Integrated radar sensing and wireless power transfer (ISWPT) is an emerging paradigm that seeks to combine the functionalities of radar sensing and wireless power transfer into a unified system, utilizing a shared hardware platform to maximize resource efficiency. In this paper, we investigate the performance of an ISWPT system that is enhanced by extremely large-scale reconfigurable intelligent surfaces (XL-RIS), which serve to dynamically control the propagation environment and improve both radar sensing and wireless power transfer. Specifically, we consider a system where energy receivers are placed within the near-field region of the XL-RIS and investigate the joint optimization of beamforming at the base station and the XL-RIS reflection phases. The goal is to maximize the efficiency of wireless power transfer while simultaneously enhancing radar sensing performance. The formulated problem, though non-convex in nature, is efficiently addressed through the introduction of an alternating optimization algorithm. Numerical simulations demonstrate the effectiveness of the proposed algorithm, showing substantial improvements in both radar sensing accuracy and wireless power transfer performance compared to existing baseline schemes.

This paper investigates near-field integrated sensing and communication systems, where both communication users and radar targets are located within the near-field region of a base station equipped with an extremely large-scale antenna array. The near-field operation introduces new challenges and opportunities for joint radar sensing and communication tasks, especially when the traditional assumption of far-field propagation no longer holds. In this work, we focus on the design of near-field beam-focusing, a technique that allows for more focused and efficient energy distribution in specific regions. Specifically, our goal is to maximize the minimum beam pattern gain for radar sensing, ensuring that the sensing capabilities meet the required performance while simultaneously satisfying the communication requirements of the users. The originally formulated problem is non-convex due to the complex coupling between the radar and communication tasks. Nevertheless, we solve the problem globally optimally by leveraging semi-definite relaxation. Subsequently, we propose a low-complexity suboptimal solution that reduces the computational burden. Numerical results demonstrate the potential of near-field beam-focusing, showing that it can successfully detect multiple radar targets located at identical angular directions.

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.

Spectrum sensing and communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, we utilize intelligent reflecting surfaces (IRS) to simultaneously enhance spectrum sensing accuracy and the secrecy performance of secondary users (SUs) through physical layer security (PLS) techniques. Additionally, we employ IRS as a novel approach to achieve the target probability of detection. We formulate a joint sensing and transmission security optimization problem to maximize the sum secrecy rate of SUs under both perfect and imperfect channel state information (CSI). To transform the probability of detection into a tractable expression, we adopt a safe approximation for theQ-function. We use a computationally-efficient block coordinate descent (BCD)-based algorithm to optimize the beamforming design and IRS phase shifts alternately. Specifically, we employ theS-procedure to handle the semi-infinite constraints under the imperfect CSI case. Simulation results demonstrate that by leveraging IRS for spectrum sensing, we can significantly reduce the sensing time while achieving the required probability of detection and the probability of false alarm. Furthermore, our proposed scheme improves both sensing accuracy and secrecy rate in both cases compared to the benchmark schemes.

Maximum Inner Product Search (MIPS) plays an essential role in many applications ranging from information retrieval, recommender systems to natural language processing. However, exhaustive MIPS is often expensive and impractical when there are a large number of candidate items. The state-of-the-art quantization method of approximated MIPS is product quantization with a score-aware loss, developed by assuming that queries are uniformly distributed in the unit sphere. However, in real-world datasets, the above assumption about queries does not necessarily hold. To this end, we propose a quantization method based on the distribution of queries combined with sampled softmax. Further, we introduce a general framework encompassing the proposed method and multiple quantization methods, and we develop an effective optimization for the proposed general framework. The proposed method is evaluated on three real-world datasets. The experimental results show that it outperforms the state-of-the-art baselines.

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.

