RIS-assisted ISAC systems.

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.

International audience

This paper investigates optimal distributed Medium Access Control (MAC) strategies for wireless Internet of Things (IoT) networks, incorporating both terrestrial and non-terrestrial relays, including UAVs. While prior research has primarily focused on single-relay forwarding, we address the complexities of energy-efficient multi-relay data forwarding. This involves managing the challenges of relay probing, optimized relay utilization, and balancing energy trade-offs to maximize system energy efficiency (EE). To address these challenges, we propose a novel strategy called Distributed Data Collection with Opportunistic Relaying (DDC/OR), designed to optimize MAC performance in multi-relay IoT networks. Our approach leverages a decision-theoretic framework based on optimal sequential planning, extending traditional cooperative MAC models to support multiple relays. The proposed DDC/OR strategy offers a statistically optimized solution that maximizes average EE, with a rigorous proof of optimality. Additionally, we present a low-complexity implementation of the DDC/OR algorithm, suitable for practical deployments. For scenarios involving dynamic non-terrestrial movements and varying relay inter-distances, we introduce a two-timescale, self-organized algorithm to adaptively reconfigure relay strategies. To ensure scalability for large relay networks, several optimizations are introduced to enhance the feasibility of the DDC/OR approach. We validate the effectiveness of the DDC/OR strategy through extensive simulations, demonstrating significant EE gains in both terrestrial and non-terrestrial relay configurations. Notably, it also achieves comparatively better performance in latency, throughput, and overall energy consumption.

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International audience

Unmanned aerial vehicles (UAVs) serve as flexible aerial platforms, enriching air-ground communication networks in various ways. To support massive connectivity within limited time-frequency blocks, non-orthogonal multiple access (NOMA) is proposed to be integrated into UAV networks. However, a common issue associated with almost all NOMA schemes is the susceptibility to inter-user interference (IUI). Therefore, in this paper, we propose a multi-UAV cooperative system aided by NOMA with index modulation (IM), termed MCU-NOMA-IM, to improve the performance of air-ground networks by mitigating IUI and also avoiding the successive interference cancellation (SIC) decoding method that is prone to error floors. With MCU-NOMA-IM, the information bits pertaining to multiple UAVs are mapped into multiple dimensions, including the modulated symbols, subcarrier indices, and energy allocation patterns. To fully investigate the performance of MCU-NOMA-IM on air-ground networks, we consider scenarios in the presence of three and four UAVs and derive upper-bounds for the bit error rates (BERs). In addition, we propose a multi-clustered-UAV cooperative system aided by NOMA with IM (MCCU-NOMA-IM), which groups closely located UAVs into several clusters to reduce the requirement for time resources. Simulation results demonstrate that both MCU-NOMA-IM and MCCU-NOMA-IM greatly outperform cooperative NOMA and non-cooperative NOMA-IM schemes, especially for distant UAVs when the signal-to-noise ratio is sufficiently high. Also, we show that the derived BER upper bounds are asymptotically tight.

This work proposes a quantum-aided deep reinforcement learning (DRL) framework designed to enhance the accuracy of direction-of-arrival (DoA) estimation and the efficiency of computational task offloading in integrated sensing and communication systems. Traditional DRL approaches face challenges in handling high-dimensional state spaces and ensuring convergence to optimal policies within complex operational environments. The proposed quantum-aided DRL framework that operates in a military surveillance system exploits quantum computing’s parallel processing capabilities to encode operational states and actions into quantum states, significantly reducing the dimensionality of the decision space. For the very first time in literature, we propose a quantum-enhanced actor-critic method, utilizing quantum circuits for policy representation and optimization. Through comprehensive simulations, we demonstrate that our framework improves DoA estimation accuracy by 91.66% and 82.61% over existing DRL algorithms with faster convergence rate, and effectively manages the trade-off between sensing and communication and by optimizing task offloading decisions under stringent ultra-reliable low-latency communication requirements. Comparative analysis also reveals that our approach reduces the overall task offloading latency by 43.09% and 32.35% compared to the DRL-based deep deterministic policy gradient and proximal policy optimization algorithms, respectively.

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

Rydberg atomic receivers have been envisaged as a revolutionary technology for future wireless communications and sensing. In order to detect the angle of arrival (AoA), researchers have typically constructed arrays comprisingmultipleRydberg atomic receivers. This paper presents a novel finding: The AoA of an incident signal can be accurately recovered even with asingleRydberg atomic receiver, by harnessing the phenomenon of inner-vapor interference. Firstly, we apply the micro-element method to the atomic vapor and derive a closed-form expression for the laser transmission in the presence of interference between the incident and local oscillator (LO) radio frequency (RF) signals. Secondly, we propose a robust method to estimate the AoA based on the particle swarm optimization (PSO) algorithm. Simulation results substantiate the effectiveness of our proposed scheme with practical parameter settings, verifying the applicability of AoA detection using a single Rydberg atomic receiver.

Integrating aerial reconfigurable intelligent surfaces (ARIS) with unmanned aerial vehicles (UAVs) presents a significant opportunity to enhance the performance of wireless networks. This integration allows ARIS to be mounted on UAVs, providing greater configuration flexibility, establishing reliable air-ground connections, and enabling three-dimensional signal reflections. However, this integration also introduces unique challenges related to physical layer security (PLS). Addressing these security considerations is essential, given the significance of secure and reliable communication. In this paper, we investigate the PLS for ARIS to assist wireless communication systems. Our objective is to select the ARIS that maximizes the secrecy capacity of the proposed system model. Two selection approaches are considered, namely, optimal and sub-optimal ARIS selection, and analytical expressions for the secrecy outage probability and probability of non-zero secrecy capacity over Nakagami-m fading channels are derived. Additionally, we examine the impact of varying the number of UAVs and the locations of the eavesdropper in practical scenarios. Moreover, the collaborative scenario is investigated, where all UAVs cooperate to improve secrecy transmission. Each ARIS reflects identical copies of the transmitted signal on the same time-frequency channel without mutual interference. The optimization problem of UAV locations and RIS phase shifts to maximize the secrecy capacity under specific constraints is formulated and addressed using an improved particle swarm optimization technique. These scenarios highlight the potential of ARIS in achieving secure and efficient wireless communications. Simulation results verify the analytical derivations, highlighting the critical role of selecting the ARIS in enhancing secrecy performance. As revealed by simulations, doubling the number of UAVs leads to a notable improvement in the average secrecy rate by approximately 77.78%. The obtained results highlight the significance of the proposed ARIS-assisted system in enhancing the PLS for wireless communications.

This paper presents a theoretical and mathematical framework for the design of a conformal reconfigurable intelligent surface (RIS) that adapts to non-planar geometries, which is a critical advancement for the deployment of RIS on non-planar and irregular surfaces as envisioned in smart radio environments. Previous research focused mainly on the optimization of RISs assuming a predetermined shape, while neglecting the intricate interplay between shape optimization, phase optimization, and mutual coupling effects. Our contribution, the Tailored 3D RIS (T3DRIS) framework, addresses this fundamental problem by integrating the configuration and shape optimization of RISs into a unified model and design framework, thus facilitating the application of RIS technology to a wider spectrum of environmental objects. The mathematical core of T3DRIS is rooted in optimizing the 3D deployment of the unit cells and tuning circuits, aiming at maximizing the communication performance. Through rigorous full-wave simulations and a comprehensive set of numerical analyses, we validate the proposed approach and demonstrate its superior performance and applicability over contemporary designs. This study—the first of its kind—paves the way for a new direction in RIS research, emphasizing the importance of a theoretical and mathematical perspective in tackling the challenges of conformal RISs.

Recent advancements in smart radio environment technologies aim to enhance wireless network performance through low-cost electromagnetic (EM) devices. Among these, metaprisms (MTPs)—a class of static, frequency-selective metasurfaces—stand out for their ability to create multiple beams at different frequencies without requiring channel state information (CSI) or active reconfiguration. Unlike reconfigurable intelligent surfaces (RIS), which rely on programmable elements and periodic tuning, MTPs operate passively, significantly reducing system complexity and overhead. The working principle of MTPs is specifically tailored for scenarios in which a large number of devices must be served simultaneously, each requiring low data rate and low latency, as envisioned by, e.g., Industrial Internet-of-Things (IoT). In this paper, we address the design of an ideal MTP by considering frequency-dependent reflection coefficients, and by identifying the general properties that are necessary to achieve the desired beam steering function in the angle-frequency domain. We also discuss the limitations of previous studies that employed oversimplified models, which may compromise performance. Key contributions include a detailed exploration of the equivalence of the MTP to an ideal S-parameter multiport (MP) model and an analysis of its implementation using Foster’s circuits. Additionally, we introduce a realistic MP network model that incorporates aspects overlooked by ideal scattering models, along with an ad-hoc optimization strategy for the obtained model. The performance of the proposed optimization approach and circuits implementation are validated through simulations using a commercial full-wave EM simulator, showcasing the effectiveness of the proposed method.

This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users’ signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.

Reconfigurable intelligent surfaces (RISs) are an emerging technology for improving spectral efficiency and reducing power consumption in future wireless systems. This paper investigates the joint design of the transmit precoding matrices and the RIS phase shift vector in a multi-user RIS-aided multiple-input multiple-output (MIMO) communication system. We formulate a max-min optimization problem to maximize the minimum achievable rate while considering transmit power and reradiation mask constraints. The achievable rate is simplified using the Arimoto-Blahut algorithm, and the problem is broken into quadratic programs with quadratic constraints (QPQC) sub-problems using an alternating optimization approach. To improve efficiency, we develop a model-based neural network optimization that utilizes the one-hot encoding for the angles of incidence and reflection. We address practical RIS limitations by using a greedy search algorithm to solve the optimization problem for discrete phase shifts. Simulation results demonstrate that the proposed methods effectively shape the multi-beam radiation pattern towards desired directions while satisfying reradiation mask constraints. The neural network design reduces the execution time, and the discrete phase shift scheme performs well with a small reduction of the beamforming gain by using only four phase shift levels.

Malware traffic classification (MTC) is a crucial step in network intrusion detection, which is significant for network security and management. With the continuous evolution of malware traffic, traditional MTC methods are difficult to adapt efficiently to new traffic categories, and manually designed neural network structures suffer from performance bottlenecks and low design efficiency. Hence, we propose an enhanced MTC method based on expandable class incremental learning (CIL) with architecture search. The architecture search can automatically design the optimal neural network structure tailored to different network traffic characteristics, avoiding the limitations of manually designing network structures and improving classification performance. Meanwhile, expandable CIL allows the MTC model to gradually learn new traffic categories without forgetting previous knowledge, avoiding the computational overhead and efficiency loss caused by frequent retraining of the model. The experimental results demonstrate that the proposed CIL-MTC approach surpasses advanced incremental learning methods on both the Edge-IIoTset and ISCX VPN-nonVPN datasets, achieving superior classification performance while maintaining lower average trainable parameters and training costs. Especially, it achieves an average incremental accuracy of 98.55% and 99.09% on the Edge-IIoTset dataset with incremental tasks of 5 and 2, respectively.

Reconfigurable intelligent surfaces (RISs) have been shown effective in strengthening the physical layer security of wireless systems, and the two-timescale design was proposed to tackle the challenges in channel estimation and phase-shift control. However, existing maximum ratio transmission (MRT) based precoding design is not efficient in mitigating information leakage. To this end, this paper considers the performance analysis and two-timescale design for RIS-aided multiple-input single-output (MISO) secure communications with regularized zero-forcing (RZF) and zero-forcing (ZF) precoding, which is not available in the literature. The major challenges come from the two-hop channel and the inverse structure in the precoding matrix. By utilizing random matrix theory, we first evaluate the fundamental limits of the considered system by deriving a closed-form expression for the ergodic secrecy sum rate (ESSR). Then, we determine the optimal regularization factor of the RZF precoder and evaluate the ESSR over independent and identically distributed (i.i.d.) channels in the high SNR regime. The results indicate that when the number of reconfigurable elements at the RIS is overwhelmingly larger than that of transmit antennas and users, the ESSR of the two-hop channel approaches that of the single-hop channel. Based on the performance analysis, we propose a two-timescale algorithm to maximize the ESSR by optimizing the regularization factor of RZF and the phase shifts of the RIS alternatively. Simulation results validate the accuracy of the theoretical analysis and the effectiveness of the proposed algorithm.

The integration of Smart Surfaces in 6G communication networks, also dubbed as Reconfigurable Intelligent Surfaces (RISs), is a promising paradigm change gaining significant attention given its disruptive features. RISs are a key enabler in the realm of 6G Integrated Sensing and Communication (ISAC) systems where novel services can be offered together with the future mobile networks communication capabilities. This paper addresses the critical challenge of precisely localizing users within a communication network by leveraging the controlled-reflective properties of RIS elements without relying on more power-hungry traditional methods, e.g., GPS, adverting the need of deploying additional infrastructure and even avoiding interfering with communication efforts. Moreover, we go one step beyond: we build COLoRIS, anOpportunistic ISACapproach that leverages localization-agnostic RIS configurations to accurately position mobile users via trained learning models. Extensive experimental validation and simulations in large-scale synthetic scenarios show$\mathbf{5\%}$positioning errors (with respect to field size) under different conditions. Further, we show that a low-complexity version running in a limited off-the-shelf (embedded, low-power) system achieves positioning errors in the$\mathbf{11\%}$range at a negligible$\mathbf{+2.7\%}$energy expense with respect to the classical RIS.

This research focuses on the challenges of distributed Medium Access Control (MAC) protocols involving Reconfigurable Intelligent Surfaces (RISs), which are still in early development. The study explores optimal channel access for multiple source-destination pairs in distributed networks with the assistance of multiple RISs. Three key issues are addressed: joint scheme for channel contention, RISs’ channel state information (CSI) acquisition, and RIS-assisted channel access; tradeoff between overhead and effective data transmission; and low-complexity distributed network operation. To achieve maximum network throughput, the paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance based on statistical optimization. The proposed MAC strategy's optimality in terms of average network throughput is rigorously derived. Closed-form expressions for threshold functions of rewards for making decisions are derived, and an easy-to-implement distributed channel access algorithm is provided with online complexity$\mathcal {O}(2^{L})$, where$L$denotes the number of distributed RISs. The proposed MAC strategy is then refined, and a low-complexity distributed algorithm is developed with complexity$\mathcal {O}(L)$. Numerical simulations verify the theoretical results, demonstrating the efficiency of the proposed strategy. This work introduces innovative solutions and analytical frameworks for the distributed MAC problem with RIS assistance, significantly advancing existing research.

Intelligent metasurfaces have recently emerged as a promising technology that enables the customization of wireless environments by harnessing large numbers of low-cost reconfigurable scattering elements. However, prior studies have predominantly focused on single-layer metasurfaces, which have limitations in terms of wave-domain processing capabilities due to practical hardware limitations. In contrast, this paper introduces a novel stacked intelligent metasurface (SIM) design. Specifically, we investigate the integration of SIM into the downlink of a multiuser multiple-input single-output (MISO) communication system, where an SIM, consisting of a multilayer metasurface structure, is deployed at the base station (BS) to facilitate transmit beamforming in the electromagnetic wave domain. This eliminates the need for conventional digital beamforming and high-resolution digital-to-analog converters at the BS. To this end, an optimization problem is formulated to maximize the sum rate of all user equipments by jointly optimizing the transmit power allocation at the BS and the wave-based beamforming at the SIM, subject to constraints on the transmit power budget and discrete phase shifts. Furthermore, we propose a computationally efficient algorithm for solving the formulated joint optimization problem and elaborate on the potential benefits of employing SIM in wireless networks. Numerical results are illustrated to corroborate the effectiveness of the proposed SIM-enabled wave-based beamforming design and to evaluate the performance improvement achieved by the proposed algorithm compared to various benchmark schemes. It is demonstrated that considering the same number of transmit antennas, the proposed SIM-based system achieves about 200% improvement in terms of sum rate compared to conventional MISO systems. The code for this paper is available athttps://github.com/JianchengAn.

Flexible intelligent metasurface (FIM) technology shows promise in terms of enhancing both the spectral and energy efficiency of wireless networks. An FIM is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals, while flexibly adjusting its position along the direction perpendicular to the surface by a process termed as “morphing”. This is of particular interest for wireless communication systems operating at millimeter-wave and terahertz frequencies, where deep fading generally occurs within a few millimeters. Hence, in contrast to conventional rigid 2D antenna arrays, the FIM surface shape may be reconfigured to improve the channel quality by beneficial 3D morphing. In this paper, we investigate the multiuser downlink, where an FIM deployed at a base station (BS) communicates with multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS, by jointly optimizing the transmit beamforming and FIM surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint of each user as well as a constraint on the maximum FIM morphing range. To solve this problem, we first consider a simple single-user scenario and show that the optimal 3D surface shape is achieved by independently adjusting each FIM element to the position having the strongest channel gain. However, in realistic multiuser scenarios, FIM surface-shape morphing involves complex tradeoffs. To address this issue, an efficient alternating optimization method is proposed to iteratively update the FIM surface shape and the transmit beamformer to gradually reduce the transmit power. Additionally, we analyze the performance gain of the FIM, showcasing a logarithmic received power scaling law versus its maximum morphing range. Finally, simulation results show that the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays at a given data rate. The code for this paper is available athttps://github.com/JianchengAn.

The advance towards 6G networks comes with the promise of unprecedented performance in sensing and communication capabilities. The feat of achieving those, while satisfying the ever-growing demands placed on wireless networks, promises revolutionary advancements in sensing and communication technologies. As 6G aims to cater to the growing demands of wireless network users, the implementation of intelligent and efficient solutions becomes essential. In particular, reconfigurable intelligent surfaces (RISs), also known as Smart Surfaces, are envisioned as a transformative technology for future 6G networks. The performance of RISs when used to augment existing devices is nevertheless largely affected by their precise location. Suboptimal deployments are also costly to correct, negating their low-cost benefits. This paper investigates the topic of optimal RISs diffusion, taking into account the improvement they provide both for the sensing and communication capabilities of the infrastructure while working with other antennas and sensors. We develop a combined metric that takes into account the properties and location of the individual devices to compute the performance of the entire infrastructure. We then use it as a foundation to build a reinforcement learning architecture that solves the RIS deployment problem. Since our metric measures the surface where given localization thresholds are achieved and the communication coverage of the area of interest, the novel framework we provide is able to seamlessly balance sensing and communication, showing its performance gain against reference solutions, where it achieves simultaneously almost the reference performance for communication and the reference performance for localization.

This paper investigates the fundamental problem of cascaded channel estimation in reconfigurable intelligent surface (RIS) aided multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. To address the high pilot overhead required by existing methods, we introduce appropriate auxiliary variables to decompose the received signal model into a subcarrier-wise bilinear sub-model and two linear sub-models with respect to the MU-to-RIS and RIS-to-base-station (BS) cascaded channels. The proposed model decomposition maintains the matrix factorization structure of the two cascaded channels and avoids the problem of parameter expansion in exiting methods. In the two linear sub-models, in addition, the cascaded channels are transformed into the delay-angle domain to leverage the joint sparsity. Based on the established models, we consider the problem of simultaneously estimating the MU-to-RIS and RIS-to-BS channel matrices as a bilinear estimation problem. To tackle this problem, we formulate a Bayesian inference framework and develop a hybrid message-passing (HVMP) algorithm to achieve approximate Bayesian inference by leveraging the Bethe method. Notably, the HVMP algorithm infers the two cascaded channels iteratively, where the covariance of each cascaded channel matrix is estimated to characterize the correlation of the matrix elements. Simulation results show that the proposed algorithm achieves accurate channel estimation with low pilot overhead while state-of-the-art baseline schemes exhibit poor performance. Furthermore, the proposed algorithm can approach the estimation oracle bound of the MU-to-RIS (or RIS-to-BS) channel which assumes perfect knowledge of the RIS-to-BS (or MU-to-RIS) channel.

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%.

This paper addresses the challenges of throughput optimization in wireless cache-aided cooperative networks. We propose an opportunistic cooperative probing and scheduling strategy for efficient content delivery. The strategy involves the base station probing the relaying channels and cache states of multiple cooperative nodes, thereby enabling opportunistic user scheduling for content delivery. Leveraging the theory of Sequentially Planned Decision (SPD) optimization, we dynamically formulate decisions on cooperative probing and stopping time. Our proposed Reward Expected Thresholds (RET)-based strategy optimizes opportunistic probing and scheduling. This approach significantly enhances system throughput by exploiting gains from local caching, cooperative transmission and time diversity. Simulations confirm the effectiveness and practicality of the proposed Media Access Control (MAC) strategy.

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM’s surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM’s surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays.

Integrating aerial reconfigurable intelligent surfaces (ARIS) with unmanned aerial vehicles (UAVs) presents a significant opportunity to enhance the performance of wireless networks. This integration allows ARIS to be mounted on UAVs, providing greater configuration flexibility, establishing reliable air-ground connections, and enabling three-dimensional signal reflections. However, this integration also introduces unique challenges related to physical layer security (PLS). Addressing these security considerations is essential, given the significance of secure and reliable communication. In this paper, we investigate the PLS of UAV-enabled ARIS-assisted wireless communication systems. Our objective is to maximize the secrecy capacity of the proposed system model. We derive analytical expressions for the secrecy outage probability and probability of non-zero secrecy capacity over Nakagami-m fading channels. Additionally, we examine the impact of varying the number of UAVs in practical scenarios. Simulation results verify the analytical derivations, highlighting the critical role of selecting the optimal UAV in enhancing secrecy performance.

This paper investigates a stacked intelligent metasurfaces (SIM)-assisted integrated sensing and communications (ISAC) system. An extended target model is considered, where the base station (BS) aims to estimate the complete target response matrix relative to the SIM. Under the constraints of minimum signal-to-interference-plus-noise ratio (SINR) for the communication users (CUs) and maximum transmit power, we jointly optimize the transmit beamforming at the BS and the end-to-end transmission matrix of the SIM, to minimize the Cramér-Rao bound (CRB) for target estimation. Effective algorithms such as alternating optimization (AO) and semidefinite relaxation (SDR) are employed to solve the non-convex SINR-constrained CRB minimization problem. Finally, we design and build a hardware platform for SIM, and experimentally evaluate the performance of SIM-aided communication and sensing tasks.

Staked intelligent metasurface (SIM) based techniques are developed to perform two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array automatically performs the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. To arrange for the SIM to carry out this task, we design a gradient descent algorithm for iteratively updating the phase shift of each meta-atom in the SIM to minimize the fitting error between the SIM's response and the 2D DFT matrix. To further improve the DOA estimation accuracy, we configure the phase shifts in the input layer of the SIM to generate a set of 2D DFT matrices having orthogonal spatial frequency bins. Extensive numerical simulations verify the capability of a well-trained SIM to perform the 2D DFT. Specifically, it is demonstrated that a SIM having an optical computational speed achieves an MSE of 10–4in 2D DOA estimation.

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a reconfigurable intelligent surface (RIS), in the presence of an eavesdropper. The mobile users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective.

Appropriate channel models tailored to the specific needs of industrial environments are crucial for the 5G private industrial network design and guiding deployment strategies. This paper scrutinizes the applicability of 3GPP’s channel model for industrial scenarios. The challenges in accurately modeling industrial channels are addressed, and a refinement strategy is proposed employing a ray-tracing (RT) based channel model calibrated with continuous-wave received power measurements collected in a manufacturing facility in Sweden. The calibration helps the RT model achieve a root mean square error (RMSE) and standard deviation of less than 7 dB. The 3GPP and the calibrated RT model are statistically compared with the measurements, and the coverage maps of both models are also analyzed. The calibrated RT model is used to simulate the network deployment in the factory to satisfy the reference signal received power (RSRP) requirement. The deployment performance is compared with the prediction from the 3GPP model in terms of the RSRP coverage map and coverage rate. Evaluation of deployment performance provides crucial insights into the efficacy of various channel modeling techniques for optimizing 5G industrial network planning.

Intelligent surfaces represent a breakthrough technology capable of customizing the wireless channel cost-effectively. However, the existing works generally focus on planar wavefront, neglecting near-field spherical wavefront characteristics caused by large array aperture and high operation frequencies in the terahertz (THz). Additionally, the single-layer reconfigurable intelligent surface (RIS) lacks the signal processing ability to mitigate the computational complexity at the base station (BS). To address this issue, we introduce a novel stacked intelligent metasurfaces (SIM) comprised of an array of programmable metasurface layers. The SIM aims to substitute conventional digital baseband architecture to execute computing tasks with ultra-low processing delay, albeit with a reduced number of radio-frequency (RF) chains and low-resolution digital-to-analog converters. In this paper, we present a SIM-aided multiuser multiple-input single-output (MU-MISO) near-field system, where the SIM is integrated into the BS to perform beamfocusing in the wave domain and customize an end-to-end channel with minimized inter-user interference. Finally, the numerical results demonstrate that near-field communication achieves superior spatial gain over the far-field, and the SIM effectively suppresses inter-user interference as the wireless signals propagate through it.

This article proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating in the millimeter-wave band. Specifically, the ISAC system consists of a radar subsystem and a communication subsystem to detect multiple targets and communicate with the users simultaneously. The IRS is used to configure the radio propagation environment by changing the phase of the radio signal to enhance the communication transmission rate. In the proposed scheme, we first derive a closed-form solution for the radar signal covariance matrix to generate a radar beampattern in the angle of interest. Then, we jointly optimize the beamforming vector of the communication subsystem and the IRS phase shifts to enhance the communication transmission rate. To decouple the multiple variables to be optimized, the alternating optimization and quadratic transformation methods are applied to determine the communication beamforming vector and the IRS phase shifts. Specifically, we utilize the majorization minimization and the complex circle manifold methods to compute the IRS phase shifts. Simulation results verify the effectiveness of the proposed algorithm and demonstrate that an IRS can improve the performance of ISAC systems.

Stacked intelligent metasurfaces (SIMs) are capable of emulating reconfigurable physical neural networks by utilizing electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. An SIM is constructed by densely integrating multiple metasurface layers, each consisting of a large number of small meta-atoms that can control the EM waves passing through it. In this paper, we harness an SIM for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array can be designed to automatically compute the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. As a result, a receiver array can directly observe the angular spectrum of the incoming signal, and it can estimate the DOA by simply using probes to detect the energy distribution on the receiver array. This avoids the need for power inefficient radio frequency chains. To enable an SIM to perform the 2D DFT in the wave domain, we formulate an optimization problem that minimizes the mean square error (MSE) between the SIM’s EM response and the 2D DFT matrix. Then, a gradient descent algorithm is customized for iteratively updating the phase shift applied by each meta-atom of the SIM. To further improve the DOA estimation accuracy, we configure the phase shifts of the input layer of the SIM to generate a set of 2D DFT matrices associated with orthogonal spatial frequency bins. Additionally, we analytically evaluate the performance of the proposed SIM-based DOA estimator by deriving a tight upper bound for the MSE. Extensive numerical simulations verify the capability of an optimized SIM to perform DOA estimation and corroborate the theoretical analysis. Specifically, we show that an SIM is capable of performing DOA estimation with an MSE of the order of$10^{-4}$.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage in challenging propagation environments. This paper studies an RIS-assisted communication system, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmark schemes in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves the system performance.

This work addresses the topic of energy efficiency (EE) maximization in wireless networks aided by a reconfigurable intelligent surface (RIS), considering both active and nearly-passive RISs. Moreover, unlike available literature, the analysis considers that the RIS reflection constraints are not enforced separately on each reflecting element, but the RIS is characterized by a single reflection constraint that is jointly enforced on all the reflecting elements. This allows for a wider set of feasible RIS reflection matrices, which leads to better performance, which is theoretically analyzed. Then, two provably convergent energy efficiency maximization algorithms with polynomial complexity are developed, which optimize the reflection coefficients of the RIS, the mobile users’ transmit powers, and the linear filters at the base station. Numerical results are illustrated to quantify the performance of the proposed methods and identify the operating points in which active or nearly-passive RISs should be preferred from the energy efficiency standpoint.

Reconfigurable intelligent surface (RIS) is a promising technique to improve the performance of future wireless communication systems at low energy consumption. To reap the potential benefits of RIS-aided beamforming, it is vital to enhance the accuracy of channel estimation. In this paper, we consider an RIS-aided multiuser system with non-ideal reflecting elements, each of which has a phase-dependent reflecting amplitude, and we aim to minimize the mean-squared error (MSE) of the channel estimation by jointly optimizing the training signals at the user equipments (UEs) and the reflection pattern at the RIS. As examples the least squares (LS) and linear minimum MSE (LMMSE) estimators are considered. The considered problems do not admit simple solution mainly due to the complicated constraints pertaining to the non-ideal RIS reflecting elements. As far as the LS criterion is concerned, we tackle this difficulty by first proving the optimality of orthogonal training symbols and then propose a majorization-minimization (MM)-based iterative method to design the reflection pattern, where a semi-closed form solution is obtained in each iteration. As for the LMMSE criterion, we address the joint training and reflection pattern optimization problem with an MM-based alternating algorithm, where a closed-form solution to the training symbols and a semi-closed form solution to the RIS reflecting coefficients are derived, respectively. Furthermore, an acceleration scheme is proposed to improve the convergence rate of the proposed MM algorithms. Finally, simulation results demonstrate the performance advantages of our proposed joint training and reflection pattern designs.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology to reconfigure the radio propagation environment in the full space. Prior works on STAR-RISs have mostly considered the energy splitting operation protocol, which has high hardware complexity in practice. Moreover, the full and instantaneous channel state information (CSI) is always assumed available for designing the STAR-RIS nearly passive beamforming, which, however, is practically difficult to obtain due to the large number of STAR-RIS elements. To address these issues, we study the mode switching design in STAR-RIS aided non-orthogonal multiple access (NOMA) communication systems. Moreover, two efficient two-timescale (TTS) transmission protocols are proposed for different channel setups to maximize the respective average achievable sum-rate. Specifically, 1) for the case of line-of-sight (LoS) dominant channels, we propose the beamforming-then-estimate (BTE) protocol, where the long-term STAR-RIS transmission and reflection coefficients are optimized based on the statistical CSI only, while the short-term power allocation at the base station (BS) is designed based on the estimated effective fading channels of all the users; 2) for the case of rich scattering environments, we propose an alternative partition-then-estimate (PTE) protocol, where the BS first determines the long-term STAR-RIS surface-partition strategy based on the path-loss information only, with each subsurface being assigned to one user; and then the BS estimates the instantaneous subsurface channels associated with the users and designs its power allocation and STAR-RIS phase-shifts accordingly. For the two proposed transmission protocols, we further propose efficient algorithms to solve the respective long-term and short-term optimization problems. Moreover, we show that both proposed transmission protocols substantially reduce the channel estimation overhead as compared to the existing schemes based on full instantaneous CSI. Last, simulation results validate the superiority of our proposed transmission protocols as compared to various benchmarks. It is shown that the BTE protocol outperforms the PTE protocol when the number of STAR-RIS elements is large and/or the LoS channel components are dominant, and vice versa.

