This paper investigates a pinching-antenna (PA)-enabled cognitive radio network, where both the primary transmitter (PT) and secondary transmitter (ST) are equipped with a single waveguide and multiple PAs to facilitate simultaneous spectrum sharing. Under a general Ricean fading channel model, a closed-form analytical expression for the average spectral efficiency (SE) achieved by PAs is first derived. Based on this, a sum- SE maximization problem is formulated to jointly optimize the primary and secondary pinching beamforming, subject to system constraints on the transmission power budgets, minimum antenna separation requirements, and feasible PA deployment regions. To address this non-convex problem, a two-stage optimization algorithm is developed, in which stage 1 designs the PT/ST pinching beamforming and stage 2 updates the ST transmit power. For the PT and ST pinching beamforming optimization, the coarse positions of PA are first determined at the waveguide-level. Then, wavelength-level refinements achieve constructive signal combination at the intended user and destructive superposition at the unintended user. For the ST power control, a closed-form solution is derived. Simulation results demonstrate that i) PAs can achieve significant SE improvements over conventional fixed-position antennas; ii) the proposed pinching beamforming design achieves effective interference suppression and superior performance for both even and odd numbers of PAs; and iii) the developed two-stage optimization algorithm enables nearly orthogonal transmission between the primary and secondary networks.

In-band full-duplex (IBFD) systems are expected to double the spectral efficiency compared to half-duplex systems, provided that loopback self-interference (SI) can be effectively suppressed. The inherent interference mitigation capabilities of the emerging fluid antenna system (FAS) technology make it a promising candidate for addressing the SI challenge in IBFD systems. This paper thus proposes a FAS-assisted self-interference cancellation (SIC) framework, which leverages a receiver-side FAS to dynamically select an interference-free port. Analytical results include a lower bound and an approximation of the residual SI (RSI) power, both derived for rich-scattering channels by considering the joint spatial correlation amongst the FAS ports. Simulations of RSI power and forward link rates validate the analysis, showing that the SIC performance improves with the number of FAS ports. Additionally, simulations under practical conditions, such as finite-scattering environments and wideband integrated access and backhaul (IAB) channels, reveal that the proposed approach offers superior SIC capability and significant forward rate gains over conventional IBFD SIC schemes.

Fluid antenna multiple access (FAMA) has recently emerged as a simple, promising scheme for large-scale multiuser connectivity, offering strong scalability with low implementation complexity. Nevertheless, most existing FAMA studies focus on downlink transmission under perfect channel state information (CSI) at the receiver side, while the uplink counterpart remains largely unexplored. This paper proposes a novel codebook-based port selection and combining (CPSC) FAMA framework for the uplink communications without CSI at the base station (BS). In the proposed scheme, a predefined codebook is designed and broadcast by the BS. Each user equipment (UE) employs a fluid antenna, acquires its local CSI and independently chooses the most suitable codeword, activates the corresponding fluid antenna ports, and determines the combining weights to achieve a two-way match between the selected codeword and the instantaneous effective channel. The BS then separates the superimposed user signals through codebook-guided projection operations without requiring global CSI or multiuser joint optimization. To handle potential codeword collisions, three lightweight scheduling strategies are introduced, offering flexible trade-offs between signaling overhead and collision avoidance. Simulation results demonstrate that the proposed CPSC-FAMA approach achieves substantially higher rates than fixed-antenna systems while maintaining low complexity. Moreover, the results confirm that amortizing the optimization cost over the UEs effectively reduces the BS processing burden and enhances scalability, making the proposed scheme a strong candidate for future sixth-generation (6G) networks.

