Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communications in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense environment called heterogeneous networks (HetNet). The optimization problem which arises while maximizing the Mean Opinion Score (MOS) is analyzed along with the QoE(Quality of Experience) metric by considering the Base Station(BS) powers in addition to the needed Quality of Service (QoS). Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature. Hence a deep reinforcement learning framework is developed for tackling the same optimization problem. In this work, a Fuzzy-based Deep Convolutional Neural Network (FDCNN) is proposed in addition to a Deep Reinforcing Learning Framework (DRLF) for extracting the features of highly correlated data. The investigational results prove that the proposed method yields the highest satisfaction to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations. The proposed work outperforms in terms of MOS with multiple antennas.

One of the essential factors in the success of future wireless networks is ensuring that the user experience is as good as possible. For a massive MIMO heterogeneous network(HetNet), we investigate how to best allocate resources for the downlink based on the user’s perception of the quality of experience [

When evaluating the quality of service (QoS) of a network, the emphasis is on measuring the network’s technical performance rather than the experience of its users, which is a common practice [

Traditionally, in wireless networks, predominantly in MaMIMO Nets, the measures for distributing resources to users and enlightening the network performance have been based on Quality of Service (QoS) parameters. In order to improve network parameters, it has only recently been proposed to look at the perception of Quality of Experience (QoE) (also known as Quality of Service) (also known as Quality of Service). When it comes to meeting user demand, it appears that QoE-based resource allocation is more operative than QoS-based resource allocation [

In heterogeneous millimeter-wave mobile networks, base station cooperation, in which multiple BSs serve the user at the same time, can result in significant coverage expansion, particularly in urban environments. The probability that a user is only connected to one or more LOS BSs was calculated and examined in the case of the example used in [

Compressive sensing techniques, which were developed specifically for this purpose, were required in order to accurately estimate the available parameters (angles of arrival as well as the departure, path gains) of the corresponding sparse channel, which were difficult to estimate otherwise. Even though compressed channel estimation based techniques which could reduce the intense training overhead, a significant amount of time is still required for the initial training phase [

In this work, the various issues in handling the millimeter-wave band are analyzed. The main problem faced is the attenuation loss which occurs due to the usage of the high frequencies. It is compensated by using the massive array of structured MIMO with heterogeneous networks. The optimization problem is also tackled here. The flow of work corresponding to the proposed model is shown in

Here the MOS, QoE, and QoS are considered the metric for dealing with optimization-related problems. In order to handle the complexity-related issues, the DRLF is implied along with FDCNN.

The occurrence of attenuation losses might be compensated with MIMO and HetNet. Here a modified massive array structured MMaMIMO with HetNet which consists of _{s} small cells which could be prearranged in the area of short coverage regions of the macro cell is shown in

The macro base station (MBS) along with the small cell base station (SBS) might use the non-coherent transmission that are coordinated with multipoint beam forming are used for providing the services related to video or internet browsing for ^{th} SBS is mentioned as N_{k}. The received signal at user U_{1} is mathematically expressed as

The macro base stations along with the small cell base stations might be connected to the available network which enables the soft cell resource allocation for non-coherent non-linear transmissions by serving the individual transmitters with coded information sysmbols which is emitted independently.

The information symbols which could be taken from the base station and the k^{th} SBS to the user U_{1} is represented with

The corresponding vectors for beamforming are represented by optimizing the needed variables which is mentioned in this paper. The elements _{1}. The assignment related to the transmitter is gathered automatically from the optimization problem solved earlier.

The MOS is considered as a measure of qualitative data for assessing the QoE and QoS that might be signified in terms of the impartial mathematical restrictions. The relation which is considered experimentally in between the QoE in addition with the QoS is articulated mathematically as given in

Here the constants T_{1} and T_{2} are selected to bring the MOS (internet) value to be in the range 1 to 10. Additionally, s_{1}(R) and s_{2}(R) represent the response time of the page or the delay that exists in between the web page request and the reception of the search contents. s_{1}(R) and s_{2}(R) might depend on the parameters representing the size of the web page, the total round trip time and the various types of protocols that are used might be expressed as

where IS[bit] represents the internet web page size and BW[Hz] represents the bandwidth in hertz and SS[bit] represents the segment size, where the maximum values is taken. Here L is represented as L = min[L_{1},L_{2}], which is observed as a parameter that characterizes the number of slow start cycles which is considered as the ideal periods.

