Collaborative Research: Practical Signal Design for Interference Management in Wireless Communication Networks

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Interference management is the bottleneck of meeting the explosive wireless data communication demand. The primary method of increasing cellular or Wi-Fi network capacity is to increase base station/access-point density, which leads to more interference from undesired transmitters due to less scatterers at shorter distance. The proposed research attacks the interference problem through joint transmit signal design among users such that it strikes an optimal balance between maximizing a data link's own rate and reducing interference to other links. The objective is to achieve maximal data rates. To make the research more relevant to real-world systems, the team takes an interdisciplinary approach of joint algorithm and hardware design with the consideration of practical constraints. Consequently, the research results are expected to significantly improve the capacity of next generation wireless communication systems. The project also provides a unique opportunity to train both graduate and undergraduate students at the intersection of signal processing and embedded system fields.The proposed research attempts to solve the optimization problem of maximizing weighted sum-rate for interference channels by research on theory, distributed optimization algorithms, and efficient hardware design. The transformative nature of this research lies in that it adopts an interdisciplinary approach of joint algorithm and hardware design, takes advantage of recent optimal signal structure result, as well as considers many practical constraints. The PI's group recently discovered that the optimal signals have a polite water-filling structure, which is analogous to the celebrated interference alignment for infinite SNR where every user can enjoy half of the resource. The polite water-filling works for all SNR range and by exploiting it, the most efficient algorithms have been designed under the ideal assumptions of full channel knowledge and centralized optimization. Under practical constraints, the design and analysis become much more challenging. The PIs will take a holistic approach of theory, design, simulation, and experiments with practical consideration of distributed optimization, distributed channel estimation, partial channel knowledge, quantization noise, hardware processing latency, and power consumption. The outcome of the project includes practical algorithms, performance analysis, and efficient hardware architectures that can serve as a blueprint for interference management in future wireless communication systems.