A More Realistic Approach to Information-Theoretic Sum Capacity of Reverse Link CDMA Systems in a Single Cell

Abstract

Abstract-In this paper, we discuss the information-theoretic approach to finding the pattern of transmission powers of the stations in a CDMA system which maximizes the aggregate capacity of the reverse link. This optimization problem is solved subject to a set of constraints. Previous research has suggested a minimum guaranteed quality of service plus bounds on individual transmission powers and the aggregate transmitted power as the constraints. Solving this problem, it is found out that the solution is very prone to including one station which transmits at a rate a hundred times as much as the others. Thus, it is concluded that the above constraints are not enough to produce a solution which can be realized in an actual system. It is suggested that lack of any constraint which explicitly controls either the maximum capacity of each station or the unfairness of the whole system is responsible for this shortcoming. In this paper, we include a maximum capacity constraint into the formulation and propose an algorithm for solving the resulting optimization problem. Then, empirical evidence are analyzed to show that the system actually becomes more balanced and practical after the new constraint is added to it.

Approximation algorithms for maximizing the information – theoretic sum capacity of reverse link CDMA systems

“…Using the approximate closed form for C( x) given in (25), the computational cost of NSC a reduces to 23M 2 −21M+14 flops, compared to the 16 3 M 3 + 55 3 M 2 − 23M + 6-flop cost of NSC [10].…”

“…This simplification results in simpler proofs for the theorems presented in [9][10][11]. Also, we show that these approximations enable us to reduce the computational cost of each algorithm by one in powers of M. Using the superscript a for the new algorithms, we propose algorithms CSC a , NSC a , and N + SC a , for solving the problems CSC, NSC, and N + SC in O(M), O(M 2 ), and O(M 2 ), respectively.…”

“…Analysis of CSC reveals that almost always there is one station in the cell which transmits at a capacity about 50 times as mush as the others [6,7,10]. To deal with this unfair behavior, and using the mathematical approach introduced in [9], a maximum capacity constraint, as C i , is added to the problem, resulting in the new single-cell (NSC) problem [10].…”

“…To deal with this unfair behavior, and using the mathematical approach introduced in [9], a maximum capacity constraint, as C i , is added to the problem, resulting in the new single-cell (NSC) problem [10]. The algorithm proposed in [10] solves the NSC for a M-station cell in O(M 3 ) flops.…”

“…To deal with this unfair behavior, and using the mathematical approach introduced in [9], a maximum capacity constraint, as C i , is added to the problem, resulting in the new single-cell (NSC) problem [10]. The algorithm proposed in [10] solves the NSC for a M-station cell in O(M 3 ) flops. The essential structure of the NSC algorithm is similar to that of the CSC, except for the fact that the definition of the intervals and the structure of the solution are different in the two cases.…”