Optimal Transmit Beamforming for Integrated Sensing and Communication

Abstract

This paper studies the transmit beamforming in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) equipped with a uniform linear array (ULA) sends combined information-bearing and dedicated radar signals to simultaneously perform downlink multiuser communication and radar target sensing. Under this setup, we maximize the radar sensing performance (in terms of minimizing the beampattern matching errors or maximizing the minimum beampattern gains), subject to the communication users' minimum individual signal-to-interference-plus-noise ratio (SINR) requirements and the BS's maximum transmit power constraints. In particular, we consider two types of communication receivers, namely Type-I and Type-II receivers, which do not have and do have the capability of cancelling the interference from the a-priori known dedicated radar signals, respectively. Under both Type-I and Type-II communication receivers, the beampattern matching and minimum beampattern gain maximization problems are non-convex and thus difficult to be optimally solved in general. Fortunately, via applying the semidefinite relaxation (SDR) technique, we obtain the globally optimal solutions by rigorously proving the tightness of SDR for both Type-I and Type-II receivers under the two design criteria. It is shown that at the optimality, dedicated radar signals are generally not required with Type-I communication receivers under some specific conditions, while dedicated radar signals are always needed to enhance the performance with Type-II communication receivers. Numerical results show that the minimum beampattern gain maximization leads to significantly higher beampattern gains at the worst-case sensing angles, and also has a much lower computational complexity than the beampattern matching design. It is also shown that by exploiting the capability of Part of this paper has been submitted to IEEE Global Communications Conference (GLOBECOM),

NOMA Inspired Integrated Sensing and Communication

“…The authors of [19] also employed the dedicated sensing signals to generate the full-rank transmit covariance matrix, which is exploited to estimate the parameters of the extended target via the maximum likelihood estimation. In [20], the optimal transmit beamforming design is studied, where it is proved and demonstrated that cancelling the interference from sensing signal to communication can significantly improve the ISAC performance. As a further advance, the application of the dirty paper coding (DPC) for interference management of communication and sensing signals was investigated in [21].…”

“…Compared to the system which merely employs the communication signals for achieving the two functions, it has been shown that additional dedicated sensing signals are generally essential to better facilitate sensing in ISAC [18]- [20]. This, however, also causes significant challenges in how to coordinate the sensing signals for supporting communications.…”

“…This, however, also causes significant challenges in how to coordinate the sensing signals for supporting communications. In the existing ISAC frameworks, the sensing signal is regarded as harmful interference to communication, which is mitigated through the beamforming design [18], [19] or the ideal interference-cancellation receiver [20]. To the best of the authors' knowledge, the potentials of exploiting the sensing signal to further convey information for benefiting the communication have not been investigated.…”

“…• Ideal sensing interference cancellation (Ideal-SenIC) ISAC [20]: The communication receiver is assumed to be capable of ideally removing the sensing interference from y k . In this case, p = 0.…”

“…The resultant MOOP can also be solved by the proposed Algorithm 1. Throughput (bit/s/Hz) NOMA-inspired ISAC Ideal-SenIC ISAC [20] No-SenIC ISAC [18], [19] NOMA-SenIC ISAC [1] Com ISAC [15] 50 100 150 200 250 300 Beampattern NOMA-inspired ISAC Ideal-SenIC ISAC [20] No-SenIC ISAC [18], [19] NOMA-SenIC ISAC [1] Com ISAC [15] - 2) Trade-off Region: In Fig. 3, the trade-off region between the communication throughput and the beampattern matching error is studied, which is obtained by averaging over 50 random channel realizations for each scheme.…”