Master Thesis Proposal
Performance Evaluation of Power Allocation Schemes in OFDM-based Mobile Radio
Systems
In this thesis, a multi-cell, multicarrier OFDM-based cellular system scenario is considered as a
reference scenario. The scenario employs a realistic interference channel model, e.g., distance
dependent pathloss, fast fading and shadowing. The performance of the global optimum power
allocation scheme which is based on D.C. programming [1] and the suboptimum power
allocation scheme known as iterative waterfilling [2] should be investigated as compared to well
known power allocation schemes, e.g., SIR balancing, waterfilling, equal power allocation and
greedy power allocation.
Tasks:
An OFDM-based downlink scenario should be implemented as a reference scenario. The
scenario consisting of K hexagonal cells with central base stations and a single mobile per cell is
assumed. A frequency selective Rayleigh fading channel consisting of N non-interfering, noncorrelated subcarriers should be considered. Additionally, every mobile is served with all N
subcarriers so that there is no need to consider a resource allocation technique. Both the sum
rate and the Jain index [6] are considered as performance measure and a fairness measure,
respectively.
This thesis should study the impact of increasing the number of subcarriers on the achieved sum
rate and on the fairness among users. Moreover, based on the assumption that the subcarriers
do not interfere each other, it should be studied whether it is possible to simplify the proposed
branch and bound algorithm [1].
References:
[1] H. Al-Shatri, T. Weber, Optimizing Power Allocation in Interference Channels Using D.C.
Programming. In 6th Workshop on Resource Allocation in Wireless Networks, (RAWNET2010)
June 2010, Avignon, France, accepted for publication.
[2] H. Al-Shatri, N. Palleit, T. Weber, Transmitter Power Allocation for Optimizing Sum Capacity
in Interference Channels. In 14th International OFDM-Workshop (InOWo'09), Hamburg,
Germany, 2009.
[3] R. Horst, P. Pardalos, N. Thoai, Introduction to Global Optimization, 2nd ed., ser. Nonconvex
Optimization and its Applications. Kluwer Academic Publishers, January 2000, vol.48.
[4] S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004
[5] A. Goldsmith, Wireless Communications, Cambridge University Press, 2005.
[6] R. Jain, D.-M. Chiu, and W. Hawe, “A quantitative measure of fairness and discrimination for
resource allocation in shared computer systems,” DEC Research Report TR-301, September
1984.