This paper investigates an Intelligent Reflecting Surface (IRS)-enhanced vehicle-to-infrastructure (V2I) multiple input multiple output (MIMO) backscatter communication network. In this network, multiple IRSs send roadside information to a multi-antenna reader using the backscatter technique, while the inevitable self-interference at the reader is taken into account. To maximize the introduced system’s weighted sum rate, we propose an optimization scheme based on minimum mean square error with successive interference cancellation (MMSE-SIC). This scheme jointly optimizes the reader’s detection matrix, beamforming vector, and IRS reflection coefficients. The formulated non-convex problem is tackled using a block coordinate descent (BCD) algorithm combined with successive convex approximation (SCA) and semi-definite relaxation (SDR) methods. The proposed framework is compared with other schemes including the linear detection technique, highlighting the trade-off between performance and computational complexity. The study provides insights into the impact of key parameters, such as the reader’s transmit power and the number of IRS elements, on system performance. Furthermore, our findings lay the groundwork for future exploration of more effective transmission approaches in IRS-enhanced V2I backscatter communication networks.

This paper proposes a hardware-software co-design approach to efficiently optimize beamforming and port selection in fluid antenna systems (FASs). To begin with, a fluid-antenna (FA)-enabled downlink multi-cell multiple-input multiple-output (MIMO) network is modeled, and a weighted sum-rate (WSR) maximization problem is formulated. Second, a method that integrates graph neural networks (GNNs) with random port selection (RPS) is proposed to jointly optimize beamforming and port selection, while also assessing the benefits and limitations of random selection. Third, an instruction-driven deep learning accelerator based on a field-programmable gate array (FPGA) is developed to minimize inference latency. To further enhance efficiency, a scheduling algorithm is introduced to reduce redundant computations and minimize the idle time of computing cores. Simulation results demonstrate that the proposed GNN-RPS approach achieves competitive communication performance. Furthermore, experimental evaluations indicate that the FPGA-based accelerator maintains low latency while simultaneously executing beamforming inference for multiple port selections.

Reconfigurable holographic surface (RHS) exhibits low energy consumption and low cost, which attracts extensive attention. Since unmanned aerial vehicle (UAV) is limited by power and volume, holographic beam is suitable for UAV Internet of things (IoT) systems. However, UAV will encounter complex airflow, causing jitter, which indicates necessary suppression method for holographic beam disturbance. To address the challenge, we explore the problem of robust rotary holographic beam, consisting of angle controlling and hybrid holographic beamforming. The problem is formulated as minimizing the sum mean squared error (SMSE) under the beam disturbance. Resolving such non-convex issues poses considerable difficulty in achieving optimality. A heuristic scheme is proposed, which decouples the angle controlling and hybrid holographic beamforming. Specifically, angle controlling aims to maximize the sum of the mathematical expectations of the disturbed holographic beamforming matrix to mitigate disturbances. Meanwhile, hybrid holographic beamforming minimizes SMSE under beam disturbances to manage multi-user interference. The suppression gain and computational complexity are analyzed theoretically to validate the effectiveness of the proposed scheme. Furthermore, simulation results show that our scheme performs better than benchmark schemes in sum-rate and bit error rate (BER).

In contrast to the intelligent reflecting surface (IRS) as a pure reflection device in the past, in this letter, from a more micro perspective, we propose to employ pseudo-random constellation rotation to secure microwave quick response (QR) code communication. Specifically, confidential information is encoded into a QR code, which is displayed on an IRS. Pseudo-random constellation rotation is applied to each element of the IRS to encrypt the QR code. This ensures that only the authenticated user (Bob) can decode and access the confidential information, while an eavesdropper (Eve) is unable to correctly interpret the QR code. We derive a closed-form expression for the bit error rate (BER) and analyze the impact of various factors on the BER for both Bob and Eve, including the distance between Bob and the IRS, Bob’s transmit power, and the Rician factor. Simulation results demonstrate the proposed scheme’s effectiveness in achieving secure communication.

