1. INTRODUCTION

Due to rapidly growing wireless communication systems fast technological advancements are needed to meet the data transmission requirements of developing communication systems. Instead of physical wireline channels, wireless channels are used in the wireless communication system. Different types of services including multimedia communication and voice to data are provided by the wireless communication system. Rapidly growing wireless systems needs rapid technological advancements to enhance the capacity of current wireline systems. Undesired effects can be occurred due to affected wireless signals because of physical properties and complex interaction with the environment.

Due to signal scattering, around obstructing and large objects the electromagnetic waves diffraction some problems that occur between transmitter and receiver channel. Because of this problem signal effect with distortion, different attenuation, phase shift, delays and inference of signal for multipath may be Beneficial or detrimental. When destructive interface occurs the signal power may be diminished slightly.

For coherent or logically related detection of signal at receiving antenna accurate channel state information (CSI) or channel estimation (CE) is needed for most appropriate and enhancement in performance of wireless communication system. If CSI is not available at the receiver, then differential demodulation technique is used by non-coherent methods for detection and demodulation of a signal transmitted. Non-coherent methods as compared with the coherent detection method, due to the non-coherent detection method loss in SNR cost about 3–4 dB loss. This situation directs the research toward a coherent detection method at the receiver for providing Channel Staten Information (CSI) in wireless communication systems.

For detection of inferring signals compressive sensing is used by multi-user detection (MUD) as receiver technology. Sparse property occurs in transmitting signal vector due to a large number of non-zero elements in a situation in which mostly devices are not inactive state and compressive signal (CS) problem occurs while decoding of the transmitted signal at a receiver. Long term evolution is more suitable for a wireless communication system that has a high activity a small number of users.

Research directs toward the enhancement of channel capacity without affecting the quality of service for the modern wireless-based networks for communication. For resolving the issues of channel capacity and enhancing the data rates MIMO technique is more effective [1–4]. In the MIMO method, the estimation of signals at receiver and sender antennas is done [5] and increased the data rate and bandwidth of the channel capacity [6–9].

To enhance the corresponding technique some antennas (transmitter) and beneficiary radio are used. Depending on the amount of data conveyed by the MIMO framework increment, the transmitted information is calculated on several transmission paths [5]. While on the receiving point, some receiver antennas receive information to perform different calculations for the restoration of the information to convert the data into the original form at the receiver [10]. For remote communication, MIMO technology is regarded as mid-point and the amount of information without any additional data transfer capacity and transmitting power because of an increase in the scope and amount of information [11,12].

For enhancing the volume growth for several correspondences, the Orthogonal Frequency Division Multiplexing (OFDM) and multicarrier code division multiple access (MC-CDMA) can also be utilized with medium MIMO innovation technique [5–7,13]. One of the optimal detectors is the maximum likelihood (ML) method but is difficult to use for the achievement of exponential complexity. Minimum mean square error (MMSE) detector/locator in Machine to Machine (M2M) and the Maximum a Posteriori (MAP) or marginal likelihood detectors, the sub-optimal MUD detectors for example null steering or the zero forcing detector are used in a less complex situation. The data sent simultaneously by multiple servers to share multi-channel access on the bases of strategies used for demodulation which is the main issue of the MUD. The last two suboptimal techniques are very simple as they use matrix inversion. Evolutionary algorithms like fuzzy adaptive differential evolution (FADE), repeated weighted boosting search (RBS) and differential evolution algorithms (DEAs) are very useful for MUD and CE. Continues Search Space used for CE and discrete search space for MUD. The Multiuser-MIMO (MU MIMO) broadcasting techniques are most widely used for improvement of the spectral efficiency. The quality of pre-coding transmitting to dominate the multiuser inference degraded because of course knowledge of CSI at the transmitter. Hence, there are chances that the interface from co-scheduled user equipment will affect system throughput.

Therefore, the several new techniques like Particle Swarm Optimization (PSO), Total Opposite Mutant Particle Swarm Optimization (TOMPSO) [5,7,8,13], Partial Opposite Mutant Particle Swarm Optimization (POMPSO), Island Differential Equation (IDE), Genetic Algorithm (GA) and Island GA could be used for enhancing the performance and capacity of modern digital communication system [6,7,13–17].

In correspond for both antennas, i.e. sender and receiver, we performed CE for high data rates in this research. Some distortion added to the sending signal during transmission through the strength of the weakens signal and the channel, due to this scenario the receiver may not be able to get actual transmitted information. For avoidance of said situation, fuzzy logic used to enhance the channel and data estimation procedure [7,8]. In this article Fuzzy Logic empowered Cognitive and Social Information (FLeCSI)–based new variant of particle swarm intelligence technique used to fulfill/meet the requirements/demands of future generation communication systems.

In this research work, the MIMO system is used because it consists of a different number of users. It is assumed that the channel is cyclo stationary and flat fading.

