Optimum Design for Coexistence Between Matrix Completion Based MIMO Radars and a MIMO Communication System (1507.01982v1)

Abstract: Recently proposed multiple input multiple output radars based on matrix completion (MIMO-MC) employ sparse sampling to reduce the amount of data that need to be forwarded to the radar fusion center, and as such enable savings in communication power and bandwidth. This paper proposes designs that optimize the sharing of spectrum between a MIMO-MC radar and a communication system, so that the latter interferes minimally with the former. First, the communication system transmit covariance matrix is designed to minimize the effective interference power (EIP) to the radar receiver, while maintaining certain average capacity and transmit power for the communication system. Two approaches are proposed, namely a noncooperative and a cooperative approach, with the latter being applicable when the radar sampling scheme is known at the communication system. Second, a joint design of the communication transmit covariance matrix and the MIMO-MC radar sampling scheme is proposed, which achieves even further EIP reduction.

• The paper proposes optimal design solutions to minimize effective interference power in shared MIMO radar and communication systems.
• It compares noncooperative, cooperative, and joint design approaches using matrix completion and alternating optimization techniques.
• Numerical results demonstrate significant improvements in target estimation and interference reduction under various system constraints.

Spectrum Sharing in MIMO Radar and Communication Systems: An Analytical Perspective

The paper "Optimum Design for Coexistence Between Matrix Completion Based MIMO Radars and a MIMO Communication System" presents a comprehensive methodology for facilitating spectrum sharing between a MIMO communication system and a matrix completion-based colocated MIMO radar system (MIMO-MC). As spectrum becomes an increasingly scarce resource, efficient spectral coexistence between radar and communication systems is imperative. This research addresses the interference issues in such spectral sharing environments by optimizing transmission parameters and sampling strategies.

Technical Summary

The authors address the spectral overlap and consequent interference between communication and radar systems that employ multiple input multiple output (MIMO) architectures. The MIMO-MC systems utilize sparse sampling to reduce data forwarding, relying on matrix completion (MC) techniques to maintain high-resolution target estimation. The focal point of this paper is enhancing the coexistence of MIMO-MC radars with communication systems by minimizing the effective interference power (EIP) at the radar while satisfying constraints for communication system capacity and power.

The paper introduces a sequential methodology comprising:

1. Noncooperative Spectrum Sharing: Where the communication system has no knowledge of the radar's sampling scheme, the optimization of the transmit covariance matrix is performed to minimize the total interference power (TIP).
2. Cooperative Spectrum Sharing: This leverages the sharing of the radar's sampling patterns with the communication system. The cooperative approach allows for a significant reduction in EIP by using knowledge of the radar's operations to optimize the communication system's transmit covariance.
3. Joint Design Approach: This introduces a co-optimization of the communication transmit covariance matrix and the MIMO radar's sampling scheme. Applying alternating optimization techniques, this approach further reduces interference.

For both Scheme I (sub-sampling) and Scheme II (random matched filter bank), the paper provides analytical insights for performance implications of varying sub-sampling rates, communication capacity constraints, number of targets, and radar transmit power. Theoretical guarantees underscore the advantage of cooperative and joint-design approaches over noncooperative spectrum management.

Implications and Future Directions

The theoretical developments and numerical results provided in this paper offer substantial implications for the design and operation of spectral-sharing systems involving radars and communication entities. The optimization strategies enhance the radar's resilience to interference and promote more efficient use of the shared spectrum.

Key numerical results showcase the advantages of cooperative methods, especially when the null space dimensions and target number do not favor noncooperative approaches. For instance, the joint-design approach achieves notable EIP reductions and improves matrix completion recovery errors, maintaining efficiency even as system parameters, such as sub-sampling rate, transmit power, and number of targets, vary.

The paper indicates several future research avenues, including exploring advanced cooperative techniques for more robust and flexible spectral sharing, extending the framework to support dynamic environments with mobile communication and radar platforms, and incorporating real-time learning algorithms to adaptively manage spectrum resources.

In summary, this research extends the landscape of spectrum sharing by advancing cooperative and jointly optimized strategies that significantly mitigate interference, paving the path for integrated radar-communication systems that efficiently share finite spectral resources.