A deep reinforcement learning (DRL) method is proposed for hybrid beamforming in near-field wideband communication systems, aiming to alleviate the harmful near-field beam splitting issue caused by the spatial-wideband effect. Compared to the far-field beam splitting issue that occurs only in the angle domain, the near-field beam splitting issue extends to the distance domain and is thus more challenging to address. A hybrid beamforming architecture with true-time delayers (TTDs) is exploited to address the near-field beam splitting issue and facilitate the maximization of spectral efficiency (SE) across large bandwidth for downlink multi-user communications. However, the conventional iterative hybrid beamforming optimization methods, such as the weighted minimum mean-square method, often exhibit high computational complexity and are thus difficult to adapt the dynamic channel in a real-time manner. Hence, a DRL-based algorithm is propose to select fully-digital codewords from the designed near-field wideband beamforming codebook. To further approximate the selected codewords in the considered hybrid-beamforming architecture, a three-stage hybrid beamforming approximation (HBA) algorithm is proposed to minimize the fully-digital approximation error by jointly optimizing the TTD-based analog beamformers and baseband digital beamformers. Numerical results show that: (1) the proposed DRL-HBA solution for hybrid beamforming significantly increases the SE; (2) the hybrid beamforming architecture with TTDs can eliminate the near-field beam splitting effect and achieve beam focusing across wide band.

In this paper, we investigate a two-way backscatter communication system assisted by a dual-sided reconfigurable intelligent surface (RIS) which consists of active or passive elements. By altering the switch status within each RIS element, different transmission and reflection coefficients can be achieved, thus enabling a binary backscatter modulation. To begin with, we propose a maximum likelihood (ML) detector and a maximal-ratio-combining (MRC)-based detector for the proposed dual-sided RIS-assisted two-way communication system to decode the backscatter signal as well as the end-users’ respective signal. Moreover, we compare the underlying system with and without backscatter modulation at the dual-sided RIS. Subsequently, we analyze the corresponding symbol error rate (SER) and throughput to highlight the performance differences of the various communication modes under perfect channel state information (CSI) and imperfect CSI. Finally, numerical results reveal that: (1) the ML detector significantly outperforms the MRC-based detector in terms of SER and throughput; (2) the throughput performance can be significantly improved by adopting a backscatter modulation, especially in the medium and high signal-to-noise ratio regimes; (3) the phase shift of the dual-sided RIS element should be aligned with the signal to be decoded first, to guarantee an improvement in the SER performance.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for the future sixth-generation mobile communication systems. The joint sensing of target range and velocity is crucial in OFDM-based ISAC systems. When the targets are highly correlated with similar range and velocity, it is challenging for the conventional two-dimensional subspace-based sensing methods to achieve accurate joint range-velocity estimation (JRVE), particularly in the low signal-to-noise ratio (SNR) region. As such, this paper proposes a high-resolution JRVE method. Specifically, the proposed method first applies equal interval sampling smoothing to the observation signal, introducing ambiguity in range-velocity-induced phase pairs. It then utilizes the translation invariance of the signal subspace to extract these ambiguous phase pairs. Finally, it leverages the orthogonality between the constructed steering vector pair and the noise subspace to resolve the ambiguity and achieve accurate estimation. Under a 5G New Radio parameter setup, simulation results demonstrate that the proposed method significantly outperforms conventional methods in terms of both resolution and accuracy. At an SNR of -10 dB, the proposed method reduces the root mean square error of range and velocity estimation by 83.7 % and 87.3 %, respectively, compared to the benchmark scheme, while its computational cost is less than 50 % of that of the benchmark.

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.

Backscatter communication emerges as a promising solution for green Internet of Things (IoT). Current research mainly focus on On-Off Keying (OOK) and Binary Phase-Shift Keying (BPSK) modulation schemes and most overlook the crucial aspect of tag circuit sensitivity. In this paper, we derive the capacity of the M-ASK modulated backscatter system under the circuit sensitivity constraint of the tag. We conducts a comparative analysis of capacity performance across three scenarios: (i) backscatter communication systems without sensitivity constraints, (ii) backscatter systems employing different modulation orders, and (iii) conventional point-to-point communication systems. It is found that the circuit sensitivity of the passive tags has a considerable impact on the system capacity. Furthermore, high-order modulation can effectively increase the channel capacity of the backscatter communication systems.

This paper proposes a reconfigurable intelligent surface (RIS) enabled symbiotic backscatter non-orthogonal multiple access (NOMA) system based on uniquely factorable constellation pair (UFCP) rule, where the RIS elements are used to passively modulate the backscatter signal over the primary NOMA signal. According to the coding design of UFCP, the backscatter signal modulated at the RIS and the primary NOMA signal emitted by the source, are jointly constructed to form a UFCP for each primary NOMA signal, thereby facilitating the decoding of the primary NOMA signal and backscatter signal at the receiver. To recover the backscatter signal modulated at the RIS as well as the primary NOMA signal, a successive interference cancellation (SIC) based detector is proposed and a closed-form expression of the symbol error rate (SER) is derived. Finally, extensive numerical results show that the proposed system possesses a signal-to-noise-ratio (SNR) gain of more than 10 dB at the SER level of 10−2in comparison with its counterpart without the UFCP design.