This paper proposes to integrate reconfigurable intelligent surface with backscatter communication (RIS-BackCom) for downlink non-orthogonal multiple access (NOMA) networks, where a RIS serves as a backscatter device to transmit the modulated signals to multiple single-antenna target users. Building upon the established system architecture, the weighted sum rate (WSR) is maximized for all the users under the constraints of total transmit power, RIS phase shift, rate fairness, and successive interference cancellation decoding rate. By employing the techniques of Lagrangian dual transform, quadratic transform and alternative optimization strategies, the original optimization problem is decomposed into three tractable sub-problems. Then, these sub-problems are effectively addressed using successive convex approximation and semidefinite relaxation methodologies. Experimental results demonstrate the feasibility and superiority of the proposed RIS-BackCom aided NOMA system.

Dual-functional radar and communication (DFRC) system is capable of sensing potential eavesdroppers close to the DFRC base station (BS) and further ensuring secure transmission using physical layer security technologies based on the obtained location information of eavesdroppers. However, such security can be threatened by a malicious intelligent reflecting surface (IRS) that simultaneously changes both radar and communication channels. To reveal this threat, an IRS-based active attack scheme is proposed in this paper under the assumption of a prevalent DFRC framework. In this scheme, the attacker, equipped with a malicious IRS, carefully controls and optimizes the IRS phase shifts in each stage of the DFRC framework to reduce its reflected radar echo power to the BS and/or strength the wiretap link gain for pilot spoofing attack according to the statistical channel state information. Numerical results show that our proposed attack scheme can significantly reduce the secrecy rate of the legitimate user, while avoid being detected by the DFRC BS.

In cell-free multiple input multiple output (MIMO) networks, multiple base stations (BSs) collaborate to achieve high spectral efficiency. Nevertheless, high penetration loss due to large blockages in harsh propagation environments is often an issue that severely degrades communication performance. Considering that intelligent reflecting surface (IRS) is capable of constructing digitally controllable reflection links in a low-cost manner, we investigate an IRS-enhanced downlink cell-free MIMO network in this paper. We aim to maximize the weighted sum rate (WSR) of all the users by jointly optimizing the transmit beamforming at the BSs and the reflection coefficients at the IRS. To address the optimization problem, we propose a fully distributed machine learning algorithm. Different from the conventional iterative optimization algorithms that require a central processing at the central processing unit (CPU) and large amount of channel state information and signaling exchange between the BSs and the CPU, in the proposed algorithm, each BS can locally design its beamforming vectors. Meanwhile, the IRS reflection coefficients are determined by one of the BSs. Simulation results show that the deployment of IRS can significantly boost the WSR and that the proposed algorithm can achieve a high WSR with a low computational complexity.

This paper proposes to leverage intelligent reflecting surface (IRS) to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT) for an integrated sensing, communication and computation system. In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick response (QR) code, rather than a familiar reflection device. Meanwhile, a dual-functional radar-communication base station (BS) is used as a scanner to collect and recognize the information on each IRS carrying the data of its associated user equipment, while detecting the radar target. Based on the established system model, partial and binary data offloading strategies are respectively considered. By defining a performance metric named weighted throughput capacity (WTC), two maximization problems of WTC are formulated. According to the coupling degree of optimization variables in the objective function and the constraints, each optimization problem is firstly decomposed into two subproblems. Then, the methods of linear programming, fractional programming, integer programming and alternative optimization are developed to solve the subproblems. The simulation results demonstrate the achievable WTC of the considered system, thereby validating RFCF-UT.

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to implement downlink communications for a multi-cell multiple-input multiple-output (MIMO) network, which represents a brand-new communication framework. In such a network, one active macro cell base station (MBS) is deployed for radiating energy signal, while each IRS acts as a small cell base station to realize its own information transmission by modulating and reflecting the signal from the MBS. Under this paradigm, we investigate two optimization problems, namely weighted sum rate maximization problem and max-min fairness problem, aiming at satisfying different communication requirements. To seek their optimal solutions, Lagrangian dual transform and alternate methods are employed to optimize the active beamforming vector at the MBS and the passive beamforming vectors at all the IRSs. Additionally, an element clustering scheme is developed to reduce computation and control complexity, in which each element cluster works like a unit and all clusters can collaborate to communicate with users. Extensive simulations are conducted to evaluate the achievable communication performance and to verify the feasibility of the proposed IRS backscatter enabled downlink multi-cell MIMO network.

