scholarly journals Hybrid NOMA/OMA-Based Dynamic Power Allocation Scheme Using Deep Reinforcement Learning in 5G Networks

2020 ◽  
Vol 10 (12) ◽  
pp. 4236 ◽  
Author(s):  
Hoang Thi Huong Giang ◽  
Tran Nhut Khai Hoan ◽  
Pham Duy Thanh ◽  
Insoo Koo
Keyword(s):  
Reinforcement Learning ◽  
Interference Cancellation ◽  
Bandwidth Allocation ◽  
Base Station ◽  
Spectrum Efficiency ◽  
Superior Performance ◽  
Joint Power ◽  
User Pairing ◽  
Fifth Generation

Non-orthogonal multiple access (NOMA) is considered a potential technique in fifth-generation (5G). Nevertheless, it is relatively complex when applying NOMA to a massive access scenario. Thus, in this paper, a hybrid NOMA/OMA scheme is considered for uplink wireless transmission systems where multiple cognitive users (CUs) can simultaneously transmit their data to a cognitive base station (CBS). We adopt a user-pairing algorithm in which the CUs are grouped into multiple pairs, and each group is assigned to an orthogonal sub-channel such that each user in a pair applies NOMA to transmit data to the CBS without causing interference with other groups. Subsequently, the signal transmitted by the CUs of each NOMA group can be independently retrieved by using successive interference cancellation (SIC). The CUs are assumed to harvest solar energy to maintain operations. Moreover, joint power and bandwidth allocation is taken into account at the CBS to optimize energy and spectrum efficiency in order to obtain the maximum long-term data rate for the system. To this end, we propose a deep actor-critic reinforcement learning (DACRL) algorithm to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment. Meanwhile, the critic can evaluate the action taken such that the CBS can optimally assign power and bandwidth to the CUs when the training phase finishes. Numerical results validate the superior performance of the proposed scheme, compared with other conventional schemes.

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

scholarly journals On the performance of non-orthogonal multiple access (NOMA) using FPGA

2020 ◽  
Vol 10 (2) ◽  
pp. 2151
Author(s):  
Mohamad Abdulrahman Ahmed ◽  
Khalid F. Mahmmod ◽  
Mohammed M. Azeez
Keyword(s):  
Gaussian Noise ◽  
Interference Cancellation ◽  
Multiple Access ◽  
Additive White Gaussian Noise ◽  
Base Station ◽  
Rate Performance ◽  
Allocation Mechanism ◽  
Close Match ◽  
Fifth Generation ◽  
Field Programmable

In this paper,  non-orthogonal multiple access (NOMA) is designed and implemented for the fifth generation (5G) of multi-user wireless communication.  Field-programmable gate array (FPGA) is considered for the implementation of this technique for two users. NOMA is applied in downlink phase of the base-station (BS) by applying power allocation mechanism for far and near users, in which one signal contains the superposition of two scaled signals depending on the distance of each user from the BS.  We assume an additive white Gaussian noise (AWGN) channel for each user in the presence of the interference due to the non-orthogonality between the two users’ signals. Therefore, successive-interference cancellation (SIC) is exploited to remove the undesired signal of the other user. The outage probability and the bit-error rate performance are presented over different signal-to-interference-plus-noise ratio (SINR). Furthermore, Monte-Carlo simulations via Matlab are utilized to verify the results obtained by FPGA, which show exact-close match.

scholarly journals Decentralized and Dynamic Band Selection in Uplink Enhanced Licensed-Assisted Access: Deep Reinforcement Learning Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fitsum Debebe Tilahun ◽  
Chung G. Kang
Keyword(s):  
Reinforcement Learning ◽  
Resource Utilization ◽  
Base Station ◽  
Band Selection ◽  
Selection Strategy ◽  
Operational Mode ◽  
Selection Scheme ◽  
Aggregation Technology

