Wireless Communications Research Group
Research Areas Overview
Heterogeneous Cellular Networks
As the wireless data traffic continues to increase as well as the way we use the cellular network changes towards multimedia applications, cellular system development has reached practical limits in many densely populated scenarios. This change has led cellular operators to opt for increased heterogenity in the design of cellular networks to improve spectral efficiency and traffic offloading. As radio link improvements such as coding, adaptive transmission and communication over multiple antennas are reaching their theoretical limits, there is a shift from centrally planned tower mounted base stations (BS) called macro BS to multiple smaller tiers inside the macro tier. This new paradigm is known as
Heterogeneous cellular networks. more...
Massive MIMO
In general, in time-division duplexing systems, as we add more and more antennas to the base station, the performance in terms of data rate, enhanced reliability, improved energy efficiency and interference increases. Consequently, Massive
MIMO, where the number of antennas is scaled up by 1-2 orders of magnitude has emerged as a recent research interest. Motivating this surge in research activities are the additional gains resulting from a large channel matrix, due to the asymptotics of random matrix theory. We are studying the properties of Massive MIMO systems, in particular the practical aspects of achieving such gains.
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Channel Prediction for Mobile MIMO Wireless Systems
Erroneous, imperfect or outdated knowledge of the wireless channel state is widely known to result in significant degradation in system performance. Accurately predicting the state of the channel can reduce these effects. We are developing parametric model based channel prediction algorithms that fully exploit both the temporal and spatial correlation structure of the channel for a variety of MIMO systems. Our algorithms are based on the double directional spatial channel model (SCM) for both 2D and 3D propagation scenarios, multidimensional parameter estimation and tracking.
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Convex Optimisation for Wireless Systems
This project focuses on the application of convex optimisation to spectrum sharing cognitive radio systems. Convex optimisation methods are widely used in the design and analysis of communications systems. Many problems that arise in communications signal processing can be cast or converted into convex optimisation problems which allow analytical or numerical solutions to be calculated easily. The problems considered are optimum power control, robust cognitive distributed beamforming and robust cognitive multi-antenna beamforming.
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Interference Alignment in Cellular Systems
As the demand for supporting more and more simultaneous users in wireless systems grows, the traditional approaches of dividing resources (time, frequency, space) equally among users (commonly referred to as 'cake-cutting') becomes less and less efficient. Interference alignment (IA) is an new idea to apply linear precoding in order to align interfering signals such that they occupy a smaller subspace at the receiver. In MIMO systems, multiple antennas are used to alignment interference in the spatial dimension. We are exploring the concept of IA in the context of cellular systems.
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Limited Feedback MIMO
Due to the large overhead required to inform a MIMO transmitter of the channel state information, in many applications the receiver feeds back a quantized version if its channel estimate. For this purpose, a codebook with the same entries and size is maintained at both transmitter and receiver; the receiver feeds back the index of the nearest codebook entry (or codeword) to the transmitter via a low-rate feedback link. The number of bits required to send the index of the selected codeword to the transmitter is considered as feedback overhead. We are working towards effective differential codebook design for multi-user systems in spatially and temporally correlated channels.
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