In 6G and future communication systems, the adoption of large scale antenna arrays and high frequency bands is an inevitable trend. This leads to most users operating in the near-field region, where higher angular resolution and sensitivity to distance changes make user mobility effects fundamentally different from those in the far-field. In this work, a systematic study of near-field channel dynamics under user mobility is investigated. We begin by analyzing the Doppler characteristics associated with both line of sight (LoS) and multipath components, and demonstrate that notable Doppler shifts can occur even at low user velocities. In particular, the LoS component exhibits antenna dependent Doppler shifts, highlighting the spatially non-uniform nature of Doppler effects in near-field scenarios. Based on this, we derive a closed form expression for the coherence time in scattering-free environments, which reveals its dependence on antenna aperture, wavelength, user motion, etc. Unlike far-field LoS channels where Doppler effects can be compensated by a phase rotation, the near-field counterpart exhibits antenna dependent Doppler shifts, resulting in a finite coherence time even without scattering. For multipath scenarios, where the near-field channel is inherently sparse, we establish upper and lower bounds for the temporal correlation function. Furthermore, to track time-varying channels, we propose an array segmentation strategy and formulate a state-space model of the near-field channel in the dictionary domain. The proposed estimation framework comprises an expectation maximization (EM) stage using Kalman filtering with Rauch-Tung-Striebel smoothing (KF-RTSS) for virtual channel statistics and a fixed-point iteration for parameter refinement, followed by a Kalman filtering stage for channel prediction. A detailed complexity analysis is also provided. Finally, extensive simulations validate our theoretical analysis and show that the proposed method accurately captures near-field channel dynamics, offering robust performance across different angles, distances, and signal to noise ratios.

With the emergence of extremely large-scale antenna arrays (ELAAs), the next generation of wireless communication is likely to occur in the radiating near-field region of base stations (BS). In such regions, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and coverage hole (dead zone). To cope with those issues, we propose a novel codebook design guideline for the uniform circular array by maximizing the overlapping coverage between the beam coverage (BC) and near-field region, where the energy spread effect in the near-field region is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we construct the beam coverage-based codebook structure and two-stage beam training (TSBT) scheme. Numerical simulations show that the TSBT scheme with the proposed codebook can potentially reduce beam training overhead while improving the success rate and patching the dead zone.

This article presents a novel concept termed integrated imaging and wireless power transfer (IWPT), wherein the integration of imaging and wireless power transfer functionalities is achieved on a unified hardware platform. IWPT leverages a transmitting array to efficiently illuminate a specific Region of Interest (ROI), enabling the extraction of ROI’s scattering coefficients while concurrently providing wireless power to nearby users. The integration of IWPT offers compelling advantages, including notable reductions in power consumption and spectrum utilization, pivotal for the optimization of future 6G wireless networks. As an initial investigation, we explore two antenna architectures: 1) a fully digital array and 2) a digital/analog hybrid array. Our goal is to characterize the fundamental tradeoff between imaging and wireless power transfer by optimizing the illumination signal. With imaging operating in the near-field, we formulate the illumination signal design as an optimization problem that minimizes the condition number of the equivalent channel. To address this optimization problem, we propose an semi-definite relaxation-based approach for the fully digital array and an alternating optimization algorithm for the hybrid array. Finally, numerical results verify the effectiveness of our proposed solutions and demonstrate the tradeoff between imaging and wireless power transfer.

Radio frequency wireless power transfer enables charging low-power mobile devices without relying on wired infrastructures. Current existing wireless power transfer systems are typically designed assuming far-field propagation, where the radiated energy is steered to towards given angles, resulting in limited efficiency and possible radiation in undesired locations. An emerging technology for wireless signaling is based on dynamic metasurface antennas (DMAs), which efficiently realize electrically large arrays. When such arrays are employed at high frequencies, wireless power transfer might take place in the radiating near-field (Fresnel) region, where spherical wave propagation holds, providing more degrees-of-freedom and improved performance. In this article, we study wireless power transfer systems charging multiple devices in the Fresnel region, where the energy transmitter is equipped with a DMA, exploring how the antenna configuration can exploit the spherical wavefront to generate focused energy beams. In particular, after presenting a mathematical model for DMA-based radiating near-field wireless power transfer systems, we characterize the weighted sum-harvested energy maximization problem of the considered system, and we propose an efficient solution to jointly design the DMA weights and digital precoding vector. Then, by accounting for hardware constraints, we further extend our study to encompass practical scenarios with discrete phase shifts in DMA elements. Simulation results show that our design generates focused energy beams capable of improving energy transfer efficiency in the radiating near-field with minimal energy pollution.

