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Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique to achieve Integrated Sensing and Communication (ISAC) for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference. RSMA relies on multi-antenna Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers, and the split and encoding of messages into common and private streams. We design the message split and the precoders of the radar sequence, common and private streams so as to jointly maximize the Weighted Sum Rate (WSR) and minimize the radar beampattern approximation Mean Square Error (MSE) subject to the per antenna power constraint. An iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed to solve the problem. Numerical results first show that RSMAassisted DFRC achieves a better tradeoff between WSR and beampattern approximation than Space-Division Multiple Access (SDMA)-assisted DFRC with or without radar sequence, and other simpler radar-communication strategies using orthogonal resources. We also show that the RSMA-assisted DFRC frameworks with and without radar sequence achieve the same tradeoff performance. This is because that the common stream is better exploited in the proposed framework. The common stream of RSMA fulfils the triple function of managing interference among communication users, managing interference between communication and radar, and beampattern approximation. Uniquely, the SIC receiver of RSMA is exploited for the dual purpose of managing interference among communication users as well as interference between communication and radar. Therefore, by enabling RSMA in DFRC, the system performance is enhanced while the system architecture is simplified since there is no need to use additional radar sequence and SIC. We conclude that RSMA is a more powerful multiple access for DFRC.

The accuracy of cooperative localization degrades significantly in the non-line-of-sight (NLOS) environments. In addition, the computational complexity of the localization problem often increases dramatically as the scale of a wireless sensor network (WSN) grows. To address these challenges, we propose a distributed NLOS cooperative localization algorithm. First, we propose a new multiplicative model based on the physical mechanism of the NLOS propagation and relax the proposed non-convex model into its convex envelope. This model has a powerful capability to mitigate the NLOS impact and remarkable robustness in changing environments. Second, we design a redundant formulation to decompose the convex problem into numerous sub-problems and then develop an efficient distributed algorithm, which enables each sensor node to locally solve each sub-problem in a parallel way, to decrease the computational complexity. The theoretical analysis and simulations show that the proposed algorithm is superior to the existing methods in both processing speed and localization accuracy.