In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.

Mobile Ad-hoc Network (MANET) is a compilation of independent wireless mobile nodes which animatedly forms a temporary network without use of any fixed infrastructure or centralized management. A major anxiety that affects such a network that characterized by dynamically changing topology is the performance, where routing with robustness performance is one of the key challenges in deploying MANET. In this work we concentrate on routing protocols which are widely used in MANET, Destination Sequenced Distance vector (DSDV), Dynamic Source Routing(DSR) and Ad hoc On demand Distance Vector(AODV) routing protocols that are widely simulated in this paper using different scenarios in terms of different Traffic types, constant bit rate(CBR), variable bit rate(VBR) then combining both classes in one scenario to scrutinize the impact of this combination. Routing protocols are analyzed against several performance metrics, Average energy consumption, Average throughput, Normalized routing load (NRL), packet delivery fraction(PDF) and total dropped packets(TDP). Combined traffic results pronounce that DSR and AODV exhibit better behaviors overall the performance metrics examined.

In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.

A mobile ad-hoc network (MANET) is a network of mobile nodes that dynamically form a transitory network lacking any existence of infrastructure and any form of centralized management. Nodes in ad hoc networks are powered by batteries with a limited lifespan and communicate in a restricted bandwidth. The unpredictable environment of a MANET may run into a major concern in the routing mechanism, therefore the need for a routing protocol with robust performance is still one of the key challenges in MANET deployment. In this work, a comparative comparison and extensive simulation analysis have been carried out for three major routing protocols: destination sequenced distance vector (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). Protocol evaluation has been extended by considering several simulation arrangements, different classes of traffic load patterns and diverse performance metrics. Based on packet rate change, node quantity and node speed, simulation scenarios were generated. Protocols were investigated against energy consumption, throughput, lost packets, routing load and packet delivery fraction for three types of traffic load patterns regular, irregular and joint traffic. DSR and AODV protocols proved to be more reliable when joint traffic was implemented when node speed and packets variations are considered. DSDV protocol verifies outstanding response over other protocols in terms of energy consumption when either regular or irregular traffic is applied. The simulation results for DSR protocol have verified the superiority over other protocols in 9 simulation scenarios when diverse metrics are considered. DSDV showed optimal performance in 7 cases, especially at low packet rates and in networks with minimum number of nodes. Similarly, AODV protocol showed outstanding performance in 6 scenarios, when higher packet rates and node mobility are considered.

This study shows how the channel estimation based Deep Learning (DL) and a power allocation method are together employed for multi-user detection in a Power domain Non-Orthogonal Multiple Access (PD-NOMA) network. Successive interference cancellation (SIC) procedure is typically employed at receiver side, where numerous users are decoded in a successive approach. Fading channels may scatter transferred signal and initiate dependencies between scattered components, this might influence the channel estimation technique and therefore impact the SIC procedure and signal recognition precision. In our proposed scheme, the influence of Deep Neural Network (DNN) in clearly approximating the channel parameters for users in NOMA cell is inspected. In our scenario, we incorporate the Long Short Term Memory (LSTM) layer with NOMA cell where the LSTM is employed for complex data management to perform training and predication. The DNN is trained online on basis of random channel models and then the trained network is used to approximate the channel taps that will be utilized by the receiver in recovering the desired symbols. Additionally, power factors for user’s devices are optimized to maximize the sum-rate of users where whole power and Quality of service (QoS) restrictions are considered. Simulation outcomes in terms of Bit Error Rate (BER), Outage probability, and sum rate have shown the dominance of the suggested channel estimation using DL over standard estimation approach. Moreover, both fixed power and optimized power schemes are also assessed when DNN is applied.

This paper analyzes the power optimization for a downlink multi-input single-output non orthogonal multiple access (MISO-NOMA) system. Power coefficients are optimized in order to maximize the sum throughput of the system users based on the total transmitted power and Quality of service (QoS) constraints. First, we formulate a simple expression for Signal to interference noise ratio (SINR) for each user in MISO-NOMA system, then an analysis for the optimization problem and the considered constraints to prove the concavity of the objective function is presented. Lagrange function and Karush-Kuhn–Tucker (KKT) optimality conditions are utilized to derive the optimal power coefficients. Simulations are conducted and optimization solver is used to investigate the improvement achieved when power coefficients are optimized compared to fixed power scheme. Simulation results are conducted in terms of sum-rate, outage probability, SIC error and users’ individual rates. Results revealed that optimized power scheme is more satisfactory than fixed power scheme for far user than near user and both schemes are superior compared to conventional orthogonal multiple access (OMA) scheme.