Robust Beamforming Design for Dual-Function Radar-Communication System

We consider a multiple-input multiple-output (MIMO) dual function radar communication (DFRC) system employing frequency hopping (FH) radar waveforms. The DFRC system embeds communication symbols using code-shift keying (CSK) strategy. Each symbol is represented by a phase modulated pulse code sequence which multiplies the radar hops in fast-time before transmitted from the MIMO radar platform. This strategy allows for reduced clutter modulation caused by changed radar waveforms within coherent processing interval (CPI). We analyze the effect of this communication symbol embedding scheme on range sildelobe levels (RSLs) and derive the DFRC probability of target detection. We show that the DFRC waveforms enhances target visibility leading to improved target detection compared with the FH radar without signal embedding.

This paper focuses on the problem of reconfigurable intelligent surface (RIS) aided co-design for multiple-input-multiple-output (MIMO) radar and MIMO communication systems with shared spectrum resources. The purpose is to maximize the radar output signal-to-interference-plus-noise-ratio (SINR) while ensuring the communication functionalities, where there is no line-of-sight (NLOS) propagation between the radar and the target. To achieve the goal, we develop a cyclic framework based on semi-definite programming (SDP), semi-definite relaxation (SDR), and alternating direction method of multiplier (ADMM) to jointly optimize the radar transmit waveforms, the receive filters, the communication codebook, and the RIS coefficients. Numerical experiments are conducted to evaluate the effectiveness of the proposed algorithm.

Dual-function radar communication (DFRC) systems can provide ubiquitous communication and sensing services on a unified platform. In environmental and drone monitoring applications, the DFRC system is often expected to monitor the target area while providing communication services. However, traditional DFRC systems are mainly based on multiple-input multiple-output (MIMO) arrays, which can only illuminate the whole specific angles, rather than a specific area. To address this, based on frequency diverse array MIMO (FDA-MIMO), this paper proposes an area monitoring and multi-user communication dual-function system. We formulate a weighted optimization problem that jointly considers the integrated mainlobe-to-sidelobe ratio (IMSR) and the communication sum rate. To solve this non-convex problem, a Riemannian manifold alternating optimization (RMAO) algorithm is developed. Specifically, the receive beamformer is derived in closed-form, and the transmit precoder is optimized via Riemannian conjugate gradient method. Numerical results illustrate that the proposed system can realize the angle–range selective area surveillance while providing communication service, which is not available in MIMO-based systems.

This paper addresses joint design of the transmitting waveform and the receivers of a dual-function radar-communication (DFRC) system that enables both multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communications. The proposed approach incorporates the design of the communication receiving (Rx) coefficients, in addition to the radar Rx filters. We seek to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) over multiple targets, subject to per-antenna power constraints, peak-to-average-power ratio (PAPR) constraints and a communication SINR constraint for each user. A successive convex approximation algorithm is developed to find a good solution for the resultant nonconvex design problem. Numerical results show that by incorporating the communication Rx coefficients into the joint design, the radar and communication capabilities of the DFRC system can be significantly enhanced over the state-of-the-art designs.

Recently, robust transmit beamforming has drawn considerable attention because it can provide guaranteed receiver performance in the presence of channel state information (CSI) errors. Assuming complex Gaussian distributed CSI errors, this paper investigates the robust beamforming design problem that minimizes the transmission power subject to probabilistic signal-to-interference-plus-noise ratio (SINR) constraints. The probabilistic SINR constraints in general have no closed-form expression and are difficult to handle. Based on a Bernstein-type inequality for quadratic forms of complex Gaussian random variables, we propose a conservative formulation to the robust single-cell beamforming design problem. The semidefinite relaxation technique can be applied to efficiently handle the proposed conservative formulation. Simulation results show that, in comparison with existing methods, the proposed method is more power efficient and is able to support higher target SINR values for receivers.

