102 Figure 4.12 BER shown by FDPM and PASTd based DDCE using adaptive NLMS and VSSNLMS predictors for normalized Doppler frequency fD=0.005. 104 Figure 4.14 MSE shown by DDCE based on FDPM and PASTd using adaptive predictors NLMS and VSSNLMS for normalized Doppler frequency fD=0.005.
Wireless Communication
Regardless of the type of communication systems used, the three main components of communication systems remain the same for all. Regardless of the channel type, its effects on the signal transmitted from the transmitter through the channel to the receiver are similar.
Wireless Communication Channels
Parameters of Fading Channels
Different modules of the proposed iterative DDCE for MIMO-OFDM systems are described in Section 6.4. The elements of the symbol vector x[n] and the bits mapped to such a vector are denoted as x [n]i and di q, [ ]n respectively.
Fading Channel Classification
MIMO-OFDM for Wireless Communication Systems
MIMO Systems
- Performance gain in MIMO Systems
- MIMO Systems Capacity
In this technique, multiple antennas are used only at the receiving end of the communication link. The capacity expression in (1.4) clearly shows that there is a linear increase in the capacity of MIMO Systems proportional to the minimum number of transmitting and receiving antennas.
OFDM Systems
- Advantages and Disadvantages of OFDM Systems
Direct implementation of OFDM systems is computationally complex due to the large number of subcarriers involved which would require an equal number of sinusoidal oscillators for coherent demodulation. By using a Fast Fourier Transform (FFT), an efficient implementation of the DFT, the computational complexity of OFDM can be further reduced.
MIMO-OFDM Systems
Regardless of the techniques used in a wireless communication system (such as OFDM, MIMO or MIMO-OFDM techniques) to combat channel effects on transmitted signals, the availability of Channel State Information (CSI) at the receiver end of the communication system remains a critical factor for the effective operation of these techniques as well as for the successful recovery of the transmitted signal.
Research Motivation
Consequently, there is savings in bandwidth usage when using the blind channel estimation method compared to the training-based method. In the decision-driven channel estimation method, all the data detected at the receiver are used for channel estimation.
Scope of the Thesis and Assumptions
Organization of the Thesis
In Chapter 5, iterative technique based on turbo principle is proposed for the Decision-oriented Channel Estimation used for SISO-OFDM systems of Chapter 4. Iterative channel estimation technique for SISO-OFDM systems of Chapter 5 is extended to MIMO-OFDM systems in Chap. 6.
Original Contributions
Journal Papers
Mneney, “Improved fuzzy iterative channel estimation for turbo equalization of time-varying frequency-selective channels,”. Mneney, “Variable step size algorithms for network echo cancellation,” Ubiquitous Computing and Communication (UBICC) Journal , vol.
Conference Papers
Mneney, "Evaluation of soft input repeater channels for turbo equalization during time-varying frequency-selective fading channel", in Proceedings of South Africa Telecommunication Networks and Applications Conference (SATNAC) 2008, Wild Coast Sun, Eastern Cape Coast, South Africa, p. Mneney, "Single and Multiple - Variable Step Size Normalized Least Mean Square Algorithms for Network Echo Cancellation," in Proceedings of the 5th International Conference on Cybernetics and Information Technologies, Systems and Applications: CISTA 2008, in the context of the International Conference on Proceedings on Engineering and Technological Innovation: IMETI 2008, pp. 1-5, Orlando, Florida, USA, June 29 - July 2, 2008.
Introduction
Multipath Channel Impulse Response Models
Details of the rigorous derivation of these models can be found in the cited references. By way of definition, the channel impulse response (CIR) can be defined as the instantaneous state of the dispersive channel encountered.
Single Input Single Output (SISO) Channel Model
- Channel Impulse Response Statistics
- Discrete-Time Channel Model
- Symbol-Spaced Channel Impulse Response Model
- Fractionally-Spaced Channel Impulse Response Model
The baseband equivalent form of the received signal ( )r t , corrupted with complex additive white Gaussian noise ( )w t , is then given as. The symbol-space discrete-time channel impulse response (SS-CIR) model is then given by .
Multiple Input Multiple Output (MIMO) Channel Model
Channel Estimation Techniques
- Pilot-Assisted Channel Estimation Techniques
- Blind and Semi-blind Channel Estimation Techniques
- Decision Directed Channel Estimation Techniques
- Iterative Decision Directed Channel Estimation Techniques
In the Decision Directed Channel Estimation (DDCE) techniques, both the pilot symbols and the remodulated detected message symbols are used for channel estimation [48]. The iterative channel estimation technique proposed in [169] is extended to the case of frequency-selective fading channels in [182].
