this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified into the moment-based and the maximum likelihood (ML) methods. We further divide these algorithms based on the modeling of the input signal. If input is assumed to be random with prescribed statistics (or distributions), the corresponding blind channel estimation schemes are considered to be statistical. On the other hand, if the source does not have a statistical description, or although the source is random but the statistical properties of the source are not exploited, the corresponding estimation algorithms are classified as deterministic. Fig. 2 shows a map for different classes of algorithms and the organization of the paper.

A method for coherently detecting and decoding turbo-- coded binary phase shift keying (BPSK) signals transmitted over frequency-flat fading channels is discussed. Estimates of the complex channel gain and variance of the additive noise are derived first from known pilot symbols and an estimation filter. After each iteration of turbo decoding, the channel estimates are refined using information fed back from the decoder. Both hard-decision and soft-decision feedback are considered and compared with three baseline turbo-coded systems: 1) a BPSK system that has perfect channel estimates; 2) a system that uses differential phase shift keying and hence needs no estimates; and 3) a system that performs channel estimation using pilot symbols but has no feedback path from decoder to estimator. Performance can be further improved by borrowing channel estimates from the previously decoded frame. Simulation results show the influence of pilot symbol spacing, estimation filter size and type, and fade rate. Performance within 0.49 and 1.16 dB of turbo-coded BPSK with perfect coherent detection is observed at a bit--error rate of 10 4 for normalized fade rates of =0005 and =002, respectively.

This paper addresses the problem of channel tracking and equalization for multi-input multi-output (MIMO) time-variant frequencyselective channels. These channels model the corrupting effects of intersymbol interference (ISI), co-channel interference (CCI), and noise. A firstorder autoregressive model describes the MIMO channel variation, and tracking is performed by a Kalman filter. Hard decisions to aid Kalman tracking come from a MIMO finite-length minimum-mean-squared-error decision-feedback equalizer (MMSE-DFE), which performs the equalization task. Since the optimum DFE for a wide range of channels produces decisions with a delay \Delta ? 0, the Kalman filter tracks the channel with a delay. A channel prediction module bridges the time gap between the channel estimates produced by the Kalman filter and those needed for the DFE adaptation. The proposed algorithm offers good tracking behavior for multi-user fading ISI channels at the expense of higher complexity than conventional adaptive algorithms. Appropriate coding options for this system are also discussed. Applications include synchronous multiuser detection of independent transmitters, as well as coordinated transmission through many transmitter/receiver antennas, for increased data rate.

In this paper, noncoherent receivers for differential space-time modulation (DSTM) are investigated. It is shown that the performance of the previously proposed conventional differential detection (DD) receiver is satisfactory only for very slow flat fading channels. However, conventional DD suffers from a considerable loss in performance even for moderately fast fading. In order to overcome this problem, multiple-symbol detection (MSD) and low-complexity decision-feedback differential detection (DF-DD) receivers are derived.

This paper presents a new fractionally-spaced maximum a posteriori (MAP) equalizer for data transmission over frequency -selective fading channels. The technique is applicable to any standard modulation technique. The MAP equalizer uses an expanded hypothesis trellis for the purpose of joint channel estimation and equalization. The fading channel is estimated by coupling minimum mean square error techniques with the (fixed size) expanded trellis. The new MAP equalizer is also presented in an iterative (turbo) receiver structure. Both uncoded and conventionally coded systems (including iterative processing) are studied. Even on frequency-flat fading channels, the proposed receiver outperforms conventional techniques. Simulations demonstrate the performance of the proposed equalizer.

In this paper, an equalization concept for the novel radio access scheme EDGE (Enhanced Data Rates for GSM Evolution) is proposed, by which high performance can be obtained at moderate computational complexity. Because high-level modulation is employed in EDGE, optimum equalization as usually performed in GSM (Global System for Mobile Communications) receivers is too complex, and suboptimum schemes have to be considered. It is shown that delayed decision-feedback sequence estimation (DDFSE) and reduced-state sequence estimation (RSSE) are promising candidates. For various channel profiles, approximations for the bit error rate of these suboptimum equalization techniques are given and compared with simulation results for DDFSE. It turns out that a discrete-time prefilter creating a minimum-phase overall impulse response is indispensible for a favourable tradeoff between performance and complexity. Additionally, the influence of channel estimation and of the receiver input filter is investigated, and the reasons for performance degradation compared to the additive white Gaussian noise channel are indicated. Finally, the overall system performance attainable with the proposed equalization concept is determined for transmission with channel coding.