Extremely large-scale antenna arrays (ELAAs) introduce a new communication paradigm called near-field communications, where users are likely to operate in the near-field region of the base-stations (BSs). In such a region, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and a coverage hole (dead zone). To cope with this issue, we developed a beam depth-based codebook and training scheme for near-field ELAA systems. As the performance of codebook design is mainly dictated by the array configurations, we study the codebook design considering uniform linear, circular and planar antenna arrays. Specifically, we first offer an integrated model to characterize the near-field channel for the considered array configurations. Then, we propose a novel codebook design guideline by maximizing the overlap depth between the near-field codeword (beam) coverage and near-field region, where the energy spread effect is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we respectively construct the beam depth based on two-stage and hierarchical codebooks as well as the corresponding beam training schemes. Numerical simulations show that the proposed codebook based beam training schemes can potentially reduce beam training overhead while improving the success rate and beam gain inside the dead zone.

Channel estimation is fundamental to leveraging the performance gains of reconfigurable intelligent surfaces (RISs), one of the key technologies for 6G. Due to the passive nature of RIS, most current research focuses on cascaded channel estimation. However, individual channel information is crucial for practical applications such as flexible precoding design. In this paper, we propose a hybrid RIS architecture integrated with dynamically controllable active elements, which reduces the cost of RIS deployment. Based on this novel architecture, we introduce an atomic norm minimization (ANM)-based individual channel estimation method, exploiting the sparse characteristics of high frequency channels. We theoretically prove that our proposed method retains its applicability even in the presence of random element damage in RIS. Furthermore, we extend the solution for individual channel estimation to passive RIS scenario under the mild condition that the locations of base station and RIS are known. Simulation experiments demonstrate that the proposed methods achieve super-resolution channel estimation, surpassing the performance of existing methods such as orthogonal matching pursuit.

Extremely large-scale multiple-input multiple-output (XL-MIMO) wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structured OFDM (SOFDM) modulation approach to reduce the CP overhead for wideband XL-MIMO systems with dual-wideband effects. As the ability to perform SOFDM is affected by the antenna architecture, we study the modulation technique considering different antenna structures, including fully-digital, phase shifter-based hybrid array, and dynamic metasurface antenna (DMA) architectures. Specifically, we first provide a mathematical model to represent a near-field channel with dual wideband effects. Based on the channel model, we develop the SOFDM modulation and then propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectral efficiency, where the solutions of precoding/combining and equalization matrices are derived for the three types of antenna array architectures. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

The employment of extremely large antenna arrays and high-frequency signaling makes future communication systems likely to operate in the near-field region, where the conventional planar wave assumption is invalid. Instead, a spherical wave assumption which provides both the user angle and distance information is more accurate. Spherical wave-based channel representation and estimation is still under investigation. In this work, we propose a distance-parameterized angular-domain sparse model to represent the near-field channel, followed by a joint dictionary learning and sparse recovery based channel estimation algorithm. The proposed sparse representation model overcomes challenges such as the storage burden and high dictionary coherence that arise in the existing polar-domain method. Simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the polar-domain method in channel estimation error and pave the way to efficient and accurate modeling in the near-field regime.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems introduce a new communication paradigm called near-field communications, which identifies users’ location within the near-field (Fresnel’s region). In the near-field, beams can be steered in the angle and distance dimensions, resulting in an enormous codebook and a prolonged two-dimensional beam alignment (BA) process. To keep a low BA overhead while achieving low BA error, in this paper, we design a novel hierarchical codebook and a BA scheme for near-field XL-MIMO systems. Specifically, we first propose a novel spatial partition where the angle-offset effect is revealed and leveraged to improve the beam gain inside the coverage area. Based on the partition, we design distance-coarse and focusing beams. Distance-coarse beams are leveraged to construct the high level of the codebook for angle dimension alignment. In contrast, focusing beams construct the last level codebook for distance dimension alignment. Corresponding to the proposed codebook structure, our BA scheme is a tree search consisting of two stages: the angle aligning stage and the distance aligning stage. Next, we formulate the desired codebook design problem as difference convex optimization problems, where three beam design guidelines are considered to minimize the BA error rate raised by the near-field angle-offset effect. After that, the proposed optimization problem is solved by the constrained concave-convex procedure. Numerical simulations verify the angle offset effect and our designed near-field beam. Furthermore, we show that our BA scheme only utilizes one percent of overhead but achieves a lower BA error rate than exhaustive searching.

