Fractional power control for decentralized wireless networks

Abstract

We consider a new approach to power control in decentralized wireless networks, termed fractional power control (FPC). Transmission power is chosen as the current channel quality raised to an exponent −s, where s is a constant between 0 and 1. The choices s = 1 and s = 0 correspond to the familiar cases of channel inversion and constant power transmission, respectively. Choosing s ∈ (0, 1) allows all intermediate policies between these two extremes to be evaluated, and we see that usually neither extreme is ideal. We derive closed-form approximations for the outage probability relative to a target SINR in a decentralized (ad hoc or unlicensed) network as well as for the resulting transmission capacity, which is the number of users/m 2 that can achieve this SINR on average. Using these approximations, which are quite accurate over typical system parameter values, we prove that using an exponent of s * = 1 2 minimizes the outage probability, meaning that the inverse square root of the channel strength is a sensible transmit power scaling for networks with a relatively low density of interferers. We also show numerically that this choice of s is robust to a wide range of variations in the network parameters. Intuitively, s * = 1 2 balances between helping disadvantaged users while making sure they do not flood the network with interference.

An Overview of the Transmission Capacity of Wireless Networks

“…Although this only ensures that γ = 1/2 is optimal for the TC approximation, results in [14] confirm that γ = 1/2 is also near-optimal for a wide range of reasonable parameter values. 2 Using γ ≫ leads to small interference levels but an under-compensation for signal fading.…”

“…The assumption of negligible thermal noise may be easily relaxed (e.g., see [13] and [14]) but at the cost of complicating the derived expressions without providing additional insight.…”

“…In [14] a fractional power control policy in which each transmitter partially compensates for the signal fading coefficient is proposed. In particular, transmit power is chosen proportional to the fading coefficient raised to the exponent −γ where γ ∈ [0, 1]:…”

“…Although transmission capacity was originally developed to analyze spread spectrum in ad hoc networks, it has proven to be a metric with considerable breadth of application. Since decentralized wireless networks are generally very difficult to characterize, the intuitive and simple-to-compute qualities of transmission capacity have made it a popular choice for a large number of possible systems, including: i) direct-sequence and frequencyhopping spread spectrum [1], [4], [9], ii) interference cancellation [5], [10], iii) spectrum sharing in unlicensed, overlaid, and cognitive radio networks [6], [7], [11], [12], iv) scheduling [10] and power control [13], [14], v) and the use of multiple antennas (which had resisted characterization by other methods) [8], [15]- [21]. Other researchers have also further studied the basic tradeoffs between outage probability, data rate, and transmission capacity for general networks [22].…”

Cooperative wireless energy harvesting and information transfer in stochastic networks

“…The distance between each source and its destination is assumed to be a constant d. In fact, the relaxed assumption about random distance cannot bring us much insight but complicates the performance analysis [21]. The source nodes are assumed to be distributed on the 2-D plane following homogeneous Poisson point process (PPP) s = {x i , i ∈ Z} with density λ s .…”