In this paper, we propose a two-way backscatter communication system assisted by an active and passive dualsided reconfigurable intelligent surface (RIS) where the active RIS element contains an amplifier while the passive one does not. By altering the switch status within each RIS element, different transmission and reflection coefficients can be achieved, enabling a binary backscatter modulation. Moreover, a maximal-ratio-combining (MRC)-based detector is proposed to decode the backscatter signal and the end-user's signal, and the corresponding symbol error rate (SER) and throughput are subsequently analyzed. Numerical results show that by avoiding the additive thermal noise within each RIS element, passive dual-sided RISenabled communications outperform the active dual-sided RISenabled communications in terms of SER, and it is also revealed that the throughput can be significantly improved through backscatter modulation.

In order to reduce the impact of obstacles and improve system performance for traditional backscatter communication (BackCom) networks, we propose a reconfigurable intelligent surface (RIS)-assisted heterogeneous BackCom network framework, where multiple backscatter clusters share the spectrum resource with macrocell users in an underlay spectrum sharing mode and achieve self-sufficient energy of each low-power-consumption backscatter device (BD) via a radio-frequency energy-harvesting way. Then, a robust resource allocation problem with imperfect channel station information is studied under the constraints of the minimum rate requirement of each BD, the minimum energy requirement of each BD, the maximum interference power of each macrocell user, the reflection coefficient of each BD, and the phase shifts of each RIS. Moreover, based on the bounded channel uncertainty model, a max-min throughput resource allocation problem of multiple backscatter clusters is formulated by jointly optimizing the time allocation factors, the reflection coefficient of each BD, and the phase shifts of each RIS. To deal with the non-convex optimization problem caused by the uncertain constraints and non-convex constraints, the worst-case approach, successive convex approximation as well as semi-definite relaxation are applied. Finally, an iteration-based robust resource allocation algorithm is proposed accordingly. Simulation results demonstrate that the proposed algorithm has good fairness and stronger robustness.

Integrated sensing and communication (ISAC) utilizing orthogonal frequency division multiplexing (OFDM) wave-forms is emerging as a critical technology for forthcoming sixth-generation mobile communication networks. The joint sensing of target range, velocity and azimuth is essential for OFDM-based ISAC systems. When the targets are highly correlated with similar range, velocity and azimuth, it is challenging for the conventional three dimensional subspace-based sensing methods to achieve accurate joint range-velocity-azimuth estimation (JRVAE), particularly in the low signal-to-noise ratio (SNR) region. Thus, this paper focuses on high-resolution JRVAE for highly correlated targets. First, a signal model is established, and the Cramér–Rao bounds for JRVAE are derived, considering communication symbols belonging to an arbitrary-order quadrature amplitude modulation constellation. Then, a high-resolution JRVAE method is proposed. Specifically, it first performs equal interval sampling smoothing on the observation signal, resulting in ambiguity in range-velocity-azimuth-induced phases, then uses the translation invariance of the signal subspace to extract the ambiguous phases, finally utilizes the orthogonality between the constructed steering vector pair and the corresponding noise subspaces to resolve ambiguity and obtaining accurate estimation. With 5G New Radio parameters setup, simulation results shows that the proposed method achieves higher resolution and accuracy compared to the conventional methods. At an SNR of -10 dB, the proposed method reduces the root mean square error in range, velocity, and azimuth by 76.0%, 82.7%, and 73.2%, respectively, compared to the benchmark scheme, while its computation cost is less than 1/3 of that of the benchmark.

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echoes of its transmitted cellular OFDM signals to sense multiple targets. The Cramér-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.

Wireless communication is vulnerable to malicious jamming and eavesdropping attacks due to the broadcast nature of wireless channels. An extremely-large-scale reconfigurable intelligent surface (XLRIS) demonstrates its abilities to enhance the physical layer security (PLS) and compensate for the severe path loss. We investigate an XL-RIS empowered near-field PLS communication system against jamming and eavesdropping attacks with the help of artificial noise (AN). To maximize the secrecy capacity, we propose an alternating optimization (AO) based algorithm to jointly optimize the beamformers at the base station (BS) and the reflection coefficient matrix at the XL-RIS, subject to the BS’s maximum transmit power and the XL-RIS’s unitmodulus constraints. For the beamforming and AN design at the BS, auxiliary variables are introduced to reformulate the subproblem into a more tractable problem, which is solved by the proposed successive convex approximation (SCA) based algorithm. For the reflection coefficient matrix design at the XL-RIS, a manifold optimization (MO) based algorithm is proposed to address the challenge of large-scale variables and unit-modulus constraints. Numerical results show that XL-RIS can ensure secure communication even if the eavesdropper is located at the same direction as the legitimate user and closer to the XL-RIS.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

