  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.