The values of L_{1} and L_{2} is represented using the formula

Then the services related to the video is considered for calculating the MOS value and is represented as

where b and e are considered as the two available coefficients which is nominated in its own way that the representing value corresponding to the MOS(video) might get fall in the specified range from 1 to 10. The PSNR value is mathematically noted as

where α, β, and γ are the parameters required for categorizing the explicit video stream. The sub channels that is available in between the users and the SBS or MBS is demonstrated as flat fading channels.

The channel exists between the i^{th} user along with the j^{th} user in SBS is expressed by ^{th} user is mathematically expressed as

where _{0}, X_{i} is attained in order to apply the appropriate corresponding vectors at the base station is mathematically expressed as

where ^{th} user and s_{l,j} is considered as the information symbol which is transmitted to the base station.

Fuzzy based deep convolutional neural networks (FDCNN) which have been exposed to ensure efficient and effective use of the temporal aspect of data in addition to simulating nonlinear relationships in input data. In millimeter wave communication the base station (BS) or the access points are concurrently serving the mobile station (MS). Here one mobile station is used and four base stations are used. The BS is fortified with ‘N’ antennas and the corresponding BS is connected to a centralised processing unit as shown in

Each base station has only one chain containing radio frequency and when it is applied to the analog communication link i.e., the beamforming-based networks with a change in phase shift. The extension for pertaining the more sophisticated millimeter wave architectures is analysed at the base station. The channel data might contain all needed information’s related to the beamforming section. The main aim of the FDCNN is to excerpt the valuable needed features from the available data. The information gathering is from video signals and some internet page contents. The collected contents are then detected, compressed and filtered for forming the contents for segmentation. Totally two different outputs are obtained one with the segmented image and other with the convoluted image. Here the mobile user is having a solitary antenna, where the designed algorithms along with the possible solutions are protracted to the multi-antenna users.

Considering the down link related transmission, the available antenna data symbol d_{s}∈μ taken at the sub carrier C = 1,2,….c. this is initially precoded by using the N × M digital precoder shown in

The final output is obtained from the resulting symbols which is transmuted to time domain in order to use the possible n-numbered K point inverse Fourier transforms. The cyclic prefix code is added to the blocks of the symbols before moving it back to the base stations by using the wired or optical fiber channels. Each BS is applied to the analog beamforming section and the obtained resulting signal is transmitted. The discrete time signal which is transmitted ro the base station is taken at the k^{th} subcarrier and is expressed as

Here the transmitted signal which is assumed to be

The downlink procedure of the HetNet (5G) is examined in this part, which involves a macro cell with an approximated radius of 350 metres and four tiny cells with a radius of 50 metres that are installed in the precise location. The four chosen SBS are evenly placed within a radius of 100 m centred at MBS. If a scenario with eight users in the macro cell and one user in each tiny cell is supplied, where the total user is (U = 12). The users are made to distribute uniformly by covering the area within the radius of 50 and 350 m for the individuals who use the macro cells and each user uses one cell (small cell). considerably all the SBS is assumed to have equal number of antennas and hence N_{c} = N, for all values of c ranges from 1 to n. The powers consumed by all the antennas are noted to be 14 dbm for MBS and −9 dbm for SBS. The proposed work is analyzed using Network Simulator-2 (NS-2) tool.

The band width of the subcarriers is marked to be 12 kHz. The penetration loss and the path loss is noted at a distance of ‘s’ km taken in between the MBS and SBS. This distance for MBS is marked to be 136.54 + 23 log_{10}(s) dB and for SBS it is marked to be 112 + 27 log_{10}(s). The standard deviation is considered to be 10 dB for log-related shadow fading. The small-scale fading part of the channels is designed by considering the separate Rayleigh variables. Here the results obtained for the different services which is offered for the browsing related to intense and the video signal related information taken out separately. The major parameter considered for analyzing the proposed work is the Average MOS for which the different antennas were used, and the performance is compared with existing methodologies.

