30,951 research outputs found
Wireless Powered Cooperative Relaying using NOMA with Imperfect CSI
The impact of imperfect channel state (CSI) information in an energy
harvesting (EH) cooperative non-orthogonal multiple access (NOMA) network,
consisting of a source, two users, and an EH relay is investigated in this
paper. The relay is not equipped with a fixed power source and acts as a
wireless powered node to help signal transmission to the users. Closed-form
expressions for the outage probability of both users are derived under
imperfect CSI for two different power allocation strategies namely fixed and
dynamic power allocation. Monte Carlo simulations are used to numerically
evaluate the effect of imperfect CSI. These results confirm the theoretical
outage analysis and show that NOMA can outperform orthogonal multiple access
even with imperfect CSI.Comment: 6 pages, 6 figures, accepted in IEEE GLOBECOM 2018 NOMA Worksho
A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment
The application of multiple-input multiple-output (MIMO) techniques to
non-orthogonal multiple access (NOMA) systems is important to enhance the
performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for
downlink and uplink transmission is proposed by applying the concept of signal
alignment. By using stochastic geometry, closed-form analytical results are
developed to facilitate the performance evaluation of the proposed framework
for randomly deployed users and interferers. The impact of different power
allocation strategies, such as fixed power allocation and cognitive radio
inspired power allocation, on the performance of MIMO-NOMA is also
investigated. Computer simulation results are provided to demonstrate the
performance of the proposed framework and the accuracy of the developed
analytical results
On secure NOMA systems with transmit antenna selection schemes
This paper investigates the secrecy performance of a two-user downlink non-orthogonal multiple access systems. Both single-input and single-output and multiple-input and single-output systems with different transmit antenna selection (TAS) strategies are considered. Depending on whether the base station has the global channel state information of both the main and wiretap channels, the exact closed-form expressions for the secrecy outage probability (SOP) with suboptimal antenna selection and optimal antenna selection schemes are obtained and compared with the traditional space-time transmission scheme. To obtain further insights, the asymptotic analysis of the SOP in high average channel power gains regime is presented and it is found that the secrecy diversity order for all the TAS schemes with fixed power allocation is zero. Furthermore, an effective power allocation scheme is proposed to obtain the non-zero diversity order with all the TAS schemes. Monte Carlo simulations are performed to verify the proposed analytical results
AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems
In this paper, we investigate the performance enhancement of Multiple Input, Multiple Output, and Non-Orthogonal Multiple Access (MIMO-NOMA) wireless communication systems using an Artificial Intelligence (AI) based Q-Learning reinforcement learning approach. The primary challenge addressed is the optimization of power allocation in a MIMO-NOMA system, a complex task given the non-convex nature of the problem. Our proposed Q-Learning approach adaptively adjusts power allocation strategy for proximal and distant users, optimizing the trade-off between various conflicting metrics and significantly improving the system’s performance. Compared to traditional power allocation strategies, our approach showed superior performance across three principal parameters: spectral efficiency, achievable sum rate, and energy efficiency. Specifically, our methodology achieved approximately a 140% increase in the achievable sum rate and about 93% improvement in energy efficiency at a transmitted power of 20 dB while also enhancing spectral efficiency by approximately 88.6% at 30 dB transmitted Power. These results underscore the potential of reinforcement learning techniques, particularly Q-Learning, as practical solutions for complex optimization problems in wireless communication systems. Future research may investigate the inclusion of enhanced channel simulations and network limitations into the machine learning framework to assess the feasibility and resilience of such intelligent approaches
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