The core contributions of the article are enumerated as follows:

1. Formulate an optimization problem for that the main objective is to minimize MMSE, Minimum Mean Channel Error (MMCE) and Bit Error Rate (BER) for optimum utilization of bandwidth.

2. For the better approximation of the user data and the channel coefficients, Fuzzy Logic empowered the Cognitive Social Information (FLeCSI) algorithm with two variants FLeCSI-TOLMPSO and FLeCSI-POLMPSO has been proposed.

3. Proposed FLeCSI method compared with various state of art algorithms like TOMPSO and POMPSO [13,18] and FL-OLMPSO [18]. Simulation results have shown that FLeCSI gives far better results as compared to other algorithms.

The sequence of the article is as follows: Literature review, MIMO system model, the proposed FLeCSI-based optimization problem is expressed in Sections 2, 3, and 4. Section 5 represents the simulation results and discussion. Afterward, the research work conclusion and future work in Sections 6 and 7.

2. LITERATURE REVIEW

PSO algorithm has been proposed by [19,20] based on the observation of bird's swarm in search of food. According to the PSO algorithm each bird acts as a particle updating his position on the bases of starting velocity, the last position visited personally, i.e. gathered information about neighboring best position from social activities, i.e. social component and cognitive component. PSO provides faster convergence in some optimization problems as compared to the evolutionary algorithm [21], the second thing is the variation of parameters is less. PSO is sensitive to fall into local optimum points when optimizing complex multidimensional functions that is its major drawback. Some qualities of PSO including greater and easy implementation researchers attracted to work with PSO. Several modifications have been done in the PSO algorithm that is applied in different applications of fuzzy logic, neural network [22] and many more.

For batter control over the velocity of swarm particles and to get overall better performance Inertia factor (w) inducted in standard PSO. On the basis of logistic human population growth, a new Incremental Particle size PSO (IPPSO) has been proposed in which the size increases with every iteration. For achieving an overall improvement in the performance of PSO a fast convergence PSO algorithm in which a new parameter named particle mean dimension has been proposed for the balance of diversity of each swarm particle [23]. The variant of the PSO algorithm that ensures convergence of the searching method with better solutions than standard PSO based on the construction factor approach [24]. Hybrid PSO incorporating dynamic chaos improved the efficiency of the algorithm [25]. For stable convergence of complex multidimensional problems, PSO particles ability have been explored by [24].

Exploration and exploitation capacity of GA and PSO have been compared [26]. A novel hybrid algorithm based on PSO and GA have been proposed [27]. Incorporated PSO with the technique of tournament selection where current best particle properties transferred into inferior ones. Suganthan et al. [28] proposed an algorithm named Local Best (LBest) model, in which the whole swarm population divided into a small neighborhood and each neighborhood maintain its LBest itself and is very well as in local optima solution it avoids trapping of the swarm but its convergence rate is very low. An adaptive inertia weight PSO algorithm has been proposed by [29].

A new variant of PSO have been introduced for reaching global optima reduce exploitation time and non-linear variation of inertia weight for improvement of search capacity in multidimensional space has been used [30]. By adding dynamic behavioral inertia factor and mutating the particles of PSO adaptively PSO performance has been improved [31]. An improved PSO technique proposed by [32] used for the identification of parameters in chaotic dynamical systems. Brits et al. [33] proposed the Niche PSO technique used for several global optimum solutions for multimodal optimization. An evolutionary algorithm BSA [34] like PSO while looking for global optima chooses the direction of an individual to move from any particles of the last generation randomly. A number of different unimodal optimization problems have been explored and exploited by using the PSO algorithm [35]. To include the effect of inertia term in PSO [36] studied the trajectories of general swarm particles. A novel reformed modified version of PSO like Cognitive and Social information-based Particle Swarm Optimization (CSI-PSO) proposed by [37] in which instead of the velocity concept of a swarm particle each swarm particle updates its position from its own position in search space using its personal and social information.

3. SYSTEM MODEL

Figure 1 shows “n” no. of antennas at the receiver end. After receiving signals frequency down is performed. Remove Cyclic Prefix which adds at the transmitter end and then convert it into Serial to Parallel (S/P). After S|P Fast Fourier Transform (FFT) is performed.

Figure 1

Proposed hybrid computational intelligence approach-based multiple input and multiple output receiver system model.

There are A transmit antennas and O receiving antennas. The channel implemented is a flat fading channel. During the communication process of S symbols, the channel is expected to be stationary. The received signal at receive antenna o is as [1]:

where i is the index of symbol, no.a is the flat fading channel coefficient that links transfer antenna a to receiver antenna o, ea(i) is i^th symbol transmitted from antenna a taking value from the symbol set {−1, +1} of Binary Phase Shift Key (BPSK) and vo(i) is Additive White Gaussian Noise (AWGN) with F|vo(i)|2=2σV2.

The MIMO channel equation below will represent the complete system:

where vi represents AWGN.