Wireless communication is vulnerable to malicious jamming attacks due to the inherent broadcasting nature of wireless channels. This paper investigates an anti-jamming communication system that employs a reconfigurable intelligent surface (RIS) to enhance desired signals and suppress jamming signals. To optimize the system performance while guaranteeing fairness, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) at the legitimate user equipments by jointly optimizing the transmit beamforming vectors at the base station (BS) and the reflecting coefficients at the RIS, subject to the BS’s maximum transmit power constraint and the RIS’s reflecting coefficient constraints. To solve the non-convex max-min-fairness optimization problem, we propose an alternating-optimization (AO)-based approach that alternates between optimizing variables using a second-order-cone program and semi-definite relaxation techniques. Considering the piratical limitation of imperfect jammer-related channel state information (CSI), we also adopt the stochastic successive convex approximation technique for tackling imperfect CSI in the AO-based approach. Furthermore, we propose a deep-reinforcement-learning (DRL)-based solving approach that does not require the jammer-related CSI. Numerical results show that both approaches improve the minimum SINR performance significantly. Although the AO-based approach with real-time CSI slightly outperforms the DRL-based approach with historical CSI, the DRL-based approach uses the trained deep neural network to obtain the beamforming decision directly without solving optimization problems.

Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) is promising for future sixth-generation mobile communication systems. For OFDM-based ISAC systems, it is important to accurately sense the target’s parameters. This paper studies the three-dimensional joint estimation (3DJE) of range, velocity, and azimuth for OFDM-based ISAC systems with multiple receive antennas. First, we establish the signal model and derive the Cramér–Rao bounds (CRBs) on the 3DJE. CRBs are widely used benchmarks that provide the theoretical lower bounds of the variances for unbiased estimation. Furthermore, an auto-paired super-resolution 3DJE algorithm is proposed by exploiting the reconstructed observation sub-signal’s translational invariance property in the delay, Doppler, and angle domains. Finally, with the 5G New Radio parameter setup, simulation results show that the proposed algorithm achieves better estimation performance and its root mean square error is closer to the square root of CRBs than existing methods.

This paper studies an extremely large-scale reconfigurable intelligent surface (XL-RIS) empowered covert communication system in the near-field region. Alice covertly transmits messages to Bob with the assistance of the XL-RIS, while evading detection by Willie. To enhance the covert communication performance, we maximize the achievable covert rate by jointly optimizing the hybrid analog and digital beamformers at Alice, as well as the reflection coefficient matrix at the XL-RIS. An alternating optimization algorithm is proposed to solve the joint beamforming design problem. For the hybrid beamformer design, a semi-closed-form solution for fully digital beamformer is first obtained by a weighted minimum mean-square error based algorithm, then the baseband digital and analog beamformers at Alice are designed by approximating the fully digital beamformer via manifold optimization. For the XL-RIS’s reflection coefficient matrix design, a low-complexity alternating direction method of multipliers based algorithm is proposed to address the challenge of large-scale variables and unit-modulus constraints. Numerical results unveil that i) the near-field communications can achieve a higher covert rate than the far-field covert communications in general, and still realize covert transmission even if Willie is located at the same direction as Bob and closer to the XL-RIS; ii) the proposed algorithm can enhance the covert rate significantly compared to the benchmark schemes; iii) the proposed algorithm leads to a beam diffraction pattern that can bypass Willie and achieve high-rate covert transmission to Bob.

Symbiotic radio (SR) is a promising energy-, spectrum-, and cost-efficient communication technology for Internet of Things. This article considers an SR network (SRN) in which backscatter devices (BDs) communicate with a primary receiver (PR) by riding on ambient radio frequency carriers from a primary transmitter (PT). First, both the achievable primary rate and backscatter-link sum rate are derived for BDs adopting spatial-division-multiple-access (SDMA) scheme and dynamic time-division-multiple-access (TDMA) scheme, respectively. Then, two optimization problems are formulated to maximize the BDs’ sum rate, by jointly optimizing the PTs’ beamforming matrix, and the BDs’ power reflection coefficients as well as the backscatter time allocation. For the single-antenna PR case, the optimal beamforming is obtained in semi-closed forms. For the multiantenna PR case, to solve the nonconvex problems with coupled variables, efficient iterative algorithms are proposed based on block coordinate descent and sequential convex approximation techniques. Finally, numerical results show that the proposed design enhances the SRNs’ throughput significantly, and give useful insights on the BDs’ multiple-access schemes.

We propose a new wireless paradigm for railways – transmissive metasurface (TMS) assisted communications. It compensates for the Doppler shift brought by high mobility and reduces signal degradation due to train carriages. Specifically, the elements of the TMS panel attached to train windows manipulate the links between the base station (BS) and the onboard users. One fundamental problem with it is: does the channel introduced by TMS outperform the traditional direct channel? To answer this, we compare the gains of direct and cascaded channels and introduce the cascaded-outperform-direct probability (CODP), which is the probability that the latter exceeds the former. We then derive two simplified closed-form CODP expressions by approximating the cascaded channel gain as mixed Gaussian and Gamma distributions. Moreover, BS-related parameters impact the CODP; we show that the CODP has (i) global maximum points concerning the azimuth angle of the path from the BS to the TMS, the distance from the BS to the railway, and the BS height, respectively, and (ii) a minimum point in terms of the azimuth angle of the path from the BS to the TMS. Finally, we provide numerical results to verify our analysis and derivations.

