Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

Abstract

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

Pilot design for underwater MIMO cosparse channel estimation based on compressed sensing

“…Therefore, the PDACS algorithm proposed in this article can continue to converge and search for pilots with the minimum cross-correlation sum. Meanwhile, the simulation shows that compared with the underwater GA algorithm, the GA algorithm used in Jiang et al 16 is for a single antenna pilot design, which does not consider the problem of the cosparsity channel in the case of underwater MIMO. Moreover, the GA algorithm is greatly affected by the parameter settings of the algorithm itself, and the running time is difficult to control.…”

“…As shown in Figure 3(a), the latitude and longitude coordinates of the ocean area are 20.373 and 113.875, respectively; the SSP (sound speed profile), seafloor, and sea surface refraction and scattering are as shown. The transmitter and the receiver are placed in a water area with a depth of 100 m. They are placed at a distance 1000 m apart and a depth of 30 m. According to the parameters in Jiang et al, 16 when the sound velocity changes from 1540 m/s on the water surface to 1512 m/s on the water bottom, the SSP is as shown in Figure 3(b). After all, these environmental profiles are input to BELLHOP model, and it produces a variety of useful outputs such as the transmission loss, the arrival time-series and amplitudes, and so on.…”

“…The pilot design depends on a better pilot search algorithm. In reference 16 an extended scheme based on a statistical random search is proposed to search for multiple antenna pilots; however, the number of pilot combinations considered in this method is limited by the pilot position replacement method.…”

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

“…In particular, an iterative algorithm is designed by applying continuous versions for channel estimation and binary versions for multi-user detection. Recently, the work in [99] applies an enhanced variant of the WOA and compressed sensing to allocate the pilot in underwater acoustic OFDM systems. To improve the original WOA, three strategies are investigated, including: 1) good initialization to avoid the prematurity issue, 2) chaotic switching to prevent traps from falling into a local minimum, and 3) nonlinear parameters to appropriately adjust the transition between exploration and exploitation.…”

Deterministic Pilot Pattern Placement Optimization for OFDM Sparse Channel Estimation

“…Furthermore, a few evolutionary schemes have been employed for solving the optimization problem to generat sub-optimal pilot pattern. These algorithms include the estimation of distribution algorithm (EDA) [23], particle swarm optimization [24], [25], bat-inspired algorithm (BA) [26], and whale optimization algorithm (WOA) [27]. However, due to the influence of parameter setting, the convergence accuracy and the time of the above-mentioned methods cannot be ensured.…”