Chapter Summary
Finally, an iterative receiver for MIMO-OFDM system with common intercarrier interference (ICI) cancellation and channel estimation is presented in [208]. The authors concluded, based on their simulation results, that for mobile transmission scenarios, the proposed method can effectively compensate the effect of intercarrier interference.
Introduction
Soft input based Iterative Channel Estimation
Among the different types of equalizers, the Turbo equalization scheme based on finite impulse response (FIR) filter proposed by Tuchler et al. Since LMS is the simplest algorithm, based on the obtained results, it is suggested that LMS should be used for iterative channel estimation while using soft information feedback from decoder/equalizer.
System Model
- Channel Interleaver
- Random Block Interleaver
- Channel Model
- System Receiver
- Computation of Mean and Variance of the Message Symbols
Traditionally, interleaving is used to "randomize" the locations of errors caused by bushy (correlative) channels, which in turn improves the performance of classical block and convolutional coding schemes designed and optimized for non-correlative channels, such as the Additive white Gaussian noise (AWGN) channel. Because of its efficiency, a type of the random interleavers (random number generator interleaver) is used in the simulation work in this chapter and the subsequent chapters.
Proposed Soft Input Channel Estimation Algorithms
- Variable Step Size Normalized Least Mean Square Algorithm
- Multiple-Variable Step Size Normalized Least Mean Square Algorithm 58
The symbol in (3.16) is a small positive constant that controls the adaptive behavior of the step size sequence n. The bound for the variable step size n, to bring about convergence of the VSSNLMS algorithm-based channel estimator, is given as follows.
Computational Complexity of the proposed Algorithms
Chapter Summary
MSE BER LMS Fairly good NLMS Fairly good APRmodRLS Good - VSSNLMS Very good Good M-VSSNLMS - Good RLS Very good Very good. Due to the negligible performance difference between VSSNLMS and multi-VSSNLMS algorithms, it can be concluded that the VSSNLMS algorithm, which is less complex than the multi-VSSNLMS and RLS algorithms, is suitable for tracking and estimating fast-changing channels in single-antenna communication systems.
Introduction
SISO OFDM System Model
Channel Model
The time-domain and frequency-domain correlation properties of the discrete CTF coefficientsH n k[ , ] associated with different OFDM blocks and subcarriers are characterized by the cross-correlation function rH[ , ]l m , for l tand m f , given as [225, 226 ]. In (4.9), x n k [ , ], w n k [ , ] and H n k [ , ] are respectively the transmitted symbol, additive white Gaussian noise sample and the complex CTF coefficient associated with the kth subcarrier of the nth OFDM symbol .
Proposed Decision Directed Channel Estimator for SISO OFDM Systems
- Temporary CTF Estimator
- Parametric CIR Estimator based on FDPM Algorithm
- Channel Impulse Response (CIR) Predictor
- Adaptive RLS Predictor
- Adaptive NLMS Predictor
- Adaptive VSSNLMS Predictor
The a priori estimate of the mth CIR component predicted by the predictor is given as. The VSSNLMS-based predictor then predicts the a priori estimate of the mth CIR component as.
![Figure 4.2 Decision Directed Channel Estimator Modules for SISO OFDM Systems [154]](https://thumb-ap.123doks.com/thumbv2/pubpdfnet/10636193.0/103.918.142.775.117.286/figure-decision-directed-channel-estimator-modules-siso-systems.webp)
Soft Demapper
As with the NLMS predictor, for n = 0, the predictor filter's coefficient, using VSSNLMS-based predictor, is initialized as pm[ ]n 1 0 0. The a priori values of bits c' and c' 1: L ca ' and L ca ' 1 respectively, are set to zero since there is no feedback from the decoder.
Soft Mapper
Simulation Results and Discussion
Figure 4.9 illustrates the MSE performance of the adaptive predictors for the fast fading channel scenario with the normalized Doppler frequency fD = 0.02, while increasing the SNR from 0 dB to 10 dB. Furthermore, Figure 4.16 and Figure 4.17 illustrate the achievable BER versus SNR and the MSE versus SNR as a function of normalized Doppler frequency (fD), respectively, for both FDPM- and PASTd-based DDCE using the adaptive VSSNLMS predictor.
Comparative Computational Complexity of the proposed DDCE Scheme
In [42] the computational complexity in terms of coefficient update of adaptive predictors NLMS and RLS are compared and given as order O MLprd and O ML2prd respectively where Lprd is the length of the CIR predictive filter. Then it turns out that the update coefficient of the proposed VSSNLMS adaptive predictor will be of the order of O 3Lprd 4 M , which is still much lower than that of the RLS-based adaptive predictor for large filter length.