Backscatter communication, which enables a pas-sive backscatter device to transmit information to a reader using incident radio-frequency signals, is a promising technology for low-power Internet of Things. To improve the backscatter communication range, we consider a multistatic backscatter communication system in which multiple dislocated readers illuminate a tag simultaneously, thus strengthening the received signals. However, this system suffers from strong direct co-channel interference between different readers. To tackle this challenge, we jointly design the tag's transmit waveform and the readers' optimal detectors to cancel out the interference first and then recover the tag information. Simulations results verify that the proposed transceiver achieves better bit-error-rate performance without error floor than the benchmark.

In this paper, we analyse the achievable rate and capacity for ambient backscatter communications with dynamic sources under the binary input and signal output (BISO) channel. Dynamic sources are the sources that transmit signals to the air intermittently, rather than continuously transmitting signals like static sources. Instead of assuming static ambient sources, we investigate the dynamic sources. Due to the complexity of the expression of mutual information, we resort to the numerical simulation results for the BISO channel capacity and the capacity-achieving distribution is obtained by one-dimensional searching. We utilize inequality to show the relationship between static sources and dynamic sources in terms of the achievable rate and capacity. The numerical studies show that the maximal of the mutual information of the BISO channel is not achieved by a uniform input distribution, and the mutual information and capacity of the BISO channel with dynamic sources are close to that of the BISO channel with static sources, scaled by the probability that the dynamic source is in the on-state.

Reconfigurable intelligent surface (RIS) enables the wireless propagation environment to be reconstructed intelligently. In this paper, we consider a symbiotic cellular UAV communication network in which the base station communicates with both its cellular users and an UAV equipped with an aerial RIS. Our objective is to maximize the energy efficiency of this network by jointly optimizing the UAV trajectory, user access indicator factor, the base station’s active beamforming and the RIS’s passive beamforming. Simulation results show that the proposed dynamic time-division-multiple-access communication scheme can significantly improve the network’s energy efficiency compared with the communication-and-hover benchmark.

This paper investigates the challenging channel estimation problem for reconfigurable intelligent surface assisted wireless communications. By exploiting the channel’s structured-sparsity and two-timescale characteristics, a three-stage channel estimation method based on structured sparse Bayesian learning (SBL) is proposed to improve the estimation accuracy and reduce the pilot overhead. The row support set is first estimated at the large timescale to reduce the dimension of sparse channel matrix. A structured SBL algorithm is then designed to estimate the block sparse channel vector at the small timescale. The estimated support set and block sparse vector are finally used to reconstruct the channel. The computational complexity of the proposed method is analyzed. Numerical results show that the proposed method outperforms state-of-the-art benchmarks in terms of estimation accuracy, pilot overhead and robustness.

In a backscatter communication system, the backscatter device (BD) transmits its messages to the backscatter receiver (BR) by reflecting the incident signal from an external radio frequency (RF) emitter, instead of using power-hungry active RF components themselves. Thus, backscatter communication has shown great potential for achieving low-power communication. The double-fading effect associated with the backscatter link, however, is a major limiting factor to achieve efficient backscatter communication. Reconfigurable intelligent surfaces (RISs), a recently developed technology, can be applied at the BD to enhance the backscatter link thanks to the fact that both RIS and backscatter communication share the same reflective principle. Such a design can also allow the backscatter communication system to capture the desired RF signal as a reflective carrier in a complex radio environment. In this article, a comprehensive overview of backscatter communication assisted by RIS is given. We first introduce the basics of backscatter communication, which covers the antenna scattering principle, backscatter modulation, and link budget calculation. Then, the details of RIS are discussed, which include antenna-based RIS and metamaterial-based RIS, followed by the discussion of the roles of RIS in backscatter communication. After that, we provide an overview of three types of backscatter communication systems assisted by RIS, including RIS-assisted unmodulated backscatter communication, RIS-assisted ambient backscatter communication, and RIS-assisted symbiotic radio. Emerging applications of these systems, technical challenges, and future opportunities in this emerging field are also presented.

This paper considers a reconfigurable intelligent surface (RIS) enhanced symbiotic radio (SR) multigroup multicast system, where the RIS passively transmits information to an Internet-of-Things receiver (IR) by modulating the incident multicasting signals, and also enhances the downlink multicast transmission from a base station (BS) to multiple primary receivers (PRs). We aim to minimize the BS's transmit power by jointly optimizing the active transmit beamforming at the BS and the passive phase shifts at the RIS, subject to the constraints on the PRs' signal-to-interference-plus-noise-ratio, the IR's successive-interference-cancellation and signal-to-noise-ratio, as well as the RIS's phase shifts. An efficient iterative algorithm is proposed to solve the non-convex problem, by employing alternating optimization, quadratic transform and semidefinite relaxation. The algorithm's convergence and complexity are also analysed. Numerical results show that the proposed scheme consumes less power than the traditional multicast scheme without RIS.

