Against this background, we will redesign turbo decoder implementations at an algorithmic level, rather than at the architectural level of the SOA approach. More specifically, we have recently been successful in devising an alternative to the BCJR algorithm, which has the same error correction capability, but does not have any data dependencies. Owing to this, our algorithm can be mapped to highly-parallel many-core processing architectures, facilitating an LTE turbo decoder processing throughput that is more than an order of magnitude higher than the SOA, satisfying future demands for gigabit throughputs. We will achieve this for the first time by developing a custom Field Programmable Gate Array (FPGA) architecture, comprising hundreds of processing cores that are interconnected using a reconfigurable Benes network. Furthermore, we will develop custom Network-on-Chip (NoC) architectures that facilitate different trade-offs between chip area, energy-efficiency, reconfigurability, processing throughput and latency. In parallel to developing these high-performance custom implementation architectures, we will apply our novel algorithm to both existing Graphics Processing Unit (GPU) and NoC architectures. This will grant us a rapid pace, allowing us to apply our novel algorithm to not only error correction, but to all aspects of receiver operation, including demodulation, equalisation, source decoding, channel estimation and synchronisation. Drawing upon our high-throughput algorithms and highly-parallel processing architectures, we will develop techniques for holistically optimising the algorithmic and implementational parameters of both the transmitter and receiver. This will facilitate practical high-performance schemes, which can pave the way for future generations of wireless communication.

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