Multi-User Multiple-Input Multiple-Output (Mimo) Communications Through Binary Spider Monkey Optimization-Based User and Antenna Scheduling

A user scheduling strategy that is sub-optimal in a multi-user multiple-input-multiple-output (MU-MIMO) communication system is taken into consideration in this paper. With the goal of improving system throughput and spectrum efficiency, MU-MIMO communication technologies are often used in networks. The signal-to-interference-plus-noise ratio (SINR) is used for user selection at the base station (BS). From the receiver side, the BS with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> transmit antennas chooses the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> best antennas from receiver side out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> antennas and sends data in parallel, separately. Accessing all of search space in the allotted time for wireless transmission is no easy feat. Searching the whole search field within the time span of wireless transmission is quite challenging. Joint scheduling of users and receive antennas is presented in this study using minimal complexity based approaches. This article proposes approaches for combined user and receive antenna scheduling that are based on minimal complexity. In this case, it has been shown that a scheduling technique based on low complexity may achieve system throughput similar to that of an exhaustive search algorithm (by exploring the full search space). A further noteworthy observation that can be made is that the number of complex multiplications and additions that are needed by these low complexity approaches is far lower than the amount that is required by an exhaustive search algorithm. The use of binary spider monkey optimization (binary SMO) soft computing technique has attained <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 97 \%$</tex> of the system throughput as compared to the traditional methods like dirty paper coding technique. Furthermore, binary SMO achieved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 2 \%$</tex> of deviation from the exhaustive search algorithm.