Spectrum sensing and the communication security are of crucial importance in cognitive radio networks (CRNs). In this paper, intelligent reflecting surface (IRS) is exploited in CRNs to simultaneously enhance the spectrum sensing accuracy and the secure performance achieved by using physical layer security (PLS) techniques. The sum secrecy rate of the secondary users (SUs) is maximized by jointly optimizing the sensing time, the beamforming design and the IRS phase shifts. A safe approximation is adopted to transform the probability of detection into a tractable expression. A computationally efficient block coordinate descent (BCD)-based algorithm with the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR) is exploited to optimize the beamforming and the phase shifts alternately. Simulation results demonstrate that our proposed algorithm can significantly improve both the sensing performance and the secrecy rate compared with the benchmark schemes.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, intelligent reflecting surface (IRS) is exploited to enhance both the accuracy of spectrum sensing and the secondary transmission in a CR network (CRN) employing the opportunistic spectrum access. A novel detection threshold based on the probability of false alarm is derived for improving the spectrum sensing performance. The average achievable rate of the secondary network is maximized under both the two-stage and one-stage IRS phase shifts case. To tackle the challenging non-convex optimization problem under the two-stage case, a computationally efficient block coordinate descent (BCD)-based algorithm is proposed coputilizing the techniques of successive convex approximation (SCA) and semidefinite relaxation (SDR). Moreover, a BCD method and a tractable approximation of the probability of detection are exploited to tackle the problem under one-stage IRS phase shifts case. Simulation results demonstrate that our proposed designs are superior to the benchmark schemes in terms of the achievable rate and the sensing performance, and IRS can greatly improve the spectral efficiency of the CRN.

Cognitive radio (CR) is one of the most disruptive techniques for enabling the next generation wireless communication networks due to its potential in improving the spectral efficiency. In this paper, an intelligent reflecting surface (IRS) is exploited to assist both spectrum sensing and secondary transmission in the CR network (CRN) employing opportunistic spectrum access. A novel IRS-enhanced spectrum sensing scheme and a redesigned detection threshold are proposed to improve the sensing performance. We formulate the throughput maximization problem by jointly optimizing the sensing time, the beamforming, and the IRS phase shifts. A computationally efficient block coordinate descent (BCD)-based algorithm is proposed to tackle the non-convex problem. Simulation results show that our proposed scheme is superior to other benchmark schemes in terms of both the throughput and the sensing performance.

Recommender retrievers aim to rapidly retrieve a fraction of items from the entire item corpus when a user query requests, with the representative two-tower model trained with the log softmax loss. For efficiently training recommender retrievers on modern hardwares, inbatch sampling, where the items in the mini-batch are shared as negatives to estimate the softmax function, has attained growing interest. However, existing inbatch sampling based strategies just correct the sampling bias of inbatch items with item frequency, being unable to distinguish the user queries within the mini-batch and still incurring significant bias from the softmax. In this paper, we propose a Cache-Augmented Inbatch Importance Resampling (XIR) for training recommender retrievers, which not only offers different negatives to user queries with inbatch items, but also adaptively achieves a more accurate estimation of the softmax distribution. Specifically, XIR resamples items from the given mini-batch training pairs based on certain probabilities, where a cache with more frequently sampled items is adopted to augment the candidate item set, with the purpose of reusing the historical informative samples. XIR enables to sample query-dependent negatives based on inbatch items and to capture dynamic changes of model training, which leads to a better approximation of the softmax and further contributes to better convergence. Finally, we conduct experiments to validate the superior performance of the proposed XIR compared with competitive approaches.

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.

Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.

This paper considers a non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system in the presence of a malicious eavesdropper. We employ the partial offloading mode such that each user can divide the individual computation task into two parts for local executing and offloading, respectively. The secrecy outage probability is adopted to measure the secrecy performance of computation ofloading by considering the practically passive eavesdropping scenario. Under this setup, we investigate the problem of minimizing the weighted sum-energy consumption for all users, subject to the secrecy ofloading rates constraints, the computation latency constraints and the secrecy outage probability constraints, and then derive the semi-closed form solution for this problem. Numerical results are provided and demonstrate that the merits of our proposed design are better than those of the alternative benchmark schemes.