Integrated sensing and communications (ISAC) is an emerging technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited number of degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RISs) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.

In this paper, we propose a secure unmanned aerial vehicle (UAV) communication system with the assistance of a reconfigurable intelligent surface (RIS), where UAV trajectory design and artificial noise are incorporated to prevent eavesdropping from multiple colluding curious users. To maximize the secrecy rate of the proposed system, we undertake a joint optimization process that encompasses the trajectory of the UAV, the RIS phase shifts, and the beamforming vectors for both information and artificial noise signals, considering the constraints of the UAV transmit power, UAV flying speed and the phase shifts. To address the non-convex nature of the joint problem and handle the coupling effects of multiple parameters, we conduct the problem decomposition by using the block coordinate descent (BCD) method, combined with an alternating algorithm to optimize the decomposed sub-problems. To further tackle the non-convexity in sub-problems, we apply the successive convex approximation (SCA) method to circumvent the trajectory optimization problem and to optimize the beamformers of information and artificial noise signals, while a majorization-minimization (MM) based scheme is adopted for the RIS phase shifts optimization. Numerical simulation results substantiate the convergence and effectiveness of the proposed algorithm through the comparison with benchmark methods, and our proposed scheme is proven to achieve a significant improvement in average secrecy rate across various conditions.

Reconfigurable intelligent surfaces (RISs) are expected to play a crucial role in reaching the key performance indicators (KPIs) for future 6G networks. Their competitive edge over conventional technologies lies in their ability to control the wireless environment propagation properties at will, thus revolutionizing the traditional communication paradigm that perceives the communication channel as an uncontrollable black box. As RISs transition from research to market, practical deployment issues arise. Major roadblocks for commercially viable RISs are i) the need for a fast and complex control channel to adapt to the ever-changing wireless channel conditions, and ii) an extensive grid to supply power to each deployed RIS. In this paper, we question the established RIS practices and propose a novel RIS design combining self-configuration and energy self-sufficiency capabilities. We analyze the feasibility of devising fully-autonomous RISs that can be easily and seamlessly installed throughout the environment, following the new internet-of-surfaces (IoS) paradigm, requiring modifications neither to the deployed mobile network nor to the power distribution system. In particular, we introduce ARES, an Autonomous RIS with Energy harvesting and Self-configuration solution. ARES achieves outstanding communication performance while demonstrating the feasibility of energy harvesting (EH) for RISs power supply in future deployments.

Multiport network theory has been proved to be a suitable abstraction model for analyzing and optimizing reconfigurable intelligent surfaces (RISs) in an electromagnetically consistent manner, especially for studying the impact of the electromagnetic mutual coupling among radiating elements that are spaced less than half of the wavelength apart and for considering the interrelation between the amplitude and phase of the reflection coefficients. Both representations in terms of Z-parameter (impedance) and S-parameter (scattering) matrices are widely utilized. In this paper, we embrace multiport network theory for analyzing and optimizing the reradiation properties of RIS-aided channels, and provide four new contributions. (i) First, we offer a thorough comparison between the Z-parameter and S-parameter representations. This comparison allows us to unveil that typical scattering models utilized for RIS-aided channels ignore the structural scattering from an RIS, which is well documented in antenna theory. We show that the structural scattering results in an unwanted specular reflection. (ii) Then, we develop an iterative algorithm for optimizing, in the presence of electromagnetic mutual coupling, the tunable loads of an RIS based on the S-parameters representation. We prove that small perturbations of the step size of the algorithm result in larger variations of the S-parameter matrix compared with the Z-parameter matrix, resulting in a faster convergence rate. (iii) Subsequently, we generalize the proposed algorithm to suppress the specular reflection due to the structural scattering, while maximizing the received power towards the direction of interest, and analyze the effectiveness and tradeoffs of the proposed approach. (iv) Finally, we validate the theoretical findings and algorithms with numerical simulations and a commercial full-wave electromagnetic simulator based on the method of moments.

Index modulation (IM) schemes for reconfigurable intelligent surfaces (RIS)-assisted systems are envisioned as promising technologies for fifth-generation-advanced and sixth-generation (6G) wireless communication systems to enhance various system capabilities such as coverage area and network capacity. In this paper, we consider a receive diversity RIS-assisted wireless communication system employing IM schemes, namely, space-shift keying (SSK) with binary modulation and spatial modulation (SM) with M-ary modulation for data transmission. The RIS lies in close proximity to the transmitter, and the transmitted data is subjected to a fading environment with a prominent line-of-sight component statistically modeled by a Rician distribution. A receiver structure based on a greedy detection rule is employed to select the receive diversity branch with the highest received signal energy for demodulation. The performance of the considered system is evaluated by obtaining a series-form expression for the probability of erroneous index detection (PED) of the considered target antenna using a characteristic function approach. In addition, closed-form and asymptotic expressions at high and low signal-to-noise ratios (SNRs) for the bit error rate (BER) of the SSK-based system, and the SM-based system employing M-ary phase-shift keying and M-ary quadrature amplitude modulation schemes are derived. The dependencies of the system performance on various parameters are corroborated via numerical results. The asymptotic expressions and results of PED and BER at high and low SNR values lead to the observation of a performance saturation and the presence of an SNR value as a point of inflection, which is attributed to the greedy detector.

The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consumption model (GPECM) for rotary-wing UAVs under the consideration of stochastic wind modeling and 3D force analysis. Based on the GPECM, we study a UAV-enabled downlink communication system, where a rotary-wing UAV flies subject to stochastic wind disturbance and provides communication services for ground users (GUs). We aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing the 3D trajectory and user scheduling among the GUs based on the GPECM. We formulate the problem as a stochastic optimization, which is difficult to solve due to the lack of real-time wind information. To address this issue, we propose an offline-based online adaptive (OBOA) design that is comprised of two phases, namely, an offline phase and an online phase. In the offline phase, we average the wind effect on the UAV by leveraging stochastic programming (SP) based on wind statistics; then, in the online phase, we further optimize the instantaneous velocity to adapt to the real-time wind. Simulation results show that the optimized trajectories of the UAV in both phases can better adapt to changes of the speed and direction of the wind, resulting in a higher EE compared with the wind-unaware scheme. In particular, the proposed OBOA design can be applied in the scenario with dramatic wind changes, and allows the UAV to adjust its velocity dynamically to achieve better performance in terms of EE.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

This paper evaluates the downlink performance of cellular networks in terms of coverage and electromagnetic field exposure (EMFE), in the framework of stochastic geometry. The model is constructed based on datasets for sub-6 GHz macro cellular networks but it is general enough to be applicable to millimeter-wave networks as well. On the one hand, performance metrics are calculated for β-Ginibre point processes which are shown to faithfully model a large number of motion-invariant networks. On the other hand, performance metrics are derived for inhomogeneous Poisson point processes with a radial intensity measure, which are shown to be a good approximation for motion-variant networks. For both cases, joint and marginal distributions of the EMFE and the coverage, and the first moments of the EMFE are provided and validated by Monte Carlo simulations using realistic sets of parameters from two sub-6 GHz macro urban cellular networks, i.e., 5G NR 2100 (Paris, France) and LTE 1800 (Brussels, Belgium) datasets. In addition, this paper includes the analysis of the impact of the network parameters and discusses the achievable trade-off between coverage and EMFE.

Reconfigurable intelligent surface (RIS) is an emerging paradigm able to control the propagation environment in wireless systems. Most of the research on RIS has been dedicated to system optimization and, with the advent of beyond diagonal RIS (BD-RIS), to RIS architecture design. However, developing general and unified electromagnetic (EM)-consistent models for RIS-aided systems remains an open problem. In this study, we propose a universal framework for the multiport network analysis of RIS-aided systems. With our framework, we model RIS-aided systems and RIS architectures through impedance, admittance, and scattering parameter analysis. Based on these analyses, three equivalent models are derived accounting for the effects of impedance mismatching and mutual coupling. The three models are then simplified by assuming large transmission distances, perfect matching, and no mutual coupling to understand the role of the RIS in the communication model. The derived simplified models are consistent with the model used in related literature, although we show that an additional approximation is commonly considered in the literature. We discuss the benefits of each analysis in characterizing and optimizing the RIS and how to select the most suitable parameters according to the needs. Numerical results provide additional evidence of the equivalence of the three analyses.

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading effect, which is a limitation in the conventional passive RIS-aided wireless systems. We aim to maximize the sum rate through jointly designing the transmit beamforming at the base station (BS), the amplification factors and the phase shifts at the active RIS. To tackle this challenging optimization problem effectively, we decouple it into two tractable subproblems. Subsequently, each subproblem is transformed into a second order cone programming problem. The block coordinate descent framework is applied to tackle them, where the transmit beamforming and the reflection coefficients are alternately designed. In addition, another efficient algorithm is presented to reduce the computational complexity. Specifically, by exploiting the majorization-minimization approach, each subproblem is reformulated into a tractable surrogate problem, whose closed-form solutions are obtained by Lagrange dual decomposition approach and element-wise alternating sequential optimization method. Simulation results validate the effectiveness of our developed algorithms, and reveal that the HWIs significantly limit the system performance of active RIS-empowered wireless communications. Furthermore, the active RIS noticeably boosts the sum rate under the same total power budget, compared with the passive RIS.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing problem. We propose a novel message-passing algorithm to solve the considered problem, and a progressive approximation method to reduce the computational complexity involved in the message passing. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters, hence mitigating the model mismatch problem. Finally, we analyze the efficacy of the proposed algorithm through the Bayesian Cramér-Rao bound. Numerical results demonstrate the feasibility of the proposed scheme and the superior performance of the proposed EM-based message-passing algorithm.

This paper investigates the integration of a reconfigurable intelligent surface (RIS) into a secure multiuser massive multiple-input multiple-output (MIMO) system in the presence of transceiver hardware impairments (HWI), imperfect channel state information (CSI), and spatially correlated channels. We first introduce a linear minimum-mean-square error estimation algorithm for the aggregate channel by considering the impact of transceiver HWI and RIS phase-shift errors. Then, we derive a lower bound for the achievable ergodic secrecy rate in the presence of a multi-antenna eavesdropper when artificial noise (AN) is employed at the base station (BS). In addition, the obtained expressions of the ergodic secrecy rate are further simplified in some noteworthy special cases to obtain valuable insights. To counteract the effects of HWI, we present a power allocation optimization strategy between the confidential signals and AN, which admits a fixed-point equation solution. Our analysis reveals that a non-zero ergodic secrecy rate is preserved if the total transmit power decreases no faster than 1/N, whereNis the number of RIS elements. Moreover, the ergodic secrecy rate grows logarithmically with the number of BS antennasMand approaches a certain limit in the asymptotic regimeN→ ∞. Simulation results are provided to verify the derived analytical results. They reveal the impact of key design parameters on the secrecy rate. It is shown that, with the proposed power allocation strategy, the secrecy rate loss due to HWI can be counteracted by increasing the number of low-cost RIS elements.

The advent of reconfigurable intelligent surfaces (RISs) brings along significant improvements for wireless technology on the verge of beyond-fifth-generation networks (B5G). The proven flexibility in influencing the propagation environment opens up the possibility of programmatically altering the wireless channel to the advantage of network designers, enabling the exploitation of higher-frequency bands for superior throughput overcoming the challenging electromagnetic (EM) propagation properties at these frequency bands. However, RISs are not magic bullets. Their employment comes with significant complexity, requiring ad-hoc deployments and management operations to come to fruition. In this paper, we tackle the open problem of bringing RISs to the field, focusing on areas with little or no coverage. In fact, we present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as D-RISA, which trains a DRL agent and, in turn, obtains an optimal RIS deployment. We validate our framework in the indoor scenario of the Rennes railway station in France, assessing the performance of our algorithm against state-of-the-art (SOA) approaches. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR), at lower computational time (up to - 25 %) while improving scalability towards denser network deployments.

To support the extremely high spectral efficiency and energy efficiency requirements, and emerging applications of future wireless communications, holographic multiple-input multiple-output (H-MIMO) technology is envisioned as one of the most promising enablers. It can potentially bring extra degrees-of-freedom for communications and signal processing, including spatial multiplexing in line-of-sight (LoS) channels and electromagnetic (EM) field processing performed using specialized devices, to attain the fundamental limits of wireless communications. In this context, EM-domain channel modeling is critical to harvest the benefits offered by H-MIMO. Existing EM-domain channel models are built based on the tensor Green function, which require prior knowledge of the global position and/or the relative distances and directions of the transmit/receive antenna elements. Such knowledge may be difficult to acquire in real-world applications due to extensive measurements needed for obtaining this data. To overcome this limitation, we propose a transmit-receive parameter separable channel model method-ology in which the EM-domain (or holographic) channel can be simply acquired from the distance/direction measured between the center-points between the transmit and receive surfaces, and the local positions between the transmit and receive elements, thus avoiding extensive global parameter measurements. Analysis and numerical results showcase the effectiveness of the proposed channel modeling approach in approximating the H-MIMO channel, and achieving the theoretical channel capacity.

Edge serverless computing has been widely used in mobile and web applications thanks to its simplicity and cost efficiency of network resource management. Serverless functions, event-driven and often stateless, are deployed on edge servers to reduce the latency caused by data transfer and the workload on cloud servers. However, in many edge serverless application scenarios, such as in IoT networks, the network is usually hierar-chical, with limited edge storage and computational capacity, and requests received by different edge nodes might be heterogeneous and time-varying. The function deployment policy becomes a key issue in achieving guaranteed network performances. In this paper, we focus on function allocation policy in edge serverless computing networks to help edge nodes dynamically choose the functions to deploy based on incoming requests, aiming to reduce latency and minimize request outage rate. A Deep Deterministic Policy Gradient (DDPG)-based function allocation algorithm is proposed for a multi-tier edge-serverless computing scenario where a set of functions are deployed to the edge nodes strategically. Simulation results show that our proposed method can reduce the latency of requests while also minimizing the outage rate.

Space-time modulated metasurfaces (STMMs) are a recently proposed generalization of reconfigurable intelligent surfaces, which include a proper time-varying phase at the metasurface elements, enabling higher flexibility and control of the reflected signals. The spatial component can be designed to control the direction of reflection, while the temporal one can be adjusted to change the frequency of the reflected signal or to convey information. However, the coupling between the spatial and temporal phases at the STMM can adversely affect its performance. Therefore, this paper analyzes the system parameters that affect the space-time coupling. Furthermore, two methods for space-time decoupling are investigated. Numerical results highlight the effectiveness of the proposed decoupling methods and reveal that the space-time phase coupling increases with the bandwidth of the temporal phase, the size of the STMM, and with grazing angles of incidence onto the STMM.

This paper focuses on achieving optimal multi-user channel access in distributed networks using a reconfigurable intelligent surface (RIS). The network includes wireless channels with direct links between users and RIS links connecting users to the RIS. To maximize average system throughput, an optimal channel access strategy is proposed, considering the trade-off between exploiting spatial diversity gain with RIS assistance and the overhead of channel probing. The paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance, based on statistics theory of optimal sequential observation planned decision. Each source-destination pair makes decisions regarding the use of direct links and/or probing source-RIS-destination links. Channel access occurs in a distributed manner after successful channel contention. The optimality of the strategy is rigorously derived using multiple-level pure thresholds. A distributed algorithm, which achieves significantly lower online complexity at$O(1)$, is developed to implement the proposed strategy. Numerical simulations verify the theoretical results and demonstrate the superior performance compared to existing approaches.

This work addresses the issue of energy efficiency maximization in a multi-user network aided by a reconfigurable intelligent surface (RIS) with global reflection capabilities. Two optimization methods are proposed to optimize the mobile users’ powers, the RIS coefficients, and the linear receive filters. Both methods are provably convergent and require only the solution of convex optimization problems. The numerical results show that the proposed methods largely outperform heuristic resource allocation schemes.

Reconfigurable intelligent surface has recently emerged as a promising technology for shaping the wireless environment by leveraging massive low-cost reconfigurable elements. Prior works mainly focus on a single-layer metasurface that lacks the capability of suppressing multiuser interference. By contrast, we propose a stacked intelligent metasurface (SIM)-enabled transceiver design for multiuser multiple-input single-output downlink communications. Specifically, the SIM is endowed with a multilayer structure and is deployed at the base station to perform transmit beamforming directly in the electromagnetic wave domain. As a result, an SIM-enabled transceiver overcomes the need for digital beamforming and operates with low-resolution digital-to-analog converters and a moderate number of radio-frequency chains, which significantly reduces the hardware cost and energy consumption, while substantially decreasing the pre-coding delay benefiting from the processing performed in the wave domain. To leverage the benefits of SIM-enabled transceivers, we formulate an optimization problem for maximizing the sum rate of all the users by jointly designing the transmit power allocated to them and the analog beamforming in the wave domain. Numerical results based on a customized alternating optimization algorithm corroborate the effectiveness of the proposed SIM-enabled analog beamforming design as compared with various benchmark schemes. Most notably, the proposed analog beamforming scheme is capable of substantially decreasing the precoding delay compared to its digital counterpart.

This work addresses the comparison between active and passive RISs in wireless networks, with reference to the system energy efficiency (EE). Two provably convergent and computationally-friendly EE maximization algorithms are developed, which optimize the reflection coefficients of the RIS, the transmit powers, and the linear receive filters. Numerical results show the performance of the proposed methods and discuss the operating points in which active or passive RISs should be preferred from an energy-efficient perspective.

Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e.g., customizing the communication environment. Therefore, deploying multiple RISs helps overcome severe signal blocking between the base station (BS) and users, which is also a practical and effective solution to achieve better service coverage. However, reaping the full benefits of a multi-RISs aided communication system requires solving a non-convex, infinite-dimensional optimization problem, which motivates the use of learning-based methods to configure the optimal policy. This paper adopts a novel heterogeneous graph neural network (GNN) to effectively exploit the graph topology in the wireless communication optimization problem. First, we characterize all communication link features and interference relations in our system with a heterogeneous graph structure. Then, we endeavor to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active beamforming at the BS, the passive beamforming vector of the RIS elements, as well as the RISs association strategy. Unlike most existing work, we consider a more general scenario where the cascaded link for each user is not fixed but dynamically selected by maximizing the WSR. Simulation results show that our proposed heterogeneous GNNs perform about 10 times better than other benchmarks, and a suitable RISs association strategy is also validated to be effective in improving the quality services of users by 30%.

Thanks to the characteristic that the signals can be reflected to a pre-defined oritention, intelligent reflecting surface (IRS) is becoming a promising technology to improve the efficiency of wireless communications. In this paper, a reflective group number based index modulation (RGNIM) scheme is proposed by exploiting the activating states of the reflecting elements of the IRS, where different group number of activated IRS elements can carry additional information in the reflective domain. The bit error ratio (BER) performance is then theoretically analysed under the imperfect channel estimation by adopting the maximum likelihood (ML) detection approach. Simulation results validate and evaluate the BER performance of the RGNIM assisted system, which also demonstrate that our RGNIM scheme outperforms other IRS aided index modulation schemes.

Abstract We consider a multiple‐input multiple‐output (MIMO) channel in the presence of a reconfigurable intelligent surface (RIS). Specifically, our focus is on analysing the spatial multiplexing gains in line‐of‐sight and low‐scattering MIMO channels in the near field. We prove that the channel capacity is achieved by diagonalising the end‐to‐end transmitter‐RIS‐receiver channel, and applying the water‐filling power allocation to the ordered product of the singular values of the transmitter‐RIS and RIS‐receiver channels. The obtained capacity‐achieving solution requires an RIS with a non‐diagonal matrix of reflection coefficients. Under the assumption of nearly‐passive RIS, that is, no power amplification is needed at the RIS, the water‐filling power allocation is necessary only at the transmitter. We refer to this design of RIS as a linear, nearly‐passive, reconfigurable electromagnetic object (EMO). In addition, we introduce a closed‐form and low‐complexity design for RIS, whose matrix of reflection coefficients is diagonal with unit‐modulus entries. The reflection coefficients are given by the product of two focusing functions: one steering the RIS‐aided signal towards the mid‐point of the MIMO transmitter and one steering the RIS‐aided signal towards the mid‐point of the MIMO receiver. We prove that this solution is exact in line‐of‐sight channels under the paraxial setup. With the aid of extensive numerical simulations in line‐of‐sight (free‐space) channels, we show that the proposed approach offers performance (rate and degrees of freedom) close to that obtained by numerically solving non‐convex optimization problems at a high computational complexity. Also, we show that it provides performance close to that achieved by the EMO (non‐diagonal RIS) in most of the considered case studies.

Targeting ultra-reliable and scalable connectivity of extremely high data rates, in the 100 Gbps to Tbps range, at almost “zero-latency” in 6G systems would require taking advantage of breakthrough novel technology concepts, including THz wireless links, broadband and spectrally efficient RF-frontends for a variety of different bands, the employment of intelligent materials (e.g., reconfigurable intelligent surfaces) and the design of machine learning-based models, protocols, and management techniques. To materialize the 6G vision, novel system techniques will need to be devised, including channel modeling and estimation, waveforms, beamforming, and multiple-access schemes, all tailored to the particularities of the adopted breakthrough technologies. As challenging Tbit/s usage scenarios are becoming ever more relevant for 6G systems, including non-line of sight connectivity based on intelligent surfaces and ad hoc connectivity in fast-moving network topologies, e.g., based on drones or V2X links, performance targets need to be reassessed. In such scenarios, apart from the high data rates in the order of Tbit/s other critical parameters may arise as more relevant: range, reliability, adaptability, reconfigurability, and agility, to name just a few.

In this paper, we propose a novel reconfigurable intelligent surface (RIS)-assisted wireless communication scheme which uses the concept of spatial modulation, namely RIS-assisted receive quadrature spatial modulation (RIS-RQSM). In the proposed RIS-RQSM system, the information bits are conveyed via both the indices of the two selected receive antennasandthe conventional in-phase/quadrature (IQ) modulation. We propose a novel methodology to adjust the phase shifts of the RIS elements in order to maximize the signal-to-noise ratio (SNR)andat the same time toconstructtwo separate PAM symbols at the selected receive antennas, as the in-phase and quadrature components of the desired IQ symbol. An energy-based greedy detector (GD) is implemented at the receiver to efficiently detect the received signal with minimal channel state information (CSI) via the use of an appropriately designed one-tap pre-equalizer. We also derive a closed-form upper bound on the average bit error probability (ABEP) of the proposed RIS-RQSM system. Then, we formulate an optimization problem to minimize the ABEP in order to improve the performance of the system, which allows the GD to act as a near-optimal receiver. Extensive numerical results are provided to demonstrate the error rate performance of the system and to compare with that of a prominent benchmark scheme. The results verify the remarkable superiority of the proposed RIS-RQSM system over the benchmark scheme.

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search.

This paper investigates the two-timescale transmission scheme for reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems, where the beamforming at the base station (BS) is adapted to the rapidly-changing instantaneous channel state information (CSI), while the nearly-passive beamforming at the RIS is adapted to the slowly-changing statistical CSI. Specifically, we first consider a system model with spatially independent Rician fading channels, which leads to tractable expressions and offers analytical insights on the power scaling laws and on the impact of various system parameters. Then, we analyze a more general system model with spatially correlated Rician fading channels and consider the impact of electromagnetic interference (EMI) caused by any uncontrollable sources present in the considered environment. For both case studies, we apply the linear minimum mean square error (LMMSE) estimator to estimate the aggregated channel from the users to the BS, utilize the low-complexity maximal ratio combining (MRC) detector, and derive a closed-form expression for a lower bound of the achievable rate. Besides, an accelerated gradient ascent-based algorithm is proposed for solving the minimum user rate maximization problem. Numerical results show that, in the considered setup, the spatially independent model without EMI is sufficiently accurate when the inter-distance of the RIS elements is sufficiently large and the EMI is mild. In the presence of spatial correlation, we show that an RIS can better tailor the wireless environment. Furthermore, it is shown that deploying an RIS in a massive MIMO network brings significant gains when the RIS is deployed close to the cell-edge users. On the other hand, the gains obtained by the users distributed over a large area are shown to be modest.

In reconfigurable intelligent surface (RIS)-aided coherent communications, channel state information (CSI) is often assumed to be perfectly estimated at the receiver. However, perfect CSI cannot be available in practice. Furthermore, the complex and ever-changing channel makes the acquisition of accurate CSI often unaffordable because of the large overhead in transmitting pilot signals. Motivated by these considerations, a novel non-coherent RIS-aided joint index keying$M$-ary differential chaos shift keying (RIS-JIK-MDCSK) system is proposed in this paper, where the receiver can retrieve information bits by performing non-coherent correlation demodulation without requiring CSI, thereby reducing the system complexity. In RIS-JIK-MDCSK, the states of the reference signal, RIS elements, and information-bearing subcarriers are jointly optimized to devise a joint index keying mechanism, where additional information bits are implicitly transmitted by these state indices, thus increasing the throughput and spectral efficiency. Furthermore, an effective joint index keying detection algorithm is proposed to recover the information bits. The analytical bit error rate (BER) of RIS-JIK-MDCSK is derived over a Rayleigh fading channel. Other evaluation metrics, including the throughput, spectral efficiency, and system complexity are also analyzed and compared against benchmark systems. Numerical simulations are performed to evaluate the superiority of RIS-JIK-MDCSK compared to existing systems.

Active intelligent reflecting surface (IRS) has been recently proposed to alleviate the product path loss attenuation inherent in the IRS-aided cascaded channel. In this paper, we study an active IRS-aided simultaneous wireless information and power transfer (SWIPT) system. Specifically, an active IRS is deployed to assist a multi-antenna access point (AP) to convey information and energy simultaneously to multiple single-antenna information users (IUs) and energy users (EUs). Two joint transmit and reflect beamforming optimization problems are investigated with different practical objectives. The first problem maximizes the weighted sum-power harvested by the EUs subject to individual signal-to-interference-plus-noise ratio (SINR) constraints at the IUs, while the second problem maximizes the weighted sum-rate of the IUs subject to individual energy harvesting (EH) constraints at the EUs. The optimization problems are non-convex and difficult to solve optimally. To tackle these two problems, we first rigorously prove that dedicated energy beams are not required for their corresponding semidefinite relaxation (SDR) reformulations and the SDR is tight for the first problem, thus greatly simplifying the AP precoding design. Then, by capitalizing on the techniques of alternating optimization (AO), SDR, and successive convex approximation (SCA), computationally efficient algorithms are developed to obtain suboptimal solutions of the resulting optimization problems. Simulation results demonstrate that, given the same total system power budget, significant performance gains in terms of operating range of wireless power transfer (WPT), total harvested energy, as well as achievable rate can be obtained by our proposed designs over benchmark schemes (especially the one adopting a passive IRS). Moreover, it is advisable to deploy an active IRS in the proximity of the users for the effective operation of WPT/SWIPT.

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all the RSs to mitigate interference amongst them as well as to leverage the secondary reflections amongst them. Motivated by these considerations, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require the joint optimization of the phase-shifts of all RS elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst the RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by effectively mitigating interference and leveraging the secondary reflections amongst the RSs, there is a great benefit of deploying more RSs to support different groups of users so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. Without properly dealing with the secondary reflections, by contrast, increasing the number of RSs can adversely impact the network throughput, especially for high transmit power.

This paper investigates reconfigurable intelligent surface (RIS)-assisted secure multiuser communication systems in the presence of hardware impairments (HIs) at the RIS and the transceivers. We jointly optimize the beamforming vectors at the base station (BS) and the phase shifts of the reflecting elements at the RIS so as to maximize the weighted minimum approximate ergodic secrecy rate (WMAESR), subject to the transmission power constraints at the BS and unit-modulus constraints at the RIS. To solve the formulated optimization problem, we first decouple it into two tractable subproblems and then use the block coordinate descent (BCD) method to alternately optimize the subproblems. Two different methods are proposed to solve the two obtained subproblems. The first method transforms each subproblem into a second order cone programming (SOCP) problem by invoking the penalty convex–concave procedure (CCP) method and the closed-form fractional programming (FP) criterion, and then directly solves them by using CVX. The second method leverages the minorization-maximization (MM) algorithm. Specifically, we first derive a concave approximation function, which is a lower bound of the original objective function, and then the two subproblems are transformed into two simple surrogate problems that admit closed-form solutions. Simulation results verify the performance gains of the proposed robust transmission methods over existing non-robust designs. In addition, the MM algorithm is shown to have much lower complexity than the SOCP-based algorithm.

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/ full-RF and multiplexing-/diversity-oriented setups.

Reconfigurable intelligent surface (RIS)-assisted unmanned areal vehicles (UAV) communications have been identified as a key enabler of a number of next-generation applications. However, to the best of our knowledge, there is no generalized framework for the quantification of the throughput performance of RIS-assisted UAV systems. Motivated by this, in this paper, we present a comprehensive system model that accounts the impact of multi-path fading, which is modeled by means of mixture gamma, transceiver hardware imperfections, and stochastic beam disorientation and misalignment in order to examine the throughput performance of a RIS-assisted UAV wireless system. In this direction, we present a novel closed-form expression for the systems throughput for two scenarios: i) in the presence and ii) in the absence of disorientation and misalignment. Interestingly, our results reveal the importance of accurate modeling the aforementioned phenomena as well as the existence of an optimal transmission spectral efficiency.

In this work, we investigate a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) scenario, where a base station (BS) communicates with multiple devices in a full-duplex mode, and senses the positions of these devices simultaneously. An RIS is assumed to be mounted on each device to enhance the reflected echoes. Meanwhile, the information of each device is passively transferred to the BS via reflection modulation. We aim to tackle the problem of joint localization and information retrieval at the BS. A grid based parametric model is constructed and the joint estimation problem is formulated as a compressive sensing (CS) problem. Moreover, an expectation-maximization (EM) algorithm is applied for tuning the grid parameters to mitigate the model mismatch problem. Finally, we analyze the efficacy of various CS algorithms through the Bayesian Cramér-Rao bound (BCRB). Numerical results demonstrate the feasibility of the proposed scenario and the superior performance of the proposed EM-tuning method.

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to validate the obtained analytical framework. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.

In this paper, we introduce a theoretical framework for analyzing the performance of multi-reconfigurable intelligence surface (RIS) empowered terahertz (THz) wireless systems subject to turbulence and stochastic beam misalignment. In more detail, we extract a closed-form expression for the outage probability that quantifies the joint impact of turbulence and misalignment as well as the effect of transceivers’ hardware imperfections. Our results highlight the importance of accurately modeling both turbulence and misalignment when assessing the performance of multi-RIS-empowered THz wireless systems.