Reconfigurable intelligent surfaces (RIS) are a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the sum rate of D2D users and cellular users by jointly optimizing the resource reuse indicators, the transmit power and the RIS's passive beamforming. To solve the formulated non-convex problem, we first propose an efficient user-pairing scheme based on relative channel strength to determine the resource reuse indicators. Then, the transmit power and the RIS's passive beamforming are jointly optimized by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. Numerical results show that the proposed design outperforms the traditional D2D network without RIS.

Symbiotic radio (SR) is a promising technology for energy- and spectrum-efficient wireless communication, which exploits passive communication for Internet-of-Things (IoT) transmission and achieves a mutualistic spectrum sharing between the passive and active transmissions. In this paper, we study an reconfigurable intelligent surface (RIS) empowered symbiotic radio over a broadcasting system, i.e., a base station (BS) broadcasts signals to multiple primary receivers (PRs) under the assistance of an RIS, while the RIS also transmits information to an IoT receiver (IR) by riding over the broadcasting signals. We formulate a problem to minimize the BS’s transmit power by jointly optimizing the BS’s active precoding and the RIS’s passive beamforming, under the signal-to-noise-ratio constraints of the primary and IoT transmissions. However, the problem is challenging to be solved optimally, since the variables are coupled and the constraints are non-convex. An iterative algorithm based on block coordinated descent (BCD) and semidefinite relaxation (SDR) techniques is first proposed, and its convergence together with complexity are analyzed. Then, to tackle the problem of high computational complexity caused by SDR technique, we further propose an alternative algorithm based on generalized power method (GPM) technique. Simulation results validate that the proposed system outperforms the traditional broadcasting system without RIS. The GPM-based algorithm achieves nearly the same transmit power performance as SDR-based algorithm, with a significantly reduced computational complexity.

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

The reconfigurable intelligent surface (RIS), which consists of a large number of passive and low-cost reflecting elements, has been recognized as a revolutionary technology to enhance the performance of future wireless networks. This paper considers an RIS assisted multicast transmission, where a base station (BS) with multiple-antenna multicasts common message to multiple single-antenna mobile users (MUs) under the assistance of an RIS. An equivalent channel model for the considered multicast transmission is analyzed, and then an optimization problem for the corresponding channel capacity is formulated to obtain the optimal covariance matrix and phase shifts. In order to solve the above non-convex and non-differentiable problem, this paper first exploits the gradient descent method and alternating optimization, to approach the locally optimal solution for any number of MUs. Then, this paper considers a special case, which can obtain the global optimal solution, and shows the sufficient and necessary condition for this special case. Finally, the order growth of the maximal capacity is obtained when the numbers of the reflecting elements, the BS antennas, and the MUs go to infinity.

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV's limited on-board energy and the CEs' transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV's trajectory, the BDs' scheduling, and the CEs' transmission power, subject to the BDs' throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach's method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the hover-and-fly scheme, the state-of-the-art scheme, and the CE-relay scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained.

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.

This paper studies reconfigurable intelligent surface (RIS) empowered symbiotic radio over broadcasting signals, i.e., a base station (BS) broadcasts to multiple primary receivers (PRs) under the assistance of a RIS, while the RIS also transmits information to an Internet-of-Things receiver (IR) by modulating the incident broadcasting signals. We formulate a problem to minimize the BS's transmit power by jointly optimizing the BS's active beamforming and the RIS's phase shifts (i.e., passive beamforming), under the signal-to-noise-ratio constraints of the primary and IoT transmission as well as the RIS's phase-shift constraints. However, the problem is challenging to be solved optimally, since the optimization variables are coupled and the constraints are non-convex. An efficient iterative algorithm based on the block coordinated descent and modified semidefinite relaxation techniques is proposed to solve this problem for both discrete and continuous phase shift scenarios. The convergency of the algorithm is proved and the complexity of the algorithm is analyzed. Numerical results validate that the proposed system outperforms the benchmark of traditional broadcasting system without RIS.

Intelligent reflecting surface (IRS) is a new and disruptive technology to achieve spectrum-, energy, and cost-efficient wireless networks. In this paper, we consider an IRS-assisted non-orthogonal-multiple-access (NOMA) system in which a base station (BS) transmits superposed downlink signals to multiple users. A combined-channel-strength (CCS) based user ordering scheme is first proposed. In order to optimize the rate performance and ensure user fairness, we further maximize the minimum decoding signal-to-interference-plus-noise-ratio (i.e., equivalently the rate) of all users, by jointly optimizing the power allocation at the BS and the phase shifts at the IRS. However, the formulated problem is non-convex and difficult to be solved optimally. By leveraging the block coordinate descent and semidefinite relaxation techniques, an efficient algorithm is then proposed to obtain a suboptimal solution. Simulation results show that the IRS-assisted downlink NOMA system can enhance the rate performance significantly, compared to traditional NOMA without IRS and traditional orthogonal multiple access with/without IRS, and the rate degradation due to the IRS's finite phase resolution is slight.

