Phase-only beampattern synthesis is widely used in radar, communication and other fields, which manipulates only the phases of the array antenna weights to achieve the expected beampattern. This letter proposes a manifold alternating direction method of multipliers (MADMM)-based method to yield phase-only beampattern with mainlobe gain maximization and sidelobe level (SLL) control, in which the subproblem with phase-only constraint is solved by the Riemannian gradient descent algorithm. Numerical results illustrate that the proposed method can obtain desired beampattern with high mainlobe gain and suppressed sidelobe level. Compared with other representative methods, the proposed method has lower computational time when the antenna number increases.

Multi-functional antenna array has an ability to significantly simplify the antenna system in multi-functional radar or communication systems since it can work in multiple frequency bands simultaneously. To further reduce the system overhead, such as RF front-ends and digital processing channels, this paper discusses the joint design of antenna selection and receive beamforming for multi-functional antenna arrays. With the assistance of a RF switch network, K antennas selected from an N-element linear array (K < N) only require K RF frontends and digital processing channels. To maximize the output signal-to-interference-plus-noise ratio (SINR) and control the sidelobe levels concurrently, the proposed multi-functional sparse array design can be formulated into a nonconvex constrained optimization problem with a l2,0-mixed norm regularization. This problem ensures that the selected antenna positions are the same in all operating frequency bands while the beamforming weights of each frequency band are jointly optimized. In virtue of the consensus alternating direction method of multipliers (CADMM) and reweighted l2,1-norm approximation techniques, a CADMMbased iteratively reweighted algorithm is proposed to handle this problem effectively, where the original problem is converted into a series of subproblems whose optimal solutions are all readily obtained. The convergence and computational complexity are also analysed. Numerical results show that the proposed multifunctional sparse array remarkably reduces the sidelobe level in all operating frequency bands concurrently while maintains the maximum output SINR, and thus has superior performance on interference suppression.

The optical wireless communication (OWC) system has been widely studied as a promising candidate for indoor high-speed wireless communications. In particular, the spatial diversity scheme has shown to be effective to improve the performance of OWC systems. However, the delay amongst multiple channels in this scheme may result in severe inter-symbol-interference (ISI) and degradations of the system performance. Recent studies have shown that the symbol decision schemes based on recurrent neural networks (RNNs) can mitigate the impact of delays. However, their performances are limited when the channel delays are long. In this paper, we propose a delay-tolerant indoor OWC system, which utilizes an attention-augmented long-short term memory (ALSTM) RNN decision scheme to handle long channel delays. A 10 Gb/s repetition-coded indoor OWC system with the proposed ALSTM RNN decision scheme is experimentally demonstrated. Compared with traditional OWC systems which add cyclic prefix (CP) /zero-postfix (ZP) to combat the impact of channel delays, the proposed system does not reduce the effective data rate or throughput. Compared with the previous study which adopts the vanilla RNN decision scheme, the proposed ALSTM RNN decision scheme can better learn dependencies amongst received symbols with the help of attention mechanism, especially amongst non-neighboring symbols. Hence, it is more robust against the ISI induced by long channel delays. Experimental results show that compared with the previously studied vanilla RNN, over one-order-of-magnitude bit-error-rate improvement is achieved when the channel delay is more than 4.7-symbol-period.

Multiband antenna arrays have the capability of effectively working in multiple frequency bands and thus significantly simplify the antenna system. To further reduce the system overhead, this paper discusses the joint design of antenna selection and adaptive beamforming for multiband antenna arrays, where the sidelobe level is also controlled so as to alleviate the effect of unknown sporadic interference. Based on the maximum signal-to-interference-plus-noise ratio (SINR) criterion and sidelobe level constraints, the proposed multiband sparse array design is formulated into a nonconvex constrained nonlinear optimization problem with an $l_{0,2}$-mixed norm regularization. This problem ensures that the same antenna positions are selected at all operating frequencies while the beamformer weights of each frequency are optimized independently. By exploiting the semi-definite relaxation and the reweighted $l_{1,\infty }$-norm approximation, the problem is converted into a series of convex subproblems and is then effectively solved by the proposed iterative reweighted method. Numerical results show that the proposed multiband sparse array significantly reduces the sidelobe levels in all operating frequencies while maintaining the maximum SINR, so it provides superior performance of interference suppression.

On the basis of compressive sensing radar, this study presents a new method of reconstructing the moving target's high‐resolution range profile (HRRP) with low computational complexity. With regard to the spatial sparsity of the radar scene, only a few sub‐pulses of frequency‐stepped chirp signal (FSCS) are used to sample the target's frequency responses. To reduce the computational complexity of traditional compressive sensing (CS) algorithm which uses FSCS to reconstruct moving target's HRRP, a dynamically deduced sensing matrix is established based on target's velocity estimation. Besides, the orthogonality of the deduced sensing matrix is analysed from the perspective of FSCS frequency encoding types to enhance the HRRP construction accuracy using CS algorithms. Numerical simulations demonstrate that the proposed method performs better than traditional algorithm with smaller estimation error and better robustness against noise.

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.