The need for high speed robust communications systems has grown dramatically in recent years. Such a demand has been fueled by the need to support various communications market segments, e.g., ever increasing numbers of voice calls, higher information transfer rates, better connectivity to the Internet. Both consumer and business market segments have witnessed unparalleled increases in growth, and such growth is predicted to continue for the foreseeable future. In theory, communications systems could accommodate the demand for increased data throughput by securing additional bandwidth over which to transmit. However, bandwidth is a limited resource and in most cases is limited by regulation. Accordingly, communication systems designers have sought to extract greater data throughput from existing bandwidth, either by using more efficient modulation schemes, or by overcoming practical limitations posed by the communications environment, e.g. the communications channel.
One difficulty with many communications systems is that the communications channel itself introduces amplitude and phase distortion, as well as noise contributions, into the transmitted signal. In order to improve the performance of a given communications system, it is necessary to remove the amplitude and phase distortion introduced by the communications channel. Accordingly, in order to mitigate the deleterious effects of the channel distortion, it is necessary to develop an estimate of the amplitude and phase distortion components introduced by the communications channel. Such an estimation process is called channel estimation.
Typically, channel estimation is performed using one of a variety of methods. While there are many differences between the various channel estimation methods available, many differences can be reduced to the fundamental tradeoff made between the complexity of the method and the performance of that method. Conceptually, outstanding channel estimation performance can be achieved, albeit at the expense of inordinately complex methods. In some cases, the complexity of such methods may be such that those methods cannot be reasonably implemented in practical communications systems.
In one traditional approach, known pilot symbols are transmitted such that the communications channel can be calibrated by the receiver. Pilot symbols are reference symbols that are known a priori by both the transmitter and the receiver such that a calibration process may be implemented. Upon receipt of the transmitted pilot symbols, this channel estimation algorithm analyses the received pilot symbols in order to generate an estimate of the distortion introduced by the communications channel.
An alternative channel estimation approach does not rely on the transmission of pilot symbols that are known a priori, but instead relies on certain known properties of the regular data signals transmitted by the communications system transmitter. For example, modulation schemes that use phase shift keying (PSK) rely on changes in phase of the signal carrier to capture the information required to be communicated. Accordingly, since the amplitude of the PSK-modulated signal is unaltered, the transmitted signal maintains a predictably fixed energy level. As such, channel estimation algorithms can be designed to capitalize on such known properties of the transmitted signal; in the case of PSK modulation-based communication systems, that known property is a fixed energy signal. Channel estimation techniques that do not use reference pilot symbols in the channel estimation process but rely on known properties of regular transmitted data signal are often referred to as “blind” channel estimation techniques.
Differential quadrature phase shift keying (DQPSK) modulation-based communication systems are widely used. In practice however, such systems do not attempt channel estimation due to the lack of sufficient reference symbols. In the research and academic literature, several blind channel estimation methods have been proposed. Most of these methods are based on second or higher order statistics, or the maximum likelihood (ML) principle. Selected references from the literature are listed as follows: B. Muquet and M. de Courville, “Blind and semi-blind channel identification methods using second order statistics for OFDM systems,” Proceeding of IEEE ICASSP 1999, vol. 5, pp. 2745-2748; C. Li and S. Roy, “Subspace-based blind channel estimation for OFDM by exploiting virtual carriers,” IEEE Transactions on Wireless Communications, vol. 2, no. 1, January 2003, pp. 141 150; N. Chotikakamthom and H. Suzuki, “On indentifiability of OFDM blind channel estimation,” Proceeding of VTC 1999-Fall, Amsterdam, Netherlands, vol. 4, September 1999, pp. 2358-2361. One major drawback of these methods is the huge computational complexity, which make these methods not suitable to be implemented in practical systems. Therefore, what is needed is a blind channel estimation technique that has reliable performance and low computation complexity for a DQPSK-based modulation communications system.
USPTO Backgrounds
USPTO Backgrounds