Reconfigurable Intelligent Surfaces (RISs) are considered one of the key disruptive technologies towards future 6G networks. RISs revolutionize the traditional wireless communication paradigm by controlling the wave propagation properties of the impinging signals at will. A major roadblock for RIS is though the need for a fast and complex control channel to continuously adapt to the ever-changing wireless channel conditions. In this paper, we ask ourselves the question: Would it be feasible to remove the need for control channels for RISs? We analyze the feasibility of devising Self-Configuring Smart Surfaces that can be easily and seamlessly installed throughout the environment, following the new Internet-of-Surfaces (IoS) paradigm, without requiring modifications of the deployed mobile network. To this aim we design MARISA, a self-configuring metasurfaces absorption and reflection solution. Our results show that MARISA achieves outstanding performance, rivaling with state-of-the-art control channel-driven RISs solutions.

International audience

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) system in a sophisticated 3-D environment, where the UAV’s trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified 2-D scenario and the availability of perfect channel state information (CSI), this article considers a practical 3-D urban environment with imperfect CSI, where the UAV’s trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired by the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV’s trajectory and we present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of the UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of the IoT nodes, the UAV’s position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV’s movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional nonlearning-based baseline methods.

The quality of experience (QoE) requirements of wireless virtual reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via the abundant bandwidth in the terahertz (THz) band. However, THz waves experience severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with programmable reflecting elements. Meanwhile, the low VR interaction latency can be achieved with the mobile edge computing (MEC) network architecture due to its computation capabilities. Motivated by these considerations, in this paper, we propose an MEC-enabled and RIS-assisted THz VR network in an indoor scenario, by taking into account the uplink viewpoint prediction and position transmission, the MEC rendering, and the downlink transmission. We propose two methods, which are referred to as centralized online gated recurrent unit (GRU) and distributed federated averaging (FedAvg), to predict the viewpoints of the VR users. In the uplink, an algorithm that integrates online long-short term memory (LSTM) and convolutional neural networks (CNN) is deployed to predict the locations and the line-of-sight and non-line-of-sight statuses of the VR users over time. In the downlink, we develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS under latency constraints. Simulation results show that our proposed learning architecture achieves near-optimal QoE as that of the genie-aided benchmark algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme.

International audience

To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.

A reconfigurable intelligent surface (RIS) is a planar structure that is engineered to dynamically control the electromagnetic waves. In wireless communications, RISs have recently emerged as a promising technology for realizing programmable and reconfigurable wireless propagation environments through nearly passive signal transformations. With the aid of RISs, a wireless environment becomes part of the network design parameters that are subject to optimization. In this tutorial article, we focus our attention on communication models for RISs. First, we review the communication models that are most often employed in wireless communications and networks for analyzing and optimizing RISs and elaborate on their advantages and limitations. Then, we concentrate on models for RISs that are based on inhomogeneous sheets of surface impedance and offer a step-by-step tutorial on formulating electromagnetically consistent analytical models for optimizing the surface impedance. The differences between local and global designs are discussed and analytically formulated in terms of surface power efficiency and reradiated power flux through the Poynting vector. Finally, with the aid of numerical results, we discuss how approximate global designs can be realized by using locally passive RISs with zero electrical resistance (i.e., inhomogeneous reactance boundaries with no local power amplification) even for large angles of reflection and at high power efficiency.

Wireless connectivity is regarded as a fundamental need for our society. Between 2020 and 2030, it is forecast that the data traffic of the global internet protocol will increase by 55% each year, eventually reaching 5016 EB, with data rates scaling up to 1 Tb/s. Besides supporting very high data rates, future wireless networks are expected to offer several other heterogeneous services, which include sensing, localization, low-latency, and ultrareliable communications. Fifth-generation (5G) networks are, however, not designed to meet these requirements. As the demands and needs become more stringent, in fact, fundamental limitations arise, which are ultimately imposed by the inherent nature of the wireless operation.

This work considers the problem of energy efficiency (EE) maximization in a reconfigurable intelligent surface (RIS)-aided multiple input multiple output (MIMO) communication link, subject to maximum power constraints and to additional constraints on the maximum exposure of the end-users to electromagnetic radiations. The RIS phase shifts, the transmit beamforming, the linear receive filter, and the transmit power are jointly optimized, and two provably convergent and low-complexity algorithms are developed. One algorithm applies to the general system setup, but does not guarantee global optimality. The other is provably optimal in the notable special case of isotropic electromagnetic field (EMF) exposure constraints. The numerical results show that an RIS ensures an EE of the same order of magnitude as when no EMF constraints are enforced.

Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted wireless communication systems. In this paper, we refine our previously proposed free-space path loss model for RISs to make it simpler, more applicable, and easier to use. The impact of the antenna’s directivity of the transmitter, receiver, and the unit cells of the RIS on the path loss is explicitly formulated as an angle-dependent loss factor. The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size. Based on the proposed model, the properties of a single unit cell are evaluated in terms of scattering performance, power consumption, and area, which allows us to unveil fundamental considerations for deploying RISs in high frequency bands. Two fabricated RISs operating in the millimeter-wave (mmWave) band are utilized to carry out a measurement campaign. The measurement results are shown to be in good agreement with the proposed path loss model. In addition, the experimental results suggest an effective form to characterize the power radiation pattern of the unit cell for path loss modeling.

The intelligent omni-surface (IOS) is a dynamic metasurface that has recently been proposed to achieve full-dimensional communications by realizing the dual function of anomalous reflection and anomalous refraction. Existing research works provide only simplified models for the reflection and refraction responses of the IOS, which do not explicitly depend on the physical structure of the IOS and the angle of incidence of the electromagnetic (EM) waves. Therefore, the available reflection-refraction models are insufficient to characterize the performance of full-dimensional communications. In this paper, we propose a complete and detailed circuit-based reflection-refraction model for the IOS, which is formulated in terms of the physical structure and equivalent circuits of the IOS elements, as well as we validate it with the aid of full-wave EM simulations. Based on the proposed circuit-based model for the IOS, we analyze the asymmetry between the reflection and transmission coefficients. Moreover, the proposed circuit-based model is utilized for optimizing the hybrid beamforming of IOS-assisted networks and hence improving the system performance. To verify the circuit-based model, the theoretical findings, and to evaluate the performance of full-dimensional beamforming, we implement a prototype of IOS and deploy an IOS-assisted wireless communication testbed to experimentally measure the beam patterns and to quantify the achievable rate. The obtained experimental results validate the theoretical findings and the accuracy of the proposed circuit-based reflection-refraction model for IOSs.

In this paper, we investigate the performance of an RIS-aided wireless communication system subject to outdated channel state information that may operate in both the near- and far-field regions. In particular, we take two RIS deployment strategies into consideration: (i) the centralized deployment, where all the reflecting elements are installed on a single RIS and (ii) the distributed deployment, where the same number of reflecting elements are placed on multiple RISs. For both deployment strategies, we derive accurate closed-form approximations for the ergodic capacity, and we introduce tight upper and lower bounds for the ergodic capacity to obtain useful design insights. From this analysis, we unveil that an increase of the transmit power, the Rician-$K$factor, the accuracy of the channel state information and the number of reflecting elements help improve the system performance. Moreover, we prove that the centralized RIS-aided deployment may achieve a higher ergodic capacity as compared with the distributed RIS-aided deployment when the RIS is located near the base station or near the user. In different setups, on the other hand, we prove that the distributed deployment outperforms the centralized deployment. Finally, the analytical results are verified by using Monte Carlo simulations.

Channel reciprocity-based key generation (CRKG) has recently emerged as a new technique to address the problem of key distribution in wireless networks. However, as this approach relies upon the characteristics of fading channels, the corresponding secret key rate may be low when the communication link is blocked. To enhance the applicability of CRKG in harsh propagation scenarios, this paper introduces a novel multiuser key generation scheme, which is referred to as RIS-assisted multiuser key generation (RMK) that leverages the reconfigurable intelligent surface (RIS) technology for appropriately shaping the environment and enhancing the sum secret key rate between an access point and multiple users. In the RMK scheme, an RIS-induced channel, rather than the direct channel, serves as the key source. We derive a general closed-form expression of the secret key rate and optimize the configuration of the RIS to maximize the sum secret key rate over independent and correlated fading channels in the presence of multiple users. In the presence of independent fading, we introduce a low-complexity algorithm based on the Karush-Kuhn-Tucker (KKT) condition. In the presence of correlated fading, the optimization problem is non-convex and challenging to solve. To tackle it, we propose a new optimization algorithm based on the semi-definite relaxation (SDR) and successive convex approximation (SCA) methods. Simulation results demonstrate that the proposed RMK scheme outperforms existing RIS-assisted algorithms and achieves a near-optimal sum secret key rate over independent and correlated fading channels.

Cell-Free Massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surface (RIS) are two promising technologies for application to beyond-5G networks. This paper considers Cell-Free Massive MIMO systems with the assistance of an RIS for enhancing the system performance under the presence of spatial correlation among the engineered scattering elements of the RIS. Distributed maximum-ratio processing is considered at the access points (APs). We introduce anaggregated channelestimation approach that provides sufficient information for data processing with the main benefit of reducing the overhead required for channel estimation. The considered system is studied by using asymptotic analysis which lets the number of APs and/or the number of RIS elements grow large. A lower bound for the channel capacity is obtained for a finite number of APs and engineered scattering elements of the RIS, and closed-form expressions for the uplink and downlink ergodic net throughput are formulated in terms of only the channel statistics. Based on the obtained analytical frameworks, we unveil the impact of channel correlation, the number of RIS elements, and the pilot contamination on the net throughput of each user. In addition, a simple control scheme for optimizing the configuration of the engineered scattering elements of the RIS is proposed, which is shown to increase the channel estimation quality, and, hence, the system performance. Numerical results demonstrate the effectiveness of the proposed system design and performance analysis. In particular, the performance benefits of using RISs in Cell-Free Massive MIMO systems are confirmed, especially if the direct links between the APs and the users are of insufficient quality with high probability.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism, which does not require any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which may result in some power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for better utilizing the reflection power in RIS-aided downlink multiple-input single-output (MISO) wireless systems. To this end, RIS-QRM partitions the RIS elements into two subsets in order to reflected the incident signals towards two orthogonal directions by using passive beamforming and encodes its local data onto the two partitions. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming maximum-likelihood detection at the receiver. Moreover, a low-complexity detection method that decouples the joint search of the constellation symbol and the RIS element partition is proposed. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM improves the error performance of the additional bits delivered by the RIS without deteriorating the error performance of the bits modulated on the constellation symbol, as compared to ON/OFF-based RIS-aided schemes.

Massive machine-type communications (mMTC) are poised to provide ubiquitous connectivity for billions of Internet-of-Things (IoT) devices. However, the required low-latency massive access necessitates a paradigm shift in the design of random access schemes, which invokes a need of efficient joint activity and data detection (JADD) algorithms. By exploiting the feature of sporadic traffic in massive access, a beacon-aided slotted grant-free massive access solution is proposed. Specifically, we spread the uplink access signals in multiple subcarriers with pre-equalization processing and formulate the JADD as a multiple measurement vectors (MMV) compressive sensing problem. Moreover, to leverage the structured sparsity of uplink massive access signals among multiple time slots, we develop two computationally efficient detection algorithms, which are termed as orthogonal approximate message passing (OAMP)-MMV algorithm with simplified structure learning (SSL) and accurate structure learning (ASL). To achieve accurate detection, the expectation maximization algorithm is exploited for learning the sparsity ratio and the noise variance. To further improve the detection performance, channel coding is applied and successive interference cancellation (SIC)-based OAMP-MMV-SSL and OAMP-MMV-ASL algorithms are developed, where the likelihood ratio obtained in the soft-decision can be exploited for refining the activity identification. Finally, the state evolution of the proposed OAMP-MMV-SSL and OAMP-MMV-ASL algorithms is derived to predict the performance theoretically. Simulation results verify that the proposed solutions outperform various state-of-the-art baseline schemes, enabling low-latency random access and high-reliable massive IoT connectivity with overloading.

The massive machine-type communications (mMTC) paradigm based on media modulation in conjunction with massive multi-input multi-output base stations (BSs) is emerging as a viable solution to support the massive connectivity for the future Internet-of-Things, in which the inherent massive access at the BSs poses significant challenges for device activity and data detection (DADD). This paper considers the DADD problem for both uncoded and coded media modulation based mMTC with a slotted access frame structure, where the device activity remains unchanged within one frame. Specifically, due to the slotted access frame structure and the adopted media modulated symbols, the access signals exhibit adoubly structured sparsityin both the time domain and the modulation domain. Inspired by this, a doubly structured approximate message passing (DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we derive the state evolution of the DS-AMP algorithm to theoretically characterize its performance. As for the coded case, we develop a bit-interleaved coded media modulation scheme and propose an iterative DS-AMP (IDS-AMP) algorithm based on successive inference cancellation (SIC), where the signal components associated with the detected active devices are successively subtracted to improve the data decoding performance. In addition, the channel estimation problem for media modulation based mMTC is discussed and an efficient data-aided channel state information (CSI) update strategy is developed to reduce the training overhead in block fading channels. Finally, simulation results and computational complexity analysis verify the superiority of the proposed DS-AMP algorithm over state-of-the-art algorithms in the uncoded case. Also, our results confirm that the proposed SIC-based IDS-AMP algorithm can enhance the data decoding performance in the coded case and verify the validity of the proposed data-aided CSI update strategy.

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.

Intelligent reflecting surfaces (IRSs), which are capable of adjusting radio propagation conditions by controlling the phase shifts of the waves that impinge on the surface, have been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, an IOS is an important instance of a reconfigurable intelligent surface (RIS) that can provide service coverage to mobile users (MUs) in a reflective and a refractive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS jointly perform beamforming, for improving the received power of multiple MUs on both sides of the IOS, through different reflective/refractive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to efficiently solve the resulting non-convex program. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.

This paper considers Cell-Free Massive Multiple Input Multiple Output (MIMO) systems with the assistance of an RIS for enhancing the system performance. Distributed maximum-ratio combining (MRC) is considered at the access points (APs). We introduce an aggregated channel estimation method that provides sufficient information for data processing. The considered system is studied by using asymptotic analysis which lets the number of APs and/or the number of RIS elements grow large. A lower bound for the channel capacity is obtained for a finite number of APs and engineered scattering elements of the RIS, and closed-form expression for the uplink ergodic net throughput is formulated. In addition, a simple scheme for controlling the configuration of the RIS scattering elements is proposed. Numerical results verify the effectiveness of the proposed system design and the benefits of using RISs in Cell-Free Massive MIMO systems are quantified.

The quality of experience (QoE) requirement of wireless virtual reality (VR) can only be satisfied with high data rate, high reliability, and low VR interaction latency. This high data rate over short transmission distances may be achieved via the abundant spectrum in the terahertz (THz) band. However, THz waves suffer from severe signal attenuation, which may be compensated by the reconfigurable intelligent surface (RIS) technology with adjustable phase-shift of each reflecting element. Motivated by these considerations, in this paper, we propose an RIS-assisted THz VR network in an indoor scenario, taking into account the viewpoint prediction and downlink transmission. We first propose a genie-aided online gated recurrent unit (GRU) and integration of online long-short term memory (LSTM) and convolutional neural network (CNN) algorithm to predict the viewpoint, location, and the line-of-sight (LoS) and non-line-of-sight (NLoS) statuses of the VR users over time, with the aim to optimize the long-term QoE of the VR users. We then develop a constrained deep reinforcement learning algorithm to select the optimal phase shifts of the RIS for the downlink transmission under latency constraints. Simulation results show that the proposed ensemble learning architecture achieves near-optimal QoE as that of an exhaustive algorithm, and about two times improvement in QoE compared to the random phase shift selection scheme.

To achieve 360ºcoverage, we investigate a simulta-neous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) aided downlink non-orthogonal multiple access (NOMA) network with randomly deployed users. For different scenarios, we first derive two STAR- RIS-aided channel models, namely the central limit model and the curve fitting model. More specifically, the central limit model fits the scenarios with numerous RIS elements while the curve fitting model can be extended to multi-cell scenarios. The analytical results reveal that 1) the central limit model has closed-form expressions calculated as the error functions, and 2) the curve fitting model can be closely modeled as a Gamma distribution. We then derive the closed-form outage probability expressions for the NOMA users. Numerical results indicate that 1) the two channel models match the simulation results well in low signal-to-noise-ratio (SNR) regions and perform as boundaries in high SNR regions, 2) the central limit model performs as an upper bound of the simulation results, while a lower bound can be obtained by the curve fitting model, and 3) the both users in the NOMA pair have no error floor.

Reconfigurable intelligent surfaces (RISs) are regarded as an emerging technology for the next generation of wireless communications. In this paper, we consider a multiple-input multiple-output network where each base station serves a user equipment with the aid of an RIS equipped with N reconfigurable elements. We characterize the interference at one user equipment that is caused by the signal emitted by its non-serving (interfering) RIS. By assuming Rayleigh fading channels, we study the corresponding interference-to-noise- ratio (INR) under the assumption of large values of N, and we prove that the INR is the product of a Chi-Square random variable (RV) and an RV that is approximated with a Gamma distribution. In addition, we prove that the amount of fading of the INR is equal to one in the large N regime.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism that conveys information by reconfiguring incident electromagnetic waves without using radio frequency chains. Existing reflection modulation schemes propose to convey additional information by manipulating the ON/OFF states of reflecting elements, which underuse the potential of the RIS. In this paper, we first propose a new reflection modulation scheme, called RIS-based quadrature reflection modulation (RIS-QRM), to achieve joint passive beamforming and information transfer for RIS-aided single-input single-output wireless communications by assuming that the direct link between the transceivers is blocked. We then extend the RIS-QRM scheme to the multiple-input single-output setting with the direct link between transceivers being taken into account. In this case, an optimization problem is formulated to jointly optimize the active beamforming and the phase shifts of the RIS in order to improve the received signal power at the user. Computer simulation results corroborate the effectiveness of the RIS-QRM scheme. It is shown that in contrast to ON/OFF-based schemes, the RIS-QRM scheme is able to improve the error performance of the additional bits delivered by the RIS without deteriorating that of the bits carried by the constellation symbols.

Reconfigurable intelligent surfaces (RISs) are typically used in multi-user systems to mitigate interference among active transmitters. In contrast, this paper studies a setting with a conventional active encoder as well as a passive encoder that modulates the reflection pattern of the RIS. The RIS hence serves the dual purpose of improving the rate of the active encoder and of enabling communication from the second encoder. The capacity region is characterized, and information-theoretic insights regarding the trade-offs between the rates of the two encoders are derived by focusing on the high- and low-power regimes.

The aerial-terrestrial communication system constitutes an efficient paradigm for supporting and complementing terrestrial communications. However, the benefits of such a system cannot be fully exploited, especially when the line-of-sight (LoS) transmissions are prone to severe deterioration due to complex propagation environments in urban areas. The emerging technology of reconfigurable intelligent surfaces (RISs) has recently become a potential solution to mitigate propagation-induced impairments and improve wireless network coverage. Motivated by these considerations, in this paper, we address the coverage and link performance problems of the aerial-terrestrial communication system by proposing an RIS-assisted transmission strategy. In particular, we design an adaptive RIS-assisted transmission protocol, in which the channel estimation, transmission strategy, and data transmission are independently implemented in a frame. On this basis, we formulate an RIS-assisted transmission strategy optimization problem as a mixed-integer non-linear program (MINLP) to maximize the overall system throughput. We then employ multi-task learning to speed up the solution to the problem. Benefiting from multi-task learning, the computation time is reduced by about four orders of magnitude. Numerical results show that the proposed RIS-assisted transmission protocol significantly improves the system throughput and reduces the transmit power.

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and locations of 3D objects. By programming its beamformer patterns, the metasurface can provide desirable propagation properties. However, achieving a high sensing accuracy is challenging, since it requires the joint optimization of the beamformer patterns and mapping of the received signals to the sensed outcome. To tackle this challenge, we formulate an optimization problem for minimizing the cross-entropy loss of the sensing outcome, and propose a deep reinforcement learning algorithm to jointly compute the optimal beamformer patterns and the mapping of the received signals. Simulation results verify the effectiveness of the proposed algorithm and show how the size of the metasurface and the target space influence the sensing accuracy.

This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches.

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

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the locations of the mobile users. As a consequence, the proposed approach does not require the estimation of either instantaneous channel state information (CSI) or second-order channel statistics for IRS optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical position information still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed.

In this paper, we introduce a physics-consistent analytical characterization of the free-space path-loss of a wireless link in the presence of a reconfigurable intelligent surface. The proposed approach is based on the vector generalization of Green's theorem. The obtained path-loss model can be applied to two-dimensional homogenized metasurfaces, which are made of sub-wavelength scattering elements and that operate either in reflection or transmission mode. The path-loss is formulated in terms of a computable integral that depends on the transmission distances, the polarization of the radio waves, the size of the surface, and the desired surface transformations. Closed-form expressions are obtained in two asymptotic regimes that are representative of far-field and near-field deployments. Based on the proposed approach, the impact of several design parameters and operating regimes is unveiled.

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless communication systems, enhances the spectral and energy efficiency by intelligently adjusting the propagation conditions between base stations (BSs) and mobile equipments (MEs). An RIS consists of many low-cost passive reflecting elements that are optimized to improve the quality of the received signal. In this paper, we study the problem of power control at the BS and RIS optimization for application to physical-layer broadcasting. Our goal is to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BS and the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposed optimization methods, we derive lower bounds to quantify the average transmit power at the BS as a function of the number of MEs, the number of RIS elements, and the number of antennas at the BS. The simulation results demonstrate that the average transmit power at the BS is close to the lower bound in an RIS-aided system, and is significantly lower than the average transmit power in conventional schemes without an RIS.

A communication link aided by a reconfigurable intelligent surface (RIS) is studied in which the transmitter can control the state of the RIS via a finite-rate control link. Channel state information (CSI) is acquired at the receiver based on pilot-assisted channel estimation, and it may or may not be shared with the transmitter. Considering quasi-static fading channels with imperfect CSI, capacity-achieving signalling is shown to implement joint encoding of the transmitted signal and of the response of the RIS. This demonstrates the information-theoretic optimality of RIS-based modulation, or “single-RF MIMO” systems. In addition, a novel signalling strategy based on separate layered encoding that enables practical successive cancellation-type decoding at the receiver is proposed. Numerical experiments show that the conventional scheme that fixes the reflection pattern of the RIS, irrespective of the transmitted information, as to maximize the achievable rate is strictly suboptimal, and is outperformed by the proposed adaptive coding strategies at all practical signal-to-noise ratio (SNR) levels.

This paper proposes a precoded optical space-domain index modulation scheme for indoor visible light communications, which is based on the optimization of the minimum Euclidean distance of optical spatial modulation (OSM) with real-valued modulation constellations. We find that the precoding matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP), whose solution is generally intractable. To tackle this problem, we first consider the case of two optical transmit antennas ( Nt= 2) in the precoded OSM and derive a closed-form solution for arbitrary M-order pulse amplitude modulation (PAM). Based on the derived solutions and the error vector reduction method, we then propose a low-complexity iterative (LCI) algorithm to identify the precoding matrix for the setup Nt> 2. To strike a flexible complexity-BER (bit error rate) tradeoff, we propose a successive convex approximation (SCA)-assisted matrix-based optimization method to transform the non-convex QCQP problem into a series of linear convex subproblems, which can be solved by low-complexity solvers. Simulation results show that these proposed algorithms are capable of substantially improving the system error performance compared with conventional OSM systems. Besides, a symbol-based SCA algorithm is introduced and it is shown to outperform the matrix-based SCA and the suboptimal LCI algorithm in terms of the BER.

In this paper, we consider non-coherent detection schemes for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memory-bits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.

In this paper, we develop an innovative approach to quantitatively characterize the performance of ultra-dense wireless networks in a plethora of propagation environments. The proposed framework has the potential of simplifying the cumbersome procedure of analyzing the coverage probability and allowing the unification of single- and multi-antenna networks through compact analytical representations. By harnessing this key feature, we develop a novel statistical machinery to study the scaling laws of wireless networks densification considering general channel power distributions including small-scale fading and shadowing as well as associated beamforming and array gains due to the use of multiple antenna. We further formulate the relationship between network density, antenna height, antenna array seize and carrier frequency showing how the coverage probability can be maintained with ultra-densification. From a system design perspective, we show that, if multiple antenna base stations are deployed at higher frequencies, monotonically increasing the coverage probability by means of ultra-densification is possible, and this without lowering the antenna height. Simulation results substantiate performance trends leveraging network densification and antenna deployment and configuration against path loss models and signal-to-noise plus interference thresholds.

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

Reconfigurable intelligent surfaces (RISs) have the capability of reconfiguring the wireless environment in a favorable way to improve the quality-of-service (QoS) of wireless communications. This makes RISs a promising candidate to enhance vehicle-to-everything (V2X) applications. This paper investigates the spectrum sharing problem in RIS-aided vehicular networks, in which multiple vehicle-to-vehicle (V2V) links reuse the spectrum already occupied by vehicle-to-infrastructure (V2I) links. To overcome the difficulty of acquiring instantaneous channel state information (CSI) due to the fast varying nature of some V2X channels, we rely upon large-scale (slowly varying) CSI in order to fulfill the QoS requirements of V2I and V2V communications. Particularly, we aim to maximize the sum capacity of V2I links that are used for high-rate content delivery and to guarantee the reliability of V2V links that are used for the exchange of safety information. The transmit power of vehicles, the multi-user detection (MUD) matrix, the spectrum reuse of V2V links, and the RIS reflection coefficients are jointly optimized, which results in a mixed-integer and non-convex optimization problem. To tackle this problem, the outage probability of each V2V link is first approximated by introducing an analytical expression to simplify the formulated problem. By leveraging the block coordinate descent (BCD) method, the considered optimization problem is decomposed into three sub-problems, whose optimal solutions are provided independently and updated alternately to obtain a near-optimal solution. Simulation results verify the theoretical analysis and the effectiveness of the proposed algorithm, as well as unveil the benefits of introducing RISs for enhancing the QoS performance of vehicular communications.

This paper investigates a novel unmanned aerial vehicles (UAVs) secure communication system with the assistance of reconfigurable intelligent surfaces (RISs), where a UAV and a ground user communicate with each other, while an eavesdropper tends to wiretap their information. Due to the limited capacity of UAVs, an RIS is applied to further improve the quality of the secure communication. The time division multiple access (TDMA) protocol is applied for the communications between the UAV and the ground user, namely, the downlink (DL) and the uplink (UL) communications. In particular, the channel state information (CSI) of the eavesdropping channels is assumed to be imperfect. We aim to maximize the average worst-case secrecy rate by the robust joint design of the UAV’s trajectory, RIS’s passive beamforming, and transmit power of the legitimate transmitters. However, it is challenging to solve the joint UL/DL optimization problem due to its non-convexity. Therefore, we develop an efficient algorithm based on the alternating optimization (AO) technique. Specifically, the formulated problem is divided into three sub-problems, and the successive convex approximation (SCA),$\mathcal {S}$-Procedure, and semidefinite relaxation (SDR) are applied to tackle these non-convex sub-problems. Numerical results demonstrate that the proposed algorithm can considerably improve the average secrecy rate compared with the benchmark algorithms, and also confirm the robustness of the proposed algorithm.

Recent research on reconfigurable intelligent surfaces (RISs) suggests that RISs can perform passive beamforming and information transfer (PBIT) simultaneously via smart reflections. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation (RPM) to achieve PBIT, where the joint active and passive beamforming is carefully designed by taking into account the communication outage probability. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS under the assumption that the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. Moreover, a closed-form expression for the asymptotic outage probability of the proposed scheme in Rician fading is derived. The achievable rate of the proposed scheme is also investigated under the assumption that the transmitted symbols are drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate. It is shown that the proposed scheme outperforms, in terms of achievable rate, the conventional RIS-assisted system without information transfer.

Reconfigurable intelligent surfaces (RISs) represent a new technology that can shape the radio wave propagation in wireless networks and offers a great variety of possible performance and implementation gains. Motivated by this, we study the achievable rate optimization for multi-stream multiple-input multiple-output (MIMO) systems equipped with an RIS, and formulate a joint optimization problem of the covariance matrix of the transmitted signal and the RIS elements. To solve this problem, we propose an iterative optimization algorithm that is based on the projected gradient method (PGM). We derive the step size that guarantees the convergence of the proposed algorithm and we define a backtracking line search to improve its convergence rate. Furthermore, we introduce the total free space path loss (FSPL) ratio of the indirect and direct links as a first-order measure of the applicability of RISs in the considered communication system. Simulation results show that the proposed PGM achieves the same achievable rate as a state-of-the-art benchmark scheme, but with a significantly lower computational complexity. In addition, we demonstrate that the RIS application is particularly suitable to increase the achievable rate in indoor environments, as even a small number of RIS elements can provide a substantial achievable rate gain.

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RISs, which may pave the way for further theoretical studies and practical applications in this field.

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with$M$RF chains and$N$antennas, where$M < N$. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of$M$antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

In this paper, a reconfigurable intelligent surface (RIS)-aided millimeter wave (mmWave) non-orthogonal multiple access (NOMA) system is analyzed. In particular, we consider an RIS-aided mmWave-NOMA downlink system with a hybrid beamforming structure. To maximize the achievable sum-rate under a minimum rate constraint for the users and a maximum transmit power constraint, a joint RIS phase shifts, hybrid beamforming, and power allocation problem is formulated. To solve this non-convex optimization problem, we develop an alternating optimization (AO) algorithm. Specifically, first, the non-convex problem is transformed into three subproblems, i.e., power allocation, joint phase shifts and analog beamforming optimization, and digital beamforming design. Then, we solve the power allocation problem by keeping fixed the phase shifts of the RIS and the hybrid beamforming. Finally, given the power allocation matrix, an alternating manifold optimization (AMO)-based method and a successive convex approximation (SCA)-based method are utilized to design the phase shifts, analog beamforming, and transmit beamforming, respectively. Numerical results reveal that the proposed AO algorithm outperforms existing schemes in terms of sum-rate. Moreover, compared to a conventional mmWave-NOMA system without RIS, the proposed RIS-aided mmWave-NOMA system is capable of improving the achievable sum-rate.

Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

The uplink of a reconfigurable intelligent surfaces (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system is analyzed, where the channel state information (CSI) is estimated using uplink pilots. First, we derive analytical expressions for the achievable rate of the system with zero forcing (ZF) receiver, taking into account the effects of pilot contamination, channel estimation error and the distributed RISs. The max-min rate optimization problem is considered with per-user power constraints. To solve this non-convex problem, we propose to decouple the original optimization problem into two sub-problems, namely, phase shift design problem and power allocation problem. The power allocation problem is solved using a standard geometric programming (GP) whereas a semidefinite programming (SDP) is utilized to design the phase shifts. Moreover, the Taylor series approximation is used to convert the nonconvex constraints into a convex form. An iterative algorithm is proposed whereby at each iteration, one of the sub-problems is solved while the other design variable is fixed. The max-min user rate of the RIS-aided cell-free massive MIMO system is compared to that of conventional cell-free massive MIMO. Numerical results indicate the superiority of the proposed algorithm compared with a conventional cell-free massive MIMO system. Finally, the convergence of the proposed algorithm is investigated.

Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beam-forming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's information is statistically known by the AP. Since the formulated problem is non-convex and thus difficult to solve optimally, we propose an efficient algorithm based on the alternating optimization technique to find a high-quality solution. Simulation results validate the effectiveness of the proposed reflection pattern modulation and beamforming design. It is shown that the proposed scheme outperforms the conventional RIS-assisted system with full RIS reflection in terms of achievable rate performance.

As a recently proposed idea for the future wireless systems, intelligent reflecting surface (IRS) can assist communications between entities which do not have high-quality direct channels in between. Specifically, an IRS comprises many low-cost passive elements, each of which reflects the incident signal by incurring a phase change so that the reflected signals add coherently at the receiver. In this paper, for an IRS-aided wireless network, we study the problem of power control at the base station (BS) for physical-layer broadcasting under quality of service constraints, by jointly designing the transmit beamforming at the BS and the phase shifts of the IRS units. Furthermore, we derive a lower bound of the minimum transmit power at the BS to validate the proposed optimization method. Simulation results show that, the transmit power at the BS approaches the lower bound with the increase of the number of IRS units, and is much lower than that of the communication system without the IRS.

Indoor millimeter-wave (mmWave) environment channels are typically sparsely-scattered and dominated by a strong line-of-sight (LOS) path. Therefore, communication over such channels is in general extremely difficult when the LOS path is not present. However, the recent introduction of reconfigurable intelligent surfaces (RISs), which have the potential to influence the propagation environment in a controlled manner, has the potential to change the previous paradigm. Motivated by this, we study the channel capacity optimization utilizing RISs in indoor mmWave environments where no LOS path is present. More precisely, we propose two optimization schemes that exploit the customizing capabilities of the RIS reflection elements in order to maximize the channel capacity. The first optimization scheme exploits only the adjustability of the RIS reflection elements; for this scheme we derive an approximate expression which explains the connection between the channel capacity gains and the system parameters. The second optimization scheme jointly optimizes the RIS reflection elements and the transmit phase precoder; for this scheme, we propose a low-complexity technique called global co-phasing to determine the phase shift values for use at the RIS. Simulation results show that the optimization of the RIS reflection elements produces a significant channel capacity gain, and that this gain increases with the number of RIS elements.

A communication link aided by a Reconfigurable Intelligent Surface (RIS) is studied, in which the transmitter can control the state of the RIS via a finite-rate control link. Prior work mostly assumed a fixed RIS configuration irrespective of the transmitted information. In contrast, this work derives information-theoretic limits, and demonstrates that the capacity is achieved by a scheme that jointly encodes information in the transmitted signal as well as in the RIS configuration. In addition, a novel signaling strategy based on layered encoding is proposed that enables practical successive cancellation-type decoding at the receiver. Numerical experiments demonstrate that the standard max-SNR scheme that fixes the configuration of the RIS as to maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly suboptimal, and is outperformed by the proposed strategies at all practical SNR levels.

Ambient backscatter communications have been introduced as low-power communications for green networking. This technology is very promising as it recycles ambient radio frequency waves, however such systems have limitations and suffer from poor performance due to their low-power. In this paper, we present and evaluate the performance of an ambient backscatter system equipped with a massive multiple input multiple output (MIMO) antenna at the reader side. A mobile device transmits a signal that is backscattered by a tag and received by the reader. Thanks to the spatial diversity of the massive MIMO antenna, the reader is able to identify, for each received signal, the state of the tag (backscattering or transparent) that corresponds to a bit of the tag message. First, we experimentally determine the channel between the device and the reader for two states of the tag. Then using a minimum mean square error algorithm we evaluate the performance of the ambient backscatter communication. We demonstrate that the performance of the ambient backscatter system is significantly improved by the massive MIMO reader.

Ambient backscatter communication system is an emerging and promising low-energy technology for Internet of Things. In such system, a device named tag, sends a binary message to a reader by backscattering a radio frequency signal generated by an ambient source. Such tag can operate without battery and without generating additional wave. However, the tag-to-reader link suffers from the source-to-reader direct interference. In this paper, for the first time, we propose to exploit a "polarization reconfigurable" antenna to improve robustness of the tag-to-reader link against the source-to-reader direct interference. Our proposed new tag sends its message by backscattering as an usual tag. However, it repeats its message several times, with a different radiation pattern and polarization, each time. We expect one polarization pattern to be better detected by the reader. We show by simulations and experiments, in line-of-sight and in richly scattering environment, that a polarization reconfigurable tag limited to 4 polarization directions outperforms a non-reconfigurable tag and nearly equals an ideally reconfigurable tag in performance.

In this paper, we present an ambient backscatters-friendly 5G network that creates locations with large power (hot spots) for tags and good reception locations (quiet spots or coherent spots) for readers. Tags are devices that communicate information to readers by backscattering ambient radiofrequency signals. The massive multiple input multiple output (M-MIMO) antenna and beamforming capability of the 5G network is used as follows. In a first step, a training device (separate from tags and readers) is used to send pilots and train the 5G network to perform focusing and/or nulling onto marked locations. In a second step, tags and readers are positioned onto the marked locations. The robustness of M-MIMO beamforming to slight changes in the environment is exploited. Our initial simulation results, in a multipath propagation channel environment, show that creating a hot spot on a tag improves the tag-reader range however with a low probability of detection. Creating a hot spot on a tag and a good reception spot on a reader at the same time, improves the tag-reader range with 99% probability of detection. The study also shows that beamforming does not degrade the performance of a legacy 5G communication.

This paper embodies the Fox's H-transform theory into a unifying modeling and analysis of HetNets. The proposed framework has the potential, due to the Fox's N-functions versatility, of significantly simplifying the cumbersome analysis and representation of cellular coverage, while subsuming those previously derived for all known simple and composite fading models. The paper reveals important insights into the practice of densification in conjunction with signal-to-noise plus interference (SINR) thresholds and path-loss models.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

The interference management technique that treats interference as noise (TIN) is optimal when the interference is sufficiently weak. Scheduling algorithms based on the TIN optimality condition have recently been proposed, e.g., for application to device-to-device communications. TIN, however, has never been applied to cellular networks. In this work, we propose a scheduling algorithm for application to cellular networks that is based on the TIN optimality condition. In the proposed scheduling algorithm, each base station (BS) first randomly selects a user equipment (UE) in its coverage region, and then checks the TIN optimality conditions. If the latter conditions are not fulfilled, the BS is turned off. In order to assess the performance of TIN applied to cellular networks, we introduce an analytical framework with the aid of stochastic geometry theory. We develop, in particular, tractable expressions of the signal-to-interference-and-noise ratio (SINR) coverage probability and average rate of cellular networks. In addition, we carry out asymptotic analysis to find the optimal system parameters that maximize the SINR coverage probability. By using the optimized system parameters, it is shown that TIN applied to cellular networks yields significant gains in terms of SINR coverage probability and average rate. Specifically, the numerical results show that average rate gains of the order of 21% over conventional scheduling algorithms are obtained.

We consider a network-coded cooperative (NCC) system that consists of N ≥ 2 sources, M ≥ 1 decode- and-forward (DF) relays, and a single destination. The relays perform network coding (NC) on the received sources' symbols using maximum distance separable (MDS) codes. For this system, we propose the most generalized user-relay selection (GURS) scheme in the literature that selects any arbitrary subsets of K users and any arbitrary subsets of L relays subject to practical constraints such as load balancing conditions and scheduling policy. Our analytical results and design guidelines generalize and subsume all existing results as special cases. To this end, we derive a new closed-form outage probability (OP) expression, assuming non-identically and independently distributed (n.i.i.d.) Rayleigh fading channels. The asymptotic outage expression at high signal-to-noise ratio (SNR) regime is further derived, based on which, the achievable diversity order and coding gain are quantified. The theoretical derivations are also validated through Monte-Carlo simulation.

In this paper, a novel dual-hop spatial modulation (SM) relay network is proposed, in which the relay is enabled to transmit its own information while forwarding the SM signal from the source over the same frequency band. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are studied. For DF relaying, the relay embeds its own information into one out of two spatial dimensions of quadrature SM. For AF relaying, the relay activates only one transmit antenna to forward the received signal, and encodes its own information by the index of the active antenna. Under both relaying protocols, the throughput of the network is increased without consuming extra power. The bit error rate (BER) performance is analyzed, and tight upper bounds on the BERs are derived in closed form for the source and relay over Rayleigh fading channels for both DF and AF relaying. The performance analysis is verified by Monte Carlo simulations, showing that the proposed scheme provides a simple yet effective solution to the implementation of SM relay networks in which the relay has its own information to be transmitted.

In this paper, we introduce an analytical approach for modeling and analyzing the performance of multi-tier cellular networks that are powered by the power grid and by renewable energy sources. The proposed approach relies on modeling the locations of the base stations, either powered by the power grid or by renewable energy sources, by using Poisson point processes. The availability of renewable energy is modeled by using a Poisson point process in the time domain. In addition, the temporal dynamics of the batteries of the base stations powered by renewable energy sources are modeled by using a discrete Markov chain with a number of states that is equal to the finite storage capacity of the batteries. In particular, the coverage probability, the spectral efficiency, and the energy efficiency of the considered network model are formulated in an analytical, closed-form, expression, which depends on the probability that the typical base station is available, i.e., it has sufficient power to serve at least one mobile terminal in its cell. This latter probability is shown to be the solution of the steady state equation of the Markov chain that models the temporal dynamics of the batteries of the base stations. Under the assumption that the batteries of the base stations can be either empty or fully charged, we formulate an optimization problem in order to maximize the energy efficiency as a function of the transmit power and the deployment density of the base stations, and identify sufficient conditions for which the problem admits a unique solution. The accuracy of the proposed approach and the performance trends inferred from it are substantiated with the aid of extensive Monte Carlo simulations.

Future wireless networks are expected be more than allowing people, mobile devices, and objects to communicate with each other. Future wireless networks will constitute a distributed intelligent communications, sensing, and computing platform. Small cells, Massive MIMO, millimeter-wave communications are three fundamental approaches to meet the requirements of 5G wireless networks. Their advantages are undeniable. The question is, however, whether these technologies will be sufficient to meet the requirements of future wireless networks that integrate communications, sensing, and computing in a single platform. Wireless networks, in addition, are rapidly evolving towards a software-defined design paradigm, where every part of the network can be configured and controlled via software. In this optimization process, however, the wireless environment remains an uncontrollable factor: It remains unaware of the communication process undergoing within it. Apart from being uncontrollable, the environment has a negative effect on the communication efficiency: signal attenuation limits the network connectivity, multi-path propagation results in fading phenomena, reflections and refractions from objects are a source of uncontrollable interference. In the recent period, a brand-new technology, which is referred to as Reconfigurable Intelligent Surfaces (RISs), was brought to the attention of the wireless community. The wireless future that can be envisioned by using this technology consists of coating every environmental object with man-made reconfigurable surfaces of electromagnetic material (software-defined reconfigurable metasurfaces) that are electronically controlled with integrated electronics and wireless communications. In contrast to any other technology currently being used in wireless networks, the distinctive characteristic of the RISs consists of making the environment fully controllable by the telecommunication operators, by allowing them to shape and control the electromagnetic response of the objects distributed throughout the network. The RISs are a promising but little understood technology that has the potential of fundamentally changing how wireless networks are designed today. In this talk, we will discuss the potential of RIS in 6G wireless networks.

We study the performance of generalized user-relay selection (GURS) scheme in network-coded cooperation systems. In particular, we propose the most general case of user-relay selection mechanism that selects any arbitrary subsets of users and relays subject to any practical constraints such as load balancing conditions, scheduling policy, and other factors. Our results thus can be applied to a large set of situations and include all existing results in the literature as special cases. We develop performance characterizations of the system under consideration in terms of outage probability over non-identically and independently distributed (n.i.i.d.) Rayleigh fading channels. The asymptotic outage expressions at high signal-to-noise ratio (SNR) regime are further derived and then, based on the derived expressions, we quantify the diversity order. The theoretical derivations are validated through Monte-Carlo simulations.

In this paper, a cooperative single carrier system comprising multiple cooperating remote radio heads and a single interferer operating in the presence of co-existing line-of-sight and non-line-of-sight paths is investigated. Distributed cyclic delay diversity is employed as the transmit diversity scheme for cyclic-prefixed single carrier transmissions over independent but non-identically distributed frequency selective fading channels. The main focus of this paper is to investigate the achievable diversity gain in the interference-limited and noise-limited regions. In contrast to the outage probability in the noise-limited region, it is shown that the diversity gain is not achievable in the interference-limited region. To justify this finding, the outage probability is derived first, and then verified by link-level simulations.

Recently released 5G networks empower the novel Network Slicing concept. Network slicing introduces new business models such as allowing telecom providers to lease a virtualized slice of their infrastructure to tenants such as industry verticals, e.g. automotive, e-health, factories, etc. However, this new paradigm poses a major challenge when applied to Radio Access Networks (RAN): how to achieve revenue maximization while meeting the diverse service level agreements (SLAs) requested by the infrastructure tenants? In this paper, we propose a new analytical framework, based on stochastic geometry theory, to model realistic RANs that leverage the business opportunities offered by network slicing. We mathematically prove the benefits of slicing radio access networks as compared to non-sliced infrastructures. Based on this, we design a new admission control functional block, STORNS, which takes decisions considering per slice SLA guaranteed average experienced throughput. A radio resource allocation strategy is introduced to optimally allocate transmit power and bandwidth (i.e., a slice of radio access resources) to the users of each infrastructure tenant. Numerical results are illustrated to validate our proposed solution in terms of potential spectral efficiency, and compare it against a non-slicing benchmark.

We propose a novel scheme for downlink multiuser multiple-input multiple-output (MIMO) systems, called dual-layered transmit-receive generalized spatial modulation (DL-TR-GSM). The proposed scheme is based on the concept of dual-layered transmission (DLT) which uses two receive antenna power levels instead of receive antenna activation/inactivation to transmit data in the receive spatial domain. Hence, in order to minimize the bit error rate (BER) for DL-TR-GSM, the optimal ratio between the two power levels is determined. To further characterize DL-TR-GSM, we fully derive the computational complexity and show a significant computational complexity reduction as well as a required hardware complexity reduction of DL-TR-GSM, compared to a state-of-the-art benchmark scheme. Simulation results confirm the performance advantages of DL-TR-GSM.

Ultra dense networks (UDNs) allow for efficient spatial reuse of the spectrum, giving rise to substantial capacity and power gains. However, recent findings have demonstrated that the reduction of inter-site distances after a certain threshold, has a detrimental effect on the network performance. In such cases, interference mitigation strategies to counteract the effect of primarily the line of sight (LOS) interference are imperative. In this course, the present paper investigates a minimum coordination strategy among immediate neighbors. The adopted scheme realizes the interference mitigation objective, enhancing system capacity, while being implementable in practice. Building upon this scheme, the present paper provides tractable closed form expressions for the downlink (DL) ergodic capacity of a reference user under the examined coordination scheme. The analysis is performed for a scenario consisting of base stations (BSs) whose positions follow a Poisson point process of a given spatial density. The tractability and accuracy of the derived expressions renders them ideal for quantifying the performance of additional coordination strategies and for revealing trends in complex optimization problems. Thus, constituting a valuable tool for network operators, toward assessing different interference mitigation techniques and coordination schemes in UDNs.

This IEEE Journal on Selected Areas in Communications (JSAC) special issue (SI) aims to provide a comprehensive overview of the state-of-the-art advances and a view of emerging research challenges and opportunities forSpatial Modulation in Emerging Wireless Systems. This SI solicits high-quality original research papers regarding theoretical studies, and application-oriented contributions dealing with architectures, platforms, and multiple access schemes.

Simultaneous wireless information and power transfer (SWIPT) has gained significant popularity in the recent past owing to its applications in a wide range of use cases. Although SWIPT has been fairly well investigated in the literature, the existing work has mainly focused on attaining the optimal rate energy (RE) tradeoff assuming Gaussian input alphabet. However, practical systems operate with finite input alphabets such as quadratic-amplitude modulation (QAM)/phase-shift keying. We characterize the attainable RE tradeoff in SWIPT systems employing finite input alphabet for transmission over parallel Gaussian channels of say orthogonal frequency-division multiplexing subcarriers or multiple-input multiple-output streams. Some of the key results in the literature that assume Gaussian input alphabet are shown to be special cases of our results. Furthermore, we provide insights into our results with the aid of graphical illustrations, which throw light on the optimal power allocation policy for various energy-harvesting constraints. Furthermore, we consider practically relevant time-sharing and power-splitting schemes operating with finite input alphabet and characterize their RE tradeoff. Their optimal solutions in the asymptotic regime are obtained, which serve as low-complexity solutions suitable for practical implementation. Our simulation studies have demonstrated that the Gaussian input assumption significantly over-estimates the attainable RE tradeoff, especially when the signal set employed is small. Furthermore, it is observed through numerical simulations that the proposed optimal power allocation performs significantly better than the power allocation based on the Gaussian input assumption. Specifically, as much as 30% rate improvement is observed when employing the classic 4-QAM signal set.

Spatial modulation (SM) is an innovative and promising digital modulation technology that strikes an appealing tradeoff between spectral efficiency and energy efficiency with a simple design philosophy. SM enjoys plenty of benefits and shows great potential to fulfill the requirements of future wireless communications. The key idea behind SM is to convey additional information typically through the ON/OFF states of transmit antennas and simultaneously save the implementation cost by reducing the number of radio-frequency chains. As a result, the SM concept can have widespread effects on diverse applications and can be applied in other signal domains, such as frequency/time/code/angle domain or even across multiple domains. This survey provides a comprehensive overview of the latest results and progresses in SM research. Specifically, the fundamental principles, variants of system design, and enhancements of SM are described in detail. Furthermore, the integration of the SM family with other promising techniques, applications to emerging communication systems, and extensions to new signal domains are also extensively studied.

This paper investigates a user-fairness relay selection (RS) problem for decode-and-forward (DF) full-duplex (FD) relay networks, where multiple users cooperate with multiple relays in each coherence time. We consider two residual self-interference (RSI) models with or without direct links. We propose a sub-optimal relay selection (SRS) scheme which requires only the instantaneous channel state information (CSI) of source-to-relay and relay-to-destination links. To evaluate the performance, the outage probability of SRS is derived for different scenarios depending on RSI models and the availability of direct links. To further investigate, asymptotic expressions are derived for the high-transmit power regime. For comparison purposes, 1) the average throughputs of the FD and half-duplex (HD) modes are derived; 2) non-orthogonal transmission is considered and its performance is discussed with approximations; and 3) the impact of imperfect CSI is investigated with the aid of analysis. While simulation results are provided to verify the analytical results, they reveal interesting fundamental trends. It turns out that a significant throughput degradation occurs with FD mode over HD mode when self-interference is fully proportional to the transmit power. Since all users can communicate in the same coherence time with the FD mode, these joint RS schemes are useful for user-fairness low-latency applications.

Network-coded cooperation (NCC) has recently gained interest as it improves the network throughput in multi-source cooperative systems. NCC has been studied for single-antenna terminals only. Employing multiple-input multiple-output (MIMO) techniques can significantly improve the performance of NCC systems. Furthermore, the existing relay selection (RS) strategies for NCC utilize the “max-min” end-to-end (E2E) criterion. This selection strategy (called Strategy A) is complicated even for a network with single-antenna terminals as it requires global channel state information (CSI). This requirement makes it hard to implement RS-based NCC. To counter this issue, we introduce a new RS strategy (Strategy B), which utilizes only the local CSI (not global CSI), significantly reducing the signaling overhead without sacrificing the performance. The performance of MIMO-NCC under Strategies A and B is studied over independent and non-identically distributed (i.n.i.d.) Rayleigh fading channels. Relays and the destination are equipped with multiple antennas, whereas sources have a single antenna. The exact outage probability expressions of the system under consideration are derived. The asymptotic outage expressions are further provided to obtain valuable insights into the practical system-design parameters such as the diversity order and coding gain. Furthermore, numerical results are provided in support of the analytical results.

In this paper, a distributed cognitive underlay single carrier system is investigated. A secondary users' network consists of a control unit (CU) and a group of secondary user remote radio heads (S-RRHs). To effectively access the radio spectrum licensed to the primary users, a distributed cyclic delay diversity (dCDD) scheme is employed between the CU and S-RRHs as the transmit diversity scheme. Multiple primary user transmitters (PTXs) are assumed to be located isotropically within the secondary users' network, so that a mixture of line-of-sight (LoS) and non-line-of-sight (nLoS) paths from the PTXs to the secondary user receiver is considered. In addition, a mixture of LoS and nLoS paths is considered in the secondary users' network. For a new transmit diversity scheme and channel model, the performance of the secondary users' network achieved by the dCDD in the presence of isotropically distributed multiple PTXs is investigated. The dCDD enables the CU to use multiple S-RRHs at the same time, so that determining the effects of a different number of S-RRHs in the presence of a new channel model is an open research issue. To this end, a new closed-form expression for the outage probability is derived, and then its accuracy is verified by link-level simulations.

This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field.

In this paper, a cooperative single carrier system comprising multiple cooperating remote radio heads and spatially distributed interferers is investigated. Due to the random location of the interferers within the communication range, a mixture of line-of-sight (LoS) and non-line-of-sight (nLoS) paths is considered in the channel model. Under a frequency selective fading channel with a mixture of the LoS and nLoS paths, the distributed cyclic delay diversity is employed to achieve the maximum transmit diversity gain without the exact knowledge of the channel state information at the transmitter side. It is shown that the operating signal-to-noise regions are divided into two regions, i.e., noise-limited and interference-limited. In this paper, the main focus is on the interference-limited region, in which diversity gain is not achieved due to performance limits determined by the system and channel parameters. The existence of these limits on the performance metrics, such as the outage probability and ergodic capacity, is derived analytically and then verified by link-level simulations.

Small-cell densification is a strategy enabling the offloading of users from macro base stations (MBSs), in order to alleviate their load and increase the coverage, especially, for cell-edge users. In parallel, as the network increases in density, the BS cooperation emerges as an efficient design method towards the demands for drastic improvement of the system performance against the detrimental overall interference. We, therefore, model and scrutinize a heterogeneous network (HetNet) of two tiers (macro and small cells) with multiple-antenna BSs serving a multitude of users, which differ with respect to their basic design parameters, e.g., the deployment density, the number of transmit antennas, and transmit power. In addition, the tiers are enhanced with cell association policies by introducing the concept of the association probability. Above this and motivated by the advantages of cooperation among BSs, the small base stations (SBSs) are enriched with this property in their design. The SBS cooperation allows shedding light into its impact on the cell selection rules in multi-antenna HetNets. Under these settings, software-defined networking (SDN) is introduced smoothly to play the leading role in the orchestration of the network. In particular, heavy operations such as the coordination and the cell association are undertaken by virtue of an SDN controller performing and managing efficiently the corresponding computations due to its centralized adaptability and dynamicity towards the enhancement and potential scalability of the network. In this context, we derive the coverage probability and the mean achievable rate. Not only we show the outperformance of BS cooperation over uncoordinated BSs, but we also demonstrate that the SBS cooperation enables the admittance of more users from the macro-cell BSs (MBSs). Furthermore, we show that by increasing the number of BS antennas, the system performance is improved as the metrics under study reveal. Moreover, we investigate the performance of different transmission techniques, and we identify the optimal bias in each case when SBSs cooperate. Finally, we depict that the SBS densification is beneficial until a specific density value since a further increase does not increase the coverage probability.

In this paper, a cooperative spectrum sharing system is investigated. To effectively access the radio spectrum licensed to the primary users, the distributed cyclic delay diversity (dCDD) scheme is employed as the transmit diversity scheme for distributed cyclic-prefixed single carrier transmissions. As a setup for the secondary users' network, it is assumed that it comprises the control unit and a group of remote radio heads. In the secondary users' network, a single primary user transmitter is assumed to be located isotropically, so that a mixture of line-of-sight and non-line-of-sight paths over frequency selective fading channels is considered. One of the objectives of this paper is to investigate the effects of this new channel model on the outage probability probability promised by dCDD. A closed-form expression for the outage probability is derived first, and then its asymptotic expression is derived to characterize the maximum achievable diversity gain. Link-level simulations are also conducted to verify the performance analysis.

A key challenge in nanonetworking is to develop a means of coordinating a large number of nanoscale devices. Molecular communication has emerged as a promising technique to assist in the coordination problem. Devices in molecular communication systems-once information molecules are released-are typically viewed as passive, not reacting chemically with the information molecules. While this is an accurate model in diffusion-limited links, it is not the only scenario. In particular, the dynamics of molecular communication systems are more generally governed by reaction-diffusion, where the reaction dynamics can also dominate. This leads to the notion of reaction-limited molecular communication systems, where the concentration profiles of information molecules and other chemical species depends largely on reaction kinetics. In this regime, the system can be approximated by a chemical reaction network. In this paper, we exploit this observation to design new protocols for both point-to-point links with feedback and networks for event detection. In particular, using connections between consensus and advection theory and reaction networks lead to simple characterizations of equilibrium concentrations, which yield simple-but accurate-design rules even for networks with a large number of devices.

In this paper, a cooperative cyclic-prefixed single carrier (CP-SC) system to improve physical layer security is investigated. By considering a distributed cyclic delay diversity (dCDD) scheme, a jamming method is proposed to maximize the signal-to-noise ratio (SNR) over the channels from the transmitters to the legitimate user, while degrading the signal-to-interference-plus-noise ratio (SINR) over the channels from the transmitters to the illegitimate user. A CDD transmitter among the set of CDD transmitters is selected as the sentinel transmitter, and it transmits a jamming signal to the illegitimate user. The sentinel transmitter is the transmitter that provides the best channel gain in order to maximize the SNR at the legitimate user and minimize the SINR at the non-legitimate users. This allow us to enhance the security of the CP-SC system. New closed form expressions for the SNR and SINR for the dCDD protocol are derived for frequency selective fading channels. Monte-Carlo simulations are conducted to verify the analytic derivations of the performance metrics for various simulation scenarios.

This paper investigates the diversity gain of a distributed cyclic delay diversity (CDD) scheme for cyclic-prefixed single carrier systems in non-identical frequency selective fading channels. Two conditions are used to obtain an equivalent channel matrix that is free of intersymbol interference. These conditions allows the system to achieve the maximum diversity order at a full rate in frequency selective fading channels. A given number of CDD transmitters is obtained from the set of cooperative transmitters in the system and is shown to be determined by the symbol block size and the maximum time dispersion of the channel. A new expression for the received signal-to-noise ratio (SNR) is derived by using order statistics. To estimate the achievable maximum diversity gain provided by the distributed CDD scheme, we employ asymptotic analysis in the high SNR regime. From the analytical framework, it is shown that the maximum diversity is achieved even for non-identical frequency selective fading channels. Link-level simulations are conducted to verify the maximum achievable diversity gain.

Caching popular content at the wireless edge is recently proposed as a means to reduce congestion at the backbone of cellular networks. The two main actors involved are Mobile Network Operators (MNOs) and Content Providers (CPs). In this work, we consider the following arrangement: an MNO pre-installs memory on its wireless equipment (e.g. Base Stations) and invites a unique CP to use them, with monetary cost. The CP will lease memory space and place its content; the MNO will associate network users to stations. For a given association policy, the MNO may help (or not) the CP to offload traffic, depending on whether the association takes into account content placement. We formulate an optimization problem from the CP perspective, which aims at maximizing traffic offloading with minimum leasing costs. This is a joint optimization problem that can include any association policy, and can also derive the optimal one. We present a general exact solution using Benders decomposition. It iteratively updates decisions of the two actors separately and converges to the global optimum. We illustrate the optimal CP leasing/placement strategy and hit probability gains under different association policies. Performance is maximised when the MNO association follows CP actions.

Future wireless networks are expected to adopt many different network technologies and architectures that promise to greatly enhance data rate and provide ubiquitous coverage for end users, all while enabling higher spectral efficiency. These benefits come with an increased risk of co-channel interference and possible correlation in the aggregated interference, which impacts the communication performance. This paper introduces a mathematical framework to quantify the spatial correlation of the interference in finite and clustered wireless networks with signals subject to Rayleigh fading. Expressions are derived for the correlation coefficient of the outage probability when the interferers are located according to some of the most common point processes used in the literature to model the spatial distribution of the devices: binomial point process (i.e., relay networks with fixed users), Poisson point process (i.e., heterogeneous cellular networks), and Thomas point process (i.e., device-to-device networks).

Caching popular content at the wireless edge promises performance benefits as well as business perspectives. In this paper, we study the following arrangement: a mobile network operator (MNO) pre-installs memory on its wireless equipment, sets a price, and invites a unique content provider (CP) to invest. The CP leases memory space and places its content; the MNO then associates network users to stations, aiming for offloading CP traffic or not. We formulate an optimization problem, which maximizes offloading with minimum leasing costs for the CP. This is an NP-hard mixed-integer non-linear optimization problem. We present an iterative exact solution using Generalized Benders decomposition into a content-related master problem and a user-association slave problem. Master is integer linear. Slave is convex for various association policies, including: 1) join-the-closest cache and 2) cache-aware association. For slave, we introduce a distributed exact solution, named generalized bucket-filling. Extensive simulations illustrate the performance benefits under different association policies. The solution helps to determine the optimal leasing price for the MNO, and the optimal investment budget for the CP. As a general conclusion, both actors profit when the MNO association supports CP decisions.

In this paper, we study the capacity and symbol error probability (SEP) of generalized spatial modulation (GSM) multiple-input multiple-output (MIMO) using measured channels that are obtained by channel sounding in an indoor office environment at 60GHz. Spatial modulation (SM) and GSM are emerging low-complexity MIMO schemes that have been extensively researched for low-GHz (below 6GHz) communications. Recently, they have been considered and shown to be promising also for (mmWave) communications. In the simplest possible case, they require only one RF chain both at the transmitter (TX) and receiver (RX), and thus, are especially attractive for mmWave communications, in which the number of RF chains needs to be as low as possible. Despite of some early works on the theoretical analysis of SM/GSM for mmWave communications, there have been no investigations using real-world channel data. We focus on the office line-of-sight (LOS) scenario and investigate three problems: 1) the performance of GSM using the extracted LOS component of measured channels; 2) the impact of non-LOS NLOS components on the performance of GSM; and 3) possible simple modulation and reception algorithms for GSM that rely only on the LOS component of the channel. The results being reported in this paper not only validate the main claims of previous studies based on ideal pure LOS channels, but also lead to novel findings. One major conclusion is that NLOS components are harmful to the SEP of GSM and should be avoided. As another important outcome, our results strongly motivate the use of precoding in GSM systems to simultaneously improve the channel capacity and reduce the physical size of MIMO arrays (thus eliminating one major issue of LOS GSM).