This article proposes a novel intelligent reflecting surface (IRS) backscatter enhancing strategy to secure multi-input multioutput (MIMO) transmission in the presence of an eavesdropper and a malicious jammer. To be specific, the IRS is employed to backscatter the jamming signal into the desired signal to enhance the reception of the user. Utilizing this strategy, we maximize the system secrecy rate by jointly designing the reflection coefficients of IRS and active beamforming at the base station (BS). To efficiently handle this nonconvex optimization problem, we adopt an iterative block coordinate descent (BCD)-based algorithm, where the active beamforming is optimized through the Lagrange multiplier method and the backscatter coefficient matrix of IRS is optimized via the majorization-minimization (MM) method. Then, we examine the robustness of the proposed scheme when considering channel estimation errors. Extensive simulations confirm the secrecy performance gains achieved by our proposed strategy and verify its superiority compared to conventional IRS-based physical layer security (PLS) strategy, IRS backscatter-aided anti-eavesdropping strategy, and other baselines.

This paper investigates a novel framework of intelligent reflecting surface (IRS) backscatter enabled multi-tier computing system. In such a hierarchical network, the data bits of computational task requested by each user equipment (UE) are broken up into three parts, which are respectively computed at tier-1 UEs, tier-2 access points (APs) and a tier-3 central server. Distinguished from conventional active antennas, IRS backscatter at the UEs is leveraged to offload data bits to the APs. Based on the established network framework, an optimization problem is formulated, which aims at maximizing the sum computational bits of system during the considered time block by jointly optimizing the active beamforming at the power beacon, the passive beamforming at the UEs, the active beamforming at the APs, the bandwidth and power allocation among all the UEs, as well as the time of local computing. To seek the optimal solution, the optimization problem is decomposed into two, namely the maximization of stage-1 sum computational bits and the minimization of stage-2 delay. By the objective function conversion and alternative optimization methods, the two problems are addressed. Extensive simulations are performed to confirm the feasibility of the proposed system and show the achievable performance in processing computational bits.

This paper proposes an intelligent reflecting surface (IRS) backscatter based uplink coordinated transmission strategy for a radio cellular network, where IRS serves as a transmitter enabling uplink transmission from each user to the associated base station (BS). To be specific, the considered network is made up of multiple cells, each of which consists of one multi-antenna BS and its served users. While one multi-antenna power beacon (PB) is deployed to radiate energy-bearing electromagnetic wave, the radio signal received by each IRS is modulated to send its connective user’s information to the associated BS. Based on such a network framework, this paper aims to maximize the weighted sum rate (WSR) under the constraints of total transmit power and reflecting coefficient by joint optimization of active beamforming at the PB, passive beamforming at the IRSs and uplink user scheduling. To address this challenging problem, fractional programming (FP), alternative optimization and weighted bipartite matching are employed to convert the logarithm objective function into a more tractable form and to handle the optimization variables. The simulation results demonstrate the achievable WSR of the considered network.

To have a good understanding of attackers’ behaviour and seek effective defensive measures, an optimal strategy of false data injection (FDI) and denial-of-service (DoS) attacks is proposed to intrude sensors and transmission channels of cyber-physical systems (CPSs) from the perspective of an attacker. Specifically, all sensors of CPSs make measurements of the state and send them to a fusion center for state estimation. Intruders attempt to modify the measurements and block innovations’ transmission by respectively launching FDI and DoS attacks to maliciously degrade the system estimation performance. A cost function is introduced to quantify the estimation performance. In order to maximize the cost function, it is important for the attacker to decide which sensors and transmission channels to intrude due to energy constraint. A convex optimization problem is formed, by solving which the optimal attack strategy is obtained. Simulations demonstrate the optimality of our proposed attack strategy.

