Minimum Mean Square Error Technique Research Articles

Selective decoding schemes and wireless MAC operating in MIMO ad hoc networks

Problems encountered in IEEE 802.11 medium access control (MAC) design are interferences from neighboring or hidden nodes and collision from simultaneous transmissions within the same contention floors. This paper presents the selective de- coding schemes in MAC protocol for multiple input multiple out- put ad-hoc networks. It is able to mitigate interferences by using a developed minimum mean-squared error technique. This interference mitigation combined with the maximum likelihood decoding schemes for the Alamouti coding enables the receiver to decode and differentiate the desired data streams from co-channel data streams. As a result, it allows a pair of simultaneous transmissions to the same or different nodes which yields the network utilization increase. Moreover, the presented three decoding schemes and time line operations are optimally selected corresponding to the trans- mission demand of neighboring nodes to avoid collision. The selection is determined by the number of request to send (RTS) packets and the type of clear to send packets. Both theoretical channel capacity and simulation results show that the proposed selective decoding scheme MAC protocol outperforms the mitigation interference using multiple antennas and the parallel RTS processing protocols for the cases of (1) single data stream and (2) two inde- pendent data streams which are simultaneously transmitted by two independent transmitters.

Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

In this paper, we study the performance of multiple-input multiple-output channel estimation methods using training sequences. We consider the popular linear least squares (LS) and minimum mean-square-error (MMSE) approaches and propose new scaled LS (SLS) and relaxed MMSE techniques which require less knowledge of the channel second-order statistics and/or have better performance than the conventional LS and MMSE channel estimators. The optimal choice of training signals is investigated for the aforementioned techniques. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is developed and studied.

MMSE techniques for space diversity receivers in OFDM-based wireless LANs

This paper studies the application of the minimum mean square error (MMSE) beamformer to orthogonal frequency division multiplexing (OFDM)-based wireless local area networks. The questions here addressed are mainly the design with finite-length data and the choice of the OFDM signal domain where the beamformer is applied, either frequency or time. As OFDM signals need more samples than other modulations to stabilize the estimation of the signal statistics, how to exploit the finite-length training sequence provided for the design of equalizers becomes an important issue. The paper also shows that the usual frequency processing in OFDM is not always the best choice for the spatial beamforming, mainly for channels with a very high delay spread. Then, time processing turns out to be the best suited approach in terms of the tradeoff between performance and complexity. Additionally, novel modifications of the MMSE spatial filter are proposed to improve the raw bit-error rate performance: 1) a temporal semiblind approach that exploits the cyclic prefix and 2) windowing in the frequency domain.