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.

In this study, the authors study the max–min fairness for wireless energy transfer in a multiuser multiple‐input multiple‐output communication system with simultaneous wireless information and power transfer. In particular, they aim to maximise the minimum harvested energy among the multiple multi‐antenna energy receivers while guaranteeing secure communication for multi‐antenna information receiver. The dual use of artificial noise to facilitate both wireless energy transfer and secure communication is exploited in the authors’ proposed problem. Both scenarios of perfect and imperfect channel state information (CSI) known at the transmitter are considered. For the perfect CSI case, the formulated max–min energy harvesting (MM‐EH) problem is non‐convex and intractable. To circumvent it, an iterative optimisation algorithm based on Taylor series expansion is proposed. Then, they turn their attention to the imperfect CSI case, where a max–min robust energy harvesting (MMR‐EH) problem is considered. Though the MMR‐EH problem is more complicated than the MM‐EH problem, they reveal that the iterative optimisation method can be extended to the solution of the former, wherein the S‐procedure is introduced. Simulation results show the efficiency of their proposed solutions in terms of energy harvesting.

In this paper, a robust downlink beamforming design for simultaneous wireless information and power transfer (SWIPT) in a multiuser MISO communication system is proposed. Our design is to maximize the minimum harvested energy among the multi-antenna energy receivers (ERs) while guaranteeing the secure communication requirement at the information receiver (IR) by optimizing the transmit beamforming vectors and power splitting ratio jointly. The considered max-min fair problem is non-convex and hard to tackle. Using the semi-definite relaxation (SDR) technique, we solve this problem by carrying out a one-dimensional search which refer to the solution of a series of semi-definite programs (SDPs). Also, we provide the closed-form solution based on Lagrange duality and prove that the utilized SDR is tight. Simulation results show our proposed robust scheme is more efficient than the conventional isotropic scheme in terms of energy harvesting.

In this letter, we consider a multiple-input-multiple-output (MIMO) downlink system with simultaneous wireless information and power transfer (SWIPT), where the information sent to the desired receiver (DR) may be wiretapped by the malicious energy harvesting (EH) receivers (potential eavesdroppers). Assuming the channel state information (CSI) of the EH receivers is not perfectly known at the transmitter, we aim to maximize the worst-case secrecy rate by jointly designing the precoding matrix, AN covariance matrix and power splsplitting ratio, under the constraints of total transmit power and harvested energyitting ratio, under the constraints of total transmit power and harvested energy at receivers. The formulated problem is non-convex and semi-infinite, which is hard to tackle. To solve it, we first deal with the non-convexity caused by CSI errors with the aid of the S-Procedure, and then employ the Taylor series approximation techniques to transfer the nonconvex problem into a convex optimization problem. Based on that, we propose an iterative algorithm with proved convergence to solve the original problem. Simulation results are finconvex optimization problem. Based on that, we propose an iterative algorithmally provided to show the effectiveness of our proposed robust transmit design.

In this paper, a supervised approach to online learn a structured sparse and discriminative representation for object tracking is presented. Label information from training data is incorporated into the dictionary learning process to construct a compact and discriminative dictionary. This is accomplished by adding an ideal-code regularization term and classification error term to the total objective function. By minimizing the total objective function, we learn the high quality dictionary and optimal linear multi-classifier simultaneously. Combined with multi task sparse learning, the learned classifier is employed directly to separate the object from background. As the tracking continues, the proposed algorithm alternates between multi task sparse coding and dictionary updating. Experimental evaluations on the challenging sequences show that the proposed algorithm performs favorably against state-of-the-art methods in terms of effectiveness, accuracy and robustness.

