Multi-objective optimization of cognitive radio networks

Abstract

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimal trade-off between the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst-case optimization (worst achieved result of a single KPI), our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage, and 4.3% in terms of global exposure.

Experimental Assessment of New Generation Radio Networks Based on Layered Division Multiplexing

“…2) Green-field Network Planning: First, based on the input parameters and constraints, the algorithm generates a greenfield network model with the initial settings and locations of the BSs (line 1 in Algorithm 1) This initial planning takes into account the minimum number of BSs that satisfy the Core Layer coverage requirements plus a 10% additional infrastructure as a margin for avoiding congestion. The users are randomly and uniformly distributed over the whole area [26], [28].…”

“…For each user u i demanding traffic in the area, the algorithm tries to find the connection to the BS j that best suits the network coverage objective [26]. For the BS j 's Core Layer maximum coverage range, the algorithm seeks if the u i is located in its range (line [32] 20 W Optical backhaul P obh [33] 19.5 W Digital Signal Processing (max) P DSP [34] 100 W Rectifier Prect [34] 60 W Transceiver (max) P TR [34] 100 W Amplifier efficiency ηamp 0.15 -4 to 6 in Algorithm 1) [26], [28]. The power consumption is minimized by applying two strategies.…”

Evaluation of the performance of a collaborative proposal of multiple access in cognitive radio networks

“…By 2023, more than 70 % of the world's population will have mobile connectivity, and total mobile data traffic is estimated to grow to 49 exabytes per month by 2021 (CISCO, 2021). Paradoxically, several extensive spectrum usage measurement campaigns have shown that some bands are overused (unlicensed bands) while other bands are underused (licensed bands) (Martinez Alonso et al, 2021).…”

“…The inefficient distribution of spectrum and the exponential growth of demand for wireless applications has become one of the main concerns of communications (Martinez Alonso et al, 2021). CR offers a set of solutions by using spectrum dynamically, giving the communication system the ability to reconfigure itself based on the circumstances of congestion, traffic load, propagation of wireless channels, among others (Dinesh et al, 2021).…”

Outlier Detection and Spectrum Feature Extraction Based on Nearest-Neighbors Correlation and Random Forest Algorithm

“…This negative trend is having an impact also on the energy efficiency of wireless networks and the operators' cost-efficiency. Paradoxically, spectrum surveys performed worldwide demonstrate that the spectrum utilization efficiency, as the ratio between the allocated spectrum and the spectrum really in use at any given location and instant of time, is lower than 25% [2], [3] and below 11% in rural areas [4]. In this context next generation technologies (e.g., 6G) will require a more sustainable and VLIR-UOS Project: CU2022TEA498A103 efficient paradigm for spectrum management and allocation based on Dynamic Spectrum Access (DSA) [5].…”