This paper first proposes an ambient wireless energy harvesting intelligent reflecting surface (IRS)-based backscatter communication system, where an active transmission from an access point (AP) to a primary user (PU) and IRS-based backscatter communication coexist in a certain area. Specifically, the system operation consists of two phases. In the first one, the AP communicates with the PU, while the signal energy reaching the IRS can be fed into the energy harvester of IRS. In the second one, the signal from the AP is modulated into new signal for backscatter communication. By jointly optimizing the beamformers at the AP and the IRS as well as the time scheduling of two-phase process, the throughput of backscatter communication is maximized. Simulation results verify that the proposed scheme achieves a substantially enhanced communication performance.

This article pioneers an unprecedented strategy of secure transmission, in which intelligent reflecting surface (IRS) is used as a backscatter device to form and scatter jamming signal while the transmitter (Alice) is regarded as a radio-frequency (RF) source. Specifically, Alice transmits confidential signal to a single-antenna legitimate user (Bob) while the transmission is overheard by multiple single-antenna illegitimate users (Eves). The beamformer at Alice is designed to align with the estimated channel vector from Alice to Bob, in order that the proposed strategy is completely compatible with the common communication system without respect to wiretap. To achieve secure transmission, IRS is deployed to modulate the received confidential signal to jamming signal and reflect it so as to deteriorate the reception at Eves. Based on this model, the reflection coefficient vector of IRS is optimized to minimize the eavesdropped information amount while guaranteeing the reliable communication at Bob. By comparing with the familiar IRS-based beamforming scheme and the cooperative jamming scheme in extensive simulations, the feasibility and secrecy performance gain are confirmed for the proposed strategy of IRS-based backscatter jamming.

To the best of our knowledge, this article represents the first attempt toward robust beamforming design for multiuser downlink communications in intelligent reflecting surface (IRS)-enhanced satellite (SAT) and high altitude platform (HAP) integrated network. Such network configuration is mainly composed of a single-antenna SAT, a multiple-antenna HAP, multiple single-antenna SAT and HAP terminals and an IRS. Although sharing the same spectrum resource with the SAT, the HAP suspended in the near-space intends to offer temporary higher-speed and lower-delay multiuser communication connections than the SAT. Therefore, the power budget should be carefully tuned at the HAP. Toward this end, we first formally formulate the transmit power minimization problem at the HAP under the constraints of signal-to-interference-plus-noise-ratio-outage-probability (SINR-OP) at each SAT and HAP terminal by taking into account the imperfect channel state informations and their Gaussian channel estimation errors. Note that the complicated superposition of direct SAT/HAP links and cascaded IRS links, makes the optimization problem rather challenging. Specifically, we transform the probabilistic constraints into approximate deterministic ones, by performing rank relaxation. Based on this, we are able to solve the optimization problem by alternately optimizing the two ensuring subproblems. As verified by extensive simulation results, the proposed IRS-enhanced beamforming schemes can substantially diminish the transmit power at the HAP compared to the beamforming counterparts without IRS.

This paper investigates intelligent reflecting surface based backscatter communication (IRS-BackCom), in order to realize computational task offloading of energy-constrained mobile edge computing network in a self-sustainable manner. Specifically, the system operation is divided into two phases. In the first one, the ambient signal energy from a power beacon (PB) either provides the energy supply of local computing and energy harvesting circuits, or flows into the energy storage, when reaching the IRS. In the second one, the stored energy is used to enable IRS-BackCom for partial computational data offloading and energize local computing circuit. Based on this, the maximization problem of sum computational bits is formulated. By jointly optimizing the beamforming vector at the PB, the backscatter matrix at the IRS, the time scheduling of two-phase process, as well as the time of local computing, sum computational bits are maximized. In addition, this paper proposes element clustering to realize BackCom, so as to reduce the control and computation complexity of IRS. According to different operating mechanisms, two cluster operation modes are considered, namely independent cluster operation mode and joint cluster operation mode. Simulation results demonstrate the achievable sum computational bits by the proposed IRS-BackCom schemes.

