Evaluating the Efficiency of Non-Orthogonal MU-MIMO Methods in Smart Cities Technologies & 5G Communication

Abstract

Many cutting-edge technologies, such as MIMO, cognitive radio, multi-carrier modulation, and network coding, have been proposed for wireless communication to satisfy needs for a higher data rate in the upcoming time, leading to improved quality of service (QoS) regardless of the weather. Orthogonal and non-orthogonal multiple access techniques are two categories into which multiple access technologies can be subdivided. Large networking with effective implementation of wireless devices is supported by non-orthogonal multiple access techniques. Massive NOMA has been implemented to advance access efficiency by permitting several users to share a similar spectrum. Because of the robust co-channel interference between mobile users presented by NOMA, it offers important tasks for system model and resources management. In this study, two additional sets of demanding codes are explored. Multi-user shared access methods and expanded multi-user shared access (EMUSA) methods are both employed. In the MUSA technique, an algorithm is used for the allocation of resources to achieve minimum intercorrelation to the maximum extent in 5G networks. A novel idea proposed in this paper is to create complex codes starting from PN codes (i.e., ePN), thereby achieving promising results in the overall system performance. The first part of this paper describes the fundamental principles of MUSA, and in the next part the main idea of the proposed technique will be studied in detail. Using Monte-Carlo MATLAB simulation, the performance of the suggested approach is assessed in terms of BER vs. SNR. The efficiency of the proposed approach is evaluated in various settings, and the outcomes are contrasted with those of the traditional CDMA technique, using parameters, such as the number of active users and antennas at the receiver.

Information Technology for Maximizing Energy Consumption for Useful Information Traffic in a Dense Wi-Fi 6/6E Ecosystem

“…Generally speaking, OFDMA shows better results with small RUs [5,8]. Now let us comment on the previously mentioned Multi-User Multiple-Input, Multiple-Output (MU-MIMO) system [6,7,[9][10][11]. This is a good reworking of the already well-known MIMO system (introduced at the time of Wave-2 802.11ac).…”

“…We formalize the process of static solution of the problem of maximizing energy consumption for useful information transfer in the Wi-Fi 6/6E ecosystem with an active MU-MIMO system and OFDMA technology. Let us present the solution of such an optimization problem with the objective function (9) and constraints (5) based on the branch-and-bound hierarchy [26]. With this approach, the original optimization problem is represented by a tree of subproblems, each of which describes a rectangular fragment with an upper face The information technology for obtaining the optimal solution for the objective function (9) will consist of a hierarchical sequence of the following stages:…”

“…To implement the selected method, auxiliary variables The logic of using the vector → b to estimate the upper bound for the objective function (9) repeats what was described above about the vector → a . The difference is that if the found estimate for the upper bound of the objective function (9) exceeds the previously fixed value of this characteristic by less than the value of δ, then the found values of the controlled variables are fixed, the current subtask is removed from the stack, and the technology moves to the next stage (Finalization). Otherwise, the found values of the controlled variables are fixed, the stack is supplemented with the found subtasks, and the technology proceeds to the implementation of Stage 2.…”

“…The stack is checked for current subtasks. The presence of such when activating admission to the current stage indicates the incompatibility of restrictions (5) or non-fulfilment of the additional condition (9). If the stack is empty, then the technology is considered successfully implemented, and the found suboptimal values are further considered optimal, found with an error of δ.…”

Six-element MIMO antenna structure with squared ring structure with multiband and high gain characteristics for C/X/Ku/K band applications

“…The fading of different signals can influence a channel and have a proportionate impact on the signal-tonoise ratio-SNR. In another way, this can impact the system error rate with the assumption of digital data transmission [17]. The diversity principle provides the proposed receiver with numerous versions of the same signal.…”