Computational Intelligence and Neuroscience

Volume 2018, Article ID 6759526, 12 pages

https://doi.org/10.1155/2018/6759526

## Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

^{1}Department of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan^{2}Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan^{3}Department of Computer Science, CUI, Lahore, Pakistan

Correspondence should be addressed to Muhammad Adnan Khan; kp.ude.eabcn@nahknandam

Received 12 August 2018; Accepted 28 October 2018; Published 6 December 2018

Academic Editor: Saeid Sanei

Copyright © 2018 Muhammad AsadUllah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.

#### 1. Introduction

In the field of communication systems, the wireless communication branch is rapidly growing, and fast technology developments are needed to meet the requirement. Wireless communication uses wireless channels instead of wireline channels. The rapid growth of the wireless communication system needs technological advances. Wireless connection provides a variety of services ranging from voice to data and multimedia. Due to the physical properties of the channel, the signal is affected, and unwanted effects occur in wireless communication. Interaction of wireless signals with the environment is very complex. Some problems happen on the channel between the transmitter and receiver because of large objects, diffraction of the electromagnetic waves around obstructing objects, and also signal scattering. Due to these interactions, the signal arriving at the receiver copes with different attenuation, distortion, delays, and phase shift. The inference of these multipaths may be constructive or destructive. The signal power can be slightly diminished when the destructive interface occurs.

It is essential for optimum performance of wireless communication systems to provide accurate channel state information (CSI) for coherent detection of the signal received at the receiver end. A noncoherent method differential demodulation technique is used for demodulation and detection of the transmitted signal when CSI is not available at the receiver. The deployment of the noncoherent method costs about 3-4 dB loss in SNR as compared with the coherent detection method. Due to such massive loss by the noncoherent detection method, research directed toward coherent detection for providing CSI at the receiver in wireless communication systems [1, 2].

Multiuser detection (MUD) as the receiver technology uses compressive sensing (CS) for the detection of inferring signals. If most of the devices are not in the active state, then the transmitting signal vector because of a large number of nonzero elements has a sparse property. Hence, decoding of the transmitted signal would become a compressive signal problem. For a system that has a small number of high activity users, the long-term evolution is more suitable [2, 3].

In modern communication systems, the primary issue is to enhance the channel capacity of the system without affecting the service quality of the system. The multiple-input and multiple-output (MIMO) method is found to be effective in enhancing the data rates and resolving the issue of the channel capacity [1, 4–6]. In this method, algorithms are used to estimate the signals at both the sender and the receiver ends of the antennas [7], due to which the data rates increase as well as the bandwidth of the channel capacity [8–11]. Few transmitter antennas and beneficiary radio wires are utilized in this technique to enhance the correspondence technique of the system. The transmit information is calculated on various transmission paths depending on the amount of data conveyed by the MIMO framework increments [12].

On the receiving end, some antennas collect the information received, and different calculations are performed to reassemble the information and reestablish the data at the receiver’s end accordingly. Due to the increment in the range and the amount of the information without any additional transmitting power or the data transfer capacity, the MIMO innovation is considered as the midpoint for remote communication [13, 14].

The medium MIMO innovation technique can also be utilized along with multicarrier code-division multiple access (MC-CDMA) and orthogonal frequency-division multiplexing (OFDM) to enhance the significant volume growth for numerous correspondences [7–9].

The maximum likelihood (ML) method is one of the optimal detectors in MUD, but ML is complicated to use it for achievement of exponential complexity. In a less-complicated situation, the suboptimal MUD detectors like the zero-forcing or null-steering detector, minimum mean square error (MMSE) detector in M2M, and maximum a posteriori or marginal likelihood detectors are used. The primary concern of the multiuser detection is based on the knowledge of strategies to demodulate the data sent simultaneously by several servers to share a multiaccess channel. The last two suboptimal approaches use matrix inversion and also are very simple. Some evolutionary algorithms like repeated weighted boosting search (RBS), fuzzy adaptive differential evolution (FADE), and differential evolution algorithms (DEAs) are helpful for channel estimation (CE) and multiuser detection [6]. For the CE problem, the continuous search space is used, and for multiuser detection, the discrete search space is used, and for improvement of the spectral efficiency multiuser-MIMO (MU-MIMO), broadcasting approaches are mostly used [10, 13]. At the transmitter because of course knowledge of channel state information, the quality of transmitting precoding to dominate the multiuser inference degraded [10]. Therefore, the system throughput may get affected by the interface from coscheduled user equipment.

