Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Abstract

The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

WITHDRAWN: A Survey on the Performance Analysis of NOMA-Assisted Cognitive Radio Sensor Networks

“…The secondary transmitter plays a supportive role in the primary transmission by employing NOMA for its transmission. In a study conducted by the author [58], the incorporation of spatial modulation techniques into NOMA is highlighted as a promising strategy for enhancing energy efficiency in densely connected wireless networks. Originally developed for scenarios involving multiple antennas, such as MIMO systems, the spatial modulation technique offers benefits in terms of signal-tonoise ratio performance when compared to conventional modulation schemes.…”

Clustering Method for Signals in the Wideband RF Spectrum Using Semi-Supervised Deep Contrastive Learning

“…An example spectrogram, labeled in rfspec-db [22] as 000310 with signal numbers overlaid, is shown in The distance vector for signal 4 is presented in Equation (5). From this vector, we can infer that the closest signal to signal 4 is signal 8 (distance of 0.84), followed by signal 6 (distance of 5.71).…”

“…The radio spectrum contains wideband and narrowband signals, analog and digital signals, and continuous and pulsed signals, with modulations of amplitude, phase, and frequency, as well as their combinations. New multiplexing and channel access techniques are employed, such as orthogonal frequency-division multiplexing (OFDM), non-orthogonal multiple access (NOMA) [4,5], or spectrum spreading using pseudorandom sequences (CDMA or FHSS). The methods of phase drift correction are also widely used to improve the quality of transmission [6,7].…”

The F/DR-D-10 Algorithm: A Novel Heuristic Strategy to Solve the Minimum Span Frequency Assignment Problem Embedded in Mobile Applications

“…On the other hand, a wide range of modern Bio-inspired Evolutionary Algorithms [21] have been developed for more difficult optimization problems. As examples, we find Genetic Algorithms (GA) [22][23][24][25], Neural Networks (NN) [26,27], Particle Swarm Optimization (PSO) [28,29], Ant Colony Algorithms (ACO) [30], Grey Wolf algorithms (GWO) [31,32], Artificial Bee Colony Algorithms (ABC) [33,34], Firefly Algorithms (FA) [35,36], Whale Algorithms (WOA) [37], Quantum-based Avian Navigation Algorithms (QANA) [38], Zebra Optimization Algorithms (ZOA) [39], and the more general Swarm Intelligence (SI) [40].…”