Non-cooperation Of The Low Snr Wireless Communications Signal Receiver Technology Research

Uncooperative receiving of wireless communication signals is a very important part of the information interception for technique based reconnaissance. Modern wireless communication, especially signal interception, which is characterized by low power and high transmission rate, urgently requires noncooperative receiving technologies with lower signal-to-noise-ratio threshold performance and better ability against serious channel distortion.Equalization and synchronization are key technologies for effective receiving of distorted signals. The implementations of equalization and synchronization in uncooperative receiver are usually more difficult than those in the conventional receivers, and the low SNR and severe distortion of wireless channel make them even more challenging. In this dissertation, we address the problems of equalization and synchronization under low SNR and harsh channel condition.In the exordium, the motivation of the thesis is presented. Since the knowledge of the channel is necessary for us to choose proper processing methods for effective detection and receiving, we also make a brief introduction to the frequently used models of wireless channel in the first chapter.Global optimum or near optimum detection is the only way to achieve effective receiving under harsh channel condition. The iterative detection method based on soft information is one of the ways to approach the global optimum performance at an acceptable cost of implementation complexity. In this thesis, we use iterative detection scheme to solve the problems of equalization.In iterative detection, the concatenated modules of the receiver make use of soft input and soft output (SISO) algorithms and exchange soft information of the transmitted bits. Other than the unilateral information transfer in a conventional receiver, the information transfer in an iterative detection is bidirectional, which makes it possible for the former module to benefit from the processing gain of the latter modules. As the iteration goes on, a benign circulation of soft information is set up and the performance of the receiver gradually approached the optimum bound. In chapter 2, the principle of iterative detection is discussed and several applications such as turbo coding and decoding, turbo equalization, iterative multi-user detection and iterative demodulation are introduced.In chapter 3, the universal structure of turbo equalization is presented. The modules including SISO mapping, SISO demapping and SISO decoding are discussed in detail, based on which we present our relevant contribution: a simplified algorithm for computation of extrinsic information of SISO equalization for Gray coded 16QAM and a new definition of the decoder's extrinsic information.The rest of chapter 3 investigates two turbo equalization algorithms on condition that thechannel is static and the channel response is known. The first algorithm is based on minimum mean square error (MMSE) linear equalization (LE) and the other one is based on MMSE soft-decision-feedback-equalization (SDFE). Simulations show that both the algorithms outperform the conventional equalizers quite a lot. In turbo MMSE LE, the optimum equalization algorithm is exploited, involving lots of computation of matrix inversion, involved and thus a high complexity is resulted. In turbo SDFE, the feed-forward and feed-back filters are derived by the MMSE rule in frequency domain and then the filters in time domain are obtained by the use of IDFT. In the computation of filters in SDFE, only scalar computation and IFFT are needed, so it is very easy to be implemented. However, as shown in simulations results, the performance of turbo SDFE is inferior to that of turbo MMSE-LE by 2dB, which is resulted from the approximation processing in using filters with finite length instead of optimal filters with infinite length.In chapter 4, blind turbo equalization for time-invariant channel is investigated. Because training sequences are usually unavailable in uncooperative receiving, we have to perform turbo equalization without the help of training sequences. Blind equalization can be classified into two structures. The first uses adaptive filters to retrieve the transmitted symbols directly and the other performs channel response estimation and equalization in a relatively independent way. The second one is used in this thesis. Since the structure and SISO equalization algorithms have been fully discussed in chapter 3, the key problem is to fulfill blind channel estimation. Accordingly, we exploit iterative channel estimation that is based on recursive least square (RLS) algorithm with soft decisions as its reference signal. To obtain soft decisions for the first iteration, a blind equalization is proposed be used before the iterative channel estimation.The super exponential blind equalization is based on block data processing and has the ability to deal with severe channel distortion. Hence it is suitable for the initial equalization in the iterative channel estimation. After the initial blind equalization, SISO decoding and SISO mapping, the initial soft decision can be obtained and the channel estimation can be bootstrapped. In the succeeding iteration, RLS algorithm exploits the soft-decision of the previous iteration as the input signal. The channel parameters of SISO equalization are also refreshed in every iteration. As the reliability