Spatial modulation (SM) and generalized spatial modulation (GSM) are emerging multiple input multiple output (MIMO) schemes that use transmitter (TX) antenna switching for data transmission. Their operating principle makes optimization of channel capacity and mutual information usually more difficult than for conventional MIMO schemes which are not based on antenna switching. We propose to use channel cut-off rate as a relevant and more tractable metric for performance optimization of spatial modulation (SM)/GSM systems, as it constitutes a practical lower-bound of channel capacity. In particular, we propose four TX precoding schemes for increasing the cut-off rate of SM/GSM systems. We show that those TX precoding schemes which are designed for increasing array gain provide the largest improvement of cut-off rate for low signal-to-noise ratio (SNR). On the other hand, the TX precoding schemes that are designed for increasing the minimum Euclidean distance of GSM symbols are more suitable for application to medium to high SNR setups and correlated channels. The proposed precoding schemes are shown to be able to enhance mutual information, and the gain is shown to be of the same order of magnitude as the gain of the corresponding channel cut-off rate.

Unmanned aerial vehicles (UAVs) have gained a significant popularity in the recent past owing to their easy deployability and wide range of applications. In most of the short- and medium-range applications, Wi-Fi is used as the access technology for establishing communication between the ground stations and the UAVs. Although Wi-Fi is known to perform well in most of the scenarios, it is important to note that Wi-Fi has been mainly designed for indoor communication in rich scattering environments, whereas the air-to-ground (A2G) channel is characterized by sparse scattering. Considering this important difference in the channel characteristics, we revisit some of the Wi-Fi features and propose efficient design alternatives. First, we provide a statistical model for the sparse A2G channel and design an optimal time-domain quantizer (TDQ) for its feedback. In contrast to the frequency-domain quantizer (FDQ) of the IEEE 802.11n/ac Standard, the proposed TDQ exploits the time-domain sparsity in the channel and requires about 15 times lesser quantization bits than FDQ. Second, we propose a beamforming (BF) scheme with the aid of full-diversity rotation (FDR) matrices and analytically evaluate its symbol error probability in order to quantify the attainable diversity order. Our numerical simulations demonstrate that the proposed FDR-BF scheme outperforms the relevant benchmark schemes in both coded as well as uncoded scenarios. Specifically, the proposed FDR-BF scheme was observed to attain a signal-to-noise ratio gain as high as 6dB compared with the popular geometric mean decomposition-based BF scheme, when operating at an elevation angle of 7.5°.

Deployment of low-cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming, and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beamwidth, and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in an mmWave ad hoc network using PBs.

In this paper, a cooperative cyclic-prefixed single carrier (CP-SC) system is studied and a scheme to improve its physical layer security is proposed. In particular, a distributed cyclic delay diversity (dCDD) scheme is employed and a deliberate interfering method is introduced, which degrades the signal-to-interference-plus-noise ratio (SINR) over the channels from a group of remote radio heads (RRHs) to an eavesdropper, while minimizing the signal-to-noise ratio loss over the channels from the RRHs to an intended user. This is obtained by selecting one RRH that acts as an interfering RRH and transmits an interfering artificial noise sequence to the eavesdropper. Through the use of the dCDD scheme, a channel that minimizes the receive SINR at the eavesdropper is selected for the interfering RRH. This choice enhances the secrecy rate of the CP-SC system. The system performance is evaluated by considering the secrecy outage probability and the probability of non-zero achievable secrecy rate, which are formulated in closed-form analytical expressions for the case of identically and non-identically distributed frequency selective fading channels. Based on the proposed analytical framework, the diversity order of the system is studied. Monte Carlo simulations are employed to verify the analytical derivations for numerous system scenarios.

In this paper, we introduce a new methodology for modeling and analyzing downlink cellular networks, where the base stations (BSs) constitute a motion-invariant point process (PP) that exhibits some degree of interactions among the points, i.e., spatial repulsion or spatial clustering. The proposed approach is based on the theory of inhomogeneous Poisson PPs (I-PPPs) and is referred to as inhomogeneous double thinning (IDT) approach. In a PP, the distribution of the distance from a randomly distributed (typical) user to its nearest BS depends on the degree of spatial repulsion or clustering exhibited by the PP. In addition, the average number of interfering BSs that lies within a given distance from the typical user is a function of the repulsion and clustering characteristics of the PP. The proposed approach consists of approximating the original motion-invariant PP with an equivalent PP that is made of the superposition of two conditionally independent I-PPPs. The inhomogeneities of both PPs are created from the point of view of the typical user (“user-centric”): the first one is based on the distribution of the user's distance to its nearest BS and the second one is based on the distance-dependent average number of interfering BSs around the user. The inhomogeneities are mathematically modeled through two distance-dependent thinning functions and a tractable expression of the coverage probability is obtained. Sufficient conditions on the parameters of the thinning functions that guarantee better or worse coverage compared with the baseline homogeneous PPP model are identified. The accuracy of the IDT approach is substantiated with the aid of empirical data for the spatial distribution of the BSs.

In this paper, we analyze and optimize the energy efficiency of downlink cellular networks. With the aid of tools from stochastic geometry, we introduce a new closed-form analytical expression of the potential spectral efficiency (bit/sec/m2). In the interference-limited regime for data transmission, unlike currently available mathematical frameworks, the proposed analytical formulation depends on the transmit power and deployment density of the base stations. This is obtained by generalizing the definition of coverage probability and by accounting for the sensitivity of the receiver not only during the decoding of information data, but during the cell association phase as well. Based on the new formulation of the potential spectral efficiency, the energy efficiency (bit/Joule) is given in a tractable closed-form formula. An optimization problem is formulated and is comprehensively studied. It is mathematically proved, in particular, that the energy efficiency is a unimodal and strictly pseudo-concave function in the transmit power, given the density of the base stations, and in the density of the base stations, given the transmit power. Under these assumptions, therefore, a unique transmit power and density of the base stations exist, which maximize the energy efficiency. Numerical results are illustrated in order to confirm the obtained findings and to prove the usefulness of the proposed framework for optimizing the network planning and deployment of cellular networks from the energy efficiency standpoint.

This paper investigates the relay selection (RS) problem in full-duplex (FD) wireless networks with multiple users and multiple common relays. We consider three self-interference models at FD relays. For amplify-and-forward (AF) and decode- and-forward (DF) relaying, the exact and asymptotic expressions for the outage probability are derived over Rayleigh fading channels with distance-dependent path loss for three RS schemes: i) optimal RS (ORS); ii) naive RS; and iii) random RS. Simulation results are provided to verify the analytical results.

5G wireless communication envisions unprecedented changes in current mobile networks in order to guarantee significant higher performance mainly in terms of data rates, latencies and efficiency. This paper provides an analytical model based on stochastic geometry, in order to address the planning and the dimensioning of 5G Cloud RAN. The results shows the requirements in terms of number of virtual base-band units, and the energy gain achieved by virtualisation both in micro-cell and pico-cell scenarios. Thus, the proposed methodology represents a step forward to analyse and compare traditional RAN design with the emerging Cloud RAN paradigm.

In this work, it is studied the performance of a scheduling algorithm where the Mobile Terminals (MTs) may be turned off if they cause a level of interference greater than a given threshold. This approach, which is referred to as Interference Aware Muting (IAM), may be regarded as an interference-aware scheme that is aimed to reduce the level of interference. The performance of IAM is studied in terms of transmit power, coverage probability, Spectral Efficiency (SE), and Binary Rate (BR). Simplified expressions of SE and BR for adaptive modulation and coding schemes are proposed, which better characterize practical communication systems. Our analysis unveils that, compared with conventional power control schemes, IAM increases the BR and reduces the transmit power.

We consider a diffusion-based nano-network with N spatially distributed transmitters and one receiver. While the transmitters are linked in a unified entity to perform one transmission, the receiver is large enough to be viewed as a plane. Messages are encoded into the number of nano-scale molecules. Based on these assumptions, we analyze the signal-to-interference ratio, which is based on the average numbers of absorbed molecules. Moreover, we present an approach to interference alignment for molecular communications.

We consider the case where a reference full-duplex (FD) node (e.g., base station), equipped with arbitrary number of transmit/receive antennas, utilizes generalized linear beamformers to simultaneously communicate with multiple FD radios (e.g., user equipments). The fading coefficients for the residual self-interference (SI) channels are drawn from the complex Gaussian distribution with arbitrary mean and variance. Here, it is not possible to directly derive the distribution of the bidirectional channel power gain. As a result, we adopt the method of moments in order to provide a new Gamma approximation for the residual SI distribution over FD multi-user MIMO Rician fading channels. The proposed theorem holds under arbitrary linear precoder/decoder design, number of antennas and streams, and SI cancellation capability. The validity of the theoretical findings is confirmed via extensive simulations of the entire transmit/receive processing chain.

Caching of popular content on wireless nodes is recently proposed as a means to reduce congestion in the backbone of cellular networks and to improve Quality of Service. From a network point of view, the goal is to offload as many users as possible from the backbone network to the wireless caches while at the same time offering good service to cache-unrelated users. Aggressive offloading can lead to an unbalanced user association. Some wireless nodes can be overloaded by cache-related traffic while the resources of others remain underused. Given a fixed content placement, this work proposes an efficient distributed algorithm to control and balance the association of cache-related traffic among cellular cache memories. The algorithm allows the network to achieve the globally optimal solution and can be executed on base stations using a limited amount of information exchange between them. It is based on a novel algorithm we call Bucket-filling. The solution limits the cache-users per node by balancing the total load among the nodes in a fair way. The improvement compared to common user assignment policies is highlighted for single-as well as for multi-tier random networks.

This paper investigates a distributed cyclic delay diversity (CDD) transmission scheme for cyclic-prefixed single carrier systems in non-identically and identically distributed frequency selective fading channels. The distinguishable feature of the proposed scheme lies in providing a transmit diversity gain while reducing the burden of estimating the channel state information, which is a challenging task in distributed and cooperative systems. To effectively use the distributed CDD scheme at the transmitters, two sufficient conditions are derived to eliminate the intersymbol interference at the receiver and leveraged to convert the multi-input single-output channel into a single-input single-output channel. These conditions allow the system to achieve the maximum diversity for frequency selective fading channels at a full rate. To achieve this maximum diversity, a fixed number of CDD transmitters are selected based on the channel conditions, symbol block size, and maximum time dispersion of the channel, and a new two-stage transmission mode is proposed. Based on the distributed CDD and the proposed selection schemes, a new expression for the signal-to-noise ratio at the receiver is obtained with the aid of order statistics, and then closed-form expressions for the outage probability and average symbol error rate (ASER) are derived. As far as the identically distributed frequency selective fading channel model is concerned, the achievable maximum diversity gain is proved, with the aid of asymptotic analysis, to be equal to the product of the total number of transmitters in the system and the number of multipath components. Link-level simulations are also conducted to validate the analytical expressions for outage probability, ASER, and maximum achievable diversity gain.

In this paper, we propose to use variable-Nugeneralized spatial modulation (VGSM), which is a specific type of generalized spatial modulation, in indoor line-of-sight (LOS) millimeter-wave (mmWave) communication. As compared with fixed-Nugeneralized spatial modulation, VGSM needs less number of transmitter (TX) antennas to achieve the same data rate. Reducing the number of antennas is especially important for LOS mmWave MIMO communication, since it means reduced array lengths and routing loss in the RF front-end. We first derive and analyze the channel capacity for LOS VGSM, and then propose a novel switching structure for implementation of the VGSM TX at mmWave frequencies and analyze its performance in terms of power added efficiency and capacity. Two optimizations of the system performance of VGSM are performed: 1) power allocation optimization for the proposed TX, and 2) antenna separation optimization in order to optimize the capacity in LOS. Our results show that VGSM is a feasible and promising scheme for indoor LOS mmWave communication. For example, we show that an 8 × 8 VGSM system with practical component values can achieve more than 20bpcu spectral efficiency within a distance of 1-5m and more than 17bpcu within 5-10m.

In this paper, we perform a system-level feasibility analysis of full-duplex (FD) relay-aided cellular networks that are equipped with multiple antennas at the base stations (BSs) and the relay nodes (RNs). The aim is to understand whether FD relaying is capable of enhancing the rate of cellular networks. With the aid of tools from stochastic geometry, we develop a tractable approach for computing the percentile rate, which allows us to gain insights on the impact of FD relaying for both the cell-edge and the cell-median mobile terminals subject to network interference. Contrary to previous works that do not consider the network interference, the framework reveals that even in the absence of self-interference at the FD RNs, a network with half-duplex (HD) RNs can outperform its FD counterpart for a moderate number of antennas at the BSs and RNs. On the other hand, the FD-based network can substantially outperform both the HD-based one and the one without RNs for a sufficiently large number of antennas at the BSs and RNs and substantially small self-interference power effect at the RNs. Finally, the aforementioned analytical insights are validated by means of Monte Carlo simulations.

We provide a theoretical framework for the study of massive multiple-input multiple-output (MIMO)-enabled full-duplex (FD) cellular networks in which the residual self-interference (SI) channels follow the Rician distribution and other channels are Rayleigh distributed. In order to facilitate bi-directional wireless functionality, we adopt: 1) in the downlink (DL), a linear zero-forcing (ZF) with SI-nulling precoding scheme at the FD base stations and 2) in the uplink (UL), an SI-aware fractional power control mechanism at the FD mobile terminals. Linear ZF receivers are further utilized for signal detection in the UL. The results indicate that the UL rate bottleneck in the FD baseline single-input single-output system can be overcome via exploiting massive MIMO. On the other hand, the findings may be viewed as a reality-check, since we show that, under state-of-the-art system parameters, the spectral efficiency gain of FD massive MIMO over its half-duplex counterpart is largely limited by the cross-mode interference between the DL and the UL. In point of fact, the anticipated twofold increase in SE is shown to be only achievable when the number of antennas tends to be infinitely large.

In this paper, the performance analysis of Receive Space Modulation (RSM) in the shadowing Multiple-Input Multiple-Output (MIMO) broadcast channel is presented. This is undertaken by considering Zero Forcing (ZF) precoding and both small and large scale fading. In particular, a closed form and accurate framework for the evaluation of the Symbol Error Rate (SER) is derived. In addition, the diversity order and the coding gain of the new architecture are also obtained. The derived framework can be directly extended to the conventional spatially multiplexed shadowing MIMO broadcast channel. It is shown that RSM achieves the same diversity order and, in certain scenarios which are well defined, higher coding gain than spatially multiplexing (SMX). Also, the performance difference between RSM and SMX in the shadowing broadcast channel is mathematically quantified. Finally, numerical results that verify the new framework and conclusions are provided.

We analyze the secrecy outage probability in the downlink for wireless networks with spatially (Poisson) distributed eavesdroppers (EDs) under the assumption that the base station employs transmit antenna selection (TAS) to enhance secrecy performance. We compare the cases, where the receiving user equipment (UE) operates in half-duplex (HD) mode and full-duplex (FD) mode. In the latter case, the UE simultaneously receives the intended downlink message and transmits a jamming signal to strengthen secrecy. We investigate two models of (semi)passive eavesdropping: 1) EDs act independently and 2) EDs collude to intercept the transmitted message. For both of these models, we obtain expressions for the secrecy outage probability in the downlink for the HD and FD UE operation. The expressions for the HD systems have very accurate approximate or exact forms in terms of elementary and/or special functions for all path loss exponents. Those related to the FD systems have exact integral forms for general path loss exponents, while exact closed forms are given for specific exponents. A closed-form approximation is also derived for the FD case with colluding EDs. The resulting analysis shows that the reduction in the secrecy outage probability is logarithmic in the number of antennas used for TAS and identifies conditions, under which HD operation should be used instead of FD jamming at the UE. These performance trends and exact relations between system parameters can be used to develop adaptive power allocation and duplex operation methods in practice. Examples of such techniques are alluded to herein.

Energy harvesting brings a key solution to the increasing energy bill and environmental concerns but, at the same time, potential energy shortage may deteriorate the network availability. In this paper, we analyze the performance of off-grid small-cell base stations (scBS) with finite battery capacity and design a new power-availability-aware cell association based on periodical broadcast of the scBS battery level. Each mobile terminal (MT) targets its own set of available scBSs before association, i.e., the set of scBSs that can guarantee service provided 1) the scBS battery level; 2) the power required to satisfy a received power constraint at each MT, given the scBS-MT distance and the shadowing attenuation; and 3) the estimated power consumed to serve other MTs potentially associated to the same scBS, which is computed using stochastic geometry tools. Next, we develop for it a tractable performance analysis and derive closed-form expressions for the probability of power outage and the coverage probability. By dynamically adapting to the fluctuations of the base station battery and the MT received power requirement, the proposed cell association allows a more even distribution of the available energy in the network, brings robustness against harvesting impairment and thereby, significantly outperforms conventional strategies.

In this paper, low-complexity time-domain turbo equalization (TDTE) detectors based upon soft-interference-cancellation (SIC)-aided minimum mean-square error (MMSE) criterion are proposed for single carrier generalized spatial modulation (SC-GSM) systems. First, a symbol-by-symbol-aided TDTE detector for application to the small-scale GSM systems is proposed, where the zero symbols are considered as constellation points when performing SIC. Then, vector-by-vector-aided TDTE (VV-TDTE) detectors for application to larger-scale antenna systems are introduced, where the GSM symbol is treated as an entire vector when performing SIC. As for the proposed VV-TDTE detectors, in addition, different time-varying filter coefficients are designed, in order to strike a flexible tradeoff between complexity and performance. By relying upon extrinsic information transfer chart analysis, we show that the proposed TDTE detectors are capable of providing considerable bit error rate performance gains over existing TDTE detectors and over the classic frequency-domain equalization-based MMSE detector, especially for the unbalanced antenna configurations.

This paper investigates the secrecy connectivity probability for wireless networks with transmit antenna selection in the presence of randomly located eavesdroppers. Firstly, we propose an antenna selection scheme for use at the base station with a half-duplex receiver to enhance secrecy connectivity performance. Then in order to further improve the secrecy connectivity, a full-duplex (FD) receiver, which broadcasts a jamming signal while receiving the downlink message, is considered in this work. The probabilities of secrecy connectivity are given in the closed form and integral form for half-duplex and full-duplex receivers, respectively. The derived analytical results are verified by Monte Carlo simulations. The resulting analysis shows that the application of antenna selection at the transmitting base station and full-duplex communication at the receiving terminal leads to significant improvements in secrecy connectivity.

Stochastic geometry (SG) has been widely accepted as a fundamental tool for modeling and analyzing cellular networks. However, the fading models used with SG analysis are mainly confined to the simplistic Rayleigh fading, which is extended to the Nakagami-m fading in some special cases. However, neither the Rayleigh nor the Nakagami-m accounts for dominant specular components (DSCs) which may appear in realistic fading channels. In this paper, we present a tractable model for cellular networks with generalized two-ray (GTR) fading channel. The GTR fading explicitly accounts for two DSCs in addition to the diffuse components and offers high flexibility to capture diverse fading channels that appear in realistic outdoor/indoor wireless communication scenarios. It also encompasses the famous Rayleigh and Rician fading as special cases. To this end, the prominent effect of DSCs is highlighted in terms of average spectral efficiency.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs). Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical mobile terminal (MT) is allowed to harvest energy from the serving BS by direct beamforming, as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we derive exact expressions for the maximum transmit power at MT and the UL average ergodic rate. Our results show that the UL average ergodic rate per random MT is not significantly improved by increasing the energy conversion efficiency.

A mathematical framework for system-level analysis and optimization of uplink cellular networks that use optimum combining at the Base Stations (BSs) is introduced. Fractional power control is explicitly taken into account. The locations of the BSs are modeled as points of a homogeneous Poisson point process. With the aid of stochastic geometry, coverage and rate are formulated in integral but mathematically tractable expressions. Based on them, performance trends for small-and large-scale multi-antenna BSs are discussed. Coverage and rate are shown to be highly dependent on several parameters, including the path-loss exponent, the fractional power control compensation factor and the maximum transmit power of the mobile terminals. Based on the proposed frameworks, a heuristic algorithm for system-level optimization is proposed and its effectiveness is demonstrated with the aid of Monte Carlo simulations.

Contemporary technologies as implemented in the field of healthcare have provided the everyday clinical practice with a plethora of tools to be used in various settings. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most promising technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. WSN4QoL is a Marie Curie project involving academic and industrial partners from three EU countries, which aims to show how new WSNs-based technologies suit the specific requirements of pervasive healthcare applications. In particular, in this paper, the WSN4QoL's system architecture is presented as designed to exploit the Network Coding (NC) mechanisms to achieve energy efficiency in the wireless communications and distributed positioning solutions to locate patients in indoor home environments. The system has been validated through experimental activities using commercial off the shelf (COTS) WSN testbeds and medical devices prototypes offered by a commercial partner. Results demonstrate that NC helps in achieving substantial gains in terms of energy efficiency as compared to traditional relay mechanisms, while the proposed positioning solution is able to locate people in indoor environments at a sub-room accuracy level, without requiring any extra dedicated hardware.

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We propose a further generalization of spatial modulation (SM) in a downlink multiuser MIMO system with multicast in order to improve the system's spectral and energy efficiencies. A base station equipped with multiple antennas sends three types of information to several users who are also equipped with multiple antennas. The individual information to each user is conveyed in the form of receive SM as well as through conventional QAM modulation. On top of that, common transmit SM information is conveyed to all users. To this end, specific preprocessing and postprocessing are obtained based upon the singular value decomposition technique. A simple additional processing based on phase shifts is proposed to improve detectability. Computer simulation results reveal that the proposed scheme has improved average bit-error rate (BER) over a reference scheme under the same spectral efficiency.

In this paper, we model and analyze the downlink (DL) wireless power transfer and uplink (UL) information transmission of K-tier heterogeneous cellular networks (HCNs) with randomly located base stations (BSs) and mobile terminals (MTs). In the DL and UL, each energy-constrained MT pairs up with its corresponding BS, which provides the maximum received power at the MT. Due to the densely located BSs and universal frequency reuse between all tiers in HCNs, the typical MT is allowed to harvest energy from the serving BS by direct beamforming as well as from the other interfering BSs. Equipped with large storage battery, the typical MT utilizes the harvested energy to provide constant transmit power for the UL information transmission. Stochastic geometry is used to model and evaluate the intrinsic relationship between the energy harvested from the BSs in the DL and the information transmission performance in the UL. To well evaluate the system performance, we first derive exact expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. As the number of BS antennas goes to infinity, we further derive asymptotic expressions for the maximum transmit power at MT, the UL outage probability, and the UL average ergodic rate per MT. Our results show that the UL outage probability per MT first decreases and then increases with increasing the time allocation factor (the fraction of time allocated to the DL), and the UL outage probability, and the UL average ergodic rate per MT, can be largely improved by using the massive antenna arrays at the BSs.

In this paper, mathematical frameworks for system-level analysis and design of uplink heterogeneous cellular networks with multiple antennas at the base station (BS) are introduced. Maximum ratio combining (MRC) and optimum combining (OC) at the BSs are studied and compared. A generalized cell association criterion and fractional power control scheme are considered. The locations of all tiers of BSs are modeled as points of homogeneous and independent Poisson point processes. With the aid of stochastic geometry, coverage probability and average rate are formulated in integral but mathematically and computationally tractable expressions. Based on them, performance trends for small- and large-scale multiple-antenna BSs are discussed. Coverage and rate are shown to highly depend on several parameters, including the path-loss exponent, the fractional power control compensation factor, and the maximum transmit power of the mobile terminals. The gain of OC compared with MRC is proved to increase, if a more aggressive power control is used and if the number of BS antennas increases but is finite. For the same number of BS antennas, OC is shown to reach the noise-limited asymptote faster than MRC. All findings are validated via Monte Carlo simulations.

In this paper, we propose to use spatial modulation (SM) and the generalized spatial modulation (GSM) MIMO schemes in indoor line-of-sight (LOS) millimeter-wave (mmWave) communication at 60GHz. SM/GSM are known but only at low-GHz frequencies where the channels are typically rich scattered and characterized by Rayleigh or Rician distribution. However, 60-GHz indoor channels are typically not rich but rather sparsely scattered and dominated by the LOS component, thus making them different from low-GHz fading channels and our work novel. We first seek to optimize SM in LOS by finding the channel conditions that minimize its symbol error probability (SEP). Then, we extend our studies to LOS GSM and derive its channel capacity and SEP. Furthermore, we present numerical studies on the behavior and performance of SM/GSM in LOS. LOS spatial multiplexing and beamforming MIMO schemes are used as benchmarks for comparison. At last, we propose novel TX and RX hardware architectures, both of which use only a single RF chain, for implementation of SM/GSM at mmWave frequencies. Both simplicity and good performance are exhibited by LOS SM/GSM thus making them very attractive techniques for short-range indoor mmWave communications at 60GHz.

Inter-cell interference is one of the main limiting factors in current heterogeneous cellular networks. Uplink fractional power control (FPC) is a well-known method that aims to cope with such a limiting factor and to save the battery life of the mobile terminals (MTs). In order to do that, the transmit power of each MT is adjusted as a function of a set of parameters that usually depend only on the link between MTs and serving base station (BS), such as the desired received power at the serving BS or the path loss between the MT and its serving BS. Contrary to these classical FPC schemes, in this paper, we use stochastic geometry to analyze a power control mechanism that keeps the interference generated by each MT under a given threshold. We also consider a maximum transmitted power and a partial compensation of the path loss. Our analysis reveals that such an interference aware method can reduce the average power consumption and increase the average spectral efficiency at once. In addition, the variance of the interference is reduced, thus improving the performance of adaptive modulation and coding schemes, since the interference can be better estimated.

The intensity matching approach for tractable performance evaluation and optimization of cellular networks is introduced. It assumes that the base stations are modeled as the points of a Poisson point process (PPP) and leverages stochastic geometry for system-level analysis. Its rationale relies on observing that system-level performance is determined by the intensity measure of transformations of the underlaying spatial PPP. By approximating the original system model with a simplified one, whose performance is determined by a mathematically convenient intensity measure, tractable yet accurate integral expressions for computing area spectral efficiency and potential throughput are provided. The considered system model accounts for many practical aspects that, for tractability, are typically neglected, e.g., line-of-sight (LOS) and non-LOS propagation, antenna radiation patterns, traffic load, practical cell associations, and general fading channels. The proposed approach, more importantly, is conveniently formulated for unveiling the impact of several system parameters, e.g., the density of base stations and blockages. The effectiveness of this novel and general methodology is validated with the aid of empirical data for the locations of base stations and for the footprints of buildings in dense urban environments.

In this paper, Multi-Stream Receive-Spatial Modulation (MSR-SM) for application to the Multiple-Input Multiple-Output (MIMO) broadcast channel is introduced and studied. MSR-SM is a closed-loop transmission scheme, which applies the concept of multistream space modulation at the receiver side. An accurate mathematical framework for the evaluation of the Bit Error Rate (BER) is proposed. In addition, the diversity order and coding gain of the new architecture are derived. Note that the proposed analytical framework takes into account both the small-scale fading and the system topology, and is directly applicable to the conventional MIMO broadcast channel. Compared with the state-of-the-art MIMO transmission in the broadcast channel, it is mathematically shown that MSR-SM achieves the same diversity order and a better coding gain, in the high Signal-to-Noise Ratio (SNR) regime. Finally, the proposed mathematical framework and the new findings are validated via Monte Carlo simulation results.

In this paper, we investigate a pair of transmit precoding (TPC) algorithms conceived for spatial modulation (SM) systems communicating over flat-fading multiple-input multiple-output (MIMO) channels. In order to retain all the benefits of conventional SM, we design the TPC matrix to be diagonal and introduce two design criteria for optimizing the elements of the TPC matrix. Specifically, we first investigate a TPC design based on maximizing the minimum Euclidean distance dmin(max-dmin) between the SM signal points at the receiver side. A closed-form solution of the optimal max-dmin-based TPC matrix is derived. Then, another TPC design algorithm is proposed for directly minimizing the bit error ratio (BER) upper bound of SM, which is capable of jointly optimizing the overall Euclidean distance between all received signal points. In the minimum BER (min-BER)-based TPC algorithm, the theoretical gradient of the BER with respect to the diagonal TPC matrix is derived and a simplified iterative conjugate gradient (SCG) algorithm is invoked for TPC optimization. Our simulation results demonstrate that the proposed max-dmin-based TPC algorithm is optimal in terms of the minimum distance. However, increasing dmindoes not achieve a further BER improvement. We also confirm that the min-BER-based TPC outperforms the max-dmin-based TPC schemes in terms of the achievable BER performance.

In this paper, a new mathematical framework to the analysis and optimization of wireless-powered cellular networks is introduced. The proposed approach leverages stochastic geometry for system-level analysis of cellular networks, by modeling base station locations as points of a Poisson point process. The trade-offs emerging from simultaneous wireless information and power transfer are characterized through the joint cumulative distribution function of average harvested energy and average rate, which is conveniently formulated in terms of an easy-to-compute two-fold integral. The analysis shows that an optimal operating point for system-level optimization exists, as well as that network densification and directional beamforming constitute essential enablers for enhancing the performance. Finally, the fundamental trade-offs emerging in wireless-powered cellular networks are quantified through the concept of feasibility regions.

In this paper, a novel architecture for the Multiple-Input Multiple-Output (MIMO) broadcast channel is proposed and studied. The new architecture is based on the concept of Multi- Stream Receive-Spatial Modulation (MSR-SM). MSR-SM is a closed-loop transmission scheme, which applies the concept of multi-stream space modulation at the receiver side. A new and accurate framework for computing the Average Bit Error Probability (ABEP) of the new architecture is proposed. In addition, the new architecture is compared against the state- of-the-art MIMO transmission in the broadcast channel and it is shown to: i) provide superior Bit Error Rate (BER) performance in the high Signal-to-Noise-Ratio (SNR) regime and ii) reduce the signal processing complexity at the transmitter.

Compared to the existing lower frequency wireless power transfer, millimeter wave (mmWave) power transfer takes advantage of the high-dimensional multi-antenna and narrow beam transmission. In this paper we introduce wireless power transfer for mmWave cellular networks. Here, we consider users with large energy storage that are recharged by the mmWave base stations prior to uplink information transmission, and analyze the average harvested energy and average achievable rate. Numerical results corroborate our analysis and show that the serving base station plays a dominant role in wireless power transfer, and the contribution of the interference power from the interfering base stations is negligible, even when the interfering base stations are dense. By examining the average achievable rate in the uplink, when increasing the base station density, a transition from a noise-limited regime to an interference-limited regime is observed.

The Equivalent-in-Distribution (EiD)-based approach to the analysis of Single-Input-Single-Output (SISO) cellular networks has recently been introduced [1]. Its rationale relies upon formulating the aggregate other-cell interference in terms of an infinite summation of independent and conditionally distributed Gaussian Random Variables (RVs). This approach leads to exact integral expressions of the error probability for arbitrary bi-dimensional modulations. In this paper, the EiD-based approach is generalized to the performance analysis of multi-user Multiple-Input-Multiple-Output (MIMO) cellular networks, which use Zero-Forcing (ZF) precoding for transmission over Rayleigh fading channels. In the presence of othercell interference and noise, the error probability is formulated in terms of a twofold integral. In interference-limited cellular networks, the mathematical framework simplifies to a single integral expression. The mathematical analysis is substantiated with the aid of extensive Monte Carlo simulations and the performance of multi-user MIMO cellular networks is discussed.

