James Okello

A Cordic-Based Reconfigrable Systolic Array Processor for MIMO-OFDM Wireless Communications

A reconfigurable systolic array processor based on a coordinate rotation digital computer (CORDIC) algorithm is proposed for MIMO-OFDM baseband processing. With CORDIC, the processor provides high computation efficiency, and a multi-thread interleaving architecture offers the advantage of a simple data transfer mechanism. Also presented are an array mapping method for calculating MMSE filter coefficients and a comparison of the processors performance with that of dedicated hardware. Despite its flexibility, the processor achieves a computational density of 57% that of dedicated hardware.

Tagging-filter based blind MIMO equalization without signal permutation

In this paper, we propose a signal processing technique for blindly separating constant modulus sources in wireless communication systems. With the proposed technique, each source is pre-filtered using a unique allpass filter that we refer to here as a transmit tagging filter (TT-filter). At the receiver, an inverse model of the TT-filter is used to blindly equalize a multiple input multiple output (MIMO) channel and to retrieve desired signals without any permutation of the originally transmitted signals. This represents an important improvement over the conventional blind method, in which permutation of transmitted signals is known to accompany blind MIMO equalization. Computer simulations with constant modulus sources verify the effectiveness of the proposed system

A study on noise estimation based on robust equation error IIR ADF for speech

In this paper, we propose the noise estimation method based on system identification with an equation error infinite impulse response (IIR) adaptive digital filter (ADF). We have investigated the speech enhancement based on a linear prediction error filter (LPEF) and a system identification model. However, the conventional system estimates the background noise by a finite impulse response (FIR) filter, therefore, a kind of background noise, which can be estimated, is limited. In addition, the estimation accuracy of the background noise is deteriorated due to disturbance in a speech section. Thus, the robust IIR ADF is introduced to noise estimation. In case that an output error IIR ADF is used, the system has the problem of stability. On the other hand, though the equation error IIR ADF guarantees system stability, the tap coefficients converge with a bias due to disturbance. In order for an adaptive algorithm to converge without a bias, the equation error IIR ADF, which uses the correlation as an input signal, is adopted. The IIR ADF takes advantage of independence between a desired signal and disturbance. Since the disturbance does not have the correlation from the desired signal, the tap coefficients converge without the bias.

MCMA equalization without signal permutation for MIMO-OFDM systems using M-QAM

We propose a blind equalization system using an all-pass filter for de-mixing orthogonal frequency division multiplexing (OFDM) symbols in multiple-input multiple-output (MIMO) - OFDM system. In a conventional blind MIMO equalization, there is a possibility that originally transmitted streams are permutated, resulting in being unable to retrieve desired signals at the receiver. In order to solve the problem, each transmitted stream of the proposed MIMO-OFDM system is pre-filtered by a unique all-pass filter. The filter is referred to as Transmit tagging filter (TT-Filter) in this paper. At a receiver, an inverse filter of the TT-filter is used to blindly equalize a MIMO channel without any permutation. The system was originally designed for constant modulus symbol such as PSK signal. This paper evaluates the performance of a QAM Transmission which is non-constant.

A study on adaptive algorithm based on correlation and variable delay for IIR ADF

In adaptive signal processing, the equation-error algorithm for an infinite impulse response (IIR) adaptive digital filter (ADF) has been well known as a stability system. However, the algorithm degrades the estimate accuracy due to a bias. In order to solve the problem, the equation error algorithm based on correlation and variable delay is proposed in this paper. Since an input signal is independent from disturbance, the bias is reduced by using the correlation between an input signal and desired signal as the tap input of the ADF. However, when an input signal is stationary, the tap input is constant and tap coefficient cannot converge. Therefore, the delay of correlation is varied to avoid the situation.

A study on tagging filter based on allpass filter for blind MIMO equalization

A blind equalization system using on a pre-filter is proposed for blindly separating constant modulus sources in multiple-input multiple-output (MIMO) wireless communication systems?? in this paper. With the proposed technique, each modulated signal is pre-filtering based on an allpass filter (Transmit Tagging Filter: TT-Filter). At the receiver, a desired signal is generated by filtering with an inverse filter of the TT-filter, and blindly separating a constant modulus signal from non-constant modulus signals and to retrieve desired signals without any permutation of the originally transmitted signals. In addition, the proposed tagging system transforms a blind MIMO problem into a blind single-input multiple-output (SIMO) problem.

A study on equation error algorithm based on correlation function

Equation error adaptive algorithm (EQ-E) for adaptive infinite impulse response (IIR) digital filter is one of the IIR adaptive algorithms that exhibit global convergence. In the presence of a color disturbance signal, the coefficients of the equation error based adaptive digital filter (ADF) converge to solutions with bias. In this paper, we propose a kind of block adaptive algorithm for the EQ-E IIR ADF. By estimating the correlation functions, the effect of the disturbance signal onto the convergence of the algorithm is reduced.

A Simplified Tagged MIMO Communications System with Blind Equalization Capability

This paper proposes a signal processing technique for blindly separating constant modulus sources in multiple-input multiple-output (MIMO) wireless communications systems. With the proposed technique, each modulated source is tagged by pre-filtering using an allpass filter (transmit tagging filter: TT-Filter) and scrambling using a BPSK modulated scrambling code (tagging code). Also, the TT-filter is designed without any multipliers. At the receiver, desired signal is generated by descrambling, filtering using an inverse model of the TT-filter, and blindly separating a constant modulus signal from non-constant modulus signals. The proposed technique enables all transmitting sources to use the same kind of TT-filter, hence allowing low cost system implementation. Furthermore, by using the same kind of TT-filter, systems latency associated with TT-filters becomes independent of the number of tagged transmitting sources. Computer simulations with constant modulus signals verify the effectiveness of the proposed system