A practical two-stage MMSE based MIMO detector for interference mitigation with non-cooperative interferers

Abstract

Wireless Multiple Input Multiple Output systems provide system designers with additional degrees of freedom. These can be used to increase throughput, reliability, or even combat spatial interference. The classical Minimum Mean Squared Error (MMSE) solution is the optimal linear estimator for these systems. Its primary drawback is that it requires an estimate of the channel response. This is generally not an issue when interference is absent. However, in environments where interference power is stronger than the desired signal power, this can become difficult to estimate. The problem is even worse in packet-based systems, which rely on training data to estimate the channel before estimating the signal. A strong interference will hinder the receiver's ability to detect the presence of the packet. This makes it impossible to estimate the channel, a critical component for the classical MMSE estimator. For this reason, the classical solution is infeasible in real environments with stronger interferences. We propose a two-stage system that uses practically obtainable channel state information. We will show how this approach significantly improves packet detection, and how the overall solution approaches the performance of the classical MMSE estimator.

An adaptive MMSE-SIC soft detector with error regularization for iterative MIMO receivers

“…, NT}. The computation of the LLRs is then performed according to (8). Similar to the linear MMSE filter and the MMSE-SIC without regularization, the resulting MMSE-SIC-ER soft detection algorithm has a computational complexity of O(N 3 T ).…”

“…With the following detected symbols, the procedure is repeated until all symbols have been detected. The LLRs can be computed after the detection of each symbol analogously to (8).…”

“…As a result, the performance of the detection of the data is improved in comparison to stand-alone linear filtering. The application of this principle is manifold, it, e.g., has been applied to remove inter-cell interference in a cellular network [2]- [3], interference from neighboring sub-carriers in orthogonal-frequencydivision-multiplexing (OFDM) systems [4]- [5], relay systems [6]- [7], or to obtain channel estimates in the presence of interferers [8].…”

Addressing IQ mismatch in Spatial Interference Suppression systems

“…Two such filters that depend only on the second order statistics of the signal have been presented in [15], and are discussed below. For any filter W, the Signal to Interference plus Noise Ratio (SINR) ρ at the output can be computed as follows…”

“…Several frontend filters based on the spatial covariance (second order statistics) of the received signal have been considered for this purpose [15]. The objective generally is to maximize the signal to interference plus noise ratio (SINR) at the output of the frontend filter.…”