Broadcast channels with correlated sources are considered from a joint source-channel coding perspective, where each receiver is kept in ignorance of the source intended for the other receiver. This setting can be seen as a generalization of Han-Costa's broadcast channel with correlated sources under additional secrecy constraints on both receivers. General outer and inner bounds for this reliable and secure communication are determined. The joint source-channel coding is proved to be optimal for two special cases, including the sources satisfying a certain Markov property sent over semi-deterministic broadcast channels, and arbitrary correlated sources sent over less-noisy broadcast channels.

In this paper, a class of state-dependent relay channel with orthogonal channels from the source to the relay and from the source and the relay to the destination is studied. The two orthogonal channels are corrupted by common channel state which is known to both the source and the relay non-causally. The lower bound on the capacity for the channel is derived firstly. Then, we show that if the receiver output Y is a deterministic function of the relay input Xr, the channel state S and one of the source inputs XD, i.e. Y = f(Xr, XD, S), the explicit capacity can be characterized.

The overwhelming amounts of digital images on the Web and personal computers have triggered the requirement of an effective tool to retrieve images of interest using semantic concepts. Due to the semantic gap between low level content features and its high level semantic features of an image, however, the performances of many existing automatic image annotation algorithms are not so satisfactory. In this paper, a novel approach based on the cognitive science is proposed to improve the quality of annotations. The main idea is that the tags of an image are considered as nodes in a semantic network, and the relevance between the tags and image contents is regulated using the spreading activation theory. After the spreading activation process finishes, each tag will be assigned an appropriate value with respect to its relation to other tags. Experimental results conducted on the 50,000 Flickr images demonstrate that the proposed scheme can effectively improve the performance in automatic image annotation.

Recently learning to rank has become one of the popular means to create a ranking model for social image search. However, the results of existing approaches are not as satisfactory for the large gap between low-level visual features and high-level semantic concepts, and these sophisticated approaches require a significant amount of parameters tuning to be effective and efficient. In this paper, we propose a novel framework for social image re-ranking based on a non-parametric quantum technique, which reranks top retrieved images by considering the interrelationship between images through the quantum estimation and requires no explicit parameter tuning. The basic idea of the proposed framework is inspired by the photon polarization experiment supporting the theory of quantum measurement. Experimental results conducted on the Flickr dataset demonstrate the effectiveness and efficiency of the proposed framework.

Recently, a combined transmitter-selection combining/ receiver-maximal-ratio -combining (TAS/MRC) scheme has been proposed to reduce the complexity of system and retain the diversity advantage. An accurate bit error rate expression is derived for arbitrarily correlated Nakagami fading channels. The Gauss-Laguerre quadrature based numerical method is employed to evaluate the derived expression. For the channels with integer fading parameter, an exact BER expression is obtained. The numerical results illustrate that the derived approximate formula of BER is in excellent agreement with the Monte Carlo results.

Recently, a combined transmitter antenna selection/receiver-maximal-ratio-combining (TAS/MRC) scheme has been proposed to reduce the complexity of system and retain the diversity advantage. In this paper, we investigate the performance of the TAS/MRC scheme over correlated Nakagami fading channels. The characteristics function method is used to derive an exact bit-error rate expression, in which only very simple functions are included. Two special cases are discussed. The theoretical findings are supported by computer simulations.

The complex dynamics of the chaotic neural networks makes it possible for them to escape from local minimum of the simple gradient descent neurodynamics. In this letter we use a transiently chaotic neural network to detect the CDMA multiuser signals and hence obtain an implementation scheme of the CDMA multiuser detector (TCNN-MD). Computer simulation results show that the proposed detector is clearly superior to Hopfield neural-network-based detector.

In this paper, we addressed a theoretical analysis for the capacity of parallel intra-connected bidirectional associative memory (MIBAM) and proved the conclusions: two MIBAM with the equal total number of neurons have the equal recalling probability for m pairs of stored pattern pairs if m is not too large. The results of computer simulation support the conclusions well.

By computer simulation, we detect a new type of strange attractor in a three-cell cellular neural network which defers from that found by other researchers. Bifurcation phenomena are analyzed.