Joint Transmit and Receive Beamforming Design for Integrated Sensing and Communication

Passive wireless devices have increasing civilian and military applications, especially in the scenario with wearable devices and Internet of Things.In this paper, we study indoor localization of a target equipped with radio-frequency identification (RFID) device in ultra-wideband (UWB) wireless networks.With known room layout, deterministic multipath components, including the line-of-sight (LOS) signal and the reflected signals via multipath propagation, are employed to locate the target with one transmitter and a single inaccurate receiver.A factor graph corresponding to the joint posterior position distribution of target and receiver is constructed.However, due to the mixed distribution in the factor node of likelihood function, the expressions of messages are intractable by directly applying belief propagation on factor graph.To this end, we approximate the messages by Gaussian distribution via minimizing the Kullback-Leibler divergence (KLD) between them.Accordingly, a parametric message passing algorithm for indoor passive localization is derived, in which only the means and variances of Gaussian distributions have to be updated.Performance of the proposed algorithm and the impact of critical parameters are evaluated by Monte Carlo simulations, which demonstrate the superior performance in localization accuracy and the robustness to the statistics of multipath channels.

Stackelberg game based relay selection for physical layer security and energy efficiency enhancement in cognitive radio networks

The time reversal (TR) technique combined with the ultra-wideband (UWB) system offers a new potential for decreasing the cost and complexity of the UWB receivers. In spite of TR–UWB's good performance in perfect channel state information (CSI), it is very sensitive to the channel estimation error. The effect of channel imperfection on the TR–UWB system is considered in this paper. At first, based on a minimum mean square error (MMSE) equalizer receiver, a prefilter is calculated in closed form to improve the performance of the TR–UWB system in an imperfect CSI scenario. Furthermore, for comparison purposes, a similar calculation for prefilter is carried out based on a simple matched filter (MF) receiver. Then, in order to improve the MF receiver performance, a two-stage iteration-based algorithm is developed. The initial value for this iteration-based improved algorithm is considered to be a prefilter which is calculated in the TR–UWB system with MMSE equalizer. This optimized algorithm causes the channel estimation error in the TR–UWB system to become zero in some steps. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the improved algorithm.

In this paper, we investigate the design of relay processing for arbitrary unicast transmission over a multi-way relay channel, where each user employing a single antenna communicates with the other user via a relay station having multiple antennas. The proposed relay processing approach is based on the idea of network coding and the minimum mean-square-error (MMSE) based optimization with relay power constraints. Moreover, iterative algorithms to solve the optimization problem are proposed under the scenarios of both perfect and imperfect channel state information (CSI) at the relay station. Numerical simulation results show that the proposed relay processing algorithms can outperform existing schemes under the perfect CSI assumption, and that the worst-case sum mean-square-error (MSE) could be reduced effectively under the imperfect CSI scenario.

A cooperative bidirectional relaying scheme based on distributed relay beamforming is proposed for an underlay cognitive radio network which consists of two secondary transceivers and K cognitive relay nodes and a primary network with a transmitter and receiver, all equipped with single antenna. For this network we assume that the second-order statistics of the channel coefficients between the relays and primary network is available. Also the perfect channel state information (CSI) is assumed for secondary network. Here, we propose a signal-to-interference plus noise-ratio (SINR) Balancing based on distributed relay beamforming. Our aim is to design the beamforming coefficients to maximize quality of service (QoS) for the secondary network while satisfying tolerable interference constraint for the primary network. Using semidefinite relaxation we turned this problem into convex feasibility problem which can be solved along with bisection search method. Our simulation results show that the uncertainty about the channel coefficients can be degrade the performance of the network considerably. Also as we increased the tolerable interference on primary receiver, the maximum achievable SINR is increased.

In this paper, a two-hop decode-and-forward cognitive radio system with deployed interference alignment is considered. The relay node is energy-constrained and scavenges the energy from the interference signals. In the literature, there are two main energy harvesting protocols, namely, time-switching relaying and power-splitting relaying. We first demonstrate how to design the beamforming matrices for the considered primary and secondary networks. Then, the system capacity under perfect and imperfect channel state information scenarios, considering different portions of time-switching and power-splitting protocols, is estimated.

We consider the downlink of a multiple-input multiple-output broadcast channel where the transmitter uses per user unitary and rate control (PU2RC) to perform the user scheduling and beamforming. The main shortcoming of the PU2RC scheme is in networks where the number of users is less than or comparable with the codebook size. In these networks, PU2RC cannot properly exploit the available channel dimensions. To deal with this problem, they propose two different approaches to enhance the throughput performance of the system. In the first approach, they try to find and assign unscheduled users to the unused channel dimensions. This is done through a signal-to-interference plus noise ratio (SINR) estimation method in which they approximate the SINR of the users along new beamforming vectors. In this approach, they usually achieve the maximum multiplexing order of the system. In the second approach, they adjust the SINR feedbacks according to the number of scheduled users. When the number of scheduled users is less than the number of transmitting antennas, the feedback SINRs are in fact underestimated. In this case, they correct the SINRs and improve the average throughput. The simulation results show that both proposed methods can effectively improve the performance of the PU2RC scheme.

