Abstract
The soft-output multiple-input multiple-output (MIMO) detection problem has been extensively studied, and a large number of heuristics and metaheuristics have been proposed to solve it. Unlike classical tree-search based detectors, geometrical heuristic algorithms involved two consecutive steps: (i) an exploration step based on the geometry of the channel matrix singular vectors; (ii) a local exploitation step is performed in order to obtain better final solution. In this paper, new enhancements for geometrical heuristics are introduced to significantly reduce the complexity in quadrature phase-shift keying (QPSK) and allow 16 quadrature amplitude modulation (QAM) capability through new exploration techniques. The performance-complexity trade-off between the new detector and two tree-based algorithms is investigated through Pareto efficiency. The Pareto framework also allows us to select the most efficient tuning parameters based on an exhaustive search. The proposed detector can be customized on the fly using only one or two parameters to balance the trade-off between computational complexity and bit error rate performances. Moreover, the Pareto fronts demonstrate that the new geometrical heuristic is especially efficient with QPSK since it provides a significant reduction in regards to the computational complexity while preserving good bit error rate (BER) performance and ensuring high flexibility.
Highlights
I N the last decades, the increase in the quantity of data sent over wireless channels has led to a shortage of available frequency bands
We previously investigated the interest of geometrical heuristics to solve the multiple-input multiple-output (MIMO) detection problem [22]
ENHANCEMENTS TO GEOMETRICAL DETECTION we present some enhancements to the geometrical-based detection framework that was presented in the previous section
Summary
I N the last decades, the increase in the quantity of data sent over wireless channels has led to a shortage of available frequency bands. The space-division multiplexing (SDM) MIMO technology can transmit several data streams in the same time-frequency slot and separate them according to spatial considerations This multiplexing technique increases the spectrum efficiency through the addition of antennas. The more antennas there are, the more complex the receiver design is These systems require new algorithms to exploit the spatial information to separate data streams efficiently. The separation of received streams has been widely studied, and many algorithms have already been proposed in the literature This detection problem is known to be NPhard [1], which implies that an optimal solution cannot be computed in polynomial time (unless under the unattainable assumption of P=NP).
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