Optimal decoding order under target rate constraints

Abstract

In this contribution, we derive the optimal power allocation under target rate constraints in the case of an uplink multiuser system. Using asymptotic results based on random matrix theory, we provide a unified framework for determining the optimal decoding order when using Successive Interference Cancellation (SIC) receivers, namely the MMSE (Minimum Mean Square Error) and Matched filter SIC. Moreover, for decentralized systems, we show that each user can, under certain conditions, solely determine the power allocation based only on his channel energy and the statistics of the other users. Simulations in the case of finite systems validate the asymptotic claims.

Using cross-system diversity in heterogeneous networks: Throughput optimization

“…It turns out that each of these SINRs converges to a limit, and is especially easy to express in the large dimensions regime (see e.g. [5,25]). Letη (mmse) ,s be the asymptotic SINR for user in the output of the MMSE receiver at base station s. The achievable approximate ergodic sum-rate is then given by:…”

Cross-System Resource Allocation Based on Random Matrix Theory

“…However, it is now wellknown (see e.g. [8,9]) that many asymptotic results from random matrix theory under the large system assumption apply for relatively small systems. More precisely, we consider a scenario where K → +∞, ∀s ∈ {1, ..., S}, Ns → +∞ with limK→∞,N s →∞ K Ns = cs and 0 < cs < +∞.…”

“…Indeed we also have a water-filling equation for the optimum power allocation scheme. One can give two special cases of equation (9). The case where the base stations have the same bandwidth (e.g.…”

“…-Update the power fractions by using the waterfilling equation (9). -Update the value of rs At each step of the iterative procedure, the total capacity of the system is increasing (or keeps constant if the power fraction is not modified), and since it is bounded above, it follows directly that it will converge to a limit.…”