Extremely large antenna array (ELAA) is regarded as one of the most crucial technologies for the next-generation communications due to its ability to significantly improve spectral efficiency. However, larger antenna aperture and higher frequency make the Rayleigh distances dramatically increased, resulting in more and more communications taking place in the near-field region. Different from traditional far-field communications, near-field communications are widely considered to be spherical wavefront-based rather than planar wavefront based, thus techniques designed for far-field scenarios might be no longer applicable. In this paper, we study the near-field communication system with an extremely large cylindrical antenna array (CLA). Under such a setup, we study the near-field beamforming, exploiting the geometrical relationship between CLA and user with the spherical-wavefront model. Specifically, we analyze the beamforming gain in the elevation angle, azimuth angle domain and distance domains, respectively. We then study the beam focusing properties in near-field CLA systems, namely the asymptotic orthogonality and the depth of focused beams. Moreover, a three-dimensional (3-D) near-field CLA codebook is proposed to make beam focusing more effective. Simulation results demonstrate that the proposed near-field codebook can effectively focus the beam to a certain location and thus improve the system achievable rate.

The employment of extremely large antenna arrays and high-frequency signaling makes future 6G wireless communications likely to operate in the near-field region. In this case, the spherical wave assumption which takes into account both the user angle and distance is more accurate than the conventional planar one that is only related to the user angle. Therefore, the conventional planar wave based far-field channel model as well as its associated estimation algorithms need to be reconsidered. Here we first propose a distance-parameterized angular-domain sparse model to represent the near-field channel. In this model, the user distance is included in the dictionary as an unknown parameter, so that the number of dictionary columns depends only on the angular space division. This is different from the existing polar-domain near-field channel model where the dictionary is constructed on an angle-distance two-dimensional (2D) space. Next, using this model, joint dictionary learning and sparse recovery based channel estimation methods are proposed for both line of sight (LoS) and multi-path settings. To further demonstrate the effectiveness of the suggested algorithms, recovery conditions and computational complexity are studied. Our analysis shows that with the decrease of distance estimation error in the dictionary, the support set of the angular-domain sparse vector can be exactly recovered under certain Restricted Isometry Property (RIP) based conditions. The high storage burden and dictionary coherence issues that arise in the polar-domain 2D representation are also well addressed. Finally, simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the existing polar-domain method in channel estimation error.

Massive multiple-input multiple-output (mMIMO) channels in wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structure OFDM modulation approach to reduce the CP overhead for wideband mMIMO systems with dual-wideband effects. Specifically, we propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectrum efficiency, where the closed-form solutions of precoding vectors and equalization matrices are derived by exploiting the unique characteristic of composite channel matrix. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems. In this paper, we study the downlink millimeter wave (mmWave) DMA systems with limited number of radio frequency (RF) chains. By using the specific DMA structure, an equivalent mmWave channel model with hybrid beamforming is first explicitly characterized. Based on that, we propose an effective joint microstrip selection and beamforming scheme to accommodate for the limited number of RF chains. A low-complexity digital beamforming solution with channel gain-based microstrip selection is developed, while the analog beamformer is obtained via a coordinate ascent method. The proposed scheme is numerically shown to approach the performance of DMAs without RF chain reduction, verifying the effectiveness of the proposed schemes.

Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency bandwidths give rise to new considerations for localization applications. Emerging antenna architectures, such as dynamic metasurface antennas (DMAs), are expected to be frequently utilized thanks to the achievable high angular resolution and low hardware complexity. Further, wireless localization is likely to take place in the radiating near-field (Fresnel) region, which provides new degrees of freedom, because of the adoption of arrays with large apertures. While current studies mostly focus on the use of costly fully-digital antenna arrays, in this paper we investigate how DMAs can be applied for near-field localization of a single user. We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy. Next, we propose an algorithm for configuring the DMA to optimize near-field localization, by first tuning the adjustable DMA coefficients to minimize the estimation error using postulated knowledge of the actual user position. Finally, we propose a sub-optimal iterative algorithm that does not rely on such knowledge. Simulation results show that the DMA-based near-field localization accuracy could approach that of fully-digital arrays at lower cost.

Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role in future wireless communications, enabling programmable radio propagation environments. They are usually considered as almost passive planar structures that operate as adjustable reflectors, giving rise to a multitude of implementation challenges, including the inherent difficulty in estimating the underlying wireless channels. In this paper, we focus on the recently conceived concept of Hybrid Reconfigurable Intelligent Surfaces (HRISs), which do not solely reflect the impinging waveform in a controllable fashion, but are also capable of sensing and processing an adjustable portion of it. We first present implementation details for this metasurface architecture and propose a convenient mathematical model for characterizing its dual operation. As an indicative application of HRISs in wireless communications, we formulate the individual channel estimation problem for the uplink of a multi-user HRIS-empowered communication system. Considering first a noise-free setting, we theoretically quantify the advantage of HRISs in notably reducing the amount of pilots needed for channel estimation, as compared to the case of purely reflective RISs. We then present closed-form expressions for the Mean-Squared Error (MSE) performance in estimating the individual channels at the HRISs and the base station for the noisy model. Based on these derivations, we propose an automatic differentiation-based first-order optimization approach to efficiently determine the HRIS phase and power splitting configurations for minimizing the weighted sum-MSE performance. Our numerical evaluations demonstrate that HRISs do not only enable the estimation of the individual channels in HRIS-empowered communication systems, but also improve the ability to recover the cascaded channel, as compared to existing methods using passive and reflective RISs.

Finite-rate-of-innovation (FRI) signals are ubiquitous in radar, ultrasound, and time of flight imaging applications. In this paper, we propose a model-based deep learning approach to jointly design the subsampling and reconstruction of FRI signals. Specifically, our framework is a combination of a greedy subsampling algorithm and a learning-based sparse recovery method. Unlike existing learning-based techniques, the proposed algorithm can flexibly handle changes in the sampling rate and does not suffer from differentiability issues during training. Moreover, exact knowledge of the FRI pulse is not required. Numerical results show that the proposed joint design leads to lower reconstruction error for FRI signals compared with existing benchmark methods for a given number of samples. The method can easily adapt to other sparse recovery problems.

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and conveyed. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with the quantization of these samples, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Our numerical evaluations demonstrate that the proposed scheme achieves reconstruction accuracy within a small gap of that achievable with infinite resolution quantizers, while compressing high-dimensional graph signals into finite bit streams.

Recently, dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing massive multiple-input multiple-output (MIMO) wireless systems. The usage of large arrays, jointly with higher transmitted frequencies, often results in the communicating devices operating in the near-field (Fresnel) region, thus requiring different considerations compared to traditional systems, assumed to operate in the far-field regime. In this paper, we study the potential of beam focusing, feasible in near-field operation, for multi-user MIMO systems, where the base station is equipped with a DMA. We introduce a mathematical model for DMA-based near-field MIMO communications. Then, we characterize the sum-rate maximization problem of the considered system, and propose an efficient solution to jointly design the DMA weights and digital precoding vector. Simulation results show that our design generates focused beams such that users residing at the same angular direction can communicate reliably without interfering, which is not achievable using conventional far-field beam steering.

Cooperative jamming is a widely used approach for improving the security of wireless networks. In this approach, a friendly jammer sends jamming signals to disrupt the reception of the eavesdropper, which inevitably interferes with the legitimate receiver. In this paper, we propose a novel approach, namely, information jamming, to exceed the secrecy rate achieved by the traditional cooperative jamming approach in a multiple-input single-output (MISO) wiretap channel. We propose that multiple multi-antenna information jammers (iJammers) transmit the source signals of the legitimate transmitter rather than independent noise signals for simultaneously enhancing the signal strength at the legitimate receiver and canceling the received signal at the eavesdropper. Specifically, we aim at maximizing the achievable secrecy rate by jointly optimizing the beamforming vectors at Alice and iJammers, subject to the individual transmit power constraints. We first propose a semi-definite relaxation based approach to solve the original non-convex problem optimally. For ease of implementation, we then provide a suboptimal distributed information beamforming scheme, whose optimal solution is obtained in closed-form. Finally, we extend our study to the imperfect channel state information case. Simulation results show that our proposed information jamming approach significantly outperforms the traditional cooperative jamming approach in terms of achievable secrecy rate.