of the soft-decisions become better and better, the resultant channel estimation become more and more accurate. Simulation results show that the proposed blind turbo equalization can perform effectively.Chapter 5 addresses the equalization issues of frequency-selective fading channel.In conventional communications, training sequences are often necessary for channel probing, especially when the channel is severely fading. Generally, only blind technologies are effective in most of the uncooperative receiver. However, when the communication standard is known, it is possible and reasonable for the receiver to exploit training sequence to improve its performance. Hence we firstly discussed the training sequence aided channel estimation. There are two periods in channel estimation: the initial period and the iterative period. In the initial estimation, channel response is assumed to be static and the estimation obtained in the trainingperiod is used for the overall sequence. In the iterative period, the RLS based channel estimation takes the soft-decision as its input signal and the channel parameter of SISO equalization is updated symbol by symbol.Chapter 5 lays emphasis on the blind equalization of fading channel. A blind equalization algorithm based on Monte Carlo methods is used. In most Bayes detection problems, a multi-dimension integration or summation with extremely high complexity is usually involved, thus one has to resort to some numerical approaches for computation. Monte Carlo methods are one kind of such numerical approaches and Gibbs sampler is one of the most commonly used Monte Carlo methods. The Gibbs sampler in general form is firstly described and then the blind equalization based on the method is discussed. In Gibbs sampler equalization, the distribution of transmitted symbols and channel response are simultaneously computed in an iterative way. When the iteration converges, the estimations of the symbols and channel response can be obtained. This algorithm can only deal with time-invariant channel. However, being different from most of the blind algorithms, it is able to fulfill data recovery and channel estimation based on very short data segment. Bearing the fact in mind that the fading channel can be well approximated by a static channel in a very short period, we propose to divide a long faded data frame into a number of short segments and assume a static channel within each segment and then use Gibbs sampler equalization algorithm on them. What's more, because Gibbs sampler is a typical SISO algorithm, it is easy to substitute it into the turbo equalization structure given in chapter 3 to fulfill turbo equalization for frequency-selective fading channel. Simulations results demonstrating performance of the equalization are also presented.Carrier synchronization is another key problem in uncooperative receiver. Though there have been many achievements about this issue, most of them are designed for high or medium SNR. When SNR is low, these methods often fail to achieve good synchronization. Therefore, chapter 6 is contributed to carrier synchronization for MPSK modulation signals with low SNR.Firstly, an NDA carrier frequency offset estimator based on auto-correlation of the signal's nonlinear transformer is discussed. For a tinny frequency offset, this estimator can approach MCRB at low SNR. However, the frequency range within which it can perform effectively is limited because phase wrapping and error will occur when frequency offset is beyond the range. Towards to this problem, we propose an improved estimator, in which the frequency out of the range is coarsely measured and then is shifted so that the residual frequency is within the range and thus can be effectively estimated. Simulations show that the improved estimator performs much better than several other commonly used estimators when SNR is low.Secondly, we investigated carrier phase recovery method for MPSK signals with low SNR. In a transmitting system with an error control coding, the likelihood ratio output of the SISO decoder can reflect the carrier phase error and thus can be exploited to construct a performance function for carrier phase recovery. Based on this performance function, wepropose a phase searching method that can achieve high estimation accuracy at relatively low computation cost. As demonstrated by simulation results; this carrier phase recovery method performs well at low SNR and can overcome phase ambiguity problem that other methods often suffer from.SISO channel decoding is the topic of chapter 7. Channel decoding is very important for receiving signal with low SNR. It is also a key part of iterative detection. In iterative processing such as turbo equalization; iterative multi-use detection and iterative demodulation; the SISO decoder is different from the one used for error control. For example; the input of turbo equalization only consists of a priori information of the coded bits whereas the common decoding algorithm often takes both channel observation and a priori information as input. The output of decoder in turbo equalization consists of log-likelihood-ratio of all coded bits other than only LLR of information bits in the common decoder. According to these differences; we describe the log-MAX-MAP decoding algorithm for convolutional code; which is suitable for iterative processing. Because the description is not based on assumption of certain code; it is also suitable for decoding of non-systematic codes.