In this paper, a new mathematical framework to the analysis of millimeter wave cellular networks is introduced. Its peculiarity lies in considering realistic path-loss and blockage models, which are derived from experimental data recently reported in the literature. The path-loss model accounts for different distributions for line-of-sight and non-line-of-sight propagation conditions and the blockage model includes an outage state that provides a better representation of the outage possibilities of millimeter wave communications. By modeling the locations of the base stations as points of a Poisson point process and by relying upon a noise-limited approximation for typical millimeter wave network deployments, exact integral expressions for computing the coverage probability and the average rate are obtained. With the aid of Monte Carlo simulations, the noise-limited approximation is shown to be sufficiently accurate for typical network densities. Furthermore, it is shown that sufficiently dense millimeter wave cellular networks are capable of outperforming micro wave cellular networks, both in terms of coverage probability and average rate.

Due to the increasing heterogeneity and deployment density of emerging cellular networks, new flexible and scalable approaches for their modeling, simulation, analysis and optimization are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance evaluation and optimization. In this paper, we investigate the accuracy of this emerging abstraction for modeling cellular networks, by explicitly taking realistic base station locations, building footprints, spatial blockages and antenna radiation patterns into account. More specifically, the base station locations and the building footprints are taken from two publicly available databases from the United Kingdom. Our study confirms that the abstraction model based on stochastic geometry is capable of accurately modeling the communication performance of cellular networks in dense urban environments.

In this paper, an approximation to the mathematical evaluation of the spectral and energy efficiency of multi-antenna downlink cellular networks is introduced. The locations of the Base Stations (BSs) are modeled as points of a Poisson Point Process (PPP) and stochastic geometry is used for system-level performance assessment. The approximation is applicable to spatial multiplexing Multiple-Input-Multiple-Output (MIMO) systems having an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). A Rayleigh fading channel model is assumed. The accuracy of the proposed approximation is substantiated with the aid of Monte Carlo simulations.

In this paper, a mathematical framework for evaluating the error probability of downlink Multiple-Input-Multiple-Output (MIMO) cellular networks is introduced. It is based on the Poisson Point Process (PPP)-based abstraction for modeling the spatial locations of the Base Stations (BSs) and it exploits results from stochastic geometry to characterize the distribution of the other-cell interference. The framework is applicable to spatial multiplexing MIMO systems with an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). It is shown that the proposed framework leads to easy-to-compute integral expressions, which provide insights for network design and optimization. The accuracy of the mathematical analysis is substantiated through extensive Monte Carlo simulations for various MIMO cellular network setups.

Network Coded Cooperation (NCC) is known to provide full diversity order and high spectral efficiency for uncoded cooperative networks. However, the understanding of NCC applied to signals that have been protected by some Forward Error Correction (FEC) codes is still limited. This letter analyzes the diversity order attainable by NCC with channel coding (Coded-NCC) in a network topology with multiple sources, one relay and in the presence of fast Rayleigh fading. Due to the difficulty of characterizing the exchange of information between the network decoder and the channel decoder, iterative network and channel decoding algorithms are usually studied with the aid of simulations. In this letter, we overcome this limitation by proposing a near-optimal receiver that performs network decoding and channel decoding in a single decoding step of an equivalent super code. An upper bound and a tight approximation of the Bit Error Rate (BER) for all sources are derived. Based on the upper bound, we analytically show that Coded-NCC achieves a diversity order equal to 2f, where f is the minimum distance of the FEC code. This result generalizes those available for cooperative networks in the absence of channel coding (Uncoded-NCC), where the diversity order is equal to 2, as well as those available for coded transmission but without cooperation, where the diversity order is equal to f.

In this paper, a tractable mathematical framework for the analysis and optimization of two-hop relay-aided cellular networks is introduced. The proposed approach leverages stochastic geometry for system-level analysis, by modeling the locations of base stations, relay nodes and mobile terminals as points of homogeneous Poisson point processes. A flexible cell association and relay-aided transmission protocol based on the best biased average received power are considered. Computationally tractable integrals and closed-form expressions for coverage and rate are provided, and the performance trends of relay-aided cellular networks are identified. It is shown that coverage and rate highly depend on the path-loss exponents of one- and two-hop links. In the interference-limited regime, in particular, it is shown that, if the system is not adequately designed, the presence of relay nodes may provide negligible performance gains. By capitalizing on the proposed mathematical framework, a system-level and interference-aware optimization criterion of the bias coefficients is proposed. Numerical results confirm the effectiveness of the proposed system-level optimization to enhance the coverage probability in the interference-limited regime. The presence of relays, on the other hand, is shown to have a limited impact on average/coverage rate under the same assumptions.

The equivalent-in-distribution (EiD)-based approach to the analysis of single-input-single-output (SISO) cellular networks for transmission over Rayleigh fading channels has recently been introduced [1]. Its rationale relies upon formulating the aggregate other-cell interference in terms of an infinite summation of independent and conditionally distributed Gaussian random variables (RVs). This approach leads to exact integral expressions of the error probability for arbitrary bi-dimensional modulations. In this paper, the EiD-based approach is generalized to the performance analysis of multiple-input-multiple-output (MIMO) cellular networks for transmission over Rayleigh fading channels. The proposed mathematical formulation allows us to study a large number of MIMO arrangements, including receive-diversity, spatial-multiplexing, orthogonal space-time block coding, zero-forcing reception and zero-forcing precoding. Depending on the MIMO setup, either exact or approximate integral expressions of the error probability are provided. In the presence of other-cell interference and noise, the error probability is formulated in terms of a two-fold integral. In interference-limited cellular networks, the mathematical framework simplifies to a single integral expression. As a byproduct, the proposed approach enables us to study SISO cellular networks for transmission over Nakagami-m fading channels. The mathematical analysis is substantiated with the aid of extensive Monte Carlo simulations.

Wireless body area networks (WBANs) are composed of sensors that either monitor and transmit vital signals or act as relays that forward the received data to a body node coordinator (BNC). In this paper, we introduce an energy efficient vital signal telemonitoring scheme, which exploits compressed sensing (CS) for low-complexity signal compression/reconstruction and distributed cooperation for reliable data transmission to the BNC. More specifically, we introduce a cooperative compressed sensing (CCS) approach, which increases the energy efficiency of WBANs by exploiting the benefits of random linear network coding (RLNC). We study the energy efficiency of RLNC and compare it with the store-and-forward (FW) protocol. Our mathematical analysis shows that the gain introduced by RLNC increases as the link failure rate increases, especially in practical scenarios with a limited number of relays. Furthermore, we propose a reconstruction algorithm that further enhances the benefits of RLNC by exploiting key characteristics of vital signals. With the aid of electrocardiographic (ECG) and electroencephalographic (EEG) data available in medical databases, extensive simulation results are illustrated, which validate our theoretical findings and show that the proposed recovery algorithm increases the energy efficiency of the body sensor nodes by 40% compared to conventional CS-based reconstruction methods.

In this paper, we consider Analog Network Coding (ANC) in the Multiple Access Relay Channel (MARC) with multiple relays, and provide the following three-fold contribution: 1) we introduce a tractable mathematical framework for computing the Symbol Error Rate (SER) of Maximum-Likelihood (ML), Zero-Forcing (ZF), and Minimum Mean Square Error (MMSE) receivers; 2) by capitalizing on this tractable mathematical framework, we formulate a power allocation problem that is proved to be convex for ML, ZF and MMSE receivers; and 3) we provide closed-form expressions of the optimal power to be allocated to the sources and the relays for ZF and MMSE receivers. With the aid of Monte Carlo simulations, we validate the accuracy of the proposed mathematical framework for various network topologies and channel conditions, as well as study the effectiveness of optimal power allocation. It is shown, in particular, that power optimization is beneficial as the number of sources increases and if the quality of the source-relay links is better than the quality of the relay-destination links.

In this paper, a new mathematical framework to the analysis of millimeter wave cellular networks is introduced. Its peculiarity lies in considering realistic path-loss and blockage models, which are derived from recently reported experimental data. The path-loss model accounts for different distributions of line-of-sight and non-line-of-sight propagation conditions and the blockage model includes an outage state that provides a better representation of the outage possibilities of millimeter wave communications. By modeling the locations of the base stations as points of a Poisson point process and by relying on a noise-limited approximation for typical millimeter wave network deployments, simple and exact integral as well as approximated and closed-form formulas for computing the coverage probability and the average rate are obtained. With the aid of Monte Carlo simulations, the noise-limited approximation is shown to be sufficiently accurate for typical network densities. The noise-limited approximation, however, may not be sufficiently accurate for ultra-dense network deployments and for sub-gigahertz transmission bandwidths. For these case studies, the analytical approach is generalized to take the other-cell interference into account at the cost of increasing its computational complexity. The proposed mathematical framework is applicable to cell association criteria based on the smallest path-loss and on the highest received power. It accounts for beamforming alignment errors and for multi-tier cellular network deployments. Numerical results confirm that sufficiently dense millimeter wave cellular networks are capable of outperforming micro wave cellular networks, in terms of coverage probability and average rate.

In this paper, we provide mathematical frameworks to the analysis of amplify-and-forward dual-hop cooperative relaying protocols in the presence of Nakagami-m fading and additive Gaussian noise at the relay, as well as additive Gaussian noise and symmetric alpha-stable interference at the destination. Quasi-static and fast-varying interference scenarios are investigated, which arise, e.g., when either the same or different interferers are active during the broadcast and relaying phases, respectively. A maximal ratio combining demodulator is studied, by assuming that the aggregate interference is either unknown (interference-oblivious) or can be estimated (interference-aware) at the destination. Closed-form expressions of the end-to-end moment generating function are provided, and the achievable diversity order is studied for different setups. The diversity order is shown to depend on the path-loss exponent of the interfering network. Under the assumption that the transmit-powers of cooperative and interfering networks are independent, it is proved that the interference-aware maximal ratio diversity combiner is capable of achieving second-order diversity only asymptotically, as the amplitude path-loss exponent tends to one.

In this paper, we study outage probability (OP) and diversity order of a M-source and N-relay wireless network that combines network coding (NC) and relay selection (RS). More specifically, a decode-and-forward (DF) relaying protocol is considered and the network-encoding vectors at the relays are assumed to constitute a maximum distance separable (MDS) code. Single relay selection (SRS) and multiple relay selection (MRS) protocols are investigated, where the best relay and the L best relays forward the network-coded packets to the destination, respectively. An accurate mathematical framework for computing the OP is provided and from its direct inspection the following conclusions on the achievable diversity are drawn: 1) the SRS protocol achieves diversity order equal to two regardless of M and N and 2) the MRS protocol achieves diversity order equal to L + 1 if L <; MandequaltoN + 1 if L ≥ M.These analytical findings are substantiated with the aid of Monte Carlo simulations, which also show that RS provides a better OP than NC based on repetition coding if L ≥ M.

In this paper, we propose an optimum transmit structure for spatial modulation (SM), a unique single-stream multiple-input multiple-output (MIMO) transmission technique. As a three-dimensional modulation scheme, SM enables a trade-off between the size of the spatial constellation diagram and the size of the signal constellation diagram. Based on this fact, the novel method, named transmission optimized spatial modulation (TOSM), selects the best transmit structure that minimizes the average bit error probability (ABEP). Unlike the traditional antenna selection methods, the proposed method relies on statistical channel state information (CSI) instead of instant CSI, and feedback is only needed for the optimal number of transmit antennas. The overhead for this, however, is negligible. In addition, TOSM has low computational complexity as the optimization problem is solved through a simple closed-form objective function with a single variable. Simulation results show that TOSM significantly improves the performance of SM at various channel correlations. Assuming Rayleigh fading channels, TOSM outperforms the original SM by up to 9 dB. Moreover, we propose a single radio-frequency (RF) chain base station (BS) based on TOSM, which achieves low hardware complexity and high energy efficiency. In comparison with multi-stream MIMO schemes, TOSM offers an energy saving of at least 56% in the continuous transmission mode, and 62% in the discontinuous transmission mode.

This paper presents a novel algebraic framework of collaborative network channel coding scheme in a network with multiple-access relay channel. The proposed scheme helps reduce the computational complexity while enhancing energy efficiency of the wireless network. The devised scheme is intended

Wireless Body Area Networks (WBANs) are composed of various sensors that either monitor and transmit real time vital signals or act as relays that forward the received data packets to a nearby Body Node Coordinator (BNC). The design of an accurate and energy efficient wireless telemonitoring system can be achieved by: i) minimizing the amount of data that should be transmitted for an accurate reconstruction at the BNC, and ii) increasing the robustness of the telemonitoring system to link failures due to the nature of wireless medium. To this end, we present a novel Compressed Sensing (CS) based telemonitoring scheme, called Cooperative Compressed Sensing (CCS), that exploits the benefits of Random Linear Network Coding (RLNC) along with key characteristics of the transmitted biosignals in order to achieve an energy efficient signal reconstruction at the BNC. Simulation studies, carried out with real electrocardiographic (ECG) data, show the benefits of: i) employing RLNC, compared to the case where relays simply store and forward the original data packets, and ii) applying the proposed CCS scheme, compared to traditional CS recovery approaches.

In this paper, we study amplify-and-forward dual-hop cooperative relaying protocols in the presence of Rayleigh fading, additive noise at the relay, as well as additive noise and symmetric alpha-stable interference at the destination. A quasi-static interference scenario is considered, which arises, e.g., when the same interferers are active during broadcast and relaying phases. At the destination, a maximal ratio combining demodulator is developed and studied, by assuming that the aggregate interference can be estimated, i.e., interference-aware design. A closed-form expression of the end-to-end moment generating function is provided and the achievable diversity order is studied. Two main results emerge from the paper: 1) if the ratio of the transmit-powers of cooperative and interfering networks is a constant, the diversity order is equal to 1 and 2) if the transmit-power of the interfering network is a constant, the diversity order is equal to 1 + 1/bI, where bI> 1 is the amplitude path-loss exponent. In the latter case, thus, second-order diversity is achieved asymptotically, as the amplitude path-loss exponent tends to one. Mathematical frameworks and findings are validated with the aid of Monte Carlo simulations.

In this paper, we examine multi-source multi-relay systems that employ Analog Network Coding for which we provide a two-fold contribution: i) We derive a closed-form upper bound of the average symbol error rate (SER) per source of the system, which is shown to be tight in the high-Signal-to-Noise Ratio (SNR) region, especially for an adequate number of relays, and ii) based on this bound, for a given total power budget to be distributed among the source and relay nodes we formulate the power allocation optimization problem with the aim of minimizing the SER per source. Results show that for a common target SER among the sources, an increase in their number results in an increase in the expected energy gains over the equal power allocation for all the nodes policy.

This work investigates the impact of power allocation for channel estimation (CE) in spatial modulation (SM). SM is a unique single-stream multiple-input multiple-output (MIMO) transmission technique that achieves spatial multiplexing gains. While SM completely avoids inter-channel interference, a single active antenna leads to a challenge for CE, i.e., all transmit antennas have to be activated sequentially to send the pilot signal. More time as well as energy is therefore consumed for CE. Driven by this motivation, we propose a closed-form optimal power allocation (OPA) for SM in this paper. Regardless of the SM transceiver structure, the optimal solution only depends on the pilot sequence length and the ratio between the number of pilots and the number of total symbols. Simulations results show that the bit error ratio performance of OPA-based SM tightly approaches the case of perfect channel state information with a gap of less than 0.8 dB.

One of the main challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRN) is spectrum sensing falsification (SSDF) attack. A SSDF attack consists in a cognitive user providing false data about the spectrum status. SSDF attack can hugely degrade the achievable detection accuracy and energy efficiency of CRNs. In this paper, a robust CSS algorithm against SSDF attack is proposed. The proposed algorithm assigns a specific weight to each user, which is able to (i) completely eliminate the resulting effects on CSS caused by many types of SSDF attacks, (ii) convert some types of SSDF attacks to be honest users, and (iii) alleviate the influence of other honest users that suffer from poor sensing performance or/and very noisy reporting channels. Simulation results show that, compared to many previous works, a significant improvement in detection accuracy and energy efficiency can be attained by the proposed algorithm.

Spatial modulation (SM) is a unique single-stream, multiple-input multiple-output (MIMO) transmission technique. Unlike multi-steam MIMO schemes, a single transmit antenna is activated in SM at any given time. Therefore, inter-channel interference is completely avoided. In addition, SM requires only one radio-frequency (RF) chain, regardless of the number of transmit antennas used. This key property results in a significant saving in quiescent power of power amplifiers. However, a challenge occurs when channel estimation (CE) is implemented for SM: the transmit antennas have to send pilots sequentially. Thus, in order not to compromise the throughput, the pilot number of each transmit antenna is restricted. This means we focus on improving the CE performance without increasing the number of pilots. In this paper, we propose a novel CE method for SM, in which the channel is jointly estimated across receive antennas. Simulation results show that the proposed approach outperforms the conventional method for various channel correlations between the receive antennas.

Spatial Modulation (SM) is a recently proposed single-RF multiple-input-multiple-output (MIMO) technique, which is capable of outperforming many conventional MIMO transmission schemes with low implementation and computational complexity. Recently, there have been some attempts in understanding the performance of SM in multi-user environments. However, most of the work has been oriented towards uplink multi-access scenarios. Also, conventional downlink/broadcast MIMO precoding techniques such as Zero Forcing (ZF) or Minimum Mean Square Error (MMSE) cannot be used in Multi-User SM (MU-SM), as part of the data in SM is also encoded into the Channel Impulse Responses (CIRs). In this paper, a novel precoding scheme for single-cell downlink MU-SM systems is proposed with a two-fold objective: i) the precoder needs to be able to completely eliminate the Multi-User Interference (MUI) by taking advantage of the Channel State Information (CSI) at the transmitter and ii) it needs to allow the users to use a single-user Maximum Likelihood (ML) optimum detector while achieving the same performance as interference-free point-to-point SM transmission. Finally, we also develop an interference-aware multi-user detection scheme, which does not require any CSI at the transmitter, and compare its performance with that of single-user detection schemes based on precoding.

A key challenge of future mobile communication research is to strike an attractive compromise between wireless network's area spectral efficiency and energy efficiency. This necessitates a clean-slate approach to wireless system design, embracing the rich body of existing knowledge, especially on multiple-input-multiple-ouput (MIMO) technologies. This motivates the proposal of an emerging wireless communications concept conceived for single-radio-frequency (RF) large-scale MIMO communications, which is termed as SM. The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system family. The research of SM has reached sufficient maturity to motivate its comparison to state-of-the-art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay-aided, cooperative, small-cell, optical wireless, and power-efficient communications. Furthermore, it has received sufficient research attention to be implemented in testbeds, and it holds the promise of stimulating further vigorous interdisciplinary research in the years to come. This tutorial paper is intended to offer a comprehensive state-of-the-art survey on SM-MIMO research, to provide a critical appraisal of its potential advantages, and to promote the discussion of its beneficial application areas and their research challenges leading to the analysis of the technological issues associated with the implementation of SM-MIMO. The paper is concluded with the description of the world's first experimental activities in this vibrant research field.

This paper proposes a multiple-input multiple-output (MIMO) transmission scheme for M-ary modulations, called Spatially Modulated Orthogonal Space-Time Block Coding (SM—OSTBC), based on the concept of Spatial Constellation (SC) codewords introduced by Le at el. . In the proposed scheme, transmit codeword matrices are generated by multiplying SC matrices with codewords constructed from Orthogonal Space-time Block Codes (O—STBC). The maximum spectral efficiency of the proposed scheme is equal to (n_T-2+ log_2 M) bpcu, where n_T is the number of transmit antennas and M is the modulation order. The SC matrices provide a means of carrying information bits together with the O—STBC codewords and allow the SM—OSTBC scheme to achieve second-order transmit diversity by satisfying the non-vanishing determinant property. A systematic method to design the SC codewords for an even number of transmit antennas greater than 3 is presented. A single-stream maximum-likelihood (ML) decoder, which requires a low computational complexity thanks to the structure of the SM—OSTBC codewords and to the orthogonality of the O—STBCs, and a sphere decoder with further reduced signal processing complexity are developed. The bit error rate (BER) performance of the proposed scheme is studied by using both theoretical union bound analysis and computer simulations. Finally, simulation results are presented in order to compare BER performance, energy efficiency and decoding complexity of the proposed scheme with those of several existing MIMO transmission schemes.

In this paper, we provide a mathematical framework for the analysis, design, and optimization of network-coded cooperative diversity (NCCD) protocols. The analysis is applicable to relay-aided protocols based on the error propagation model, which rely on appropriately designed diversity-combining demodulators at the destination. Wireless networks with an arbitrary number of sources and relays are considered. Arbitrary multilevel modulation schemes and network codes constructed over a non-binary Galois field (GF) are analyzed. NCCD protocols based on repetition-based and relay selection cooperation are investigated. Two takeaway messages emerge from our analysis: 1) repetition-based NCCD protocols are capable of achieving full-diversity, e.g., if the GF size is sufficiently large and the network code satisfies the maximum distance separable (MDS) property; and 2) NCCD protocols based on relay selection are capable of achieving full-diversity under more restrictive assumptions than repetition-based protocols, e.g., if the number of active relays is no fewer than the number of sources and an MDS network code is used.

In this paper, a mathematical framework to the computation of the error probability of downlink cellular networks is introduced. It is based on the Poisson point process (PPP)-based abstraction for modeling the spatial locations of the base stations (BSs), and it exploits results from stochastic geometry for characterizing the distribution of the other-cell interference. The framework is applicable to spatial multiplexing multiple-input-multiple-output (MIMO) systems with an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). If Nt= Nr= 1, the mathematical approach can be used for arbitrary fading distributions on both useful and interfering links. If either Nt> 1 or Nr> 1, it can be applied to arbitrary fading distributions on the useful link and to Rayleigh fading on the interfering links. It is shown that the proposed approach leads to easy-to-compute integral expressions, which reduce to closed-form formulas in some asymptotic regimes. Furthermore, the framework is shown to provide insights for system design and optimization. The accuracy of the mathematical analysis is substantiated through extensive Monte Carlo simulations for various cellular network setups.

In this paper, we propose a novel Virtual Multiple-Input-Multiple-Output (VMIMO) architecture based on the concept of Spatial Modulation (SM). Using a dual-hop and Decode-and-Forward protocol, we form a distributed system, called Dual-Hop Hybrid SM (DH-HSM). DH-HSM conveys information from a Source Node (SN) to a Destination Node (DN) via multiple Relay Nodes (RNs). The spatial position of the RNs is exploited for transferring information in addition to, or even without, a conventional symbol. In order to increase the performance of our architecture, while keeping the complexity of the RNs and DN low, we employ linear precoding using Channel State Information (CSI) at the SN. In this way, we form a Receive-Spatial Modulation (R-SM) pattern from the SN to the RNs, which is able to employ a centralized coordinated or a distributed uncoordinated detection algorithm at the RNs. In addition, we focus on the SN and propose two regularized linear precoding methods that employ realistic Imperfect Channel State Information at the Transmitter. The power of each precoder is analyzed theoretically. Using the Bit Error Rate (BER) metric, we evaluate our architecture against the following benchmark systems: 1) single relay; 2) best relay selection; 3) distributed Space Time Block Coding (STBC) VMIMO scheme; and 4) the direct communication link. We show that DH-HSM is able to achieve significant Signal-to-Noise Ratio (SNR) gains, which can be as high as 10.5 dB for a very large scale system setup. In order to verify our simulation results, we provide an analytical framework for the evaluation of the Average Bit Error Probability (ABEP).

In this paper, a novel channel estimation (CE) method is proposed for spatial modulation (SM), a unique single-stream multiple-input-multiple-output transmission technique. In SM, there is only one transmit antenna being active at any time instance. While this property completely avoids inter-channel interference, it results in a challenge to estimate the channel information. In conventional CE (CCE) methods for SM, all transmit antennas have to be sequentially activated for sending pilots. Therefore, the time consumed in CE is proportional to the number of transmit antennas, which significantly compromises the throughput. By exploiting channel correlation, the proposed method, named transmission cross CE (TCCE), has the following characteristics: i) the entire channel is estimated by sending pilots through one transmit antenna; ii) it requires no overhead or feedback; and iii) it achieves a low computational complexity at the receiver. In addition, we propose an analytical framework to compute the distribution of the CE errors over time-varying fading channels. The corresponding average bit error probability (ABEP) bound of SM is also derived for the proposed method. Results show that the proposed ABEP bound matches with the simulations very well. When compared with CCE, the new method obtains a signal-to-noise ratio gain of up to 7.5 dB for medium and high correlations between the transmit antennas. Moreover, an adaptive CE technique can be readily implemented for SM via switching between CCE and TCCE.

Although employing cooperation in spectrum sensing for cognitive radio (CR) systems improves the detection accuracy by mitigating the shadowing and multi-path fading faced by cognitive users, it increases the energy consumption especially because spectrum sensing is a periodic process. Therefore, for battery-powered terminals, energy efficiency represents a favorable metric in system design. One of the ways to improve the energy efficiency in CR is to optimize the fusion rule (FR) by which the individual results are processed. In this paper, we optimize the well known FR K-out-of-N for maximizing energy efficiency and detection accuracy. Mathematical expressions for the optimal N and K for both objectives are obtained. Simulation and analytical results show that significant improvement in energy efficiency can be achieved through FR optimization while satisfying a predefined threshold on the missed detection probability.

In this paper, we provide a theoretical analysis of the optimal Maximum-Likelihood (ML) detector for the recently proposed multistream Spatial Modulation (SM) concept for multiple-input-multiple-output (MIMO) systems. A Union Bound that is based on closed-form formulas is derived for the average bit error probability (ABEP) of the ML detection, which shows an excellent fit with respect to Monte Carlo simulations for the high signal-to-noise ratio (SNR) region. Furthermore, numerical results for different rates are provided regarding the comparison of the ABEP of multistream SM with ML detection with its recently proposed counterpart with suboptimal detection and with the ABEPs of other well-known MIMO schemes, such as conventional Spatial Multiplexing, Alamouti, and SM, which show its advantage over them. In addition, for the examined MIMO configuration and rates, the relative transmit energy efficiency gain of multistream SM with ML detection over the aforementioned methods is calculated for a low target ABEP.

Life expectancy is projected to increase significantly in the coming years. This fact has pushed the need for designing new and more pervasive healthcare systems. In this field, distributed and networked embedded systems, such as Wireless Sensor Networks (WSNs), are the most suitable technology to achieve continuous monitoring of aged people for their own safety, without affecting their daily activities. This paper proposes recent advancements in this field by introducing WSN4QoL, a Marie Curie project which involves academic and industrial partners from three EU countries. The project aims to propose new WSN-based technologies to meet the specific requirements of pervasive healthcare applications. In particular, in this paper, a Network Coding (NC) mechanism and a distributed localization solution are presented. They have been implemented on WSN testbeds to achieve efficiency in the communications and to enable indoor people tracking. Preliminary results in a real environment show good system performance that meet our expectations.

In this paper, we propose a closed-loop and single RF multiple-input-multiple-output (MIMO) method, particularly suitable for the Uplink of cellular systems, which is based on the low-complexity and recently devised method of Spatial Modulation. By selecting a subset of the available transmit antennas to implement Spatial Modulation based on the instantaneous Bit Error Rate (BER), the proposed method achieves both multiplexing and transmit-diversity gains by utilizing only one RF chain. By taking into account the total power consumption at the transmit side, we numerically show that our proposed method is more energy efficient than the single RF transmit antenna selection method for the same target performance and several MIMO configurations and correlation values among the transmit antennas.

This paper studies the four-node Multiple Access Relay Channel (MARC) under quasi-static block Rayleigh fading channels and Gaussian noise environment. A relay employs Demodulate-and-Forward (DMF) protocol in order to help two channel-encoded sources to communicate with a destination. The contributions of the paper are threefold: i) we propose a Near Optimal Joint Network/Channel decoding (NO-JNCD) algorithm at the destination. The NO-JNCD employs Cooperative Maximum Ratio Combining (C-MRC) detector and the BCJR algorithm applied to a compound code which trellis consists of all possible states of two single trellises at sources; ii) we compute extended distance spectrum of the compound code containing input weights for each source and output weights on each fading channel; and iii) from the extended distance spectrum, we derive the Bit Error Rate (BER) upper bound for the compound code as well as for each source. It is shown by simulation that the proposed decoder provides performance very close to that of the optimal JNCD with both DMF and Decode-and-Forward (DF) relaying protocols. Finally, the analysis is checked by simulation.

In single-stream multiple-input multiple-output (MIMO) schemes, such as spatial modulation (SM) and space shift keying (SSK), a single transmit antenna is activated at any given time. Therefore, unlike multi-stream MIMO transmitters, simultaneous pilot transmissions are prohibitive because of a single radio-frequency (RF) chain. In state-of-the-art literature, the channels of different transmit antennas are individually estimated. As a result, more time is required to transfer pilots and the effective data rate is compromised. In this paper, we propose a novel channel estimation (CE) technique for single-stream MIMO systems. Given a pilot ratio, the proposed scheme achieves the same estimation period as multi-stream MIMO systems without needing any additional information or feedback. Simulations are implemented under a practical base station environment. Results show that for various speeds of the mobile user, the proposed approach significantly improves the performance of SM in comparison to the conventional CE method.

Wireless Sensor Networks (WSNs) are fostering pervasive healthcare applications, where people are remotely monitored, without the full time assistance of professional care-givers. A key enabling element is the possibility to locate people in indoor environments, especially in emergency situations. Framed within the WSN4QoL project, this paper proposes the use of a ray tracing model for network planning, to support efficient people localization and performance analysis. Preliminary results show accurate radio coverage estimations, with a minimal calibration effort.

Energy efficiency in cooperative spectrum sensing in cognitive radio is investigated in this paper, where a novel approach is proposed for reducing the energy consumed in spectrum sensing and improving the resultant energy efficiency of the cognitive transmission. The proposed approach is based on limiting the number of users that participate in the spectrum sensing task. The participation decision of each user is taken individually by the user itself, where each user estimates the expected amount of consumed energy based on its distance from the base station, and compares it to a predefined threshold. The user will participate only if the estimated energy is less than the threshold. Besides reducing energy consumption, our proposal increases the amount of successfully transmitted data as well. Moreover, an optimization of the threshold is carried out through simulation in order to optimize the energy efficiency. Our results show a considerable amount of reduction in energy consumption (up to 80%) compared to the conventional approach.