The soft-input soft-output (SISO) module is the basic building block for established iterative detection (ID) algorithms for a system consisting of a network of finite state machines. The problem of performing ID for systems having parametric uncertainty has received relatively little attention in the open literature. Previously proposed adaptive SISO (A-SISO) algorithms are either based on an oversimplified channel model, or have complexity that grows exponentially with the observation length (or the smoothing lag ). In this paper, the exact expressions for the soft metrics in the presence of parametric uncertainty modeled as a Gauss--Markov process are derived in a novel way that enables the decoupling of complexity and observation length. Starting from these expressions, a family of suboptimal (practical) algorithms is motivated, based on forward/backward adaptive processing with linear complexity in . Recently proposed A-SISO algorithms, as well as existing adaptive hard-decision algorithms are interpreted as special cases within this framework. Using a representative application---joint iterative equalization-decoding for trellis-based codes over frequency-selective channels---several design options are compared and the impact of parametric uncertainty on previously established results for ID with perfect channel state information is assessed.

A novel adaptive nonlinear equalizer for fast timevarying multipath channels that combines the channel-estimation and data-detection tasks is presented. The a posteriori probabilities (APP's) of the states of the intersymbol interference (ISI) channel are recursively computed from the received data by a symbol-by-symbol (SbS) detector [7] and are then employed by a Kalman-type nonlinear channel estimator. Robust channel tracking and good data-detection performance are obtained, with a reasonable receiver complexity.

fading channel in which the fading process varies slowly over time. Assuming that neither the transmitter nor the receiver have knowledge of the fading process, do multiple transmit and receive antennas provide significant capacity improvements at high signal-to-noise ratio (SNR)? For regular fading processes, recent results show that capacity ultimately grows doubly logarithmically with the SNR independently of the number of transmit and receive antennas used. We show that for the Gauss–Markov fading process in all regimes of practical interest the use of multiple antennas provides large capacity improvements. Nonregular fading processes show completely different high-SNR behaviors due to the perfect predictability of the process from noiseless observations. We analyze the capacity of MIMO channels with nonregular fading by presenting a lower bound, which we specialize to the case of band-limited slowly varying fading processes to show that the use of multiple antennas is still highly beneficial. In both cases, regular and nonregular fading, this capacity improvement can be seen as the benefit of having multiple spatial degrees of freedom. For the Gauss–Markov fading model and all regimes of practical interest, we present a communication scheme that achieves the full number of degrees of freedom of the channel with tractable complexity. Our results for underspread Gauss–Markov and band-limited nonregular fading channels suggest that multiple antennas are useful at high SNR. Index Terms—Channel capacity, decision-oriented training, fading number, high signal-to-noise ratio (SNR), multiple antennas, noncoherent communication. I.

At the TCP sender side, the arrival of an ack packet always triggers the sender to send data packets, which establishes a correspondency between the arrived ack packet and the sent data packets. In a TCP connection, the correspondency between every ack packet and its corresponding data packets forms a sequence. This sequence characterizes the sender’s behavior. In this paper, we propose a method to estimate this correspondency sequence from the dump trace measured at the receiver side. Because many possible correspondency sequences can be constructed based on the trace, the problem here is an estimation problem, which is to select a most possible one from those candidate sequences. The method proposed first eliminates some candidates that violate basic TCP congestion behavior. Then, it chooses the most possible one among the remaining sequences using the statistical characteristics of delays between the acks and their corresponding data packets under maximum-likelihood criterion. The method can work in the condition when the TCP connection experiences various network delay and loss, and it applies to TCP senders of different versions. Simulations and Internet experiments have been performed to validate the method.