Joint Detection in Massive Overloaded Wireless Systems via Mixed-Norm Discrete Vector Decoding

We propose a novel ℓ0-norm based multi dimensional signal detection scheme for overloaded wireless systems such as Non-orthogonal Multiple Access (NOMA) and underdetermined Multiple-Input Multiple-Output (MIMO), in which the discreteness of maximum likelihood (ML) detection is transformed into a continuous ℓ0-norm constraint, subsequently convexized via fractional programming (FP). As a consequence, the proposed signal detection algorithm possesses the potential to achieve ML-like performance in terms of bit error rate (BER) by properly adjusting weighting parameters, at a fraction of the cost. Simulation comparisons with state-of-the-art (SotA) alternatives are given, which illustrate the effectiveness of the proposed method both in terms of its capability of outperforming the SotA and of possibilities of further improvement towards ML-like performance via the optimization of the weighting parameters.