This work proposes a low‐complexity detector for medium‐ and high‐order modulation large‐scale multiple‐input multiple‐output (LS‐MIMO) systems based on the set of Markov chain Monte‐Carlo techniques. Such efficient signal detection algorithm is based on the mixed Gibbs sampling with multiple restarts (MGS‐MR) strategy with sample‐averaged approach during the coordinate updating process, named averaged MGS (aMGS). The proposed strategy applies multiple samples average procedure to restrict the range of the random solution, which comes from the mixture proposed by the original MGS. Numerical simulation results considering higher‐order M ‐QAM demonstrated that the proposed detection method can substantially improve the convergence of the MGS‐MR algorithm, while no extra computational complexity is required. The proposed aMGS‐based detector suitable for medium‐ and high‐order modulation LS‐MIMO further exhibits improved performance when the system loading is high, i.e. when (K /N) ≥ 0.75. In addition, the proposed numerical simulation analyses have shown that the optimal value of the mixing ratio parameter can vary regarding system and channel configuration scenarios, resulting somewhat different from the 1/2K value disseminated in the literature.
A message passing detector based on belief propagation (BP) algorithm for Markov random fields (MRF-BP) and factor graph (FG-BP) graphical models is analysed under different large-scale (LS) multiple-input multiple-output (MIMO) scenarios, including system parameters, such as damping factor (DF), number of users and number of antennas, from N = 20 to 200 antennas. Specifically, the DF variation under different number of antennas configuration and signal-to-noise ratio (SNR) regions is extensively evaluated; bit error rate (BER) performance and computational complexity are assessed over different scenarios. Numerical results lead to a great performance gain with damped MRF-BP approach, overcoming FG-BP scheme in specific scenarios, with no extra computational complexity. Also, message damping (MD) method resulted in faster convergence of MRF-BP algorithm in LS scenarios, evidencing that, besides the performance gain, MD technique can lead to a computational complexity reduction. Specifically under low number of transmit antennas scenarios, the DF value needs to be carefully chosen. Furthermore, based on the proposed analysis, optimal value for the DF is determined considering wide LS antennas scenarios and SNR regions.
Here, the authors develop a prototype filter design for filter bank multi-carrier (FBMC) systems focusing on shortlength pulses (K ≤ 3) with near-perfect reconstruction features, favouring operational conditions for FBMC systems. The proposed design is formulated as the optimisation of the weights of a discrete prototype filter that takes into account high symbol reconstruction rates and desirable spectrum features. Despite not being convex, the formulated optimisation problem is relaxed via semi-definite programming (SDP), enabling solutions that can over-perform other popular prototype filters. Numerical results show that the proposed design considering overlapping factors K = {1, 2, 3} can deliver prototype filters with a good signal-to-interference ratio (SIR) versus spectrum performance trade-off. Indeed, for K ≤ 2, the proposed methodology delivered prototype filters with the highest SIR levels among the available options. Furthermore, the proposed methodology can be extended and combined with other filter optimisation criteria, enabling to comply with different FBMC design requirements.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.