Consider an optimal design problem of robust adaptive beamforming of minimum variance distortionless response, based on estimation of steering vector. The optimal beamformer is obtained by computing the sample matrix inverse and an optimal estimate of the desired signal steering vector. The common criteria to select a best estimate include high performance in terms of output signal-to-noise-plus-interference ratio (SINR) and array output power, but less prior knowledge. However there is often a trade-off between the two criteria. Herein, in order to find an optimal steering vector estimate, a beamformer output power maximization problem is formulated subject to a double-sided norm perturbation constraint, a similarity constraint, and a quadratic constraint to guarantee the desired source direction-of-arrival (DOA) to get away from the DOA region of all linear combinations of the interference steering vectors. In the new robust design, all the prior information includes some allowable error norm bounds, besides the antenna array geometry and angular sector of the desired signal. It turns out that the optimization problem is a non-convex quadratically constrained quadratic program with inhomogeneous constraints, but we show that it is still solvable, and propose how to efficiently get an optimal estimate, via a semidefinite programming relaxation technique. To validate our results, simulation examples are presented to demonstrate the improved performance of the new robust beamformer in terms of the output SINR as well as the output power.

Joint design of the transmit and receive weights for coherent FDA radar

This paper considers coordinated multicast beamforming in a multi-cell\nmultigroup multiple-input single-output system. Each base station (BS) serves\nmultiple groups of users by forming a single beam with common information per\ngroup. We propose centralized and distributed beamforming algorithms for two\ndifferent optimization targets. The first objective is to minimize the total\ntransmission power of all the BSs while guaranteeing the user-specific minimum\nquality-of-service targets. The semidefinite relaxation (SDR) method is used to\napproximate the non-convex multicast problem as a semidefinite program (SDP),\nwhich is solvable via centralized processing. Subsequently, two alternative\ndistributed methods are proposed. The first approach turns the SDP into a\ntwo-level optimization via primal decomposition. At the higher level,\ninter-cell interference powers are optimized for fixed beamformers while the\nlower level locally optimizes the beamformers by minimizing BS-specific\ntransmit powers for the given inter-cell interference constraints. The second\ndistributed solution is enabled via an alternating direction method of\nmultipliers, where the inter-cell interference optimization is divided into a\nlocal and a global optimization by forcing the equality via consistency\nconstraints. We further propose a centralized and a simple distributed\nbeamforming design for the signal-to-interference-plus-noise ratio (SINR)\nbalancing problem in which the minimum SINR among the users is maximized with\ngiven per-BS power constraints. This problem is solved via the bisection method\nas a series of SDP feasibility problems. The simulation results show the\nsuperiority of the proposed coordinated beamforming algorithms over traditional\nnon-coordinated transmission schemes, and illustrate the fast convergence of\nthe distributed methods.\n

We present a dual-function radar communication (DFRC) system applying signal embedding through a code shift keying (CSK) strategy. The information symbol is phase encoded into a sequence and then modulated by the continuous phase modulation (CPM) before embedding into frequency hopping (FH) radar sub-pulses. The CPM modulated communication symbol phase coded sequence is multiplied with the FH radar waveforms in fast-time and transmitted through multiple-input multiple-output (MIMO) radar platforms. We address the problem of range sidelobe modulation (RSM) and propose the design of the optimum CSK sequences to reduce the degree of change in the range sidelobe levels (RSLs) of the ambiguity function (AF). It is shown that the optimum CSK sequences yield significant reduction in the RSLs of the DFRC system. They also maintain the same degree of correlation between FH sub-pulses regardless of the communication symbol sequences embedded. Additionally, the proposed DFRC system with the optimized waveform design provides good spectrum containment and achieves high communication data rates.

Power resource allocation scheme for distributed MIMO dual-function radar-communication system based on low probability of intercept

In this letter, we consider secrecy communications over multiple-input single-output $K$ -user interference channel in the presence of multiple eavesdroppers. Each legitimate data link is wiretapped by several eavesdroppers. To keep the eavesdroppers ignorant of the confidential messages, we propose a co-ordinated secure beamforming scheme for maximizing the secrecy sum-rate subject to the individual power constraint of each transmitter. To solve the resultant nonconvex problem, we approximate it into a few semidefinite programs (SDPs) by employing the semidefinite relaxation (SDR) technique and a conservative convex approximation. It is demonstrated experimentally that the proposed algorithm converges within a few iterations. Furthermore, we prove that the SDP problem obtained via SDR always yields a rank-one solution such that the SDR is tight. Simulations show the efficacy of the proposed scheme.

