Avoidance or Alignment: Interference Mitigation Strategies for Wireless Communications

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Wireless communications is transforming society, enabling rapid growth in developing areas without infrastructure and removing barriers. The proliferation of over 1 billion mobile devices (smart phones, etc.) represent convergence of computing and telecommunications. While such devices are capable of operating at very high transmission rates, networks operate in reality at rates orders of magnitude slower. This gap is growing as mobile broadband data traffic is projected to increase by a factor of 18 over the next 3 years. The proposed research program investigates key signal processing techniques that address limitations on spectrum efficiency due to multi-user interference.Nearer term research efforts aim to avoid interference by organizing transmissions orthogonally over time, frequency, and space. Traditionally, frequencies may be spatially reused by shrinking network cell sizes. However, servicing spatially non-uniform user traffic (hot spots) requires the overlapping of different cell types. The resulting heterogeneous networks challenge orthogonality. Multiple antenna systems, known as multiple-input multiple-output (MIMO) systems, may achieve directional transmission via beamforming and/or multiplex multiple data streams via space-time coded transmission.In the shorter term, we aim to maintain user orthogonality and achieve more opportunistic and dynamic use of spectrum. We propose to better identify time periods of unused spectrum via decision theory and optimization. The knowledge gained would then be applied to interference avoidance with large potential payoffs for emerging products and standards. First, we plan to develop true two-sided sequential statistical tests to more efficiently detect both appearance and absence of users over time, frequency and space. Next, we seek distributed solutions with minimum communication overhead and sensor observation energy expenditure. A parallel investigation will address increasing spectrum efficiency by reducing signaling overhead. Increasingly, advanced methods exploit channel state information, that is typically assumed to be constant over short periods, i.e., quasi-static. In the short to medium term we propose new “predictive training” methods that account for predictable channel variations. Here, we plan to extend and generalize our recently developed approach to channel estimation by taking advantage of physical properties of mobile wireless signal transmission including bandlimited fading and Doppler effects in MIMO systems. In the longer term, we aim to disrupt the interference avoidance paradigm for orthogonal transmission by advanced signal processing that achieves interference alignment (IA). This is in contrast to conventional multi-user interference cancellation that costs a spatial dimension (antenna) per interferer, or to multi-user detection that must jointly decode all user signals. In IA, user signals from different transmitters are processed to project interference onto reduced-dimension subspaces to regain lost orthogonality. Under idealized conditions of low noise levels, systems employing IA have the potential to increase throughput proportionally with numbers of users, i.e., interference becomes a benefit! However, practical realization of IA requires highly accurate channel state information, including timing and frequency synchronization. We plan to study performance limitations and propose realizable IA algorithms at finite SNR that incorporate novel approaches to channel estimation, multiple timing and frequency offset recovery. Here, we plan to leverage our extensive recent research on channel estimation and training of imperfectly synchronized multiple relay networks, which shares key “multiple access” features with the IA problem.