Random Access Channel Coding in the Finite Blocklength Regime

Consider a random access communication scenario over a channel whose operation is defined for any number of possible transmitters. Inspired by the model recently introduced by Polyanskiy for the Multiple Access Channel (MAC) with a fixed, known number of transmitters, we assume that the channel is invariant to permutations on its inputs, and that all active transmitters employ identical encoders. Unlike Polyanskiy, we consider a scenario where neither the transmitters nor the receiver know which transmitters are active. We refer to this agnostic communication setup as the Random Access Channel, or RAC. Scheduled feedback of a finite number of bits is used to synchronize the transmitters. The decoder is tasked with determining from the channel output the number of active transmitters ($k$) and their messages but not which transmitter sent which message. The decoding procedure occurs at a time $n_t$ depending on the decoder's estimate $t$ of the number of active transmitters, $k$, thereby achieving a rate that varies with the number of active transmitters. Single-bit feedback at each time $n_i, i \leq t$, enables all transmitters to determine the end of one coding epoch and the start of the next. The central result of this work demonstrates the achievability on a RAC of performance that is first-order optimal for the MAC in operation during each coding epoch. While prior multiple access schemes for a fixed number of transmitters require $2^k - 1$ simultaneous threshold rules, the proposed scheme uses a single threshold rule and achieves the same dispersion.Comment: Presented at ISIT18', submitted to IEEE Transactions on Information Theor

Blind adaptive equalizer for broadband MIMO time reversal STBC based on PDF fitting

In this paper, we propose a new blind adaptive technique used for the equalisation of space-time block coded (STBC) signals transmitted over a dispersive MIMO channel. The proposed approach is based on minimising the difference between the probability density function (PDF) of the equalizer output — estimated via the Parzen window method — and a desired PDF based on the source symbols. The cost function combines this PDF fitting with an orthogonality criterion derived from the STBC structure of the transmitted data in order to discourage the extraction of identical signals. This cost function motivates an effective and low-cost stochastic gradient descent algorithm for adapting the equaliser. The performance is demonstrated in a number of simulations and benchmarked against other blind schemes for the equalisation of STBC over broadband MIMO channels

Distributed estimation in wireless sensor networks under a semi-orthogonal multiple access technique

This thesis is concerned with distributed estimation in a wireless sensor network (WSN) with analog transmission. For a scenario in which a large number of sensors are deployed under a limited bandwidth constraint, a semi-orthogonal multiple-access channelization (MAC) approach is proposed to provide transmission of observations from K sensors to a fusion center (FC) via N orthogonal channels, where K≥N. The proposed semi-orthogonal MAC can be implemented with either fixed sensor grouping or adaptive sensor grouping. The mean squared error (MSE) is adopted as the performance criterion and it is first studied under equal power allocation. The MSE can be expressed in terms of two indicators: the channel noise suppression capability and the observation noise suppression capability. The fixed version of the semi-orthogonal MAC is shown to have the same channel noise suppression capability and two times the observation noise suppression capability when compared to the orthogonal MAC under the same bandwidth resource. For the adaptive version, the performance improvement of the semi-orthogonal MAC over the orthogonal MAC is even more significant. In fact, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to that of the hybrid MAC, while requiring a much smaller amount of feedback. Another contribution of this thesis is an analysis of the behavior of the average MSE in terms of the number of sensors, namely the scaling law, under equal power allocation. It is shown that the proposed semi-orthogonal MAC with adaptive sensor grouping can achieve the optimal scaling law of the analog WSN studied in this thesis. Finally, improved power allocations for the proposed semi-orthogonal MAC are investigated. First, the improved power allocations in each sensor group for different scenarios are provided. Then an optimal solution of power allocation among sensor groups is obtained by the convex optimization theory, and shown to outperform equal power allocation. The issue of balancing between the performance improvement and extra feedback required by the improved power allocation is also thoroughly discussed