In this article, a symbiotic radio (SR) system is proposed to support passive Internet of Things (IoT), in which a backscatter device (BD), also called IoT device, is parasitic in a primary transmission. The primary transmitter (PT) is designed to assist both the primary and BD transmissions, and the primary receiver (PR) is used to decode the information from the PT as well as the BD. The symbol period for BD transmission is assumed to be either equal to or much greater than that of the primary one, resulting in parasitic SR (PSR) or commensal SR (CSR) setup. We consider a basic SR system which consists of three nodes: 1) a multiantenna PT; 2) a single-antenna BD; and 3) a single-antenna PR. We first derive the achievable rates for the primary and BD transmissions for each setup. Then, we formulate two transmit beamforming optimization problems, i.e., the weighted sum-rate maximization (WSRM) problem and the transmit power minimization (TPM) problem, and solve these nonconvex problems by applying the semidefinite relaxation (SDR) technique. In addition, a novel transmit beamforming structure is proposed to reduce the computational complexity of the solutions. The simulation results show that for CSR setup, the proposed solution enables the opportunistic transmission for the BD via energy-efficient passive backscattering without any loss in spectral efficiency, by properly exploiting the additional signal path from the BD.

The potential of multiuser MIMO (MU-MIMO) to increase network capacity has been intensively studied. To enable concurrent transmission in Frequency-Division Duplex (FDD) systems with limited feedback, users have to report the estimated channel state information (CSI) to the base station (BS) for interference elimination, which however has been proven to be vulnerable. In this paper, we reveal how to utilize the CSI feedbacks to launch sniffing attacks in a deterministic way, where thesniffing attackenables the attackers to eavesdrop other users in the same transmission group. We conduct a rigorous theoretical analysis on the construction of the forged CSI. In the proposed sniffing attacks, the malicious users can successfully sniff other users’ messages without knowing their own messages for signal cancellation. To make sniffing attacks more practical, we explore the possibilities of sniffing multiple users by a coalition of malicious users or a single malicious user. To thwart sniffing attacks, we design a novel secure CSI feedback (SCF) protocol based on the random masking technique, which requires simple modifications at the BS and incurs a little additional workload. Extensive theoretical analysis and experimental results are provided to further validate the effectiveness of our proposed sniffing attacks and the corresponding countermeasure.

This paper considers a UAV-enabled backscatter communication network (UBCN) in which multiple backscatter devices (BDs) on the ground are illuminated by their associated ground carrier emitters (CEs) and transmit information to a flying UAV in a dynamic time-division-multiple-access manner. To tackle the critical issue of limited UAV on-board energy and CE transmission energy, we maximize the energy efficiency (EE) by jointly optimizing the BD scheduling, the BDs' power reflection coefficients, the CEs' transmission powers, and the UAV trajectory, subject to the BDs' throughput constraints and other practical constraints. Furthermore, we propose an iterative algorithm to solve the formulated non-convex problem, by leveraging the block coordinated decent and the successive convex approximation techniques. Finally, extensive simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared to the benchmark hover-and-fly scheme.

This paper considers an ambient backscatter communication network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing signals to its legacy user (LU) and receives uplink signals backscattered from multiple backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. For the case with a single BD, we obtain closed-form solutions and propose an efficient algorithm by using the Lagrange duality method. For the general case with multiple BDs, we propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. In addition, we study the throughput region which characterizes the Pareto-optimal throughput tradeoffs among all BDs. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

This paper considers an ambient backscatter communication (AmBC) network in which a full-duplex access point (FAP) simultaneously transmits downlink orthogonal frequency division multiplexing (OFDM) signals to its legacy user (LU) and receives uplink signals backscattered from multiple wireless-powered backscatter devices (BDs) in a time-division-multiple-access manner. To maximize the system throughput and ensure fairness, we aim to maximize the minimum throughput among all BDs by jointly optimizing the backscatter time and reflection coefficients of the BDs, and the FAP's subcarrier power allocation, subject to the LU's throughput constraint, the BDs' harvested-energy constraints, and other practical constraints. However, the formulated problem is non-trivial to solve in general, since the variables are mutually coupled and result in non-convex constraints. We thus propose an iterative algorithm by leveraging the block coordinated decent and successive convex optimization techniques. We further show the convergence performances of the proposed algorithm and analyze its complexity. Finally, extensive simulation results show that the proposed joint design achieves significant throughput gain as compared to the benchmark schemes.

