ITLinQ: A New Approach for Spectrum Sharing in Device-to-Device Communication Systems

Abstract

We consider the problem of spectrum sharing in device-to-device communication systems. Inspired by the recent optimality condition for treating interference as noise, we define a new concept of information-theoretic independent sets (ITIS), which indicates the sets of links for which simultaneous communication and treating the interference from each other as noise is information-theoretically optimal (to within a constant gap). Based on this concept, we develop a new spectrum sharing mechanism, called information-theoretic link scheduling (ITLinQ), which at each time schedules those links that form an ITIS. We first provide a performance guarantee for ITLinQ by characterizing the fraction of the capacity region that it can achieve in a network with sources and destinations located randomly within a fixed area. Furthermore, we demonstrate how ITLinQ can be implemented in a distributed manner, using an initial 2phase signaling mechanism which provides the required channel state information at all the links. Through numerical analysis, we show that distributed ITLinQ can outperform similar stateof-the-art spectrum sharing mechanisms, such as FlashLinQ, by more than a 100% of sum-rate gain, while keeping the complexity at the same level. Finally, we discuss a variation of the distributed ITLinQ scheme which can also guarantee fairness among the links in the network and numerically evaluate its performance.

Optimal Wireless Resource Allocation With Random Edge Graph Neural Networks

“…While these optimization problems can be easily formulated, they tend to be intractable as they are most often non-convex and infinite dimensional [2]. Some simplification is attained by working in the Lagrangian dual domain [2], [3] and subsequently using dual descent methods [4]- [6], or, alternatively, with heuristic optimization and scheduling methods [7]- [10].…”

Opportunistic Treating Interference as Noise

“…By simple layered superposition coding and opportunistic TIN decoding, the achievable GDoF value of the message W [m] k via polyhedral TIN, with the max{0, ·} term ignored in (16), is given by…”

Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization–Maximization Approach

“…While some of existing works [4], [21], [22] adopt a quality-of-service (QoS) model for the scheduling problem, many other works (including this paper) consider maximizing the weighted sum rate across the D2D network, where the fairness is taken into account by appropriate setting of the weights. This paper is most closely related to a series of works that propose algorithms called FlashLinQ [23], ITLinQ [24], and ITLinQ+ [25], which address the D2D scheduling problem using greedy search while utilizing information theoretic intuition based on generalized degree-of-freedom (GDoF); we review these algorithms in details in Section II-C.…”

“…We further examine the current state-of-the-art methods for D2D link scheduling in the literature: FlashLinQ [23], ITLinQ [24], and ITLinQ+ [25]. These works assume that each terminal has a single antenna, and further that each transmitter (or receiver) is only associated with one receiver (or transmitter) respectively, namely the fixed single association case shown in Fig.…”

“…Because deciding the ON-OFF state for all the links at the same time is difficult, all three algorithms propose to schedule the links in a greedy fashion sequentially, as stated in Algorithm 1. The main difference between FlashLinQ [23], ITLinQ [24], and ITLinQ+ [25] lies in the criterion for deciding whether the new link conflicts with already scheduled ones in Step 3 of Algorithm 1.…”