Spectrum congestion and competition over frequency bandwidth could be alleviated by deploying dualfunction radar-communications systems, where the radar platform presents itself as a system of opportunity to secondary communication functions. In this paper, we propose a new technique for communication information embedding into the emission of multiple-input multiple-output (MIMO) radar using sparse antenna array configurations. The phases induced by antenna displacements in a sensor array are unique, which makes array configuration feasible for symbol embedding. We also exploit the fact that in a MIMO radar system, the association of independent waveforms with the transmit antennas can change over different pulse repetition periods without impacting the radar functionality. We show that by reconfiguring sparse transmit array through antenna selection and reordering waveform-antenna paring, a data rate of megabits per second can be achieved for a moderate number of transmit antennas. To counteract practical implementation issues, we propose a regularized antenna selection based signaling scheme. The possible data rate is analyzed and the symbol/bit error rates are derived. Simulation examples are provided for performance evaluations and to demonstrate the effectiveness of proposed DFRC techniques.

In this paper, we analyze the effect of non-uniform array configurations on adaptive beamforming for enhanced signal-to-interference-plus-noise ratio (SINR). The array is configured using a given number of antennas or through a selection of subset of antennas from a larger available set, leading to a sparse array in both cases. The bounds on the highest achievable SINR for a given number of antennas are formulated and used to offer new insights into open-loop adaptive beamforming. The upper and lower bounds underscore the role of array configurations in optimizing performance for interference-free and interference-active environments, respectively. This paper considers the general case of multiple sources and interferences in the field of view. We formulate three angles, namely, eigenspace angle, conventional angle, and minimum canonical angle for characterizing spatial separation between the source and interference subspaces, and as such, represents performance loss incurred by interference nulling. Three sparse array design methods, incorporating these angles, are proposed. Simulation examples confirm the role of subspace angles in optimum beamforming and validate the utility of antenna selection algorithms for sparse array design.

In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression. In order to achieve an implementable design of array reconfiguration, the RCAS is conducted in the framework of regularized antenna switching, whereby the full array aperture is collectively divided into separate groups and only one antenna in each group is switched on to connect with the processing channel. A set of deterministic complementary sparse arrays with good quiescent beampatterns is first designed by RCAS and full array data is collected by switching among them while maintaining resilient interference suppression. Subsequently, adaptive sparse array tailored for the specific environment is calculated and reconfigured based on the information extracted from the full array data. The RCAS is devised as an exclusive cardinality-constrained optimization, which is reformulated by introducing an auxiliary variable combined with a piece-wise linear function to approximate the l ₀ -norm function. A regularization formulation is proposed to solve the problem iteratively and eliminate the requirement of feasible initial search point. A rigorous theoretical analysis is conducted, which proves that the proposed algorithm is essentially an equivalent transformation of the original cardinality-constrained optimization. Simulation results validate the effectiveness of the proposed RCAS strategy.

Dual-functional radar communications (DFRC) systems, which jointly design radar and communication functionalities on one common platform, have been recognized as an attractive approach for leveraging the scarce spectral efficiently in diverse applications, such as autonomous driving. Index modulation (IM), a promising communication technique with high energy utilization and spectral efficiency, has been successfully integrated into DFRC systems. Most existing IM-based DFRC schemes utilize the degrees of freedom (DoFs) from one or two domains for separate information embedding, without utilizing the full diversity of the integrated waveform. In this paper, we propose a novel DFRC scheme, referred to as hybrid index modulation (HIM), operating on frequency hopping multiple-input multiple-output (FH-MIMO) radar. The HIM embeds communication information into the index of the 3-tuples in the hybrid dictionary, which is the outer product of DoFs from the frequency, phase, and antenna index of the integrated waveform. Since the HIM entwines waveform diversities from different domains, it increases the communication rate by multiples. We also apply the HIM to the non-orthogonal FH-MIMO radar to improve spectral efficiency, in addition to the conventional orthogonal case. Furthermore, we propose a constrained HIM scheme to enhance the radar detection performance, where the FH pattern has been optimized in terms of ambiguity function. System performance is evaluated both theoretically and experimentally in metrics of communication rate, ambiguity function and symbol error rate.

Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation for safe driving, while remaining a low cost device for feasible mass production. Sparse multiple input multiple output (MIMO) arrays emerge as a primary candidate to meet these requirements. As DOA estimation accuracy is a main indicator of tracking performance, Cramer Rao Bound (CRB) is chosen as the goodness measurement for sparse MIMO array optimization, but its application requires prior information of the road environment. We propose a cognitive sparse MIMO array automotive radar, which “perceives” the road environment via automotive sensing supplemented by co-existed communication from roadside unit to vehicle (R2V). This information is used for co-designing a sparse MIMO array for enhanced automotive sensing and vehicle to roadside unit (V2R) communication. Note that both static RSU and dynamic RSU are usually deployed in Internet of Vehicles (IoV), which can provide continuous transmission coverage and permanent connectivity. The bi-directional communications are integrated into the automotive radar. This is achieved by joint transmit waveform design with spectral nulls for communications and with shared sparse MIMO array co-design for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bi-directional communications in the cycle of cognitive-driven optimization.

In this paper, we cast thinned array beampattern synthesis as a linear inverse problem (LIP) and apply three iterative soft-thresholding based optimization algorithms to solve it effectively. We combine synthesis algorithms with beampattern error reweighting and beampattern phase adjustment to achieve accurate approximation of the desired reference pattern. The proposed methods can be used to synthesize arbitrarily shaped beam-patterns, including multibeam forming and radiation suppression within several angular regions. These methods offer a number of advantages in regard to array scale, beampattern specifics and computational time compared to existing methods. Since this paper focuses on synthesizing receiver arrays, there are no explicit constraints applied to the excitation weights. Numerical results demonstrate the efficiency of three proposed approaches for designing non-uniformly spaced arrays. In particular, the fast iterative soft-thresholding algorithm can synthesize of up to a few hundred antennas.