Cognitive Engine Design for Link Adaptation: An Application to Multi-Antenna Systems

In this paper, we present a Cognitive Engine (CE) design for link adaptation and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding. Our design moves forward the state of the art in several ways while having a simple structure. Specifically, the CE only needs to observe the number of successes and failures associated with each set of channel conditions and communication method. From these two numbers, the CE can derive all of its functionality. First, it can estimate confidence intervals of the packet success rate (PSR) using the Beta distribution. A low computational approximation to the CDF of the Beta distribution is also presented. Second, the designed CE balances the tradeoff between learning and short-term performance (exploration {vs.} exploitation) by applying the Gittins index. Third, the CE learns the radio abilities independently of the operation objectives. Thus, if an objective changes, information regarding the radio´s abilities is not lost. Finally, prior knowledge such as capacity, BER curves, and basic communication principles are used to both initialize the CE´s knowledge and maximize the learning rate across different channel conditions. The proposed CE is demonstrated to have the ability to learn in a dynamic scenario and quickly approach maximal performance.