The alternate emerging numerous strategies like particle swarm optimization (PSO) [15], partial opposite mutant particle swarm optimization (POMPSO), total opposite mutant particle swarm optimization (TOMPSO) [7, 9, 10], genetic algorithm (GA), island GA, differential equation (DE), and island DE can be used to further enhance the performance of the digital communication system [15]. In this article, we performed the channel estimation for high data rates in correspondence to both the sender and the receiver ends. As some distortion adds up to the signal during communication through the channel, the signal strength weakens and the receiver end might not be able to collect the accurate information. To overcome this issue, fuzzy logic is implemented to improve the data and channel estimation process [9, 10]. In this article, fuzzy logic empowered the opposite particle swarm optimization-based new variant for the communication system and implemented it using the PSO technique.

In this research work, we consider the MIMO system that consists of different numbers of users. It also assumed that the channel is flat fading and cyclostationary. The main contributions of the paper are listed as follows:(a)We formulate an optimization problem in which the objective is to minimize the MMSE and BER.(b)A fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm has been proposed for the estimation of the user data and the channel coefficients.(c)We compare our proposed method with other studied algorithms like TOMPSO and POMPSO in the literature. Simulation results show that the proposed algorithms give attractive results as compared to different algorithms.

The rest of the paper is organized as follows: the MIMO system model is explained in Section 2. The FL-OLMPSO-based optimization problem is formulated in Section 3. Section 4 presents the simulation results and discussion. Finally, the research work is concluded in Section 5.

#### 2. System Model

There are *A* transmitting antennas and *B* receiving antennas. The flat fading channel is implemented. The channel is expected to be stationary during the communication process of *Q* symbols. The received signal at the receiver antenna *b* is as follows [1]:where is the index of the symbol, is the flat fading channel coefficient that links the transfer antenna *a* to the receiver antenna *b*, is the *i*th symbol transmitted from the antenna *a* taking value from the symbol set {−1,+1} of binary phase shift key (BPSK), and is the additive white Gaussian noise (AWGN) with .

The following MIMO channel equation will represent the complete system:where represents AWGN:

The transmitted symbol vector isand the received signal vector is

The channel gain at the receiver antenna can always be normalized to unity:where .

Now define a received data matrix with *B* × *V* dimensions and transmitted data matrix with *A* ∗ *V* dimensions as follows [1]:respectively. Then, the PDF of the received signal matrix conditioned on the MIMO channel matrix and the transmitted data matrix can be written as follows:

The ML estimation of the transmitted symbols and the MIMO channel matrix can be obtained by maximizing over and mutually. Equally, the joint ML estimation can be obtained by minimizing the following cost function:

Namely, the joint ML CDE is obtained as follows:

Equation (10) demonstrates that the search for the optimal joint ML solution is over the discrete space of the transmitted symbols and the continuous space of the MIMO channel matrix mutually.

##### 2.1. Improved Cost Function

Equation (10) can be written as follows:where *B* represents the receiver antennas and *Q* symbols are transmitted. It is also shown that **H** and **D** accrue in second and third terms. Then, we let

Substituting the values from equation (13) in equation (12), we get

Equation (12) can be written as follows:

It means the joint ML CDE can be written as follows:

In this article, we have consigned fuzzy logic-empowered opposite learning mutant particle swarm optimization (FL-OLMPSO) for the joint channel and symbol estimation for the MIMO system. We have used three-layered methods. At one layer, a continuous version of FL-OLMPSO was exploited, and at the next layer, a soft version of discrete FL-OLMPSO was applied as shown in Table 1. FL-OLMPSO is the updated version of the OLMPSO algorithms proposed by Khan et al. [10].