Abstract:We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters
f
,
g
. We present a series of simple and practical-for-implementation greedy algorithms … Show more
“…[43,56,57] and [58,59], respectively. 20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20] P2 D 2 := [5, 5,5,8,12,25,30,25,30,40,40,45,20,30,25,15,15,30,20,20,25] P3 25,8,8,8,15,18,52,77,28,…”
Section: Methodsmentioning
confidence: 99%
“…These strategies rely on logical or procedural rules closely aligned with the problem's nature. Noteworthy among the successful heuristics employed in channel assignment are Taboo Search [13,14], Simulated Annealing [15,16], and Greedy Algorithms [17][18][19][20]. On the other hand, a wide range of modern Bio-inspired Evolutionary Algorithms [21] have been developed for more difficult optimization problems.…”
Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%.
“…[43,56,57] and [58,59], respectively. 20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20] P2 D 2 := [5, 5,5,8,12,25,30,25,30,40,40,45,20,30,25,15,15,30,20,20,25] P3 25,8,8,8,15,18,52,77,28,…”
Section: Methodsmentioning
confidence: 99%
“…These strategies rely on logical or procedural rules closely aligned with the problem's nature. Noteworthy among the successful heuristics employed in channel assignment are Taboo Search [13,14], Simulated Annealing [15,16], and Greedy Algorithms [17][18][19][20]. On the other hand, a wide range of modern Bio-inspired Evolutionary Algorithms [21] have been developed for more difficult optimization problems.…”
Wireless communication supports various real-world applications, such as aeronautical navigation, satellite and TV broadcasting, wireless LANs, and mobile communications. The inherent characteristics of the electromagnetic spectrum impose constraints on telecommunication channels and their frequency bandwidths within mobile networks. A persistent challenge in these applications is providing high-demand services to mobile users, where frequency assignment problems, also known as channel assignment problems, assume significance. Researchers have developed several modeling approaches to address different facets of this problem, including the management of interfering radio signals, the assessment of available frequencies, and optimization criteria. In this paper, we present improved algorithms for solving the Minimum Span Frequency Assignment Problem in mobile communication systems using the greedy optimization approach known as F/DR. We solved and evaluated twenty well-known benchmark cases to assess the efficacy of our algorithms. Our findings consistently demonstrate that the modified algorithms outperform the F/DR approach with comparable computational complexity. The proposed algorithm notably achieves the following benchmarks: The modified algorithms consistently produce at least one local optimum better than the traditional algorithm in all benchmark tests. In 95% of the benchmarks evaluated, the probability of discovering a local optimum value (calculated by the modified algorithm) that is better than or equal to the one found by the conventional algorithm exceeds 50%.
“…Take, for example, a W-MHz SR-WLAN. A normal-range WLAN surrounds the SR-WLAN (that is, using more power than the target WLAN) [31][32][33][34]. The SR-WLAN should be able to discover an open basic channel.…”
Channel bonding is considered by the IEEE 802.11ac amendment to improve wireless local area network (WLAN) performance. In this article, the channel bonding and aggregation method were proposed to increase wireless local area network performance (WLANs). It combines many channels (or lanes) to boost the capacity of modem traffic. Channel bonding is the combination of two neighbouring channels within a certain frequency band to increase wireless device throughput. Wi-Fi employs channel bonding, also known as Ethernet bonding. Channel bandwidth is equal to the uplink/downlink ratio multiplied by the operational capacity. A single 20 MHz channel is divided into two, four, or eight power channels. At 80 MHz, there are more main and smaller channels. Performance of short-range WLANs is determined through graph-based approach. The two-channel access techniques including channel bonding proposed for the IEEE 802.11ac amendment are analysed and contrasted. The novel channel sizing algorithm based on starvation threshold is proposed to expand the channel size to improve WLAN performance. Second-cycle throughput is estimated at 20 Mbps, much beyond the starvation threshold. Our test reveals access points (AP) 1, 2, and 4 have enough throughput. A four-AP WLAN with a 5-Mbps starvation threshold is presented. C160 = 1 since there is only one 160 MHz channel. MIR (3, 160 (a, a, a)) =0, indicating that AP 3’s predicted throughput is 0. The algorithm rejects the 160 MHz channel width since ST is larger than 0. The channel width in MHz is given by B =0,1 MIR. The MIR was intended to maximise simultaneous broadcasts in WLANs. The authors claim that aggregation with channel bonding outperforms so all WLAN APs should have a single-channel width. It usually outperforms fairness-based measures by 15% to 20%. Wi-Fi standards advise “channel bonding,” or using higher frequency channels. Later standards allow channel bonding by increasing bands and channel lengths. Wider channels enhance average WLAN AP throughput, but narrower channels reduce appetite. Finally, it is concluded that APs are more useful than STAs.
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