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ICC 2021-IEEE International Conference on Communications
In this paper, we present a gradient descent and
Metropolis-Hastings (MH) principle based low-complexity, highly
parallelizable, near-optimal MIMO demodulation framework.
The optimal maximum-likelihood (ML) demodulation is a dis-
crete optimization problem and is computationally infeasible
for large MIMO dimensions. Many continuous-relaxed versions
of ML, such as least-squares (LS) demodulation, have closed-
form solutions but are highly suboptimal to ML. However, the
continuous surface of, for example, LS cost function can serve
as a proxy for the discontinuous staircase-like surface of the ML
cost function. The key idea in this work is to search for the
ML solution by performing an MH random walk around the
gradient-descent direction of the LS surface (scheme referred
hereafter as MHGD). In MH algorithm, a Markov Chain Monte
Carlo (MCMC) technique, first a random perturbation over the
previous state is proposed. Then, the proposal is accepted/rejected
with non-zero probability. In MHGD, gradient-descent and MH
work synergistically- gradient-descent shows the likely directions
to MH making it very efficient and MH navigates the discrete
space, which the gradient-descent alone cannot. To achieve near-
ML performance, MHGD uses a preconditioner on the gradient
and channel-matrix based covariance for the MH random step.
The complexity of MHGD algorithm is comparable or lower than
the well-known low-complexity, near-ML algorithms. But more
importantly, MHGD is highly parallelizable, has simple vector-
izable operations, scalable to very large MIMO due to the use
of gradients, and can also be extended to distributed computing
paradigm. Simulations demonstrate the aforementioned strengths
and salient features of near-ML MHGD algorithm
