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Co-Designing Statistical MIMO Radar and In-band Full-Duplex Multi-User MIMO Communications


Core Concepts
The authors propose a co-design framework for a statistical (widely distributed) MIMO radar and an in-band full-duplex (IBFD) multi-user MIMO (MU-MIMO) communications system that operate concurrently within the same frequency band. The proposed approach jointly optimizes the radar waveform, communications precoders, and receive filters to maximize a compound weighted sum mutual information (CWSM) metric, while accounting for practical constraints such as transmit power limits, quality-of-service requirements, and peak-to-average-power ratio.
Abstract
The paper presents a co-design framework for a statistical (widely distributed) MIMO radar and an IBFD MU-MIMO communications system that share the same frequency spectrum. The key highlights are: The authors consider a spectral sharing problem where a statistical MIMO radar and an IBFD MU-MIMO communications system operate concurrently within the same frequency band. Prior works on joint MIMO-radar-MIMO-communications (MRMC) systems have limitations, such as focusing on colocated MIMO radars, half-duplex MIMO communications, single-user scenarios, or omitting practical constraints. The proposed framework addresses these issues by co-designing the radar waveform, communications precoders, and receive filters to maximize a compound weighted sum mutual information (CWSM) metric, while accounting for constraints like transmit power limits, quality-of-service requirements, and peak-to-average-power ratio. The radar receiver is designed to exploit the downlink communications signals reflected from the radar target to enhance target detection. Extensive numerical experiments show that the proposed methods improve radar target detection and yield higher achievable data rates compared to conventional approaches. The companion papers (Part II and III) describe the optimization algorithm and the tracking performance for multiple targets, respectively.
Stats
The target response vector to the n_r-th radar Rx is h_rt,n_r[k] = [h_1t,n_r[k], ..., h_M_rt,n_r[k]]^T, where h_m_rt,n_r[k] = α_m_rt,n_r e^(j2π(k-1)T_r f_m_r,t,n_r). The covariance matrix of the radar receive signal y_rt,n_r is R_rt,n_r(m,l) = E[y^(n_t)_r,n_rm^H] = Tr{a[m]a^H[l]Σ^(m,l)_rt,n_r}, where Σ^(m,l)_rt,n_r = E{h^*_rt,n_r[l]h^T_rt,n_r[m]} is a diagonal matrix with m_r-th diagonal element e^(j2πT_r(m-l)f_m_rt,n_r η^2_m_rt,n_r.
Quotes
"The purpose of this and companion papers (Part II and III) is to co-design an MRMC framework that addresses all of these issues." "Extensive numerical experiments show that our methods improve radar target detection over conventional codes and yield a higher achievable data rate than standard precoders."

Deeper Inquiries

How can the proposed co-design framework be extended to handle scenarios with multiple targets and time-varying channels

To extend the proposed co-design framework to handle scenarios with multiple targets and time-varying channels, several adjustments and enhancements can be made. Firstly, for multiple targets, the radar receiver signal processing algorithms need to be modified to differentiate between echoes from different targets. This may involve implementing sophisticated data association algorithms to track and identify individual targets using information from all Tx-Rx pairs. Additionally, the radar waveform design and precoding strategies may need to be optimized to enhance target detection and tracking performance in a multi-target environment. Regarding time-varying channels, the system can incorporate adaptive algorithms that can dynamically adjust the radar and communications parameters based on the changing channel conditions. This may involve real-time channel estimation and tracking techniques to optimize the transmission and reception processes for both radar and communications systems. By continuously updating the channel state information and adapting the system parameters accordingly, the co-design framework can effectively handle time-varying channels and improve overall system performance in dynamic environments.

What are the potential challenges and trade-offs in achieving perfect self-interference cancellation in the IBFD communications system, and how can they impact the overall MRMC system performance

Achieving perfect self-interference cancellation in the IBFD communications system poses several challenges and trade-offs that can impact the overall MRMC system performance. One of the main challenges is the complexity of the self-interference cancellation algorithms, which may require sophisticated signal processing techniques and hardware implementations. Imperfections in the cancellation process, such as residual self-interference or inaccuracies in the cancellation algorithms, can degrade the system performance and introduce interference to the received signals. Trade-offs may arise in terms of system complexity, power consumption, and processing latency. Implementing advanced self-interference cancellation techniques can increase the hardware complexity and cost of the system. Moreover, the cancellation process may introduce additional processing delays, affecting the real-time operation of the system. Balancing these trade-offs is crucial to optimize the self-interference cancellation performance while minimizing the impact on the overall system efficiency and reliability.

What are the implications of the co-design approach on the hardware complexity and cost of the integrated MRMC system, and how can these factors be optimized

The co-design approach has implications on the hardware complexity and cost of the integrated MRMC system, which can be optimized through careful system design and resource allocation. The integration of radar and communications functionalities in a single hardware platform may require specialized components and processing units to handle the diverse requirements of both systems. This can lead to increased hardware complexity and cost, especially in terms of antenna arrays, signal processing units, and RF front-end components. To optimize the hardware complexity and cost, system designers can explore techniques such as hardware sharing, reconfigurable hardware architectures, and efficient resource allocation strategies. By sharing common hardware components between the radar and communications systems, redundant hardware can be minimized, reducing overall system complexity and cost. Additionally, leveraging reconfigurable hardware platforms and software-defined radio technologies can provide flexibility and scalability in adapting to changing system requirements without significant hardware modifications. Overall, optimizing the hardware complexity and cost of the integrated MRMC system involves a careful balance between performance requirements, system scalability, and cost-effectiveness, ensuring that the system meets its operational objectives efficiently and economically.
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