The transmitted symbol vector is

and the received signal vector is

The channel gain at receive antenna can always be normalized to unity.

where, No,a=no.a

Now defined a received data matrix with O × V dimensions and transmitted data matrix with A*V dimensions as [1]:

and respectively. Then the PDF of the received signal matrix R conditioned on the MIMO channel matrix H and the transmitted data matrix F can be written as

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

Namely, the joint ML CDE is obtained as

Equation (7) 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.

4. PROPOSED FUZZY LOGIC EMPOWERED CSI-BASED OPPOSITE SWARM OPTIMIZATION ALGORITHM

Opposite Swarm Optimization used with fuzzy logic to enhance the velocity with the help of fuzzy logic controller which takes two input parameter as local and global intelligence and provide the cognition and social information. Algorithm performed following different steps for the preparation and evaluation of data.

Step 1: To start and initialize the populaces of data P_a = {P_a1,P_a2,………………P_ae} and velocity V_d

Step 2: To calculate cost of population utilization.

Step 3: To calculate the value of Lower and Upper Bound (LP_b) and (UP_b) from P_b independently.

Step 4: To calculate the opposite populace given as:

• Total Opposite Learning

  Opposite Data Population

  ϘP_a = {ϘP_a1,ϘP_a2,………………ϘP_ae}

  ϘP_ai = {ϘP_ai,1, ϘP_ai,2,……………… ϘP_ai,M}

  ϘP_ai,j = Ɯƃ_p+ ƕƃ_a − P_ai,j.

• Partial Opposite Learning

  Opposite Data Population

  ϘP_a = {ϘP_a1,ϘP_a2,……………… ϘP_ae/2}

  ϘP_ai = {ϘP_ai,1, ϘP_ai,2,……………… ϘP_ai,M}

  ϘP_ai,j = Ɯƃ_p + ƕƃ_a − P_ai,j.

Step 5: To calculate the appropriateness of Opposite Populations(ϘP_a)

Step 6: Choose the value of data population in Local best particleϻ_bpa from P_a and ϘP_a and Global best particle of G_bpa from P_a and ϘP_a.

After performing the above-mentioned steps, select and update the velocity of Global best particle for data population

Step 7: Calculate Local & Global Intelligence:

•    (LI) = P_ij − LP_{bij (n−1)} and (GI) = P_ij − GP_{bij (n−1)}

   [FLCO, FLSO] = FLC (LI, GI)

   V_dij(n) = V_{dij(n − 1)} FLCO (Local Intelligence) LI + FLSO (Global intelligence)

Step 8: Calculate the data population particle of fitness and update the value of P_a.

Step 9: The same iterations perform for the next received signaluntil achieved the desired MSE or NoC.

Step 10: Finally Stop

The membership function of input and output variables used in the proposed system mathematically and graphically are presented below (Table 1).

5. CONCLUSION

Multiple Input Multiple Output (MIMO) technology is used for the wireless communication systems, when it comes to the capacity development of the channel. As the dynamic environment and growing number of users, data rate demand increased in growing modern wireless communication systems. MUD with artificial intelligence plays a vital role to enhance network capacity to meet the demands of future networks with an increased number of users and multimedia services. The proposed FLeCSI-based MIMO receiver system is fast enough to fulfill the needs of a future generation growing networks. FLeCSI provides more attractive results as compared to other suboptimal-based previous techniques It is also observed that the proposed FLeCSI-based receiver system minimizes BER and MMSE as compared to other techniques like FL-OMPSO, FL-POLMPSO, FL-TOLMPSO, TOMPSO, etc. discussed previously.

6. FUTURE WORK

In this article, the Selective fading channel is ignored. Typ2 fuzzy may be used for updating the Cognition and Social information of swarm intelligence. Deep machine learning may be explored on CE along MUD for better results. The application of this proposed FLeCSI can also be applied to medical imaging, channel equalization, spectrum sensing in cognitive radio, etc. The complexity comparison of proposed FLeCSI and their solution can be another point of research.

DATA AVAILABILITY

The simulation files to support the findings of this study are available from the corresponding author upon request.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article.

AUTHORS' CONTRIBUTIONS

Muhammad Asadullah, Muhammad Adnan Khan & Sagheer Abbas proposed the Improved Mathematical System Model & Algorithm. Muhammad Adnan khan, Muhammad Asadullah & Tahir Alyas performed the MATLAB Simulations. Muhammad Asadullah, Muhammad Asif Saleem and Areej Fatima contributed in writing—original draft preparation. Muhammad Adnan Khan and Sagheer Abbas performed revision and improve the quality of the draft. All authors have read and agreed to the published version of the manuscript.

Funding Statement

This research received no external funding.

ACKNOWLEDGMENTS

The authors would like to express their deepest gratitude to Dr. Shazia Saqib from Lahore Garrison University for the helpful suggestions during proof read this article.