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Register a new userMIMO OFDM Dual-Function Radar-Communication Under Error Rate and Beampattern Constraints
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In this work we consider a multiple-input multiple-output (MIMO) dual-function radar-communication (DFRC) system that employs an orthogonal frequency division multiplexing (OFDM) and a differential phase shift keying (DPSK) modulation, and study the design of the radiated waveforms and of the receive filters employed by the radar and the users. The approach is communication-centric, in the sense that a radar-oriented objective is optimized under constraints on the average transmit power, the power leakage towards specific directions, and the error rate of each user, thus safeguarding the communication quality of service (QoS). We adopt a unified design approach allowing a broad family of radar objectives, including both estimation- and detection-oriented merit functions. We devise a suboptimal solution based on alternating optimization of the involved variables, a convex restriction of the feasible search set, and minorization-maximization, offering a single algorithm for all of the radar merit functions in the considered family. Finally, the performance is inspected through numerical examples.
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In this study, we analyze index modulation (IM) based on circularly-shifted chirps (CSCs) for dual-function radar & communication (DFRC) systems. We develop a maximum likelihood (ML) range estimator that considers multiple scatters. To improve the correlation properties of the transmitted waveform and estimation accuracy, we propose index separation (IS) which separates the CSCs apart in time. We theoretically show that the separation can be large under certain conditions without losing the spectral efficiency (SE). Our numerical results show that the IS combined ML and linear minimum mean square error (LMMSE)-based estimators can provide approximately 3 dB signal-to-noise ratio (SNR) gain in some cases while improving estimation accuracy substantially without causing any bit-error ratio (BER) degradation at the communication receiver.
A novel dual-function radar communication (DFRC) system is proposed, that achieves high target resolution and high communication rate. It consists of a multiple-input multiple-output (MIMO) radar, where only a small number of antennas are active in each channel use. The probing waveforms are orthogonal frequency division multiplexing (OFDM) type. The OFDM carriers are divided into two groups, one that is used by the active antennas in a shared fashion, and another one, where each subcarrier is assigned to an active antenna in an exclusive fashion (private subcarriers). Target estimation is carried out based on the received and transmitted symbols. The system communicates information via the transmitted OFDM data symbols and the pattern of active antennas in a generalized spatial modulation (GSM) fashion. A multi-antenna communication receiver can identify the indices of active antennas via sparse signal recovery methods. The use of shared subcarriers enables high communication rate. The private subcarriers are used to synthesize a virtual array for high angular resolution, and also for improved estimation on the active antenna indices. The OFDM waveforms allow the communication receiver to easily mitigate the effect of frequency selective fading, while the use of a sparse array at the transmitter reduces the hardware cost of the system. The radar performance of the proposed DFRC system is evaluated via simulations, and bit error rate (BER) results for the communication system are provided.
Inter-carrier interference (ICI) poses a significant challenge for OFDM joint radar-communications (JRC) systems in high-mobility scenarios. In this paper, we propose a novel ICI-aware sensing algorithm for MIMO-OFDM JRC systems to detect the presence of multiple targets and estimate their delay-Doppler-angle parameters. First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. To account for the presence of multiple targets at a given angle, we devise an iterative interference cancellation based orthogonal matching pursuit (OMP) procedure, where at each iteration the generalized likelihood ratio test (GLRT) detector is employed to form decision statistics, providing as by-products the maximum likelihood estimates (MLEs) of radar channels and CFOs. In the final step, target detection is performed in delay-Doppler domain using target-specific, ICI-decontaminated channel estimates over time and frequency, where CFO estimates are utilized to resolve Doppler ambiguities, thereby turning ICI from foe to friend. The proposed algorithm can further exploit the ICI effect to introduce an additional dimension (namely, CFO) for target resolvability, which enables resolving targets located at the same delay-Doppler-angle cell. Simulation results illustrate the ICI exploitation capability of the proposed approach and showcase its superior detection and estimation performance in high-mobility scenarios over conventional methods.
A novel multiple-input multiple-output (MIMO) dual-function radar communication (DFRC) system is proposed. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms using a small subset of the available antennas in each channel use. The proposed system assigns most carriers to antennas in a shared fashion, thus efficiently exploiting the available communication bandwidth, and a small set of subcarriers to active antennas in an exclusive fashion (private subcarriers). A novel target estimation approach is proposed to overcome the coupling of target parameters introduced by subcarrier sharing. The obtained parameters are further refined via an iterative approach, which formulates a sparse signal recovery problem based on the data of the private subcarriers. The system is endowed with beamforming capability, via waveform precoding and antenna selection. The precoding and antenna selection matrices are optimally co-designed to meet a joint sensing-communication system performance. The sparsity of the transmit array is exploited at the communication receiver to recover the transmitted information. The use of shared subcarriers enables high communication rate, while the sparse transmit array maintains low system hardware cost. The sensing problem is formulated by taking into account frequency selective fading, and a method is proposed to estimate the channel coefficients during the sensing process. The functionality of the proposed system is demonstrated via simulations.
Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) techniques, has emerged as a powerful strategy for multi-user multi-antenna systems. In this paper, RSMA is introduced as a unified multiple access for multi-antenna radar-communication (RadCom) system, where the base station has a dual communication and radar capability to simultaneously communicate with downlink users and probe detection signals to azimuth angles of interests. Using RS, messages are split into common and private parts, then encoded into common and private streams before being precoded and transmitted. We design the message split and the precoders for this RadCom system such that the Weighted Sum Rate (WSR) is maximized and the transmit beampattern is approximated to the desired radar beampattern under an average transmit power constraint at each antenna. We then propose a framework based on Alternating Direction Method of Multipliers (ADMM) to solve the complicated non-convex optimization problem. Results highlight the benefits of RSMA to unify RadCom transmissions and to manage the interference among radar and communications, over the conventional Space-Division Multiple Access (SDMA) technique.
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