TY - JOUR
T1 - Performance analysis of SIMO space-time scheduling with convex utility function
T2 - Zero-forcing linear processing
AU - Lau, Vincent K.N.
AU - Kwok, Yu Kwong
N1 - Funding Information:
Manuscript received December 15, 2002; revised July 14, 2003, November 5, 2003, and December 10, 2003. This work was supported by a grant from the Research Grants Council of the HKSAR under Project HKU 7162/03E. V. K. N. Lau is with Bell Laboratories, Lucent Technologies,Whippany, NJ 07054 USA (e-mail: [email protected]). Y.-K. Kwok is with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong (e-mail: [email protected]). Digital Object Identifier 10.1109/TVT.2004.823507
PY - 2004/3
Y1 - 2004/3
N2 - In a multiple-antenna system, an optimized design across the link and scheduling layers is crucial toward fully exploiting the temporal and spatial dimensions of the communication channel. In this paper, based on discrete optimization techniques, we derive a novel analytical framework for designing optimal space-time scheduling algorithms with respect to general convex utility functions. We focus on the reverse link (i.e., client to base station) and assume that the mobile terminal has a single transmit antenna while the base station has nR receive antennas. In order that our proposed framework is practicable and can be implemented with a reasonable cost in a real environment, we further assume that the physical layer involves only linear-processing complexity in separating signals from different users. As an illustration of the efficacy of our proposed analytical design framework, we apply the framework to two commonly used system utility functions, namely maximal throughput and proportional fair. We then devise an optimal scheduling algorithm based on our design framework. However, in view of the formidable time complexity of the optimal algorithm, we propose two fast practical scheduling techniques, namely the greedy algorithm and the genetic algorithm (GA). The greedy algorithm, which is similar to the one widely used in 3G1X and Qualcomm high-data-rate (HDR) systems (optimal when nR = 1), exhibits significantly inferior performance when nR > 1 as compared with the optimal approach. On the other hand, the GA is quite promising in terms of performance complexity tradeoff, especially for a system with a large number of users with even a moderately large nR. As a case in point, for a system with 20 users and nR = 4, the GA is more than 36 times faster than the optimal while the performance degradation is less than 10%, making it an attractive choice in the practical implementation for real-time link scheduling.
AB - In a multiple-antenna system, an optimized design across the link and scheduling layers is crucial toward fully exploiting the temporal and spatial dimensions of the communication channel. In this paper, based on discrete optimization techniques, we derive a novel analytical framework for designing optimal space-time scheduling algorithms with respect to general convex utility functions. We focus on the reverse link (i.e., client to base station) and assume that the mobile terminal has a single transmit antenna while the base station has nR receive antennas. In order that our proposed framework is practicable and can be implemented with a reasonable cost in a real environment, we further assume that the physical layer involves only linear-processing complexity in separating signals from different users. As an illustration of the efficacy of our proposed analytical design framework, we apply the framework to two commonly used system utility functions, namely maximal throughput and proportional fair. We then devise an optimal scheduling algorithm based on our design framework. However, in view of the formidable time complexity of the optimal algorithm, we propose two fast practical scheduling techniques, namely the greedy algorithm and the genetic algorithm (GA). The greedy algorithm, which is similar to the one widely used in 3G1X and Qualcomm high-data-rate (HDR) systems (optimal when nR = 1), exhibits significantly inferior performance when nR > 1 as compared with the optimal approach. On the other hand, the GA is quite promising in terms of performance complexity tradeoff, especially for a system with a large number of users with even a moderately large nR. As a case in point, for a system with 20 users and nR = 4, the GA is more than 36 times faster than the optimal while the performance degradation is less than 10%, making it an attractive choice in the practical implementation for real-time link scheduling.
KW - Fairness
KW - Genetic algorithm (GA)
KW - Multiple antenna
KW - Optimal algorithm
KW - Scheduling
KW - Single-input-multiple-output (SIMO)
KW - Utility functions
UR - http://www.scopus.com/inward/record.url?scp=1942485973&partnerID=8YFLogxK
U2 - 10.1109/TVT.2004.823507
DO - 10.1109/TVT.2004.823507
M3 - Article
AN - SCOPUS:1942485973
SN - 0018-9545
VL - 53
SP - 339
EP - 350
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 2
ER -