Chapter Summary
Similar to the case of the VSSNLMS-based estimator proposed in Chapter 3, the proposed adaptive VSSNLMS predictor only requires Lprd extra additions and Lprd 4 extra multiplications (divisions) compared to the adaptive NLMS predictor.
- Introduction
- Turbo Principle
- Generic Turbo Encoder
- Iterative Turbo Decoder
- System Model
- Iterative Decision Directed Channel Estimation Scheme
- Soft Demapper and Soft Mappers
- Soft Demapper
- Soft Mapper 1
- Soft Mapper 2
- Simulation Results and Discussion
- Computational Complexity of the Iterative DDCE Scheme
- Chapter Summary
The block diagram of the system model incorporating the iterative DDCE scheme looks as shown in Figure 5.3. Each of the iterative DDCE modules is based on the different algorithms presented in Chapter 4.
Introduction
This approach is said to be a space-time extension of the bit-interleaved coded modulation idea of [253]. The proposed iterative DDCE works in an iterative mode in conjunction with the MIMO attenuators and the Turbo decoder at the receiver of the MIMO-OFDM systems.
Iterative DDCE Scheme for MIMO-OFDM Systems
In this chapter, soft input iterative DDCE scheme using adaptive VSSNLMS and subspace detection FDPM algorithms is proposed for estimating MIMO CSI at the receiver of the bit-interleaved turbo-coded MIMO-OFDM systems. Simulation results for the proposed iterative DDCE for MIMO-OFDM system are discussed in Section 6.7.
MIMO-OFDM Systems Model
- ST-BICM Transmitter Structure
- Channel Statistics
- ST-BICM Receiver
Therefore, the SISO links of the MIMO channel can be characterized as a multipath SISO channel used in the previous chapters. Therefore, the received signal at the jth receiving antenna associated with the kth subcarrier of the nth OFDM block can be written as.
Iterative Decision Directed Channel Estimator Modules for MIMO-OFDM
- Temporary Channel Transfer Function (CTF) Estimator
- CTF Estimator based on Minimum Mean Square Error (MMSE)
- FDPM Subspace Tracking Algorithm-based MIMO CIR Estimator
- Adaptive VSSNLMS Algorithm-based MIMO CIR Predictor
The simple reason for this is due to the rank deficiency problem associated with MIMO channel estimation. The derivation of the Adaptive VSSNLMS-based CTF estimator for the proposed Iterative DDCE scheme for MIMO OFDM system is presented in this section.
Soft MIMO Demapper
- Soft MIMO Demapper Formulation
During subsequent iterations, the a priori ratios of the bits of each transmit antenna branch are derived from the output of the Map turbo decoder. The decoder's a priori log-likelihood ratios are also used by another soft mapper to compute soft symbols that are input into the proposed iterative DDCE scheme for the estimation of channel state information in the second iteration and beyond.
Soft MIMO Mapper
Ldn is set to zero in the initial step of the iterative process since there is no a priori information about the coded bit at this step.
Simulation Results and Discussions
Simulation is further performed for the proposed iterative DDCE for MIMO-OFDM systems using the PASTd-based CIR estimator proposed in [161]. Finally, the effect of antenna diversity on the proposed iterative DDCE for bit-interleaved turbo coded MIMO-OFDM systems is investigated.
Computational Complexity of the proposed Iterative DDCE scheme for
Chapter Summary
However, the order of computational complexity of the proposed iterative DDCE scheme grows linearly as MT MR (the number of transmit and receive antennas) compared to the computational complexity of the similar scheme proposed in the previous chapters for SISO- OFDM system.
THESIS SUMMARY
The VSSNLMS-based predictor is further derived for implementing the CIR predictor module of the DDCE scheme. The presented results also show that the VSSNLMS-based CIR predictor improved the performance of the DDCE scheme compared to when the NLMS-based predictor is used.
SUGGESTIONS FOR FUTURE RESEARCH WORK
Sollenberger, “Robust channel estimation for OFDM systems with fast dispersive fading channels,” IEEE Transactions on Communications, vol. Mengali, “Comparison of pilot-assisted channel estimation methods for OFDM systems,” IEEE Transactions on Signal Processing, vol.
![Figure 3.8 Receiver block diagram using soft-input soft output linear equalizer, iterative channel estimator and turbo decoder [220]](https://thumb-ap.123doks.com/thumbv2/pubpdfnet/10636193.0/89.918.145.809.564.995/figure-receiver-diagram-equalizer-iterative-channel-estimator-decoder.webp)