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor attempts to eavesdrop two suspicious communication links simultaneously. To facilitate concurrent eavesdropping, our multi-antenna legitimate monitor employs a proactive eavesdropping via jamming approach, by selectively jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the achievable eavesdropping rate region for the minimum-mean-squared-error (MMSE) receiver case, by optimizing the legitimate monitor's jamming transmit covariance matrix subject to its power budget. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping rate region is essentially the intersection of the achievable rate region for the two suspicious links and that for the two eavesdropping links. The former region can be purposely altered by the monitor's jamming transmit covariance matrix, whereas the latter region is fixed when the MMSE receiver is employed. Therefore, we first analytically characterize the achievable rate region for the two suspicious links via optimizing the jamming transmit covariance matrix and then obtain the achievable eavesdropping rate region for the MMSE receiver case. In addition, we also consider the MMSE with successive interference cancellation (MMSE-SIC) receiver case and characterize the corresponding achievable eavesdropping rate region by jointly optimizing the time-sharing factor between different decoding orders. Furthermore, extensions to the imperfect channel state information case and the more than two suspicious links scenario are also examined. Finally, numerical results are provided to corroborate our analysis and evaluate the eavesdropping performance.

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor (e.g., the National Security Agency in the USA) attempts to eavesdrop more than one suspicious communication links simultaneously to maximally protect public security. To facilitate concurrent eavesdropping, our multi-antenna monitor employs a proactive eavesdropping via jamming approach, by purposely jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the monitor's achievable eavesdropping signal-to-interference-plus-noise ratio (SINR) region of two suspicious links, by optimizing the legitimate monitor's transmit covariance matrix for jamming the two suspicious receivers. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping SINR region is essentially the intersection of the achievable region for the two suspicious links and that for the two eavesdropping links, and the former region can be purposely altered by the monitor's jamming transmit covariance matrix subject to its power budget. As both suspicious links' SINRs are affected by monitor's jamming covariance matrix, we first analyze the achievable region bounds for both suspicious links and then characterize the achievable eavesdropping SINR region. Finally, numerical results are provided to corroborate our analysis.

This paper considers a proactive eavesdropping problem, in which a full-duplex legitimate monitor aims to eavesdrop on a suspicious communication link between the secondary pairs in a cognitive radio (CR) network via jamming. For such a scenario, the jamming signals would not only disrupt the suspicious receivers, but also influences the interference received at the primary receiver, which both destroy the transmitting rate in the suspicious link. Hence, the design of the beamforming should have good tradeoff between those two affects. We aim to maximize the eavesdropping rate by designing the jamming beamforming under the transmitting power (TP) constraint at the legitimate monitor and the interference temperature (IT) constraint at the primary receiver, which is a non- convex problem. Specifically, several cases are discussed to decompose the original problem and a closed-form solution is finally presented by solving two sub-problems. In particular, some analyses on the main parameters (the maximum power at the legitimate monitor and the interference temperature at the primary receiver) are undertaken to obtain their boundaries corresponding to different modes of the optimal vector, which influence the performance of the system. Numerical results are finally presented to demonstrate the performance of our proposed schemes outperforms the reference solutions.

Cooperative jamming approach has been commonly used in the field of physical layer security to achieve a good secrecy rate of wireless networks. In this approach, the Gaussian noise signal sent by a friendly jammer can disrupt the reception of the eavesdropper, however, it may also interfere with the legitimate receiver. In this paper, we propose a novel approach, namely, information jamming, to go beyond the secrecy rate achieved by the traditional cooperative jamming approach in a multiple-input single-output (MISO) wiretap channel. We propose that a multi-antenna friendly jammer transmits the source message rather than Gaussian noise signal for simultaneously enhancing the signal strength at the legitimate receiver and canceling the received signal at the eavesdropper. In particular, we aim to maximize the achievable secrecy rate by optimizing the information jammer's beamforming vector. Though the optimization problem is non-convex, we successfully solve it optimally via a two-stage based numerical approach. To gain more useful analytical results, we also propose two suboptimal jamming schemes with low computation, by targeting at improving the legitimate reception and avoiding the eavesdropping, respectively. Finally, numerical results show that the proposed information jamming approach significantly helps go beyond the secrecy rate of the existing cooperative jamming approach, especially when the transmit power of the jammer is large enough.