Enhanced licensed-assisted access (eLAA) is an operational mode that allows the use of unlicensed band to support long-term evolution (LTE) service via carrier aggregation technology. The extension of additional bandwidth is beneficial to meet the demands of the growing mobile traffic. In the uplink eLAA, which is prone to unexpected interference from WiFi access points, resource scheduling by the base station, and then performing a listen before talk (LBT) mechanism by the users can seriously affect the resource utilization. In this paper, we present a decentralized deep reinforcement learning (DRL)-based approach in which each user independently learns dynamic band selection strategy that maximizes its own rate. Through extensive simulations, we show that the proposed DRL-based band selection scheme improves resource utilization while supporting certain minimum quality of service (QoS).

scholarly journals Self-Interference Cancellation: A Comprehensive Review from Circuits and Fields Perspectives

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 172
Author(s):  
Jiahao Zhang ◽  
Fangmin He ◽  
Wei Li ◽  
Yi Li ◽  
Qing Wang ◽  
...  
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Base Station ◽  
The Self ◽  
Spectrum Efficiency ◽  
Full Duplex ◽  
Interference Signal ◽  
Comprehensive Review ◽  
To Come ◽  
Transmitting Antenna

Increased demand for higher spectrum efficiency, especially in the space-limited chip, base station, and vehicle environments, has spawned the development of full-duplex communications, which enable the transmitting and receiving to occur simultaneously at the same frequency. The key challenge in this full-duplex communication paradigm is to reduce the self-interference as much as possible, ideally, down to the noise floor. This paper provides a comprehensive review of the self-interference cancellation (SIC) techniques for co-located communication systems from a circuits and fields perspective. The self-interference occurs when the transmitting antenna and the receiving antenna are co-located, which significantly degrade the system performance of the receiver, in terms of the receiver desensitization, signal masking, or even damage of hardwares. By introducing the SIC techniques, the self-interference can be suppressed and the weak desired signal from the remote transmitter can be recovered. This, therefore, enables the full-duplex communications to come into the picture. The SIC techniques are classified into two main categories: the traditional circuit-domain SICs and the novel field-domain SICs, according to the method of how to rebuild and subtract the self-interference signal. In this review paper, the field-domain SIC method is systematically summarized for the first time, including the theoretical analysis and the application remarks. Some typical SIC approaches are presented and the future works are outlooked.

scholarly journals Cross-Layer Resource Allocation for Multihop V2X Communications

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yanhua He ◽  
Liangrui Tang ◽  
Yun Ren ◽  
Jonathan Rodriguez ◽  
Shahid Mumtaz
Keyword(s):  
Resource Allocation ◽  
Power Allocation ◽  
Rate Control ◽  
Relay Selection ◽  
Superior Performance ◽  
Cross Layer ◽  
Joint Power ◽  
Resource Allocation Algorithm ◽  
Mobile Relay

Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

scholarly journals Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7094
Author(s):  
Jaehee Lee ◽  
Jaewoo So
Keyword(s):  
Reinforcement Learning ◽  
Computational Complexity ◽  
Power Allocation ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Spectrum Efficiency ◽  
Q Learning ◽  
User Pairing ◽  
Input Multiple Output ◽  
Radio Resources

In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).

scholarly journals Artificial Intelligence Based LTE MIMO Antenna for 5th Generation Mobile Networks

2020 ◽  
Vol 2 (3) ◽  
pp. 155-162
Author(s):  
Dr. Abul Bashar
Keyword(s):  
Artificial Intelligence ◽  
Mobile Networks ◽  
Multiple Input Multiple Output ◽  
Long Term Evolution ◽  
Base Station ◽  
Fifth Generation ◽  
Term Evolution ◽  
Multiple Input ◽  
Input Multiple Output