Correlation between the main and eavesdropping channels damages secrecy, the achievable secrecy rate under high correlation is small not to satisfy some high-rate applications. This paper optimizes an artificial noise (AN)-aided beamformer by employing a novel quality-of-service criterion, namely signal-to-interference-plus-noise-ratio outage-probability (SINR-OP), and seeks to maximize the target outage SINR under transmit power and SINR-OP constraints. Under Gaussian channel state information uncertainties, a robust ellipsoid-bounding-based approach is employed to approximate the probabilistic constraints; then, a suboptimal solution is obtained. The simulation results verify the proposed AN-aided beamforming achieves the high target outage SINR at the destination.

This paper studies a scheme of joint beamforming design combined with reconfigurable intelligent surface (RIS) selection to maximize the received signal-to-noise ratio (SNR) at the user. Specifically, we consider a communication system, in which multiple RISs are employed to assist the transmission from the base station (BS) to UE. To enhance the communication quality more effectively, the optimal RIS is selected based on the received SNR at UE. In this process, the joint beamforming at the BS and any RIS is optimized separately to maximize the received SNR at UE. Since the line of sight (LOS) propagation may be obstructed or suffer from severe power attenuation, this paper considers two scenarios that the direct link exists and is blocked. Simulation results show that the proposed RIS selection scheme is able to improve the received SNR more significantly than the counterparts without RIS selection.

Reconfigurable intelligent surface (RIS), as a revolutionary technique, appears to ameliorate undesirable propagation environment in a controllable manner, which has the potential to substantially boost security of private information in the future smart radio environment. Much recent attention has been directed to the RIS-enhanced secure communication, among which the terrestrial network scenarios are generally concerned, while the exploration of aerial-ground communication integrated with RIS remains an open issue. Furthermore, in the available research, the ideal assumption of line-of-sight channel for aerial-ground link cannot be exploited in complex urban environment. Inspired by this, we provide in this paper the secrecy rate maximization problem under a complex urban scenario, where the drone's high mobility and RIS's tunable capability are utilized to against the potential eavesdropper. Although the established problem is intractable to tackle, we devise an iterative algorithm combining alternating optimization and successive convex approximation method as the solution, which jointly optimizes the phase shifts, the drone's trajectory along with transmit power for the single-user scenario. The proposed designs are also extended to the multi-user multi-eavesdropper system. Eventually, extensive simulation experiments indicate the remarkable benefits brought by our proposed scheme on secrecy performance and the necessity of optimizing phase shifts.

An artificial noise (AN)-aided beamformer specific to correlated main and wiretap channels is designed in this paper. We consider slow-fading multiple-input-single-output wiretap channels with multiple passive single-antenna eavesdroppers in which an independent transmitter side and correlated receiver side are assumed. Additionally, the source has accurate main channel information and statistical wiretap channel information. To reduce the secrecy loss due to receiver-side correlation, this paper proposes a channel-correlation-enabled transmission optimization scheme. In particular, the correlation is viewed as a resource to acquire more knowledge about wiretap channels. Based on this, the statistical distribution of wiretap channels is described more precisely, and an elaborate channel-correlation-enabled AN-aided beamformer is designed. Then, the achievable secrecy rate under transmit power and secrecy outage constraints is derived. Finally, the study is also extended to a specific scenario of multiple-antenna eavesdroppers. Simulation results show that the secrecy rate under transmit power and secrecy outage constraints can be improved under high correlation.

