Treating Interference as Noise in Cellular Networks: A Stochastic Geometry Approach

Abstract

The interference management technique that treats interference as noise (TIN) is optimal when the interference is sufficiently low. Scheduling algorithms based on the TIN optimality condition have recently been proposed, e.g., for application to device-to-device communications. TIN, however, has never been applied to cellular networks. In this work, we propose a scheduling algorithm for application to cellular networks that is based on the TIN optimality condition. In the proposed scheduling algorithm, each base station (BS) first randomly selects a user equipment (UE) in its coverage region, and then checks the TIN optimality conditions. If the latter conditions are not fulfilled, the BS is turned off.In order to assess the performance of TIN applied to cellular networks, we introduce an analytical framework with the aid of stochastic geometry theory. We develop, in particular, tractable expressions of the signal-to-interference-and-noise ratio (SINR) coverage probability and average rate of cellular networks. In addition, we carry out asymptotic analysis to find the optimal system parameters that maximize the SINR coverage probability. By using the optimized system parameters, it is shown that TIN applied to cellular networks yields significant gains in terms of SINR coverage probability and average rate. Specifically, the numerical results show that average rate gains of the order of 21% over conventional scheduling algorithms are obtained. Index TermsTreating interference as noise (TIN), cellular networks, user scheduling, Poisson point processes, stochastic geometry, system level analysis.

Treating Interference as Noise in Cell-Free Massive MIMO Networks

“…In wireless communications, the TIN optimality conditions characterize the interference relationship between the intended link (from an intended AP to an intended UE), the link between the intended UE and the nearest interfering AP and the link between the intended AP and the nearest interfering UE, as illustrated in Fig 1 . One achieves the whole capacity region to within a constant gap of log(3n) when the TIN optimality conditions hold, where n is the number of the transceiver pairs [3]. Mathematically, the TIN optimality conditions in cellular systems can be formulated as [5]…”

“…where SNR denotes the signal-to-noise ratio (SNR) of the intended link, INR ap and INR ue denote the interference-tonoise ratios (INRs) of the link between the intended AP and the interfering UE and the link between the intended UE and the interfering AP, respectively. Parameters κ ≥ 1 and 1 ≤ µ ≤ 2 are introduced in [5] for system optimization. The TIN conditions in (4) does not apply directly in our considered CF mMIMO system since a UE is served by multiple APs instead of one single BS.…”

“…The TIN conditions were originally studied for a K-user interference channel from an information-theoretic view [3]. Then the analysis has been extended to the case with device-to-device (D2D) communications [4] and cellular networks [5]. However, there is a research gap here in investigating the probability that the TIN conditions hold, which is referred to as probability of TIN conditions for simplification in the remainder of this paper, in CF mMIMO using stochastic geometry [6].…”

Throughput Enhancement of the Edge User Equipments Based on the PowerBandwidth Tradeoff in the Optical Attocell Networks

“…In the work environment, a simplified model is used to evaluate the UE performance by relying on the two-layer hexagonal network deployment due to the location of the UE at the edge of the coverage area [26]. In addition, the cell circle radius of the LED and radius of the hexagonal cell play a vital role in determining the signal strength distribution within the coverage area of the cell and minimizing the CCI effect according to the cell size.…”

“…In addition, the condition written in Eq. (25) should be kept in order that the error probability can drop rapidly once the instantaneous ( 0 ) , surpasses ( 0 ) .The error probability over the AWGN is thus given: (26) Where…”