Ambient backscatter communication (AmBC) enables a passive backscatter device to transmit information to a reader using ambient RF signals, and has emerged as a promising solution to green Internet-of-Things (IoT). Conventional AmBC receivers are interested in recovering the information from the ambient backscatter device (A-BD) only. In this paper, we propose a cooperative AmBC (CABC) system in which the reader recovers information not only from the A-BD, but also from the RF source. We first establish the system model for the CABC system from spread spectrum and spectrum sharing perspectives. Then, for flat fading channels, we derive the optimal maximum-likelihood (ML) detector, suboptimal linear detectors as well as successive interference-cancellation (SIC) based detectors. For frequency-selective fading channels, the system model for the CABC system over ambient orthogonal frequency division multiplexing carriers is proposed, upon which a low-complexity optimal ML detector is derived. For both kinds of channels, the bit-error-rate expressions for the proposed detectors are derived in closed forms. Finally, extensive numerical results have shown that, when the A-BD signal and the RF-source signal have equal symbol period, the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios, and when the A-BD symbol period is longer than the RF-source symbol period, the existence of backscattered signal in the CABC system can enhance the ML detection performance of the RF-source signal, thanks to the beneficial effect of the backscatter link when the A-BD transmits at a lower rate than the RF source.

Ambient backscatter communication (AmBC) enables radio-frequency (RF) powered backscatter devices (BDs) (e.g., sensors and tags) to modulate their information bits over ambient RF carriers in an over-the-air manner. This technology, also called “modulation in the air,” has emerged as a promising solution to achieve green communication for future Internet of Things. This paper studies an AmBC system by leveraging the ambient orthogonal frequency division multiplexing (OFDM) modulated signals in the air. We first model such AmBC system from a spread-spectrum communication perspective, upon which a novel joint design for BD waveform and receiver detector is proposed. The BD symbol period is designed as an integer multiplication of the OFDM symbol period, and the waveform for BD bit “0” maintains the same state within the BD symbol period, while the waveform for BD bit “1” has a state transition in the middle of each OFDM symbol period within the BD symbol period. In the receiver detector design, we construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. For the system with a single-antenna receiver, the maximum-likelihood detector is proposed to recover the BD bits, for which the optimal threshold is obtained in closed-form expression. For the system with a multi-antenna receiver, we propose a new test statistic which is a linear combination of the per-antenna test statistics and derive the corresponding optimal detector. The proposed optimal detectors require only knowing the strength of the backscatter channel, thus simplifying their implementation. Moreover, practical timing synchronization algorithms are proposed for the designed AmBC system, and we also analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results are provided to verify that the proposed transceiver design can improve the system bit-error-rate performance and the operating range significantly and achieve much higher data rate, as compared with the conventional design.

Ambient backscatter communication (AmBC) enables a tag to modulate its information bits over ambient RF carriers by intentionally changing its reflection coefficient, thus has emerged as a promising technique to achieve green communications for future Internet-of-Things. In this paper, we model a cooperative AmBC system from a spectrum- sharing perspective, where a cooperative receiver (C-RX) decodes the information from both a multi-antenna primary transmitter (PT) and a single-antenna secondary transmitter (i.e., tag). We consider two scenarios: first, the tag-symbol period equals the PT-symbol period; second, the tag-symbol period is an integer multiple of the PT-symbol period. For each scenario, we analyze the data rate via successive- interference-cancellation (SIC) based decoding, and formulate a problem to maximize the sum rate by optimizing the beamforming vector at the PT. The problems are transformed into semi-definite programming (SDP), and solved by using the technique of semi-definite relaxation (SDR). Furthermore, a novel transmit beamforming structure is proposed to reduce the computational complexity of beamforming optimization. Numerical results show that the cooperative AmBC system can achieve a higher sum rate than a conventional point-to-point system without a backscatter tag.

In ambient backscatter communications (Am-BC), a backscatter device can harvest power from ambient RF signals and modulate its information symbols over the ambient carriers without using complex RF transmitter. Conventional receiver design for AmBC focuses on tackling the direct link interference from the RF source. In this paper, a novel receiver, which is called cooperative receiver, is proposed to recover signals not only from the ambient backscatter device (A-BD), but also from the RF source. We first study the optimal maximum-likelihood (ML) detection for such system. Then, by exploiting the structural property of the system model, linear detectors and successive interference cancellation (SIC) based detectors are proposed. We also derive the closed-form bit error rate (BER) expressions for both ML detection and the proposed SIC algorithms. Finally, extensive numerical results show that the existence of backscattered signal in the considered system can significantly enhance the ML detection performance of the source signal, and the proposed SIC-based detectors can achieve near-ML detection performance for typical application scenarios.

In cognitive radio networks, the performance of energy detection will be degraded significantly due to the cluster overlapping caused by noise uncertainty. To alleviate the noise uncertainty effect, a novel machine learning algorithm is proposed in this paper for cooperative spectrum sensing. The proposed algorithm incorporates fuzzy support vector machine and nonparallel hyperplane support vector machine. For membership assignment, kernel shadow c-means (KSCM) algorithm is utilized. Furthermore, the test statistics collected by the second users are arranged into a feature vector instead of being combined through weighted sum. Simulations results have shown that the proposed scheme, called NP-FSVM, is more robust to noise uncertainty than the existing methods.

