Thursday 23 January 2025
The quest for a more efficient and effective way to position devices in wireless networks has led researchers to explore new methods that combine sensing and communication capabilities. A recent study published in IEEE Transactions on Wireless Communications takes a step in this direction, proposing a novel approach to balance two competing objectives: bistatic positioning (BP) and monostatic sensing (MS).
In BP, the base station (BS) transmits pilot signals, which are received by the user equipment (UE), allowing it to estimate its own position. Meanwhile, in MS, the BS acts as a radar system, using the same pilots to detect and track passive targets, such as vehicles or pedestrians. The key challenge lies in finding the optimal beamformer design that balances these two objectives.
The researchers developed a multi-objective optimization framework that weighs the performance metrics of BP and MS. They derived the Cramér-Rao bounds (CRBs) for both paradigms, which provide a lower bound on the achievable positioning accuracy. The CRBs were then used to formulate an optimization problem that minimizes the weighted sum of the two objectives.
To further optimize the beamformer design, the researchers introduced two mismatch-minimizing approaches: one targeting the beamformer mismatch and another focusing on the variance matrix mismatch. Numerical results demonstrated the performance tradeoff between BP and MS, highlighting the superiority of the weighted-sum variance matrix mismatch approach in balancing the two objectives.
The study’s findings have significant implications for future wireless networks, which will likely integrate sensing and communication capabilities to enhance their functionality. The proposed approach can be applied to a variety of scenarios, including autonomous vehicles, smart cities, and emergency response systems.
In addition to its technical significance, this research highlights the importance of interdisciplinary collaboration between experts in signal processing, communications, and radar engineering. By combining their knowledge and expertise, researchers can develop innovative solutions that address complex problems and improve the performance of wireless networks.
The proposed approach is not without its limitations, however. The complexity of the optimization problem increases with the number of targets and the dimensionality of the beamformer design space. Future research should focus on developing efficient algorithms and scalable methods to solve these problems.
Despite these challenges, the study’s findings offer a promising direction for future research in wireless networks. By balancing bistatic positioning and monostatic sensing, researchers can develop more effective and efficient solutions that integrate sensing and communication capabilities.
Cite this article: “Balancing Bistatic Positioning and Monostatic Sensing in Wireless Networks”, The Science Archive, 2025.
Beamforming, Bistatic Positioning, Monostatic Sensing, Wireless Networks, Signal Processing, Radar Engineering, Optimization, Cramér-Rao Bounds, Multi-Objective Optimization, Variance Matrix Mismatch
