No abstract

No abstract

In this paper, exploring multicarrier waveforms, we investigate the performances of coexistence between radar and communication systems. Performances are measured in terms of radar mutual information (RMI) for the radar system and communication data rate (CDR) for the communication system. Two joint design schemes are proposed based on isolation and sharing, respectively. Firstly, for the isolationbased scheme, radar and communication signals are transmitted/received on the isolated bands. Two independent problems of RMI/CDR maximization are proposed and the optimal power allocation solutions can be characterized using Karush-Kuhn-Tucker (KKT) optimality conditions. This scheme undertakes the advantage of interference avoidance and easy implementation. Secondly, for the sharing-based scheme, radar and communication signals can be transmitted/received on the same band and the RMI/CDR maximization problems are jointly solved. The optimal power allocation solutions are optimized by the proposed sequential optimization algorithm. Compared with the isolation-based scheme, this scheme is more flexible and brings additional gain on performance at the price of higher complexity. Finally, simulation results are provided to analyze and discuss the performance and validate the theoretical results.INDEX TERMS Radar/communication co-existence, mutual information, power allocation, multicarrier system, sequential optimization. II. SYSTEM AND SIGNAL MODELS A. SYSTEM MODELAs shown in Fig. 1, we consider such a co-existence scenario where both radar and communication systems are deployed in neighborhood and multicarrier waveforms are adopted for both systems. For the considered co-existence scenario, from the radar system's point of view, transmitter/receiver (TX/RX) can receive radar echoes scattered from target as well as communication signals from communication TX, via two links: a link scattering off target h rr and a direct link between communication TX and radar RX h cr . From the communication system's point of view, RX can receive communication and radar signals via two direct links: a link between communication TX and RX h cc and a link between radar TX and communication RX h rc . Further, it is assumed that the channels are stationary over the observation period.As shown in Fig. 2, for the problem of co-existence between radar and communication systems, we propose two

No abstract

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in dual-function radar-communication (DFRC) systems, where radar and communications are performed simultaneously with a common signal. However, with random communication symbols (CS) in DFRC, the transmit signal has a random ambiguity function that affects the radar's range-velocity estimation performance, whose influence is remained uncovered. Hence, this paper focuses on minimizing the outlier probability (OP) -the probability of incorrectly estimating a target's range-velocity bin -in OFDM DFRC w.r.t the CS probability distribution (i.e., the input distribution). Conditioned on the CSs, the OP only depends on the CS magnitudes. Hence, we consider the following two schemes for the above optimization: CSs with (1) constant magnitude (phase shift keying input), and (2) random magnitude (Gaussian input). For (1), the problem reduces to the familiar power allocation design across OFDM's subcarriers and symbols, with uniform power allocation across subcarriers and a windowed power allocation across symbols being near-optimal. For (2), the mean and variance of the Gaussian distribution at each subcarrier is optimized, with an additional communication constraint to avoid the zero-variance solution where no CSs are carried. We observe that subcarriers with strong communication channels feature strong variance (i.e., favour communications) while the others are characterized by a strong mean (favouring radar). However, the overall power allocation (i.e., the sum of mean and variance) across the OFDM subcarriers and symbols is similar to (1). Simulations show that CSs with random magnitudes degrade the sensing performance, but can be compensated significantly with the proposed input distribution optimization, which highlights the importance of considering the effect of CSs' random magnitudes on DFRC signal design.