4 research outputs found
Two-Stage LASSO ADMM Signal Detection Algorithm For Large Scale MIMO
This paper explores the benefit of using some of the machine learning
techniques and Big data optimization tools in approximating maximum likelihood
(ML) detection of Large Scale MIMO systems. First, large scale MIMO detection
problem is formulated as a LASSO (Least Absolute Shrinkage and Selection
Operator) optimization problem. Then, Alternating Direction Method of
Multipliers (ADMM) is considered in solving this problem. The choice of ADMM is
motivated by its ability of solving convex optimization problems by breaking
them into smaller sub-problems, each of which are then easier to handle.
Further improvement is obtained using two stages of LASSO with interference
cancellation from the first stage. The proposed algorithm is investigated at
various modulation techniques with different number of antennas. It is also
compared with widely used algorithms in this field. Simulation results
demonstrate the efficacy of the proposed algorithm for both uncoded and coded
cases.Comment: 5 pages, 4 figure
FlexCore: Massively Parallel and Flexible Processing for Large MIMO Access Points
Large MIMO base stations remain among wireless network designers’ best tools for increasing wireless throughput while serving many clients, but current system designs, sacrifice throughput with simple linear MIMO detection algorithms. Higher-performance detection techniques are known, but remain off the table because these systems parallelize their computation at the level of a whole OFDM subcarrier, sufficing only for the less demanding linear detection approaches they opt for. This paper presents FlexCore, the first computational architecture capable of parallelizing the detection of large numbers of mutually-interfering information streams at a granularity below individual OFDM subcarriers, in a nearly-embarrassingly parallel manner while utilizing any number of available processing elements. For 12 clients sending 64-QAM symbols to a 12-antenna base station, our WARP testbed evaluation shows similar network throughput to the state-of-the-art while using an order of magnitude fewer processing elements. For the same scenario, our combined WARP-GPU testbed evaluation demonstrates a 19x computational speedup, with 97% increased energy efficiency when compared with the state of the art. Finally, for the same scenario, an FPGA-based comparison between FlexCore and the state of the art shows that FlexCore can achieve up to 96% better energy efficiency, and can offer up to 32x the processing throughput
Spectrally efficient multicarrier communication systems: signal detection, mathematical modelling and optimisation
This thesis considers theoretical, analytical and engineering design issues relating
to non-orthogonal Spectrally Efficient Frequency Division Multiplexing (SEFDM)
communication systems that exhibit significant spectral merits when compared to Orthogonal
FDM (OFDM) schemes. Alas, the practical implementation of such systems
raises significant challenges, with the receivers being the bottleneck.
This research explores detection of SEFDM signals. The mathematical foundations
of such signals lead to proposals of different orthonormalisation techniques as required
at the receivers of non-orthogonal FDM systems. To address SEFDM detection, two
approaches are considered: either attempt to solve the problem optimally by taking
advantage of special cases properties or to apply sub-optimal techniques that offer reduced
complexities at the expense of error rates degradation. Initially, the application
of sub-optimal linear detection techniques, such as Zero Forcing (ZF) and Minimum
Mean Squared Error (MMSE), is examined analytically and by detailed modelling. To
improve error performance a heuristic algorithm, based on a local search around an
MMSE estimate, is designed by combining MMSE with Maximum Likelihood (ML)
detection. Yet, this new method appears to be efficient for BPSK signals only. Hence,
various variants of the sphere decoder (SD) are investigated. A Tikhonov regularised
SD variant achieves an optimal solution for the detection of medium size signals in
low noise regimes. Detailed modelling shows the SD detector to be well suited to the
SEFDM detection, however, with complexity increasing with system interference and
noise. A new design of a detector that offers a good compromise between computational
complexity and error rate performance is proposed and tested through modelling
and simulation. Standard reformulation techniques are used to relax the original optimal
detection problem to a convex Semi-Definite Program (SDP) that can be solved
in polynomial time. Although SDP performs better than other linear relaxations, such
as ZF and MMSE, its deviation from optimality also increases with the deterioration
of the system inherent interference. To improve its performance a heuristic algorithm
based on a local search around the SDP estimate is further proposed. Finally, a modified
SD is designed to implement faster than the local search SDP concept. The new
method/algorithm, termed the pruned or constrained SD, achieves the detection of
realistic SEFDM signals in noisy environments