By offloading mobile traffic for macrocells, small cells can help to alleviate the pressure on conventional cellular networks from explosive data growth. On the other hand, the severe spectrum scarcity problem has prompted the research on spectrum sharing recently. In this paper, a spectrum sharing system of OFDMA small cells overlaying CDMA networks is considered, in which each OFDMA user in the small cells requests a specific transmission rate. We investigate the interferences among entities in the spectrum sharing system, from which a resource allocation problem is formulated along with the rate constraints, with the objective to meet the target rates of OFDMA users and to simultaneously protect CDMA system. This high- complexity and intractable problem is transformed into two separable problems by calculating the minimum required interference and selecting the activated users. We propose the on-demand resource allocation scheme to solve the resource allocation problem efficiently by deploying a semi-distributed algorithm. Simulation results are provided to evaluate the effectiveness and performance of the proposed scheme.

Ambient backscatter communications (AmBC) enables radio-frequency (RF) powered devices (e.g., tags, sensors) to modulate their information bits over ambient RF carriers in an over-the-air manner. This system, called “modulation in the air”, thus has emerged as a promising technology for green communications and future Internet-of-Things. This paper studies the AmBC system over ambient orthogonal frequency division multiplexing (OFDM) carriers in the air. We first establish the system model for such AmBC system from spread-spectrum perspective, from which a novel joint design for tag waveform and reader detector is proposed. We construct the test statistic that cancels out the direct-link interference by exploiting the repeating structure of the ambient OFDM signals due to the use of cyclic prefix. The maximum-likelihood detector is proposed to recover the tag bits, for which the optimal threshold is obtained with closed-form expression. Also, we analyze the effect of various system parameters on the transmission rate and detection performance. Finally, extensive numerical results show that the proposed transceiver design outperforms the conventional design.

Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). The use of multiple transmitters (TXs) each with one coil can be applied to enhance the WPT performance by coherently combining the magnetic fields induced by all TX coils in a beam toward the receiver (RX) coil, a technique termed "magnetic beamforming". In this paper, we study the optimal magnetic beamforming design for an MRC-WPT system with multiple TXs and a single RX. We formulate a problem to jointly optimize the currents flowing through different TXs so as to minimize the total power drawn from their voltage sources, subject to the minimum power required by the RX load as well as the practical constraints on the peak voltage and current at all TXs. For the special case of identical TX resistances and without the peak voltage and current constraints, we show that the optimal current at each TX should be proportional to the mutual inductance between its TX coil and the RX coil. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is reformulated as a semidefinite programming (SDP) with rank-one constraint. We show that the semidefinite relaxation (SDR) of the reformulated problem is tight and hence the problem is solved optimally. Numerical results show that the optimal magnetic beamforming design significantly enhances the deliverable power as well as the power efficiency over the uncoordinated WPT benchmark with equal current allocation over TXs.

In this paper, we study a wireless powered co-operative communication system, which consists of a hybrid relay node (RN), a source node (SN), and a destination node (DN). It is assumed that the hybrid RN has a constant power supply while the SN has no embedded power supply. Thus, the SN needs to first harvest energy from the radio frequency (RF) signal broadcasted by the hybrid RN before transmitting information to the hybrid RN. By assuming that the RN has an information buffer and can temporarily store the information it received, we investigate the long-term throughput of two different block-wise cooperative protocols, namely the block-wise harvest-and-transmit (BW-HaT) protocol and the block-wise mode adaptation (BW-MA) protocol. For the BW-HaT protocol, the throughput expression is obtained in closed form. For the BW-MA protocol, the optimal mode adaptation method that maximizes the throughput of the system is presented and the maximum throughput is given for different system setups. It is shown that through simultaneously transmitting information and energy to the DN and SN, respectively, the proposed transmission schemes can significantly increase the system throughput.

We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10%, compared to that obtained using the perfect F-CSI.

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number M of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame) . To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-M regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system.

We consider a multi-input-single-output (MISO) system where a wireless power transmitter sends wireless power to a receiver via beamforming, taking into account the effects of channel estimation. In this paper, we address the problem of dynamically balancing the amount of time resource used for training, as well as the energy resource used for wireless power transfer, so as to maximize the amount of energy harvested at the receiver. First, the optimal length of preamble is obtained under the case when the length is optimized offline, or dynamically optimized online, depending on the channel estimate. For the latter case, the solution is obtained by solving a dynamic programming (DP) problem, which is shown to be a threshold-type policy that depends only on the channel estimate power. We further derive the optimal dynamic power allocation scheme for DP-based wireless power delivery. The analysis results are validated by numerical simulations. The scheme based on dynamic-length preamble with power allocation is shown to significantly outperform the fixed-length preamble scheme.

We consider the scenario in which multiple sensors send spatially correlated data to the fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of the signal can be formulated as a compressive sensing (CS) problem. To model the scenario where sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor, referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. We provide theoretical guarantees on the number of measurements for reliable reconstruction, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under some mild conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of the SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. Our analysis is corroborated by numerical results.