In this paper, we investigate a bistatic integrated sensing and communications (ISAC) system, consisting of a base station (BS) with multiple transmit antennas, a sensing receiver with multiple receive antennas, a single-antenna communication user (CU), and a point target to be sensed. Specifically, the BS transmits a superposition of Gaussian information and deterministic sensing signals to support ISAC. The BS aims to deliver information symbols to the CU, while the sensing receiver aims to estimate the target’s direction-of-arrival (DoA) with respect to the sensing receiver by processing the echo signals reflected by the target. For the sensing receiver, we assume that only the sequences of the deterministic sensing signals and the covariance matrix of the information signals are perfectly known, whereas the specific realizations of the information signals remain unavailable. Under this setup, we first derive the corresponding Cram´er-Rao bounds (CRBs) for DoA estimation and propose practical estimators to accurately estimate the target’s DoA. Subsequently, we formulate the transmit beamforming design as an optimization problem aiming to minimize the CRB, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the CU and a maximum transmit power constraint at the BS. When the BS employs only Gaussian information signals, the resulting beamforming optimization problem is convex, enabling the derivation of an optimal solution. In contrast, when both Gaussian information and deterministic sensing signals are transmitted, the resulting problem is non-convex and a locally optimal solution is acquired by exploiting successive convex approximation (SCA). Finally, numerical results demonstrate that the utilization of additional deterministic sensing signals is critical for sensing performance enhancement, while solely employing Gaussian information signals leads to a notable performance degradation for target sensing. It is unveiled that the proposed transmit beamforming design achieves a superior ISAC performance boundary compared with various benchmark schemes.

We propose an adaptive transmit and receive beam forming (TRBF) scheme for Time Division Duplex Multiple Input and Multiple Output (TDD-MIMO) system. Through analyzing the anti-jamming mechanism and deficiency from the point of subspace projection, we solve the optimization problem of designing TRBF and give an adaptive scheme. Firstly, the receiver determines receive beam former according to the legitimate channel matrix and interference null space. Secondly, the receiver uses the receive beam former as the transmit beam former to send training sequences to the transmitter for estimating the receive space. Finally, the transmitter determines the transmit beam former to correct the transmit power pointing to the receive space for avoiding power leakage. Since all of the transmit power reaches receiver and is collected, the receiver can get the maximum signal-to interference plus noise ratios (SINR). Simulation results show that the proposed method outperforms conventional beam forming significantly and has a similar performance to the theoretical value in the presence of repressive and deceptive interference.

Spectrum sharing between license holders (primary users) and unlicensed secondary users has been proposed as a solution to frequency wastage. Anyhow, the coexistence among these two types of users should not affect the quality of service of primary users. In this paper, we propose an interference control approach when one primary and multiple secondary users are simultaneously transmitting on the same frequency band. We first evaluate the Signal to Interference-plus-Noise Ratio (SINR) of the primary user in the presence of multiple secondary transmissions. Assuming a constraint on the primary SINR, we explicitly compute the probability that this SINR exceeds a certain threshold in the case of cohabitation. These precise probabilities are afterwards used in the transitions between different states of the Continuous Time Markov Chain that we proposed to model the system. A new model with additional outage probabilities is then built, leading to do a cross layer design of dynamic spectrum access, by considering both the traffic queuing problem and the channel outage in physical layer. Finally, some performance metrics of the system are analyzed, namely the primary throughput, the mean number of secondary users accessing the spectrum, and the secondary deprivation rate.

In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

This study considers the joint transmission precoding design for downlink multicell multiuser multiple‐input single‐output systems where imperfect channel variances are available at the base stations. The authors aim to tackle the robust signal‐to‐interference‐plus‐noise ratio (SINR) balancing problem to maximise the minimum worst‐case user rate. To solve the non‐convex problem, a duality relationship between the downlink max–min worst‐case SINR optimisation problem and the virtual uplink min–max worst‐case SINR optimisation problem is first revealed. Based on this, a new algorithm is developed to solve the virtual problem by using jointly the sub‐gradient method and the geometric programming methods, whose achieved solution is finally converted to the downlink. Their analysis shows that the proposed algorithm is guaranteed to converge and has lower computational complexity than conventional approaches. Moreover, computer simulations validate the effectiveness of the proposed method and show that the proposed algorithm has fast convergence and achieves a performance close to that of the brute search method.

In this paper, we investigate the physical layer security of multiuser multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence systems, in which the strong radar signals are exploited as inherent jamming signals to disrupt mali-cious receptions. The transmit beamformers of communication and radar systems are jointly designed to ensure the secure transmission by minimizing the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while satisfying the quality-of-service (QoS) of legitimate transmission, the requirement of radar target detection, and the transmit power constraints of radar and communication systems. An efficient fractional programming (FP) and semi-definite relaxation (SDR) based algorithm is proposed to solve the non-convex optimization problem. Simulation results verify the advancement of the proposed joint transmit beamforming on secure transmission for radar and communication coexistence systems and the effectiveness of the associate design algorithm.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

In wireless communication systems with multiple users, the near-far effect and the cochannel interferences decrease the spectral efficiency and system performance. An efficient known method to combat these destructive effects is to properly adjust the transmit powers. A practical and efficient way for power control (PC) in cellular communication systems is the autonomous distributed PC (DPC). In this method, the transmit power of each link is independently modified based on the received signal-to-interference-plus-noise ratio (SINR) reported from the other side of that link. It is known that in a perfect scenario where the reported SINRs are exact, this algorithm converges to the optimum transmit powers. However, in the case of erroneous SINR reports, the transmit powers deviate from their optimum values. This paper addresses the destructive effect of inaccurate SINR reports on the performance of a DPC algorithm. Then, independent variable step-sizes that update the transmit powers are proposed, and convergence of the proposed DPC algorithm to the optimum solution of the original problem is analytically shown. Additionally, a protocol to adaptively tune the step-sizes in our algorithm, which improves the performance of PC in the presence of inaccurate SINR reports, is proposed. Computer simulations are used to study the performance of the proposed method in terms of both the speed of converges and the steady-state accuracy of link qualities.