Blind Adaptive Energy Estimation for Decorrelating Decision-Feedback CDMA Multiuser Detection Using Learning-Type Stochastic Approximations

This paper investigates the application of linear reinforcement learning stochastic approximation to the blind adaptive energy estimation for a decorrelating decision-feedback (DDF) multiuser detector over synchronous code-division multiple-access (CDMA) radio channels in the presence of multiple-access interference (MAI) and additive Gaussian noise. The decision feedback incorporated into the structure of a linear decorrelating detector is able to significantly improve the weaker users’ performance by canceling the MAI from the stronger users. However, the DDF receiver requires the knowledge of the received energies. In this paper, a new novel blind estimation mechanism is proposed to estimate all the users’ energies using a stochastic approximation algorithm without training data. In order to increase the convergence speed of the energy estimation, a linear reinforcement learning technique is conducted to accelerate the stochastic approximation algorithms. Results show that our blind adaptation mechanism is able to accurately estimate all the users’ energies even if the users of the DDF detector are not ranked properly. After performing the blind energy estimation and then reordering the users in a nonincreasing order, numerical simulations show that the DDF detector for the weakest user performs closely to the maximum likelihood detector, whose complexity grows exponentially with the number of users.