A low complexity SI sequence estimator for pilot-aided SLM–OFDM systems

Selected mapping (SLM) is a well-known method for reducing peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. However, as a consequence of implementing SLM, OFDM receivers often require estimation of some side information (SI) in order to achieve successful data recovery. Existing SI estimation schemes have very high computational complexities that put additional constraints on limited resources and increase system complexity. To address this problem, an alternative SLM approach that facilitates estimation of SI in the form of phase detection is presented. Simulations show that this modified SLM approach produces similar PAPR reduction performance when compared to conventional SLM. With no amplifier distortion and in the presence of non-linear power amplifier distortion, the proposed SI estimation approach achieves similar data recovery performance as both standard SLM–OFDM (with perfect SI estimation) and also when SI estimation is implemented through the use of an existing frequency-domain correlation (FDC) decision metric. In addition, the proposed method significantly reduces computational complexity compared with the FDC scheme and an ML estimation scheme

Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as a potential candidate for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method

Efficient joint maximum-likelihood channel estimation and signal detection

In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic techniques

Estimation of AR and ARMA models by stochastic complexity

In this paper the stochastic complexity criterion is applied to estimation of the order in AR and ARMA models. The power of the criterion for short strings is illustrated by simulations. It requires an integral of the square root of Fisher information, which is done by Monte Carlo technique. The stochastic complexity, which is the negative logarithm of the Normalized Maximum Likelihood universal density function, is given. Also, exact asymptotic formulas for the Fisher information matrix are derived.Comment: Published at http://dx.doi.org/10.1214/074921706000000941 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org