This paper studies cooperative-jamming (CJ)-aided beamforming design under Gaussian channel uncertainties to reduce the secrecy loss due to reception correlation. In light of the difficulty of maximizing the outage-probability-constrained secrecy rate, a novel quality-of-service (QoS)-based optimization is considered. By employing the Bernstein-type inequality to approximate the probabilistic constraints, we seek to maximize the target outage signal-to-noise ratio (SNR) at the destination under transmit power and SNR outage constraints. Simulation results verify the designed CJ-aided beamforming substantially enhances the secrecy from a QoS perspective.

2-D principal component analysis (2-DPCA) is one of the successful dimensionality reduction approaches for image classification and representation. However, 2-DPCA is not robust to outliers. To tackle this problem, we present an efficient robust method, namely R1-2-DPCA for feature extraction. R1-2-DPCA aims to seek the projection matrix such that the projected data have the maximum variance, which is measured by R1-norm. Compared with most existing robust 2-DPCA methods, our model is not only robust to outliers but also helps encode discriminant information. Accordingly, we develop a nongreedy iterative algorithm, which has not only a closed-form solution in each iteration but also a good convergence, to solve our model. Moreover, to further improve classification performance, we employ nuclear norm as the distance metric in the classification phase. Extensive experiments on several face databases illustrate that our proposed method is superior to most existing robust 2-DPCA methods.

This paper proposes to employ multiple cooperative jammers to reduce the secrecy loss due to the correlation between main and wiretap channels. We consider slow-fading multiple-input single-output (MISO) wiretap channels with a passive single-antenna eavesdropper, in which an independent transmitter side and correlated receiver side are assumed. Considering that signal processing techniques as well as the blind growth of power at the source play a limited role in reducing the secrecy loss due to the receiver-side correlation, multiple cooperative jammers equipped with multiple antennas are introduced into the system. Owing to spatial diversity, the channel correlation from the different jammers to the destination and the eavesdropper varies. To interfere with the reception at the eavesdropper efficiently and consequently enhance security, some jammers in favorable channel conditions are selected to emit artificial noise (AN) with power optimization. Based on this, the secrecy outage probability is analyzed. The simulation results verify that the proposed scheme of multiple cooperative jammers provides substantial gains in terms of secrecy.

In PHY-security, high correlation between main and wiretap channels, which are frequently observed, can cause a significant loss of secrecy. Unfortunately, signal processing techniques at the transmitter (Alice), such as precoding and artificial noise (AN) techniques, are ineffective. Under this circumstance, this paper focuses on a slowly fading and reciprocal channel scenario wherein Alice sends a confidential message to an authorised receiver (Bob) with the transmission overheard by a passive unauthorised receiver (Eve), and all of them are equipped with multiple antennas. To prevent interception and ensure secrecy, we redesign a novel scheme of discriminatory channel estimation (DCE), in which training procedures are developed to limit the channel estimation performance at Eve while producing little effect on Alice and Bob. As a result, Eve's ability to obtain the channel information would deteriorate, thereby effectively increase the difference in decoding the message between Bob and Eve. Simulation results demonstrate the proposed scheme could provide substantial gains with respect to secrecy.

This paper investigates a dynamic bandwidth allocation strategy with secrecy constraints, where big data can be viewed as a resource instead of a burden from the traditional perspective. Unlike usual cases, we take into account big data and security issues along with bandwidth allocation. It is reasonable to assume that big data derived from mobile network, by a series of processing, can generate a binary set S consisting of pairs of users. According to S, a metric closeness can be redefined to describe whether the same confidential content can be shared between two users. On this basis, data driven clusters can be formed. Then two bandwidth allocation algorithms, aiming at increasing secrecy sum capacity and individual secrecy capacity by sharing content in clusters, are proposed. The fairness among users and computation complexity are considered in the first algorithm, while the objective of the second algorithm is to maximize the secrecy sum capacity. In order to validate our proposed schemes, a concise case is presented and numerical results show that a significant performance gain over both secrecy sum capacity and individual secrecy capacity is achieved.