In this paper, we consider a new design problem of optimizing a linear transceiver for correlated multiple-input multiple-output (MIMO) interference channels in the presence of channel state information (CSI) errors, which is a more realistic and practical scenario than those considered in the previous studies on uncorrelated MIMO interference channels. By taking CSI errors into account, the optimization problem is initially formulated to minimize the average mean square error (MSE) under the general power constraints. Since the objective function is not jointly convex in precoders and receive filters, we split the original problem into two convex subproblems, and then linear precoders and receive filters are obtained by solving two subproblems iteratively. It is shown that the proposed algorithm is guaranteed to converge to a local minimum. The numerical results show that the proposed algorithm can significantly reduce the sensitivity to CSI errors compared with the existing robust schemes in the correlated MIMO interference channel.

Integrated sensing and communication (IS-AC) is an appealing approach to address spectrum congestion and beamforming is an effective method to realize ISAC. In this paper, we investigate the beamforming design problem for multiple-input multiple-output (MIMO) ISAC systems and propose to maximize the radar beampattern gain of the target direction while ensuring the signal-to-interference-plus-noise ratio (SINR) constraints of communication users. Particularly, we discuss two cases of ISAC transmit beamforming, i.e., Case-I and Case-II, which do not have and do have the dedicated probing signal, respectively. For these two cases of transmit beamforming design problems, we start from the single-user scenario and provide the closed-form solutions for MIMO ISAC beamforming vectors. Then, we consider the multiuser scenario and utilize the semidefinite relaxation technique to convert the beamforming design problems into convex semidefinite programming problems. Furthermore, we investigate the impact of the channel correlation between radar and communication on the performance gain of MIMO ISAC systems and characterize the performance tradeoff. Numerical results validate that the dedicated probing signal is unnecessary in the single-user scenario, whereas it has a slight improvement in target detection performance at low SINR thresholds in the multi-user scenario. It is also shown that the stronger the correlation between radar and communication channels, the greater the performance gain of the system.

In this paper, we study simultaneous data transmission and power transfer for multiple information receivers (IRs) and energy-harvesting receivers (ERs) in cellular networks. We propose an optimization problem to maximize a linear combination of the useful signal power at each IR and total received power at each ER subject to the following three sets of constraints: i) data reliability by maintaining the required level of signal to interference plus noise ratio (SINR) for all IRs; ii) information security by keeping leakage SINR levels of all IRs at every ER below a predefined value, which helps prevent possible eavesdroppers, i.e., ERs, from detecting information aimed for the IRs; and iii) power consumption by guaranteeing that the transmit power at each base station is within its available budget. Using semidefinite relaxation (SDR) technique, we then transform the proposed problem into a convex form. We further prove that the transformed problem always yields rank-one optimal solution. Hence, our approach does not require any computationally expensive randomization procedure, as usually utilized in a typical SDR method, in obtaining optimal solutions. Simulation results indicate that the proposed approach prevails conventional scheme in terms of providing higher IRs' total throughput, higher received power level at each ER and more secure for the transmission of information.

In downlink multiple streams multiple-input multiple-output (MIMO) system, adaptive modulation and coding (AMC) is used to increase spectrum efficiency (SE) due to selecting the modulation and coding rate scheme (MCS) dynamically. However, there is a gap between UE's signal to interference plus noise ratio (SINR) and that required by the MCS. In this paper, we propose a novel power loading algorithm using the SINR gap. The algorithm converts the SINR gap into the transmit power gap among different streams. Thus BS adjusts transmit power allocation for different streams and selects higher MCS than that without adjusting power. The results demonstrate that this proposed algorithm contributes large gain of SE for single user (SU-) MIMO based on singular value decomposition (SVD) preceding.