Dual-functional radar-communication (DFRC) technique has been viewed as a promising component in the emerging platforms. When synthesizing the desired transmit beampattern, the degrees of freedom of waveform design is limited, which introduces high multi-user interference (MUI), thus degrading the communication performance. Inspired by the applications of the Reconfigurable Intelligent Surface (RIS) in mitigating MUI, in this paper, we investigate joint waveform design and passive beamforming in RIS-assisted DFRC system. We first study the minimization of MUI under the strict beampattern constraint by jointly optimizing DFRC waveform and RIS phase shift matrix. To deal with the coupled variables, we propose an alternating algorithm based on manifold optimization. Subsequently, the trade-off between radar and communication performances is investigated. Simulation results show that for both cases of strict beampattern and trade-off design, with the help of RIS, the system throughput can be significantly improved. Moreover, compared with the scenario where no RIS is employed, the obtained beampattern matches with the target transmit beampattern better.

Reconfigurable intelligent surface (RIS) has been considered in wireless communication systems to improve spectral efficiency. This letter addresses the spectrum sharing problem between multiple-input-multiple-output (MIMO) radar and multi-user MISO communication systems, where RIS is exploited to deal with the interference imposed by the base station (BS). We aim to maximize the radar detection probability by jointly optimizing the BS transmit beamformer and RIS phase shift matrix, subject to the users’ signal-to-interference-plus-noise-ratio and power consumption constraints. Due to the coupled variables, the formulated problem is challenging to solve; hence, we convert it into a better tractable form and propose an alternating algorithm to handle the resulting problem. The simulation results validate the effectiveness of RIS in controlling interference and improving radar detection probability.

In this paper, we propose a secure multi-user uplink communicaion scheme against a mobile aerial eavesdropper (AE) in Integrated Sensing and Communication (ISAC) systems, where the ISAC base station transmits radar signals to track and jam the AE. For tracking mobile AE, we adopt an extended Kalman filter technique to predict AE's motion state. Based on the tracking information we formulate an optimization problem to jointly design radar signal and receiver beamformer for improving the secrecy performance. To deal with the non-convex constraints and the coupled variables, we propose an alternating optimization based algorithm via iteratively solving a Fractional Programming (FP) problem and a Semidefinite Program (SDP) problem. Numerical results show that the proposed algorithm can achieve higher secrecy capacity and better fairness, compared to the state-of-the-art techniques.

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.

Hybrid precoding architectures are widely studied for millimeter wave (mmWave) massive MIMO systems. A major challenge in designing hybrid precoders is the practical constraints on the number of RF chains, which can have a direct impact on the spectral and energy efficiencies of the communication systems. In this paper, we investigate tradeoff between the two performance metrics in both static and mobile communication scenarios via closed-form expressions, when the number of active RF chains can be selected. Based on these expressions, the computational complexity to configure the hybrid precoder is reduced, which can be used to adaptively activate required RF chains for the given MIMO system and channel condition. Numerical results indicate that a certain number of RF chains should be activated in order to maximize energy efficiency at high SNRs, which is generally different from the optimal configuration to maximize spectral efficiency. Furthermore, for low SNRs, we have shown that a simple analog beamforming, which uses only a single RF chain, is optimal for both spectral and energy efficiencies. In addition, the proposed mobility-aware hybrid precoding is shown to be capable of effectively achieving beamforming gain between high-speed mobile devices.