This letter studies the cooperative precoding design for a coexisting wireless energy transfer (WET) and cognitive radio (CR) system, where the WET system share the same spectrum with the CR system. Different from the traditional wireless networks, interference here is regarded as a useful rather than harmful resource. Specifically, we address the transmit covariance design to minimize the total transmit power at the energy transmitter and the secondary transmitter while satisfying secrecy rate, energy harvesting, and interference temperature constraints. We propose an iterative algorithm to tackle the formulated nonconvex optimization problem, and prove that it could converge to a Karush-Kuhn-Tucker point of the original problem. Simulation results are finally provided to illustrate the effectiveness of our proposed algorithm.

This paper studies the secure beamforming design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel with confidential messages and external eavesdroppers, where each receiver adopts the power splitting (PS) scheme to decode information and harvest energy concurrently, and it is also seen as a potential eavesdropper for messages not intended for it. Our objective is to minimize the total transmit power while guaranteeing the individual secrecy rate and energy harvesting constraints at each receiver by jointly optimizing transmit beamforming vectors, artificial noise covariance, and receive PS ratios. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are considered. For the perfect CSI case, we propose a two-stage optimization approach to solve the original non-convex problem global optimality, and also provide a low-complexity suboptimal solution based on the particle swarm optimization algorithm. Furthermore, we also extend the above result to the colluding eavesdroppers scenario. For the imperfect CSI case, we propose a worst-case-based robust formulation, where the CSI errors are norm-bounded. With the aid of S-Procedure, we derive the equivalent forms for constraints and then transform the non-convex robust design into a convex optimization problem. Simulation results are finally presented to demonstrate the performance of our proposed schemes.

In this paper, we propose a secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) broadcast channel with confidential messages, where each receiver utilizes the power splitting approach to decode information and harvest energy simultaneously, and it also acts as a potential eavesdropper for the independent message sent to the others. By jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and receive power splitting (PS) ratios for all receivers, we aim to maximize the total harvested energy while guaranteeing the secrecy rate constraint at each receiver and the total transmit power constraint at the transmitter, which is a non-convex optimization and hard to solve. In this paper, we propose a two-stage optimization approach based iterative algorithm to tackle such a challenging problem. Moreover, we prove that the proposed algorithm can achieve convergence, and also analyze its computational complexity. Finally, simulation results are provided to demonstrate the performance of our proposed algorithm.

In this paper, we design a secure transmission scheme for multiple-input single-output (MISO) broadcast channel with simultaneous wireless information and power transfer (SWIPT), where a multi-antenna transmitter simultaneously transmit independent confidential messages to multiple potentially malicious receivers, in the presence of external eavesdroppers. Our objective is to minimize the total transmit power while guaranteeing the security communication and energy harvesting constraints by jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and power splitting ratios, which is non-convex optimization and hard to tackle. We first solve this non-convex problem by using the technique of semi-definite relaxation (SDR), and then prove that the relaxation is tight and thus achieves the globally optimal solution of the original problem. Simulation results are finally presented to demonstrate the performance of our proposed scheme.

In this paper, a robust transmission scheme for simultaneous wireless information and power transfer (SWIPT) in the presence of an eavesdropper is proposed. With imperfect channel state information (CSI) at the transmitter, the optimal transmit covariance matrix is obtained based on the maximization of the worst case harvested energy for the energy receiver (ER) while guaranteeing the achievable secrecy rate constraint for the information receiver (IR). To solve such a challenging nonconvex problem, in this paper, a two-stage optimization approach is proposed. In the first stage, the original problem is transformed into a robust design problem, which can be further converted into a convex semidefinite program (SDP) problem by using S-procedure as a tool. In the second stage, the optimal transmit covariance matrix is obtained via the aid of one-dimensional search algorithm. Finally, simulation results are provided to illustrate the robustness and effectiveness of the proposed method.