Artificial intelligence based long term evolution multi in multi output antenna supporting the fifth generation mobile networks is put forth in the paper. The mechanism laid out in paper is devised using the monopole-antenna integrated with the switchable pattern. The long term evolution based multiple input and multiple output antenna is equipped with four antennas and capable of providing a four concurrent data streams quadrupling the theoretical maximum speed of data transfer allowing the base station to convey four diverse signals through four diverse transmit antennas for a single user equipment. The utilization of the long term evolution multiple input multiple output is capable of utilizing the multi-trial broadcasting to offer betterments in the signal performance as well as throughput and spectral efficiency when used along the fifth generation mobile networks. So the paper proposes the artificial intelligence based long term evolution multiple input multiple output four transmit antenna with four diverse signal transmission capacity that is operating in the frequency of 3.501 Gigahertz frequency. The laid out design is evaluated using the Multi-input Multi output signal analyzer to acquire the capacity of the passive conveyance of the various antennas with the diverse combination of patterns. The outcomes observed enables the artificial intelligence antenna to identify the choicest antenna to be integrated in the diverse environments for improving the throughput, signal performance and the data conveyance speed.

scholarly journals Multiple Unmanned Aerial Vehicles Deployment and User Pairing for Non-Orthogonal Multiple Access Schemes

2020 ◽  
Author(s):  
Jie Wang ◽  
Miao Liu ◽  
Jinlong Sun ◽  
Guan Gui ◽  
Haris Gacanin ◽  
...  
Keyword(s):  
Wireless Communications ◽  
Unmanned Aerial Vehicles ◽  
Message Passing ◽  
Multiple Access ◽  
Base Station ◽  
Spectrum Efficiency ◽  
User Pairing ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Non-orthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand of higher SE in the NOMA based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this paper proposes a multiple unmanned aerial vehicles (UAVs) aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a part of ground users (GUs) and share the sub-channels with the left GUs that communicate with the base station (BS) directly. Furthermore, this paper proposes a K-means clustering based UAV deployment and location based user pairing scheme to optimize the transceiver association for the multiple UAVs aided NOMA uplinks. Finally, a sum power minimization based resource allocation problem is formulated with the lowest quality of service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive experiments are conducted to compare the performances of the proposed schemes with those of the single UAV aided NOMA uplinks, G2G based NOMA uplinks, and the proposed multiple UAVs aided uplinks with a random UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and user pairing based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of the UAVs.

A joint power and bandwidth allocation method based on deep reinforcement learning for V2V communications in 5G

2021 ◽  
Vol 18 (7) ◽  
pp. 25-35
Author(s):  
Xin Hu ◽  
Sujie Xu ◽  
Libing Wang ◽  
Yin Wang ◽  
Zhijun Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Bandwidth Allocation ◽  
Joint Power ◽  
Allocation Method ◽  
V2v Communications

scholarly journals Interference Cancellation Techniques in Heterogeneous Networks

2018 ◽  
Vol 43 (3) ◽  
pp. 153-180
Author(s):  
Sarah Imam ◽  
Ahmed El-Mahdy
Keyword(s):  
Heterogeneous Networks ◽  
Interference Cancellation ◽  
Superior Performance ◽  
Alignment Algorithm ◽  
Zero Forcing ◽  
Fifth Generation ◽  
Data Rates ◽  
Interference Signals ◽  
Cellular Wireless Networks

Abstract The fifth generation (5G) cellular wireless networks are heterogeneous networks that will provide higher data rates, enhanced quality-of-experience (QoE) and reduced latency. Interference is one of the main problems in such systems. In this paper, a proposed cross-tier uplink interference alignment algorithm for coexisting two-tier networks is introduced. First at each receiver, the sum of the square of the channel gains from each macro user to this receiver is calculated and the average of these values is taken as a threshold. Macro users whose sum values are greater than this threshold are selected and aligned at the femto receiver. This alignment is performed by using precoders at the transmitters. Then, Zero Forcing technique is applied at the receivers in order to null the aligned interference signals. Numerical results demonstrate the superior performance of the proposed algorithm.

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