反相關性決策分碼多工多人檢測器之盲目使用者接收能量估測系統

Abstract

此篇論文研究在同步分碼多工無線頻道下, 反相關性決策回饋多人檢測器 使用線性增強學 習隨機近似法去盲目調適能量估測. 將決策回饋併入線 性反相關性檢測器後, 經由消除大 能量使用者的干擾, 則小能量使用者 的效能明顯提昇. 無論如何, 反相關性決策回饋檢測 器要知道所有使用 者能量. 在這篇論文中, 我們提出一種在不用訓練資料下, 使用隨機近 似演算法去估測所有使用者能量的新盲目估測機制. 為了增加估測能量的 收斂速度, 使用 線性增強學習技巧加速隨機近似演算法的收斂. 結果顯 示, 不論能量大小的次序如何, 盲 目調適機制能正確的估測所有使用者 能量. 完成能 量估測後, 重新依 能量大小適整所有使者的次序. 模擬數據顯示, 使用反相關性決策回饋 檢測器的最小能量使者的效能, 與使用最大可能性檢測器的效能相近. 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 CDMA radio channel in the presence of multiple access interference (MAI) and additive Gaussian noise. The decision feedback incorporated into the structure of linear decorrelating detector is able to significantly improve the weaker users' performance by cancelling the MAI from the stronger users. However, the DDF receiver requires 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 the users of DDF detector are not ranked properly. After performing the blind energy estimation and then re-ordering 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 exponentically with the number of users.