A cooperative diversity protocol for multi-relay wireless networks is proposed. Its distinguishable feature is to protect the transmission of the source through distributed diversity while simultaneously serving the data traffic of the relays. Therefore, the performance of the source is improved without reducing the aggregate throughput. The protocol exploits a recently introduced concept for multiple- antennas wireless systems, which is known as Spatial Modulation (SM). The contribution of this paper is threefold: i) the optimal receiver that takes into account demodulation errors at the relays is developed; ii) its achievable diversity is studied analytically; and iii) it is shown, with the help of Monte Carlo simulations, that it outperforms state- of-the-art cooperative diversity protocols.

In this paper, an analytical framework to compute the average rate of downlink heterogeneous cellular networks is introduced. The framework exploits the so-called Poisson Point Process (PPP-) based abstraction model for the positions of the Base Stations (BSs), and results from stochastic geometry for other-cell interference modeling and performance analysis are used. The contribution of the present paper is twofold. 1) A simplified framework is proposed, which avoids the computation of the Meijer G-function and is applicable to arbitrary path-loss exponents. The new mathematical approach relies upon the Anderssen et al. sum of exponentials approximation for the Kohlrausch function. 2) The framework is applicable to correlated log-normal shadowing. The new mathematical approach relies upon the Owen and Steck method for the generation of equi-correlated multivariate normal distributions. The frameworks are substantiated through extensive Monte Carlo simulations, and numerical examples show that: i) for low Signal-to-Noise-Ratios (SNRs), the average rate slightly decreases if shadowing correlation increases; and ii) for high-SNRs, the average rate increases if shadowing correlation increases.

In this paper, we aim to study the Energy Efficiency (EE) of Spatial Modulation (SM) at different Base Stations (BSs) taking into account the total power consumption. Compared to conventional Multiple-Input Multiple-Output (MIMO) schemes, SM benefits from a single Radio Frequency (RF) chain which results in decreased power supply (W), higher EE (Mbits/J), and reduced complexity. Using the fundamental limits of Shannon capacity, we show that SM achieves a range of average data rates with only a fraction, which can be as low as 24% for four transmit antennas, of the total power supply of conventional MIMO. In addition, we demonstrate that the EE of the studied schemes is maximized for a certain average data rate and that SM achieves the highest EE among them. Finally, we note that a BS employing SM can be up to 67% more energy efficient compared to a BS under a conventional MIMO transmission scheme, for four transmit antennas.

To improve the performance of spatial modulation (SM) over correlated MIMO channels, transmit optimized spatial modulation (TOSM) has been proposed recently. It trades off traditional signal constellation diagrams with spatial constellation diagrams to minimize the average bit error probability (ABEP). After the optimum number of transmit antennas is determined, the specific antennas need to be carefully chosen from the entire array to provide a minimum ABEP. Like in conventional transmit antenna selection (TAS) schemes, the problem can be solved by an exhaustive search. However, this results in an unaffordable complexity especially when the spectral efficiency is high. In this paper, we propose a creative TAS approach for TOSM. Given a required number of antennas, the novel technique determines the selection solution based on circle packing. Simulation results show that for various channel correlations and spectral efficiencies, the proposed method achieves performance results close to exhaustive search with a gap of less than 0.3 dB. The complexity is reduced to an extremely low level.

In this paper, for the first time real--world channel measurements are used to analyse the performance of spatial modulation (SM), where a full analysis of the average bit error rate performance (ABER) of SM using measured urban correlated and uncorrelated Rayleigh fading channels is provided. The channel measurements are taken from an outdoor urban multiple input multiple output (MIMO) measurement campaign. Moreover, ABER performance results using simulated Rayleigh fading channels are provided and compared with a derived analytical bound for the ABER of SM, and the ABER results for SM using the measured urban channels. The ABER results using the measured urban channels validate the derived analytical bound and the ABER results using the simulated channels. Finally, the ABER of SM is compared with the performance of spatial multiplexing (SMX) using the measured urban channels for small and large scale MIMO. It is shown that SM offers nearly the same or a slightly better performance than SMX for small scale MIMO. However, SM offers large reduction in ABER for large scale MIMO.

In this paper, we introduce a new framework to compute the Outage Probability (Pout) and the Average Symbol Error Probability (ASEP) of dual-hop Amplify-and-Forward (AF) relaying in the presence of noise, fading, and network interference. The interfering nodes are randomly distributed in the 2D Euclidean plane according to a homogeneous Poisson point process. Our framework provides closed-form expressions of Poutand ASEP over Rayleigh fading channels, additive noise at the relay and at the destination, and network interference at the destination. The accuracy of our analytical derivation is substantiated through extensive Monte Carlo simulations.

In this paper, we introduce an analytical framework to compute the average rate of downlink heterogeneous cellular networks. The framework leverages recent application of stochastic geometry to other-cell interference modeling and analysis. The heterogeneous cellular network is modeled as the superposition of many tiers of Base Stations (BSs) having different transmit power, density, path-loss exponent, fading parameters and distribution, and unequal biasing for flexible tier association. A long-term averaged maximum biased-received-power tier association is considered. The positions of the BSs in each tier are modeled as points of an independent Poisson Point Process (PPP). Under these assumptions, we introduce a new analytical methodology to evaluate the average rate, which avoids the computation of the Coverage Probability (Pcov) and needs only the Moment Generating Function (MGF) of the aggregate interference at the probe mobile terminal. The distinguishable characteristic of our analytical methodology consists in providing a tractable and numerically efficient framework that is applicable to general fading distributions, including composite fading channels with small- and mid-scale fluctuations. In addition, our method can efficiently handle correlated Log-Normal shadowing with little increase of the computational complexity. The proposed MGF-based approach needs the computation of either a single or a two-fold numerical integral, thus reducing the complexity of Pcov-based frameworks, which require, for general fading distributions, the computation of a four-fold integral.

In this paper, we introduce an analytical framework for performance analysis and design of Single-Input-Multiple-Output (SIMO) wireless systems in the presence of noise, fading, and radio frequency interference produced by randomly distributed active interferers surrounding an intended probe receiver. The framework leverages recent application of stochastic geometry and Poisson Point Processes (PPPs) theory to network interference modeling. To assess the impact of spatial dependence across multiple receive-antennas, three models of network interference are studied: i) the isotropic model, where all receive-antennas see interferers belonging to the same PPP; ii) the independent model, where each receive—antenna sees interferers belonging to an independent PPP; and iii) the mixture model, which is a superposition of isotropic and independent interferences. Depending on the fading distribution of the interferers, the Nakagami-m fading parameter of the probe link, and the number of receive-antennas, either exact or upper-bound formulas of the error probability averaged over noise, fading, and spatial interference are given. Our analysis shows that, depending on the interference model, performance can either improve or get worse with multiple antennas at the receiver. The proposed analytical methodology is applicable to single- and multi-PPPs interference environments.

In this paper, Sphere Decoding (SD) algorithms for Spatial Modulation (SM) are developed to reduce the computational complexity of Maximum—Likelihood (ML) detectors. Two SDs specifically designed for SM are proposed and analysed in terms of Bit Error Ratio (BER) and computational complexity. Using Monte Carlo simulations and mathematical analysis, it is shown that by carefully choosing the initial radius the proposed sphere decoder algorithms offer the same BER as ML detection, with a significant reduction in the computational complexity. A tight closed form expression for the BER performance of SM—SD is derived in the paper, along with an algorithm for choosing the initial radius which provides near to optimum performance. Also, it is shown that none of the proposed SDs are always superior to the others, but the best SD to use depends on the target spectral efficiency. The computational complexity trade—off offered by the proposed solutions is studied via analysis and simulation, and is shown to validate our findings. Finally, the performance of SM—SDs are compared to Spatial Multiplexing (SMX) applying ML decoder and applying SD. It is shown that for the same spectral efficiency, SM—SD offers up to 84% reduction in complexity compared to SMX—SD, with up to 1 dB better BER performance than SMX—ML decoder.

In this paper, we contribute to the theoretical understanding, the design, and the performance evaluation of multi—source multi—relay network—coded cooperative diversity protocols. These protocols are useful to counteract the spectral inefficiency of repetition—based cooperation. We provide a general analytical framework for analysis and design of wireless networks using the Demodulate—and—Forward (DemF) protocol with binary Network Coding (NC) at the relays and Cooperative Maximal Ratio Combining (C—MRC) at the destination. Our system model encompasses an arbitrary number of relays which offer two cooperation levels: i) full—cooperative relays, which postpone the transmission of their own data frames to help the transmission of the sources via DemF relaying and binary NC; and ii) partial—cooperative relays, which exploit NC to transmit their own data frames along with the packets received from the sources. The relays can apply NC on different subsets of sources, which is shown to provide the sources with unequal diversity orders. Guidelines to choose the packets to be combined, i.e., the network code, to achieve the desired diversity order are given. Our study shows that partial—cooperative relays provide no contribution to the diversity order of the sources. Theoretical findings and design guidelines are validated through extensive Monte Carlo simulations.

A unique characteristic of spatial modulation (SM) is the three dimensional constellation diagram. This enables to trade-off traditional signal constellation diagrams with spatial constellation diagrams where the latter is defined by the physical antenna array. In this paper we investigate the optimum pairs of signal and spatial constellation sizes with respect to average bit error probability (ABEP) and energy efficiency. The analysis is performed for varying antenna correlations and channel conditions. Both numerical and closed-form results are presented which show that a significant performance gain can be obtained when the optimal constellation pairs are used.

In this paper, we study the three-node multiple-access relay channel under realistic operating conditions. More specifically, in our setup all wireless links are subjected to Rayleigh fading and Gaussian noise, and the relay node can be located at different distances from source and destination nodes. We assume that coded bits are fully-interleaved, so that the transmitted symbols are mutually independent. By exploiting factor graph representation, we propose a joint network/channel decoding algorithm, which takes into account decoding errors introduced by the relay node. The convergence of the proposed iterative decoding algorithm is studied through EXIT chart analysis. The proposed decoder is compared with a recently proposed algorithm, which employs the demodulate-and-forward protocol and works on coded bits, and it is shown that it provides better performance. More specifically, for a bit error rate (BER) equal to 1e−3, it is 1.5dB better when the relay is located near the destination and 2.0dB better when the relay is in between source and destination.

Accurate modeling of network interference, deep understanding of its impact on the achievable performance, and development of efficient techniques to mitigate or exploit it are three important and fundamental research assets in current and next-generation cellular networks. In this context, Andrews, Baccelli, and Ganti [1] have recently introduced a new analytical approach to estimate coverage and rate of cellular networks subject to other-cell interference. In this paper, we move from the approach developed in [1], and propose an alternative analytical derivation to compute the rate of cellular networks. More specifically, by using stochastic geometry and Poisson point processes theory, we derive a simple and easy-to-compute expression of the rate, which can be used for arbitrary network and channel parameters, e.g., path-loss exponent, receiver noise, density of Base Stations (BSs), etc. Compared to [1], our framework has two main distinguishable features: i) the rate can be computed via a single numerical integral, rather than via a three-fold numerical integral; and ii) the formula is applicable to arbitrary fading distributions on the intended link, rather than being useful for Rayleigh fading only. Our analytical derivation is substantiated through extensive Monte Carlo simulations.

In this paper, we study the performance of network-coded cooperative diversity systems with practical communication constraints. More specifically, we investigate the interplay between diversity, coding, and multiplexing gain when the relay nodes do not act as dedicated repeaters, which only forward data packets transmitted by the sources, but they attempt to pursue their own interest by forwarding packets which contain a network-coded version of received and their own data. We provide a very accurate analysis of the Average Bit Error Probability (ABEP) for two network topologies with three and four nodes, when practical communication constraints, i.e., erroneous decoding at the relays and fading over all the wireless links, are taken into account. Furthermore, diversity and coding gain are studied, and advantages and disadvantages of cooperation and binary Network Coding (NC) are highlighted. Our results show that the throughput increase introduced by NC is offset by a loss of diversity and coding gain. It is shown that there is neither a coding nor a diversity gain for the source node when the relays forward a network-coded version of received and their own data. Compared to other results available in the literature, the conclusion is that binary NC seems to be more useful when the relay nodes act only on behalf of the source nodes, and do not mix their own packets to the received ones. Analytical derivation and findings are substantiated through extensive Monte Carlo simulations.

In this paper, we introduce a new framework to compute the Average Symbol Error Probability (ASEP) of an intended wireless communication system subject to network interference and noise. The interfering nodes are assumed to be randomly distributed in the 2D Euclidean plane according to a homogeneous Poisson point process. Our framework is applicable to performance prediction and optimization of, e.g., emerging heterogeneous cellular and cognitive radio networks. More specifically, we move from and generalize the semi-analytical framework recently introduced by Pinto and Win [1], and develop a new mathematical model which offers a simple single-integral expression of the ASEP under very general channel and interference conditions. The framework is exact, avoids Monte Carlo methods for its computation, and is applicable to asynchronous and synchronous scenarios. Our numerical examples show that both setups have almost the same performance, and that the ASEP in the presence of synchronous interference is a very tight upper-bound of the ASEP in the presence of asynchronous interference. This is a relevant result, as we show in this paper that in the former case all parameters of interest can be computed in closed-form. Our analytical derivation is substantiated through extensive Monte Carlo simulations.

Generalized Space Shift Keying (GSSK) modulation is a recently proposed low-complexity concept for Multiple-Input- Multiple-Output (MIMO) wireless systems. GSSK modulation is a generalized version of Space Shift Keying (SSK) modulation, which provides a better spectral efficiency through multiple active antennas at the transmitter. An apparent weakness of GSSK modulation is that it does not exploit the transmit-antennas to achieve transmit-diversity. In this paper, we propose a precoding method for GSSK modulation, which simultaneously achieves high diversity and coding gains. The solution is based on: i) \emph{co-phasing} the active antennas of each spatial-constellation point; and ii) properly \emph{rotating} the phases among spatial-constellation points. The new scheme requires Channel State Information at the Transmitter (CSIT), i.e., the channel phases of each wireless link, which can be obtained through a feedback channel. For the case of a perfect feedback channel, we analytically show that for three and four antennas at the transmitter a full transmit diversity can be achieved without reducing the achievable rate. Furthermore, for various MISO configurations and achievable rates we show through Monte Carlo simulations that our proposed scheme outperforms state-of-the-art open- loop GSSK schemes, in terms of both diversity and coding gain, when the number of bits allocated for the quantization of each channel phase is between 2 and 4.

Spatial modulation (SM) and space shift keying (SSK) use only one out of several transmit antennas at a time to transmit data via an antenna index. In such a system, the information is encoded by exploiting channel randomness i.e. the fact that channels between different transmit and receive antennas are random. This difference is used to distinguish among the transmit antennas. While SSK uses only antenna index to transmit data, SM also uses ordinary signal modulation. In wireless secrecy systems, one of the key performance measures is secrecy capacity. It specifies the rate at which the transmitter can communicates on the main link to the desired receiver while this information cannot be decoded by the eavesdropper. We investigate SM and SSK in the context of wireless secrecy capacity when the underlying modulation and the difference between the legitimate and eavesdropper signal to noise ratios (SNRs) are varied.

In this paper, motivated by the relatively new concept of Spatial Modulation (SM), we are addressing the problem of Receive-Spatial Modulation (R-SM) under two cases of imperfect channel knowledge at the transmitter side. In the first case, we adopt a statistical model for the channel uncertainties, whereas in the second one a worst-case approach is followed and the channel uncertainties are expressed as a bounded set. Based on Zero-Forcing (ZF) precoding and using standard tools from optimization theory, we derive closed form solutions that turn out to be robust. Simulation results show that the proposed schemes have a performance close to the perfect channel knowledge scenario in low and mid-low SNRs. Furthermore, these designs can be applied to wide range of channels with different correlation states combined with a transmit power gain.

In this paper, we study the performance of space modulation for Multiple-Input-Multiple-Output (MIMO) wireless systems with imperfect channel knowledge at the receiver. We focus our attention on two transmission technologies, which are the building blocks of space modulation: i) Space Shift Keying (SSK) modulation; and ii) Time-Orthogonal-Signal-Design (TOSD-) SSK modulation, which is an improved version of SSK modulation providing transmit-diversity. We develop a single-integral closed-form analytical framework to compute the Average Bit Error Probability (ABEP) of a mismatched detector for both SSK and TOSD-SSK modulations. The framework exploits the theory of quadratic-forms in conditional complex Gaussian Random Variables (RVs) along with the Gil-Pelaez inversion theorem. The analytical model is very general and can be used for arbitrary transmit- and receive-antennas, fading distributions, fading spatial correlations, and training pilots. The analytical derivation is substantiated through Monte Carlo simulations, and it is shown, over independent and identically distributed (i.i.d.) Rayleigh fading channels, that SSK modulation is as robust as single-antenna systems to imperfect channel knowledge, and that TOSD-SSK modulation is more robust to channel estimation errors than the Alamouti scheme. Furthermore, it is pointed out that only few training pilots are needed to get reliable enough channel estimates for data detection, and that transmit- and receive-diversity of SSK and TOSD-SSK modulations are preserved even with imperfect channel knowledge.

In this paper, we study the performance of Spatial Modulation (SM) applied to optical wireless communications (OWC) in indoor environments with line-of-sight (LOS) characteristics. To this end, we consider setup scenarios with different numbers of optical transmitters and receivers which are arranged within a room. SM is compared to Repetition Coding (RC). Because RC is known to achieve very good performance in OWC systems due to the use of intensity modulation and the resulting constructive superposition of the power signals. The results show that SM can outperform RC when high spectral efficiencies are desirable, e.g. 4 bit/s/Hz and greater, since it can operate with reduced signal modulation orders by conveying additional data bits in the spatial domain. We also demonstrate that SM benefits from receive-diversity to a larger extent while at the same time requiring less computational complexity. Furthermore, we give a general framework to numerically approximate the average bit error probability of both SM and RC.

In this paper, we propose a simple analytical methodology to study the performance of multi-source multi-relay cooperative wireless networks with network coding at the relay nodes and Maximum-Likelihood (ML-) optimum channel-aware detectors at the destination. Channel-aware detectors are a broad class of receivers that account for possible decoding errors at the relays, and, thus, are inherently designed to mitigate the effect of erroneous forwarded and network-coded data. In spite of the analytical complexity of the problem at hand, the proposed framework turns out to be simple enough yet accurate and insightful to understand the behavior of the system, and, in particular, to capture advantages and disadvantages of various network codes and the impact of error propagation on their performance. It is shown that, with the help of cooperation, some network codes are inherently more robust to decoding errors at the relays, while others better exploit the inherent spatial diversity and redundancy provided by cooperative networking. Finally, theory and simulation highlight that the relative advantage of a network code with respect to the others might be different with and without decoding errors at the relays.

Accurate representation of the physical layer is instrumental for a sound design and optimization of Medium Access Control (MAC) protocols for cooperative wireless networks. However, the vast majority of MAC protocols are designed and analyzed by considering simplified physical layer and channel models, which often lead to too optimistic performance predictions. In particular, even though many experimental activities have showcased the important role played by shadow-fading, most protocols are designed and evaluated by taking into account only the transmission distance (circular coverage model) or only the fast-fading. Motivated by the proved unsuitability of these models, the contribution of this paper is threefold: i) we provide important considerations on how to adequately include the effect of shadowing into the design of MAC protocols for cooperative networks; ii) we provide an analytical framework to determine the subset of active relays in order to meet a given Quality-of- Service (QoS) requirement; and iii) we study, through analysis and simulation, the performance of a promising MAC protocol for cooperative networks, which is called Persistent Relay Carrier Sensing Multiple Access (PRCSMA), by explicitly taking into account the effect of shadowing. Our study shows that shadowing can dramatically change system and protocol performance.

In this paper, we study the sensitivity and robustness of Space Shift Keying (SSK) modulation to imperfect channel knowledge at the receiver. Unlike the common widespread belief, we show that SSK modulation is more robust to imperfect channel knowledge than other state-of-the-art transmission technologies, and only few training pilots are needed to get reliable enough channel estimates for data detection. More precisely, we focus our attention on the so-called Time-Orthogonal-Signal-Design (TOSD-) SSK modulation scheme, which is an improved version of SSK modulation offering transmit-diversity gains, and provide the following contributions: i) we develop a closed-form analytical framework to compute the Average Bit Error Probability (ABEP) of a mismatched detector for TOSD-SSK modulation, which can be used for arbitrary transmit-antenna, receive-antenna, channel fading, and training pilots; ii) we perform a comparative study of the performance of TOSD-SSK modulation and the Alamouti code under the same imperfect channel knowledge, and show that TOSD-SSK modulation is more robust to channel estimation errors; iii) we point out that only few pilot pulses are required to get performance very close to the perfect channel knowledge lower-bound; and iv) we verify that transmit- and receive-diversity gains of TOSD-SSK modulation are preserved even for a mismatched receiver.

Knowing exactly where a mobile entity is and monitoring its trajectory in real-time has recently attracted a lot of interests from both academia and industrial communities, due to the large number of applications it enables, nevertheless, it is nowadays one of the most challenging problems from scientific and technological standpoints. In this work we propose a tracking system based on the fusion of position estimations provided by different sources, that are combined together to get a final estimation that aims at providing improved accuracy with respect to those generated by each system individually. In particular, exploiting the availability of a Wireless Sensor Network as an infrastructure, a mobile entity equipped with an inertial system first gets the position estimation using both a Kalman Filter and a fully distributed positioning algorithm (the Enhanced Steepest Descent, we recently proposed), then combines the results using the Simple Convex Combination algorithm. Simulation results clearly show good performance in terms of the final accuracy achieved. Finally, the proposed technique is validated against real data taken from an inertial sensor provided by THALES ITALIA.

In this paper, we investigate joint network and channel decoding algorithms for the multiple-access relay channel. We consider a realistic reference scenario with Rayleigh fading over all the wireless links, including the source-to-relay channels. Our contribution is twofold: i) first, we develop the quasi-optimal joint network and channel decoder by taking into account possible errors over the source-to-relay channels, and ii) second, we propose a low-complexity iterative joint network and channel decoding algorithm, which reduces the number of channel decoders with respect to state-of-the-art solutions. Our numerical results show that: i) in fully-interleaved Rayleigh fading channels, the proposed solution provides almost the same bit error probability as the quasi-optimal scheme but with a reduction in complexity of approximately the 65%, and ii) in Rayleigh block-fading channels, the proposed scheme yields almost the same bit error probability as state-of-the-art solutions but with a reduction in complexity of approximately the 30%.

In this paper, we propose Unequal Error Protection (UEP) coding theory as a viable and flexible method for the design of network codes for multi-source multi-relay cooperative networks. As opposite to state-of-the-art solutions available for improving the diversity gain of cooperative networks, it is shown that the proposed method allows us to assign each source node the desired diversity gain, according to, e.g., the requested Quality of Service (QoS) or power constraints. The diversity advantage of the UEP-based network code design over conventional relay-only and XOR-only solutions is shown for the canonical two-source two-relay network. Furthermore, Maximum-Likelihood (ML-) optimum channel-aware receivers for multi-source multi-relay cooperative networks are developed, and their Average Bit Error Probability (ABEP) and achievable diversity over fading channels analytically studied. It is shown that only a cross-layer (joint) implementation of demodulation and network-decoding allows the destination to fully exploit the diversity inherently provided by the distributed network code. Finally, analytical derivations and findings are substantiated via Monte Carlo simulations.

Spatial Modulation (SM) is a recently proposed low--complexity modulation scheme for multiple-antenna wireless systems. Recent works have shown that state-of-the-art SM provides a multiplexing gain with respect to single-antenna systems by avoiding inter-channel interference, but it is inherently unable to achieve transmit-diversity. The aim of this paper is to shed light on the design of multiple-antenna wireless systems that exploit the SM concept and can achieve transmit-diversity gains. More specifically, we propose a novel modulation concept, which in a unique fashion combines the high multiplexing gain offered by SM and the transmit-diversity gain provided by Space Time Block Codes (STBCs) technology. This modulation scheme is introduced here for the first time and referred to as "Spatially-Modulated STBCs" (SM-STBC). By using analysis and simulation, we show that SM-STBC achieves multiplexing and diversity gains, as well as still retains a single-stream receiver implementation. By using numerical simulations, we show that the proposed code can provide better performance than state-of-the-art Alamouti and rate-3/4 STBCs.

Space Shift Keying (SSK) is a low-complexity modulation scheme for multiple-antenna wireless systems. In this paper, we analyze the transmit-diversity/multiplexing trade-off of SSK modulation with the main objective of developing practical solutions to achieve transmit-diversity. More specifically, the contributions of this paper are as follows: i) we propose a practical scheme that achieves transmit-diversity equal to two for any number of antennas at the transmitter. The solution is based on the so-called Time-Orthogonal-Signal-Design (TOSD) principle introduced in [1], and adopts time-orthogonal shaping filters at the transmitter; ii) we show that the TOSD principle with orthogonal shaping filters can be applied to the so-called Generalized SSK (GSSK) modulation scheme in [2], and that a transmit-diversity equal to two can still be obtained while increasing the data rate with respect to SSK modulation; and iii) we propose a general encoding scheme that allows us to get transmit-diversity greater than two. The solution combines TOSD and GSSK principles in a unique fashion, and is flexible enough to accommodate various transmit-diversity gains by trading-off the number of transmit-antenna, the number of simultaneously-active transmit-antenna, and the achievable data rate. Furthermore, proposed methods and findings are substantiated via analysis and numerical simulations.

In this paper, Sphere Decoding (SD) algorithms for Spatial Modulation (SM) are developed to reduce the computational complexity of Maximum--Likelihood (ML--) optimum detectors, which foresee an exhaustive search of the whole search space and have a complexity that linearly increases with the product of number of transmit--antenna, receive--antenna, and size of the modulation scheme. Three SDs specifically designed for SM are proposed and analyzed in terms of Bit Error Probability (BEP) and computational complexity. By judiciously choosing some key parameters, e.g., the radius of the sphere centered around the received signal, it is shown that the proposed algorithms offer the same BEP as ML--optimum detection, with a significant reduction of the computational complexity. Also, it is shown that none of the proposed SDs is always superior to the others, but the best SD to use depends on the system setup, i.e., the number of transmit--antenna, receive--antenna, and the size of the modulation scheme. The computational complexity trade--off offered by the proposed solutions is studied via analysis and simulation, and numerical results are shown to validate our findings.

The demonstration proposal moves from the capabilities of a wireless biometric badge [4], which integrates a localization and tracking service along with an automatic personal identification mechanism, to show how a full system architecture is devised to enable the control of physical accesses to restricted areas. The system leverages on the availability of a novel IEEE 802.15.4/Zigbee Cluster Tree network model, on enhanced security levels and on the respect of all the users' privacy issues.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

In this paper, we introduce Dual-hop Spatial Modulation (Dh-SM). We look at the effect that Dh-SM has on the required signal to noise ratio (SNR) at the destination and how it can help alleviate the multi-hop burden. Initial bit-error-ratio (BER) results comparing the performance of Dh-SM with orthogonal decode-and-forward (DF) are presented where Dh-SM is shown to have up to a 10 dB SNR advantage.

Time switched transmit diversity (TSTD) system uses only one out of several transmit antennas at a time in order to exploit the benefit of transmit diversity while keeping the system simple by having only one RF chain. We show that while average rate of such a system remains similar to the capacity of the single transmit antenna, outage secrecy capacity is greatly improved. In particular, as the number of transmit antenna is increased or outage probability decreased, the gains over single antenna system improve significantly - gains of over 20 dB with respect to the single antenna system are achieved for 1% outage rate. Furthermore, the probability of existence of secrecy capacity is improved as well for cases when the main channel has higher SNR than the eavesdropper channel.

In this paper, we study the performance of Space Shift Keying (SSK) modulation for a generic Multiple—Input—Multiple—Output (MIMO) wireless system over correlated Rician fading channels. In particular, our contribution is twofold. i) First, we propose a very general framework for computing the Average Bit Error Probability (ABEP) of SSK—MIMO systems over a generic Rician fading channel with arbitrary correlation and channel parameters. The framework relies upon the Moschopoulos method. We show that it is exact for MIMO systems with two transmit—antenna and arbitrary receive—antenna, while an asymptotically—tight upper—bound is proposed to handle the system setup with an arbitrary number of transmit—antenna. ii) Second, moving from the consideration that conventional SSK—MIMO schemes can offer only receive—diversity gains, we propose a novel SSK—MIMO scheme that can exploit the transmit—antenna to increase the diversity order. The new method has its basic foundation on the transmission of signals with good time—correlation properties, and is called Time—Orthogonal—Signal—Design (TOSD—) assisted SSK modulation (TOSD—SSK). It is shown that the proposed method can increase twofold the diversity order for arbitrary transmit— and receive—antenna. In particular, for MIMO systems with two transmit—antenna and N_r receive—antenna full—diversity equal to 2N_r can be achieved. Analytical frameworks and theoretical findings are substantiated via Monte Carlo simulations for various system setups.

Spatial Modulation (SM) is a recently proposed joint coding and modulation scheme for Multiple-Input-Multiple-Output (MIMO) wireless systems, which is receiving a growing interest. SM offers a low-complexity alternative to the design of MIMO wireless systems, which avoids multiple Radio Frequency (RF) chains at the transmitter and high-complexity interference cancellation algorithms at the receiver, but still guarantees a multiplexing gain that only depends on the number of antennas at the transmitter. This makes this technology especially suitable for the downlink with low-complexity mobile units. So far, the feasibility and performance of SM have been assessed and studied only for point-to-point communication systems, i.e., the single-user scenario. However, the performance achievable by the vast majority of wireless communication networks is interference limited, due to the simultaneous transmission of various users over the same physical wireless channel. Therefore, the adoption of SM in the next generation of wireless communication systems requires a deep understanding of its performance over interference channels. Motivated by this consideration, in this paper we study the performance of SM over the reference multiple-access fading channel composed by two transmitters and one receiver. Two detectors at the receiver are studied, i.e., the single- and the multi-user detector. In particular, analysis and Monte Carlo simulations show that the single-user detector does not offer, in general, good error performance for arbitrary channel conditions, while the multi-user detector achieves error performance very close to the single-user lower-bound. These results clearly highlight that SM can be adopted for enabling data transmission over multiple-access fading channels as well.

In this paper, we develop an analytical framework for analyzing the performance of wireless systems adopting the recently proposed Space Shift Keying (SSK) modulation scheme. More specifically, we investigate the performance of a 2 × 1 MISO (Multiple-Input-Single-Output) system setup with Maximum-Likelihood (ML) detection at the receiver. The exact Average Bit Error Probability (ABEP) over correlated and non-identically distributed Nakagami-m fading channels is computed in closed-form. Numerical results will show that the performance of SSK modulation is significantly affected by the characteristics of fading channels, e.g., channel correlation, fading severity, and power imbalance among the wireless links. Analytical frameworks and findings will also be substantiated via Monte Carlo simulations.

