On Power Consumption Issues in FIR Filters with Application to Communication Receivers:Complexity, Word length,and Switching Activity
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Power consumption in CMOS VLSI circuits has in recent years become a major design constraint. This is in particular important for wireless networks, due to the limited life time of the batteries that wireless nodes are operating on. Orthogonal Frequency Division Multiplexing (OFDM) is one example of a technique which in recent years has become widely applied in wireless communication systems. However, the performance of OFDM and other spectrally efficient schemes depends, to a large extend, on advanced digital signal processing (DSP) and on the use of efficient and possibly adaptive resource allocation and transmission techniques. These in turn require that accurate estimates of the channel are available in the receiver and transmitter. However, accurate channel estimation of a time and frequency dispersive wireless fading channel calls for complex estimators, which might lead to significant power dissipation in such devices. Therefore, characterizing and analyzing power consumed by such devices under different channel conditions, and optimizing for power is important to reduce the overall power consumption of the system. In this thesis a certain chosen class of estimators, i.e., a linear FIR estimator, is considered, which is based on finite impulse response (FIR) filters. The work in this thesis considers the power related challenges in such estimators. The power consumed by such estimators depends, in part, on the complexity of the estimator, i.e., the length of the FIR filter. The filter length is one of the factors affecting the estimation accuracy. An analysis of the relation between the performance of such estimators and the required complexity for these devices under different channel conditions, i.e., in the presence of noise, is performed in this thesis. In this study we show that a small increase in this noise can lead to a considerable increase in the required estimator complexity if a given Normalized Mean Square Error (NMSE) performance for the channel estimation must be upheld, in particular at medium-to-high Channel Signal to Noise Ratios (CSNR). Furthermore, reducing the power consumption through word-length optimization, when realizing such estimators, is an attractive approach. Due to the characteristics of the input signal to such estimators, a special treatment of channel estimation error due to quantization of estimator filter coefficients is needed. In this thesis we investigate the impact of finite coefficient word length on channel estimator performance. A theoretical analysis of the increase in channel estimation error due to quantization of estimator coefficients is performed, and the behavior of this error in different fading environments and for different filter orders is studied. The power consumed in a channel estimator is also influenced by the switching activity in the input signal of the estimator. Characterizing the switching activity in the input signal, including how this activity changes in different environments, e.g., in the presence of noise, is a subject of the work performed in this thesis. In this study we give an expression for direct calculation of the correlation coefficient for the most significant bit in a signal, using the word-level correlation coefficient. We also derive expressions for accurately calculating the variance (σ2) and word-level correlation coefficient (ρ) for a correlated signal, when an additional noise of a given variance is added to the signal. This can be used to estimate the bit-level switching activity in a signal in the presence of noise, based on the Dual Bit Type (DBT) method. The impact the additional noise has on the switching activity of a correlated signal has also been studied. These results make it possible for a designer to model the actual input switching activity in different real life noisy environments, enabling realistic power consumption estimation. A study on switching activity reduction in estimator filters using a coefficient reordering method is another part of this thesis. Closed form analytical models for the coefficient and input data switching activity before and after reordering in an estimator filter is developed and the impact that coefficient reordering has on the input data, and consequently on the total switching activity, is studied. Using our derived models we show that the impact of coefficient reordering on data input increases first as the input signal correlation, ρz, increases, but this impact decreases again when ρz → 1. This impact is 0 for ρz ≈ 0 and ρz ≈ 1. Our results show that this impact is highest for ρz = 0.7 to ρz = 0.999, and becomes larger for large values of the estimator order N. Considering a realistic case, we further study the possibility of reducing the switching activity in a MAC-based channel estimator when realized with different orders and word lengths, and operating in different environments. This study shows that if a designer makes the right choices when reordering, it can result in higher gain in reducing the switching activity. The decision will also depend on the channel condition in which the system is operating most of the time. The results of this study show that when the word length is reduced, the use of reordering can in some cases, e.g., when estimator order is increased to N = 50 and beyond, actually lead to an increase in total switching activity if extra care is not taken. It also shows that for large N and input data with medium to high correlation, it is not possible to reduce the switching activity using reordering if the word length is reduced to W = 8 or lower. When the word length is reduced the optimization in general becomes even more sensitive to the characteristics of the input data. The designer consequently need to have this information available to experience reduction or even avoid increase in switching activity for small values for W. It should be mentioned that although we look at these power related challenges in the context of estimators, the results for several parts of this work is not limited to the channel estimators. The results concerning the switching activity reduction in MAC-based channel estimators can be generally applied to FIR filters, and the study on the input signal switching activity is valid for signals input to any digital signal processing (DSP) module.