Binary Power Optimality for Two Link Full-Duplex Network

Dayarathna, Shalanika; Senanayake, Rajitha; Evans, Jamie

doi:10.1109/wcnc45663.2020.9120530

Cited by 2 publications

(1 citation statement)

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“…Lower complexity scheduling algorithms are developed based on message-passing [40], SINR heuristics [41], [42], information-theoretic insights [43], [44], fractional programming [45], and machine learning approaches [46]- [48]. Binary power control is found to be optimal for sum-rate maximization in many cases, e.g., multiple access channels [49], 2user interference channels with single carrier [35], [36], [50] and multiple carriers [51], 𝐾-user one-sided symmetric Wyner-type interference channels [52], networks where the transmission rate is an artificial linear function of the received power [53], and networks where either a geometric-arithmetic mean or low-SINR approximation is applicable [36]. Remarkably, numerical simulations of common communication network topologies such as cellular networks and D2D networks [54]- [58] suggest that binary power control has performance comparable to optimal power control.…”

Section: Introductionmentioning

confidence: 99%

The Extremal GDoF Gain of Optimal versus Binary Power Control in $K$ User Interference Networks Is $Θ(\sqrt{K})$

Chan¹,

Pezeshkpour²,

Jafar³

2022

Preprint

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Using ideas from Generalized Degrees of Freedom (GDoF) analyses and extremal network theory, this work studies the extremal gain of optimal power control over binary (on/off) power control, especially in large interference networks, in search of new theoretical insights. Whereas numerical studies have already established that in most practical settings binary power control is close to optimal, the extremal analysis shows not only that there exist settings where the gain from optimal power control can be quite significant, but also bounds the extremal values of such gains from a GDoF perspective. As its main contribution, this work explicitly characterizes the extremal GDoF gain of optimal over binary power control as Θ √ 𝐾 for all 𝐾. In particular, the extremal gain is bounded between √ 𝐾 and 2.5 √ 𝐾 for every 𝐾. For 𝐾 = 2, 3, 4, 5, 6 users, the precise extremal gain is found to be 1, 3/2, 2, 9/4 and 41/16, respectively. Networks shown to achieve the extremal gain may be interpreted as multi-tier heterogeneous networks. It is worthwhile to note that because of their focus on asymptotic analysis, the sharp characterizations of extremal gains are valuable primarily from a theoretical perspective, and not as contradictions to the conventional wisdom that binary power control is generally close to optimal in practical, non-asymptotic settings.

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