Enhanced Cellular Coverage and Throughput Using Rateless Codes

Abstract

Rateless codes have been shown to provide robust error correction over a wide range of binary and noisy channels. Using a stochastic geometry model, this paper studies the performance of rateless codes in the cellular downlink and compares it with the performance of fixed-rate codes. For the case of Rayleigh fading, an accurate approximation is proposed for the distribution of the packet transmission time of K-bit information packets using rateless codes. The two types of channel coding schemes are compared by evaluating the typical user and per-user success probability and the rate. Based on both the analytical results and simulations, the paper shows that rateless coding provides a significant throughput gain relative to fixed-rate coding. Moreover the benefit is not restricted to the typical user but applies to all users in the cellular network.

Rate Statistics in Cellular Downlink: A Per-User Analysis of Rateless Coded Transmission

“…The per-user rate is obtained by scaling the per-user coverage probability by the fixed-rate of transmission. On the contrary, the main contribution of this letter is the characterization of per-user (location-dependent) rate in cellular downlink, which is very different from the metrics pursued in [2]- [5]. The simulation and analytical results in this letter show significant performance enhancements for the per-user rate in cellular downlink due to rateless codes relative to adaptive modulation and coding, and fixed-rate coding with power control.…”

“…The typical user is located within the typical cell, the Voronoi cell of the typical BS at origin. The distance between the typical user and the typical BS of Φ is D. Its approximate distribution is D ∼ Rayleigh(σ), with the scale parameter σ = 1/ √ 2πλ [2]. We consider a translated version of the PPP Φ so that the typical user is at the origin.…”

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

“…In the last few years, stochastic geometry has emerged as a power tool for the analysis of wireless networks due to its analytical tractability yet accuracy. For example, it has been used for the analysis of cellular networks [22]- [26], cognitive networks [27], millimeter wave cellular networks [28], [29], ad-hoc networks [30], wireless powered cellular networks [31], [32], and backscatter communication networks [33].…”

“…Its analysis and optimization are, however, still challenging. The main reason is the lack of analytical results for the distribution of the downlink distances within the typical cell of a Voronoi tessellation [26], [32]. To overcome this limitation, we introduce some approximations that lead to a tractable analytical framework, which is shown to be suitable for system optimization yet sufficiently accurate.…”

“…• In our network model, the distribution of the downlink distances within the typical Poisson-Voronoi cell are needed. It is known, however, that these distributions are unknown [26], [32]. A tractable and accurate approximation that is typically employed to overcome this limitation consists of approximating the typical cell with the Crofton (or zero) cell.…”