Robust multi‐user detection based on hybrid Grey wolf optimization

Abstract

Summary The search for an effective nature‐inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi‐user detection algorithm based on Grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi‐user detection under an impulse noise environment. The simulation results show that the iteration times of the multi‐user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, Grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.

Mathematical formulation and hybrid meta‐heuristic algorithms for multiproduct oil pipeline scheduling problem with tardiness penalties

“…The pseudo‐code of this hybrid algorithm is shown in pseudo‐code 4. In order to see the applications of more hybrid algorithms see References 34‐37.…”

Multi-User Detection Based on Improved Cheetah Optimization Algorithm

“…where A l represents the amplitude, B l [i] denotes a sequence of information consisting of 1 or −1, n(t) represents the channel noise, τ l ∈ [0, T) is the size of the signal time delay for the l user, and s l (t) denotes the pseudo-code sequence. Assuming the sampling period is equal to the width of a single code slice, the system output after sampling can be expressed as [13] y = SAB + δn (2) where S = [s 1 s 2 . .…”

“…In recent years, significant progress has been made in multi-user detection techniques covering a wide range of areas, including deep learning-based methods that utilize deep neural network structures to learn complex signal features and interference patterns [1][2][3], multitask learning techniques that model the multi-user detection problem as a unified multitask learning problem [4], information sharing between multiple receiver nodes for collaborative signal processing techniques [5][6][7], nonconvex optimization methods that improve the local search capability and convergence speed of the system by introducing nonconvex constraints and optimization methods [8], biomimetic optimization algorithms that solve the optimization problem by simulating the behavior of groups of living organisms in nature [9][10][11][12][13], and modeling the multi-user detection using methods such as graph-based models that model signal transmission relationships and interference relationships for analysis and optimization [14]. These techniques play an important role in improving system performance, reducing complexity, and enhancing real-time performance.…”

“…Based on the analytical setup of the previous paper, the algorithms are set up to establish a multi-user detection study. According to the literature [13] on fusion strategy, the cheetah optimization algorithm introduced in the DE is replaced with JADE [30] (HCO-jade) to participate in the simulation experiments in the search process part, to compare and verify the fusion advantage of this paper's algorithm. Under the aforementioned experimental conditions, the initialization parameters are set based on the optimal values obtained from the above experiments.…”

Enhancing Mean-Variance Mapping Optimization Using Opposite Gradient Method and Interior Point Method for Real Parameter Optimization Problems

“…The SOA is combined with another algorithm to solve energy problems, such as short-term wind speed forecasting problems [16]. The gray wolf optimization algorithm (GWO) [17] relies on the level of leadership and the hunting mechanism of the gray wolf population for the search process of the optimization algorithm. The algorithms, based on different animal behavior patterns, are also subject to their effectiveness, which cannot be significantly improved when modified.…”