For services related to the intenet browsing the number of users are assumed to be from 1 to 8, individually for accepting the page sizes of the website with 50, 100, 150, 200, 250, 300, 350 kB. Let us consider the minimum spectral efficiency and maximum spectral efficiency for individual users are limited to 2 bits per seconds to 10 bits per second. T_{1} and T_{2} is obtained by considering the MOS value as minimum to R minimum and then the maximum to the R maximum which delivers the output value T_{1} = 4.432 and T_{2} = 12.876.

The _{c} as N_{c} = 0. Then for obtaining the average MOS, the accumulated MOS for all the users is separated by K. Hence for obtaining the average MOS value, the 5G HetNet is made better than other homogeneous networks. The value of M = 10 or M-20 is made available by adding small cells or by removing the small cells for keeping the network lead upto about 12% to 28% which shows some certain improvement in the MOS consideration. Additionally, the MBS antenna increases from 10% to 80% for enhancing the average MOS value in the homogeneous networks and the heterogeneous networks. This could be limited for the values from 20% for 5G homogeneous networks and 6% for 5G heternogeneous networks. The employment of the small cells and the Macro cells in the whole network might leads to attract more users.Then the emergency in adding small cells will make the base stations to have more number of antennas. The fig indicates the same by increasing the SBS antenna numbers, thus improving the average MOS network.The final result indicates that the obtained fig is provided with a good averare value of MOS which could be obtained by setting the homogeneous MMaMIMO MBS and the heterogeneous 5G MMaMIMO MBS along with very less number of antennas held at the MBS.If the average MOS value is set up to about 5.4 might reached upto certain enhancement eitehr by setting N = 50 for homogeneous networks and N = 25 for heterogeneous networks along with some SBS.

The

In this work the video services are considered in addition with the internet related services. The network parameters which are considered similar might be considered as like the previous case. The considered parameters are designed by using the PSNR value that exists in between the 30 to 40 db. These parameters are x = 26.234, y = 0.054 and z = 4.564. The

The suggested synchronized deep-learning based beamforming method is assessed in this part, and its potential to serve extremely maneuverable mmWave applications is demonstrated. The suggested deep learning method is then shown to be able to accurately predict beamforming orientations and approaching the ideal effective attainable rate. The effect of the system’s primary networking and machine learning settings on effectiveness will investigate the most needed aspects of integrated communication and learning system which is suitable to adapt to the millimetre-wave environment and its sensitivity to synchronize the base station for showing its performance along with the untrained scenarios.

The FDCNN model is typically trained in order to predict the radiofrequency of the beamforming vector by considering the 512 + 512 selected beams for each base station. Normally the fuzzy-based deep convolutional neural network is used for coordinating the beamforming region which is trained by considering the dataset with size 20 k samples for achieving the better speed and the best antennas for base stations. The

Finally, it is clear that the

The enhancement in the quality of experience faced by the user for for multimedia-related applications over the millimeter-wave band is investigated in this approach. The high attenuation loss in high frequencies is compensated with a massive array structure named MMaMIMO (Multiple-Input and Multiple-Output) HetNet(5G). The optimization problem which arises while maximizing the mean opinion score (MOS) is analyzed along with the QoE (Quality of Experience) metric by considering the base station (BS) powers in addition to the needed quality of service (QoS). The proposed work yields a better MoS and Quality of Service (QoS) and is proved in result analysis section. For solving the problems related to complexity and the dynamic nature might be resolved by using a deep reinforcement framework along with a fuzzy-based deep convolutional neural network (FDCNN) for extracting the features of highly correlated statistical information. The results obtained after experimentation proves that the method proposed might yield the highest satisfaction to the user by maximizing the antenna numbers and increasing the small-scale antennas at the base stations.