Analysis of LMS algorithm with inputs from cyclostationary random processes

The least mean square (LMS) algorithm is investigated for inputs from nonstationary random processes having periodic (period P) statistics. By defining P different coefficient vectors, each vector can be shown to converge in the mean' to a biased solution which is dependent upon the algorithm step size mu .<>     

Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

In this letter we propose, for the first time, a solution to the problem of carrier frequency offset (CFO) estimation within the data dependent superimposed training (DDST) framework for channel estimation. While time division multiplexed (TDM) trained systems can use the TDM sequence to determine the CFO, the original attraction of DDST for channel estimation was that it avoided any TDM training. So in this letter we show how CFO estimation can still be very effectively performed with the DDST algorithm, while continuing to preclude the need for any additional bandwidth-consuming TDM training. Finally, simulations are presented that verify the theoretical results.     

Medical image transmission over a GSM cellular system

This authors report on an investigation of possible source/channel coders for medical image transmission over a GSM cellular link. Two source coders (JPEG and EZW) and two channel coders (convolutional and turbo codes) are used. Analysis of the results indicates that EZW with convolutional coding gives the best performance, but some kind of automatic repeat on request (ARQ) is also needed     

Channel estimation using implicit training

In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance.     

Microwave feed-forward low noise amplifier design for cellular basestation

A novel cellular base station microwave amplifier network, utilising a feed-forward design is described. A low noise figure and high small signal linearity are simultaneously achieved. Encouraging results from both a theoretical analysis and a practical implementation are presented     

Enhancing the super exponential method of blind equalisation withthe fast RLS Kalman algorithm

A method for efficiently implementing super exponential (SE) blind equalisation is proposed. The method, based on fast Kalman filtering theory, is recursive in order and in time, and leads to a significant reduction in the number of arithmetic operations. Using the order update by partitioning the covariance matrix of the first hundred data in a specific form, a fast initialisation is implemented. The resulting algorithm achieves the same theoretical fast convergence characteristics as the original SE algorithm but with a significant reduction in arithmetic operations     

Full-Rank and Rank-Deficient Precoding Schemes for Single-Carrier Block Transmissions

In block-mode transmission, inserting a cyclic prefix (CP) between two consecutive blocks provides an efficient way of removing interblock interference and simplifies equalization of frequency-selective channels at the receiver. Here, we address optimal training design for channel estimation in such systems. Our investigations focus on affine precoding schemes. In designing the precoder, least squares channel estimation is constrained to be decoupled from symbol detection, which results in orthogonal precoding schemes. If the precoding matrix is full-rank, the data rate (or bandwidth) has to be traded off to accommodate training. We propose a full-rank orthogonal single-carrier (FROSC) precoding with a low peak-to-average power ratio. Then, in order to improve bandwidth efficiency, we propose a rank deficient orthogonal single carrier (DROSC) precoding scheme. Symbol recovery is still possible thanks to the finite alphabet property of the data symbols.