In this paper, we introduce TOSD-SM (TimeOrthogonal Signal Design assisted Spatial Modulation), which is a novel space modulation scheme based on the principle of the recently proposed Spatial Modulation (SM) wireless transmission technique for Multiple-Input-Multiple-Output (MIMO) systems. We show that, unlike other SM-based methods available in the literature, the proposed approach can offer transmit-diversity gains by properly designing the transmitted signal to have a peaky time auto-correlation function. By considering the basic 2 × 1 MISO (Multiple-Input-Single-Output) system setup, the optimal Maximum-Likelihood (ML) detector with full Channel State Information (CSI) at the receiver will be developed, and its performance evaluated in closed-form. Performance comparison with other proposed SM schemes over fading channels will evidence two main benefits of TOSD-SM: i) transmit-diversity gains, and ii) intrinsic robustness to spatial correlation of channel fading. Analytical frameworks and findings will also be substantiated via Monte Carlo simulations.

Since the pioneering research work of Ahlswede et al. in 2000, Network Coding (NC) has rapidly emerged as a major research area in electrical engineering and computer science due to its wide applicability to communication through real networks. The many contributions available in the literature to date, ranging from pure theoretical studies on fundamental limits to practical experimentations in real-world environments, offer a clear evidence that the shift in paradigm envisaged by NC might revolutionize the way we manage, operate, and understand the organization of networks. NC allows intermediate nodes of communication networks to combine the information received from multiple links for subsequent transmissions, and offer a powerful and efficient generalization to network information delivery via routing, where network nodes simply store and forward data, and processing is only accomplished at the end nodes. In this paper, we have a twofold objective: i) first, we summarize fundamental information-theoretic results, which, since their publication, have been representing the foundation for all subsequent research in this field, and ii) then, we introduce and summarize the latest results related to the analysis, design, and optimization of the so-called network error correction coding problem, which is instrumental for the effective use of NC over lossy, e.g., wireless networks.

Trellis Coded Spatial Modulation (TCSM) is a novel transmission technology for Multiple-Input-Multiple-Output (MIMO) systems, which has been recently proposed to improve the performance of Spatial Modulation (SM) over correlated fading channels. The fundamental principle of TCSM is to use convolutional encoding and Maximum-Likelihood Sequence Estimation (MLSE) decoding to increase the free distance between sequences of spatial constellation points, thus improving, especially over spatially correlated fading channels, the end-to-end system performance. In this paper, we propose tight analytical bounds for performance analysis of TCSM over correlated fading channels. In particular, the contributions of this paper are as follows: i) we propose two asymptotically tight (for high Signal-to-Noise-Ratios, SNRs) upper bounds for the analysis of uncoded SM schemes, which offer a better accuracy than already existing frameworks, ii) we propose a simple Chernoff bound for performance analysis of TCSM, which, although weak, can well capture the diversity order of the system, and iii) we propose an asymptotically tight (for high SNRs) true union bound for the accurate performance prediction of TCSM over correlated fading channels. Analytical frameworks and findings will also be substantiated via Monte Carlo simulations.

In this paper, we propose a comprehensive framework for performance analysis of multi-hop multi-branch wireless communication systems over log-normal fading channels. The framework allows to estimate the performance of amplify and forward (AF) relay methods for both channel state information (CSI-) assisted relays, and fixed-gain relays. In particular, the contribution of this paper is twofold: i) first of all, by relying on the Gauss quadrature rule (GQR) representation of the moment generation function (MGF) for a log-normal distribution, we develop accurate formulas for important performance indexes whose accuracy can be estimated a priori and just depends on GQR numerical integration errors; ii) then, in order to simplify the computational burden of the former framework for some system setups, we propose various approximations, which are based on the Improved Schwartz-Yeh (I-SY) method. We show with numerical and simulation results that the proposed approximations provide a good trade-off between accuracy and complexity for both Selection Combining (SC) and Maximal Ratio Combining (MRC) cooperative diversity methods.

In this paper, we offer an accurate framework for analyzing the performance of wireless communication systems adopting the recently proposed Space Shift Keying (SSK) modulation scheme. More specifically, we study the performance of a Ntx 1 MISO (Multiple-Input-Single-Output) system setup with Maximum-Likelihood (ML) detection and full Channel State Information (CSI) at the receiver. The exact Average Bit Error Probability (ABEP) over generically correlated and non-identically distributed Nakagami-m fading channels is computed in closed-form when Nt=2, while very accurate and asymptotically tight upper bounds are proposed to compute the ABEP when Nt> 2. With respect to current literature, our contribution is threefold: i) the ABEP is computed in closed-form without resorting to Monte Carlo numerical simulations, which, besides being computationally intensive, only yield limited insights about the system performance and cannot be exploited for a systematic optimization of it, ii) the framework accounts for arbitrary fading conditions and is not restricted to identically distributed fading channels, thus offering a comprehensive understanding of the performance of SSK modulation over generalized fading channels, and iii) the analytical framework could be readily adapted to study the performance over generalized fading channels with arbitrary fading distributions, since the Nakagami-m distribution is a very flexible fading model, which either includes or can closely approximate several other fading models. Numerical results show that the performance of SSK modulation is significantly affected by the characteristics of fading channels, {e.g.}, channel correlation, fading severity, and power imbalance among the Nttransmit-receive wireless links. Analytical frameworks and theoretical findings are also substantiated via Monte Carlo simulations.

Space Shift Keying (SSK) modulation is a new and recently proposed transmission technology for Multiple-Input-Multiple-Output (MIMO) wireless systems, which has been shown to be a promising low-complexity alternative to several state-of-the-art MIMO schemes. So far, only optimal or heuristic transceivers with Full Channel State Information (F-CSI) at the receiver have been investigated, and their performance analyzed over fading channels. In this paper, we develop and study the performance of the optimal Maximum-Likelihood (ML) detector with unknown phase reference at the receiver (i.e., Partial-CSI, P-CSI, knowledge). A very accurate analytical framework for the analysis and optimization of this novel detector over generically correlated and non-identically distributed Nakagami-{m} fading channels is proposed, and its performance compared to the optimal receiver design with F-CSI. Numerical results will point out that: i) the performance of SSK modulation is significantly affected by the characteristics of fading channels, e.g., channel correlation, fading severity, and, particularly, power imbalance among the transmit-receive wireless links, and ii) unlike ordinary modulation schemes, there is a substantial performance loss when the receiver cannot exploit the phase information for optimal receiver design. This latter result highlights the importance of accurate and reliable channel estimation mechanisms for the efficient operation of SSK modulation over fading channels. Analytical frameworks and theoretical findings will also be substantiated via Monte Carlo simulations.

Trellis coded modulation (TCM) is a well known scheme that reduces power requirements without any bandwidth expansion. In TCM, only certain sequences of successive constellation points are allowed (mapping by set partitioning). The novel idea in this paper is to apply the TCM concept to the antenna constellation points of spatial modulation (SM). The aim is to enhance SM performance in correlated channel conditions. SM considers the multiple transmit antennas as additional constellation points and maps a first part of a block of information bits to the transmit antenna indices. Therefore, spatial multiplexing gains are retained and spectral efficiency is boosted. The second part of the block of information bits is mapped to a complex symbol using conventional digital modulation schemes. At any particular time instant, only one antenna is active. The receiver estimates the transmitted symbol and the active antenna index and uses the two estimates to retrieve the original block of data bits. In this paper, TCM partitions the entire set of transmit antennas into sub-sets such that the spacing between antennas within a particular sub-set is maximized. The scheme is called trellis coded spatial modulation (TCSM). Tight analytical performance bounds over correlated fading channels are proposed in this paper. In addition, the performance and complexity of TCSM is compared to the performance of SM, coded V-BLAST (vertical Bell Labs layered space-time) applying near optimum sphere decoder algorithm, and Alamouti scheme combined with TCM. Also, the performance of all schemes with turbo coded modulation is presented. It is shown that under the same spectral efficiency, TCSM exhibits significant performance enhancements in the presence of realistic channel conditions such as Rician fading and spatial correlation (SC). In addition, the complexity of the proposed scheme is shown to be 80% less than the V-BLAST complexity.

In this paper, we investigate the performance of a general multi-layer decentralized system setup for cooperative spectrum sensing, which includes realistic sensing/reporting channels and correlated log-normal shadow-fading in all wireless links of the cooperative network. Our investigations will show that, even though often overlooked in typical cooperative spectrum sensing analysis, shadowing correlation on the reporting channel can yield similar performance degradations as shadowing correlation on the sensing channel. All findings will be substantiated via theoretical arguments and Monte Carlo simulations, and, in particular, novel approximation methods to account for correlated Log-Normal shadowing in cooperative spectrum sensing problems will be introduced.

In this paper, a general framework for performance evaluation of cooperative spectrum sensing methods over realistic propagation environments is proposed. In particular, the framework accounts for correlated Log-Normal shadowing in both sensing and reporting channels, and yields simple and easy-to-use formulas for computing the Detection Probability of a distributed network of secondary users using Amplify and Forward (AF) relying for data reporting to the fusion center. Numerical results are also shown to substantiate the accuracy of the proposed framework.

The demonstration proposal focuses on presenting the capabilities of a wireless biometric badge, which integrates a localization and tracking service along with an automatic personal identification mechanism, to control the access to restricted areas with a high level of security. The wireless biometric badge is the result of R&D activities conducted by several professors, researchers, and students in the Center of Excellence in Research DEWS at the University of L' Aquila in Italy, as well as its R&D Spin-Off WEST Aquila S.r.l.

A fundamental component to enable Dynamic Spectrum Access (DSA) capabilities in the future generation of wireless communication systems is the design of robust and efficient spectrum sensing methods to detect licensee users transmitting in a given frequency band. In this context, collaborative methods have been proposed to improve spectrum sensing capabilities over wireless channels, by counteracting shadow-fading phenomena via distributed diversity. However, recent results have shown that the expected benefits of cooperation, which are obtained at the expenses of increased traffic overhead and the need for a control channel, may be significantly hampered when the signals sensed by the cooperative users experience correlated shadow- fading. Moreover, it has been argued that spatial correlation of shadow-fading yields fundamental limits to the performance of cooperative spectrum sensing methods, and practical implications in terms of protocol design and network deployment. Motivated by the above considerations, in this paper we propose an analytical framework for the analysis and design of cooperative spectrum sensing methods over correlated shadow- fading environments, when each cooperative user is equipped with a simple energy-based detector. We will show that the framework requires efficient and accurate methods for modeling the power-sum of correlated Log-Normal Random Variables (RVs), which well describe shadowing phenomena. In particular, three main technical contributions can be found in the present paper: i) we generalize some recently proposed methods to approximate the power-sum of independent Log-Normal RVs to correlated scenarios, ii) we analyze the accuracy of several approximation techniques to compute detection probability over correlated Log-Normal shadowing, which is an unexplored field so far, and iii) we rely on the proposed framework to study the detrimental effect of correlation on the performance of cooperative spectrum sensing methods. Furthermore, numerical results will be shown to validate the proposed framework.

In this manuscript, we propose an accurate framework for the analysis of cooperative dual-hop wireless systems over fading channels, which use an Amplify and Forward (AF) relaying mechanism with fixed-gain relays. In particular, the proposed framework provides either exact results or very accurate bounds for computing the Moment Generating Function (MGF) of the end-to-end Signal-to-Noise Ratio (SNR) over Nakagami- m and Weibull fading. Furthermore, with the help of the MGF-based approach for performance analysis of wireless systems over fading channels, we will show that important performance indexes can be easily derived from the MGF. Numerical results will be shown to substantiate the proposed analytical framework.

Approximating the sum of log-normal random variables (RVs) is a long-standing open issue and many studies in the old and recent literature resort to assume that the "power sum" can be well modeled via another log-normal RV. Such a log-normal approximation can be effectively used for accurate outage probability (Pout) analysis and modeling of signal-to-(noise+interference) ratio (SNIR) behaviors in the context of radio resource management in wireless systems; nevertheless, it is recognized that i) an adequate accuracy can only be obtained for a sub-range of values taken by the distribution; ii) the achievable accuracy depends on the parameters of the set of RVs; iii) log-normal approximation may not provide a satisfactory accuracy for estimating bit error probabilities. Moreover, all contributions in the open technical literature are referred to model the "power sum" distribution, i.e., the weighted linear combination of log-normal RVs with weights that may take only positive values. While this setup is consistent with typical scenarios where summands add incoherently, it has recently been shown that in some high frequency-selective multipath channels with log-normal distributed shadow- and fast-fading, e.g., in impulse radio ultra wide band (IR-UWB) systems, the signal- to-noise ratio (SNR) is more accurately modeled by the weighted linear combination of log-normal RVs with weights that may assume both positive and negative values. Motivated by the above considerations, and moving from recent results, which show that approximating the "power sum" of log-normal RVs can boil down to a model selection problem within the pearson system of distributions, the specific contribution of this paper can be summarized as follows, i) We show that the pearson type IV distribution can not only be used to accurately approximate the probability density function (PDF) of the "power sum" of log-normal RVs with positive weights, but it can also accurately model the scenario with both positive and negative weights, as well as the scenario with generically correlated RVs. ii) For the scenario with independent RVs, we propose a numerical technique, which is based on the moment generating function (MGF) matching principle and uses a least- squares criterion, to accurately estimate the four parameters of the pearson type IV distribution, iii) A byproduct of this analysis.

In, we have recently proposed a general approach for approximating the power sum of Log-Normal Random Variables (RVs) by using the Pearson system of distributions. Therein, we have also highlighted the main advantages of using Pearson approximation instead of the usual Log-Normal one, and compared the proposed method with other approaches available in the open technical literature. However, despite being very accurate, the proposed method may be, in some circumstances, computational demanding since a non-linear least-squares problem needs to be solved numerically to get an accurate approximation. Motivated by the above consideration, the aim of this Letter is to provide an alternative approach for computing the parameters of the approximating Pearson distribution. The proposed solution is based on the Method of Moments (MoMs) in the logarithmic domain. In particular, by using some known properties of the Laplace transform, we will show that the MGF of the Log-Normal power sum in the logarithmic domain (denoted as log-MGF) can be obtained from the Mellin transform of the MGF of the Log-Normal power sum in the linear domain. From the estimated log-MGF, we will then compute the desired log-moments required for Pearson approximation. Numerical results will be also shown in order to substantiate the accuracy of the proposed method.

In this paper, we develop a general and unified framework for estimating the Cumulative Distribution Function (CDF) of "quadratic-form" receivers. The expression of the CDF that we finally achieve takes on the form of a single integral with finite limits and integrand composed of elementary functions, which can be readily integrated using standard techniques. In particular, the specific contribution of this manuscript is twofold: i) we present a comprehensive framework for the analysis of multi-channel reception systems using either non-coherent or differentially-coherent detectors, diversity systems with practical (i.e., noisy) channel estimators, and Transmitted-Reference (TR) receiver architectures working in frequency-selective fading channels, and ii) we show that the framework can be efficiently used for performance assessment of wireless systems over generalized fading channels and, under mild assumptions, over generalized fading channels further impaired by in-band multi-tone interference.

In this Letter, we propose a comprehensive framework for performance analysis of cooperative wireless systems using Amplify and Forward (AF) relay methods. The framework relies on the Moment Generating Function (MGF-) based approach for performance analysis of communication systems over fading channels, and on some properties of the Laplace Transform, which allow to develop a single-integral relation between the MGF of a random variable and the MGF of its inverse. Moreover, a simple lower bound for Outage Probability (Pout) and Outage Capacity (OC) computation is also introduced. Numerical and simulation results are provided to substantiate the accuracy of the proposed framework.

In the present contribution, we propose a comprehensive framework for the analysis of cooperative dual-hop wireless systems over generalized fading channels, which use an amplify and forward (AF) relaying mechanism with blind and semi-blind relays. In particular, the proposed framework provides either exact results or very accurate bounds for computing the moment generating function (MGF) of the end-to-end signal-to-noise ratio (SNR) for various fading channel models typically encountered in real propagation environments. Furthermore, with the help of the MGF-based approach for performance analysis of wireless systems over fading channels, we will show that important performance indexes can be easily derived from the MGF. With respect to previous published articles on the matter, the main contribution of the paper is twofold: i) by relying on the properties of the Meijer-G function, either exact expressions or accurate bounds for the MGF of the end-to-end SNR are provided, and ii) the analysis encompasses the vast majority of fading channel models. Numerical and simulation results will be compared to substantiate the analytical derivation.

In this Letter, we propose an advanced framework for performance analysis and design of decentralized data fusion problems. In particular, the performance of a multilayer system setup for data detection, which includes realistic sensing/reporting channels and correlated log-normal shadow-fading in all wireless links of the cooperative network, will be studied. The system setup will be used to analyze the performance of cooperative spectrum sensing problems adopting an amplify-and-forward (AF) relay protocol.We will show that, even though often overlooked in typical cooperative spectrum sensing analysis, shadowing correlation on the reporting channel can yield similar performance degradations as shadowing correlation on the sensing channel. All findings will be substantiated via theoretical arguments and Monte Carlo simulations, and, in particular, novel approximation methods to account for correlated log-normal shadowing in cooperative spectrum sensing problems will be introduced in this paper.

In two recent contributions, we have provided a comparative analysis of various optimization algorithms, which can be used for atomic location estimation, and suggested an enhanced version of the Steepest Descent (ESD) algorithm, which we have shown to be competitive with other distributed localization algorithms in terms of estimation accuracy and numerical complexity. Moreover, therein we have conducted a preliminary statistical characterization of the positioning error distribution, by showing that it can be well approximated by the family of Pearson distributions, as well as pointed out that its knowledge may be efficiently used to speed-up the analysis of iterative-based positioning algorithms by avoiding the need of simulating the whole location discovery algorithm, and allowing simulation at the atomic level only. In this contribution, based on the preliminary results shown in, we propose a comprehensive statistical analysis of the positioning error distribution for the ESD algorithm, by providing the parameters of the Pearson fitting distribution with respect to two important design factors for wireless sensor networks (WSNs): i) the ranging error standard deviation, which represents the input parameter for every localization algorithm, and ii) the geometric dilution of precision factor, which provides a simple parameter to account for different network topologies. In particular, we report an extensive number of simulation results that may provide important insights to the system designer: i) allow a parametric analysis to figure out the joint effect of ranging errors and network topology on the performance of the localization algorithms, and ii) give a general framework for modeling the statistics of the positioning error, which may be used for network planning, as well as for the analysis and design of the upper layers of the protocol stack.

In recent years, there has been a growing interest in antenna sharing systems, and many analytical frameworks have been developed to analyze the performance of various relying protocols in a variety of propagation environments. However, among the different propagation scenarios, the less investigated are those characterized by log-normal fading. Motivated by this consideration, we propose a comprehensive framework for performance analysis of multi-hop multi-branch wireless systems over log-normal fading channels. More specifically, the developed framework allows to estimate the performance of amplify and forward (AF) relay methods for both channel state information (CSI-) assisted analog relays, and semi-blind and blind analog relays. In particular, the contribution of the paper is twofold: i) first of all, by relying on the Gauss quadrature rule (GQR) representation of the moment generation function (MGF) for a log-normal distribution, we develop very accurate formulas for important performance indexes; ii) then, in order to simplify the computational burden of the former framework for some system setups, we also propose an approximation, which is based on the improved Schwartz-Yeh (I-SY) method. We show with numerical and simulation results that the proposed approximation provides a good trade-off between accuracy and complexity.

Virtual antenna array (VAA) systems (also known as cooperative communications, cooperative diversity, cooperative relaying or antenna sharing systems) are a new and recently proposed kind of (cooperative) spatial diversity, which have been receiving a significant interest in various communities as a means for improving the performance of both infrastructure-based and ad-hoc-based transmission systems operating over multipath fading channels. In particular, in antenna sharing systems the mobile radios share their single-antennas to create a distributed virtual antenna array through distributed transmissions and distributed signal processing operations. Although cooperative wireless communications exhibit various design challenges and research opportunities at different protocol layers, in this contribution we mainly focus on physical layer issues. In particular, we propose a comprehensive and unified framework for performance analysis of multi-hop multi-branch wireless systems with channel state information (CSI-) assisted analog relays over generalized fading channels. The framework relies on the moment generating function (MGF-) approach for performance analysis of digital communication systems over fading channels and on some properties of the Laplace transform, which allow to develop a simple single-integral relation between the MGF of a random variable and the MGF of its inverse. Numerical and simulation results are also provided to substantiate the accuracy of the proposed framework.

In recent contributions [Tennina, S., et al., 2008], [Tennina, S., 2008], we have provided a comparative analysis of various optimization algorithms, which can be used for atomic location estimation, and suggested an enhanced version of the steepest descent (ESD) algorithm, which we have shown to be competitive with well-known distributed localization algorithms in terms of estimation accuracy and numerical complexity. Moreover, in [Tennina, S., 2008] we have conducted a statistical characterization of the positioning error distribution of the ESD algorithm, and shown that the latter error can be well approximated by the family of Pearson distributions. However, the analysis in [Tennina, S., et al., 2008; Tennina, S., 2008; Tennina, S., 2008] is mainly based on numerical (i.e., computer-based) simulations, which only in part allows to predict the system performance in a realistic environment where sensor nodes are expected to operate. As a consequence, the aim of this contribution is twofold: i) to analyze the error performance of the ESD algorithm in a real testbed platform working in a typical indoor environment, and ii) to compare experimental and simulated results to substantiate via real measurements our previous findings useful for network setup and analysis. In particular, we will first report on the implementation issues related on mapping the ESD algorithm on the CrossBow's MICAz sensor node platform [http://www.xbow.com/Products/wproductsoverview.aspx.], and, then, we will investigate, via real experiments, on the effect of network topology and ranging errors in estimating the final position of an unknown sensor node.

We have recently proposed a general approach for approximating the power sum of log-normal random variables (RVs) by using the Pearson system of distributions, and then used it for performance analysis of Ultra Wide Band (UWB) wireless systems. Therein, we have also highlighted the main advantages of using the Pearson approximation instead of the usual Log-Normal one, and compared the proposed method with other approaches available in the open technical literature. However, despite being very accurate, the proposed method may be, in some circumstances, computational demanding since a non-linear least-squares problem needs to be solved numerically. Motivated by the above considerations, the aim of this paper is to provide an alternative approach for computing the parameters of the approximating Pearson Type IV distribution. The proposed solution is based on the method of moments (MoMs) in the logarithmic domain. In particular, the specific contribution of this paper is to provide closed-form expressions for the log- moments of the log-normal power sum. By using some known properties of the Laplace transform, we will show that the MGF of the log-normal power sum in the logarithmic domain (i.e., the log-MGF) can be obtained from the Mellin transform of the MGF of the Log-Normal power sum in the linear domain. From the estimated log-MGF, we will then compute the desired log-moments required for Pearson Type IV approximation. Numerical results will be also shown in order to substantiate the accuracy of the proposed method.

Recent advances in the technology of wireless electronic devices have made possible to build ad-hoc wireless sensor networks (WSNs) using inexpensive nodes consisting of low power processors, a modest amount of memory, and simple wireless transceivers. Over the last years, many novel applications have been envisaged for distributed WSNs in the area of monitoring, communication, and control. One of the key enabling and indispensable services in WSNs is localization (i.e., positioning), given that the availability of nodes' location may represent the fundamental support for various protocols (e.g., routing) and applications (e.g., habitat monitoring). In the depicted context, the present contribution reports our recent research advances along two main directions: i) first of all, we provide a comparative analysis of various optimization algorithms that can be used for atomic location estimation, which include: triangulation, steepest descent, non-linear least squares, conjugate gradient, and an enhanced version of the Steepest Descent (ESD) that is introduced in this paper, and ii) then, we provide a statistical characterization of location errors, by showing that the distribution of error positions can be well approximated by the family of Pearson distributions. In particular, we will show that i) the ESD algorithm may be competitive with the other algorithms in terms of estimation accuracy and numerical complexity, and ii) the knowledge of location error distribution may be efficiently used to speed-up the analysis of iterative-based positioning algorithms by avoiding the need of simulating the whole location discovery algorithm and allowing simulation at the atomic level only.

In this letter, we propose a framework for analyzing the performance of ultra wide band (UWB) receiver architectures in sparse multipath channels with overlapped multipath components (MPCs). The proposed methodology is based on approximating the moment generating function (MGF) of the received signal-to-noise ratio (SNR) via Pade expansion. The method can be applied to any fading scenarios and to any UWB receiver architectures, provided that the MGF of the SNR can be characterized in terms of a convergent Pade expansion. By comparing the results obtained from the analysis with those obtained from simulations in selected reference scenarios, it can be observed that the proposed approximation is very accurate.

This paper deals with the analysis of the timing acquisition (synchronization) process in Impulse Radio (IR-) Ultra Wide Band (UWB) systems based on Differential Transmitted Reference (DTR) receiver architectures. The fundamental contribution includes a theoretical framework for timing acquisition analysis, which is not only able to describe the first moment (Mean Acquisition Time, MAT), and the second moment (standard deviation of the acquisition time) of the acquisition process, but which also provides a consistent and comprehensive analysis of the probability mass function of the acquisition time, i.e., Acquisition Time Mass Probability. In particular, the specific novelty insights of this paper are as follows: (i) the proposal of a new synchronization algorithm, which is based on a two-step approach and relies on the specific characteristics and peculiarities of DTR receivers, as well as the analysis of its performance via a framework based on the theory of Markov processes; (ii) the exploitation of the LATTICE- POISSON algorithm to accurately estimate the Acquisition Time Mass Probability and Overall Acquisition Probability PD(ov), i.e., the probability to get synchronized before a given time; (iii) the performance analysis and comparison of one-step and two-step solutions with respect to the acquisition threshold; and (iv) the analysis of two approaches to estimate MAT and PD(ov)in multipath fading channels, i.e., "Tacq Average" and "Pd Average', which are shown to lead to different results in different scenarios. We observe that restricting the analysis to the MAT may lead to hasty conclusions about the effect of the acquisition threshold on the design of DTR receivers, thus substantiating the need for a more general analysis using PD(ov), as well as the necessity of specifying the characteristics of the wireless channel (e.g., quasi-static, slowly- or fast-fading) for an accurate performance analysis.

In this paper, we propose an ultra wide band (UWB)-based full-body motion capture system for gait analysis. The system is designed to reduce the implementation complexity and cost of current gait analysis setups, which require expensive locomotion laboratories and equipment. The solution exploits recent developments in wearable computing (i.e., electronic textiles) and relies on estimating the relative distance between body-worn sensors through time-of-flight measurements. While such a system may find application in several areas, substantial research is still required to prove its feasibility and accuracy. From this perspective, the present contribution addresses two key aspects to enable its development: i) the interaction between UWB electromagnetic pulses and the human body is analyzed and, by contrast to most results in the literature, the body- induced pulse distortion is characterized in terms of an equivalent channel impulse response, and ii) by explicitly taking into account that body-induced distortion, the accuracy of various time-of- flight-based ranging algorithms is investigated.

Approximating the sum of Log-Normal random variables (RVs) is a long-standing open issue, in the old and recent literature, and many approaches have been proposed to deal with that problem. However, all previous contributions are referred to model the "power sum" distribution, i.e. the weighted linear combination of Log-Normal RVs with weights that may take only positive values. In this paper, we extend that analysis by assuming that the weights may also take negative values. We also point out the importance that this scenario may have for the accurate analysis of ultra wide band (UWB) systems when intra-pulse interference due to multipath propagation cannot be neglected. In this perspective, (i) we show that the weighted linear combination of Log-Normal RVs can be well approximated by the Pearson Type IV distribution in the logarithmic domain, (ii) we use the obtained results for the accurate estimation of the average bit error probability (ABEP) of UWB receiver architectures.

In this paper, we propose an analytical framework for the accurate analysis of Rake and transmitted reference (TR) ultra wide band (UWB) receivers in multipath channels with log-normal fading. The framework relies on approximating the logarithm of the signal-to-noise ratio (SNR) at the detector input by the Pearson type IV distribution, which turns out to provide a simple but surprisingly effective and accurate approximation for the whole SNR probability density function (pdf). The framework includes the analytical derivation of i) the average bit error probability (ABEP) via Gauss-Legendre Gaussian quadrature rules (GQR), ii) the SNR cumulative distribution function (CDF) via the hypergeometric function2F1(middot, middot; middot; middot), which is useful for outage probability (Pout) analysis and iii) the SNR moment generating function (MGF) via Gauss-Legendre GQR. The validity and accuracy of the theoretical approach are also substantiated via Monte Carlo simulations.

This paper deals with timing acquisition (synchronization) for ultra wide band (UWB) systems based on differential transmitted reference (DTR) receiver architectures. In particular, the paper represents an extension to multipath scenarios of our previous work [1] and aims at comparing the performance of two different search schemes (i.e. one- and two- step), by considering two verification mechanisms (i) returning false alarm and (ii) absorbing false alarm. The analysis of mean acquisition time (MAT) and overall detection (acquisition) probability (PDov) shows that the proposed two-step scheme allows to improve synchronization capabilities (i.e. faster acquisition) while guaranteeing the same bit error probability (BEP).

In this paper we propose a framework for analyzing synchronization performance of UWB systems based on differential receiver architectures. We introduce an extended signal flow graph representation based on the Markov chain theory, which is able to easily and efficiently include realistic Verification/False Alarm Recovery Systems and whose analysis has been typically simplified so far. The proposed model, which turns out to fall within the Markov chain theory with multiple terminating hypotheses and multiple absorbing states, is used to investigate the performance of one- and two-step search schemes with absorbing false alarm occurrences. The analysis of the Overall Detection (Acquisition) Probability shows that the two-step scheme can provide better acquisition probabilities with comparable synchronization accuracy.

In this paper we propose a polyphase processing solution for relaxing the strict timing constraints of all-digital ultra wide band (UWB) receiver architectures. The work is motivated by the fact that, while interleaved data acquisition systems have been investigated in the literature with the aim of mitigating the strict timing requirements imposed by the sampling theorem, less attention has been paid in analyzing efficient processing architectures aiming at reducing the overall computational complexity of UWB receiver architectures. Specifically, we evidence how to exploit the polyphase approach for designing efficient detection, synchronization and channel estimation structures

This paper proposes a general framework for analyzing the performance of ultra wide band (UWB) communication systems in a synchronous reference scenario. In this context the information flows, produced by various sources, are synchronously multiplexed to share the common radio resource. The analysis in mainly devoted to investigate the performance of differential UWB receiver architectures, with the signal plus (multiplexing) interference to noise ratio (SINR) and bit error rate (BER) being the parameters of interest. While the obtained results point out that typical channelization codes (i.e. Hadamard) do not seem to be adequate to mitigate multiplexing interference (MI), we identified exact expressions to completely remove MI. In this perspective, the developed framework turns out to be a fundamental setup to identify efficient code families that aims at optimizing the overall system performance

This paper proposes an investigation of binary and quaternary modulation schemes for ultra wide band systems using differential receiver architectures. The expressions for the signal to noise ratio (SNR) and bit error rate (BER) are derived taking into account the code effect, the trade-off between time correlation window width and front-end filter bandwidth. The analysis points out that the quaternary modulation scheme outperforms binary modulations in terms of BER.