Vector quantization based QoS evaluation in cognitive radio networks

Abstract

In this paper, we characterize the QoS that secondary users can expect in a cognitive radio network in the presence of primaries. To that end, we first define a $$K$$K-dimensional QoS space where each point in that space characterizes the expected QoS. We show how the operating condition of the system maps to a point in the QoS space, the quality of which is given by the corresponding QoS index. To deal with the real-valued QoS space, we use vector quantization to partition the space into finite number of regions each of which is represented by one QoS index. We argue that any operating condition of the system can be mapped to one of the pre-computed QoS indices using a simple look-up in $$O(log\,N)$$O(logN) time--thus avoiding any cumbersome computation for QoS evaluation. The proposed technique takes the power vector as its input from the power control unit which we consider as a black box. Using simulations, we illustrate how a $$K$$K-dimensional QoS space can be constructed. We choose capacity as the QoS metrics and show what the expected capacity would be for a given power vector. We also show the effect of having large number of partitions on the distortion. As for the implementation feasibility of the proposed concept, we implement the QoS space on an 8-bit microcontroller and show how the mathematically intensive operations can be computed in a short time. Further we use binary search to achieve scalability as the dimensionality of the space increases.