Partial-Data Superimposed Training with Data Precoding for OFDM Systems

Superimposed training (ST) is a recently addressed technique used for channel estimation where known training sequences are arithmetically added to data symbols, avoiding the use of dedicated pilot subcarriers, and thus, increasing the available bandwidth compared with traditional pilot symbol assisted modulation schemes. However, the system handles data interference over channel estimation as a result of the ST process; also, data detection is degraded by pilot interference. Recent ST methods have analyzed the data interference and presented schemes that deal with it. We propose a novel superimposed model over a precoded data scheme, named partial-data superimposed training (PDST), where an interference control factor assigns the adequate information level to be added to the training sequence in orthogonal frequency division multiplexing systems. Also, a data detection method is introduced to improve the symbol error rate performance. Moreover, a capacity analysis of the system has been derived. Finally, simulation results confirm that performance of PDST is superior to previous proposals

Advanced Statistical Signal Processing Methods in Sensing, Detection, and Estimation for Communication Applications

The applications of wireless communications and digital signal processing have dramatically changed the way we live, work, and learn over decades. The requirement of higher throughput and ubiquitous connectivity for wireless communication systems has become prevalent nowadays. Signal sensing, detection and estimation have been prevalent in signal processing and communications for many years. The relevant studies deal with the processing of information-bearing signals for the purpose of information extraction. Nevertheless, new robust and efficient signal sensing, detection and estimation techniques are still in demand since there emerge more and more practical applications which rely on them. In this dissertation work, we proposed several novel signal sensing, detection and estimation schemes for wireless communications applications, such as spectrum sensing, symbol-detection/channel-estimation, and encoder identification. The associated theories and practice in robustness, computational complexity, and overall system performance evaluation are also provided

Performance of STBC Based MIMO-OFDM Using Pilot-aided Channel Estimation

Many studies have been published to address the growing issues in wireless communication systems. Space-Time Block Coding (STBC) is an effective and practical MIMO-OFDM application that can address such issues. It is a powerful tool for increasing wireless performance by coding data symbols and transmitting diversity using several antennas. The most significant challenge is to recover the transmitted signal through a time-varying multipath fading channel and obtain a precise channel estimation to recover the transmitted information symbols. This work considers different pilot patterns for channel estimation and equalization in MIMO-OFDM systems. The pilot patterns fall under two general types: comb and block types, with a proper arrangement suitable to the multiple transmit antennas. The two main channel estimation methods, LS and MMSE, are compared by evaluating performance in terms of Bit Error Rate (BER) to analyze the performance of pilot-aided channel estimation for 2x2 and 4x4 MIMO arrangements utilizing LTE parameters and the effects of modifying different numbers of OFDM subcarriers under different channel models It has been discussed, a 4x4 system performs better than a 2x2 system in terms of BER with an acceptable amount of additional complexity

Adaptive averaging channel estimation for DVB-T2 systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn modern communication systems, the rate of transmitted data is growing rapidly. This leads to the need for more sophisticated methods and techniques of implementation in every block of the transmitter-receiver chain. The weakest link in radio communications is the transmission channel. The signal, which is passed through it, suffers from many degrading factors like noise, attenuation, diffraction, scattering etc. In the receiver side, the modulated signal has to be restored to its initial state in order to extract the useful information. Assuming that the channel acts like a filter with finite impulse, one has to know its coefficients in order to apply the inverse function, which will restore the signal back to its initial state. The techniques which deal with this problem are called channel estimation. Noise is one of the causes that degrade the quality of the received signal. If it could be discarded, then the process of channel estimation would be easier. Transmitting special symbols, called pilots with known amplitude, phase and position to the receiver and assuming that the noise has zero mean, an averaging process could reduce the noise impact to the pilot amplitudes and thus simplify the channel estimation process. In this thesis, a novel channel estimation method based on noise rejection is introduced. The estimator takes into account the time variations of the channel and adapts its buffer size in order to achieve the best performance. Many configurations of the estimator were tested and at the beginning of the research fixed size estimators were tested. The fixed estimator has a very good performance for channels which could be considered as stationary in the time domain, like Additive White Gaussian Noise (AWGN) channels or slowly time-varying channels. AWGN channel is a channel model where the only distorting factor is the noise, where noise is every unwanted signal interfering with the useful signal. The properties of the noise are that it is additive, which means that the noise is superimposed on the transmitted signal, it is white so the power density is constant for all frequencies, and it has a Gaussian distribution in the time domain with zero mean and variance σ2=N. A slowly time varying channel refers to channel with coherence time larger than the transmitted symbol duration. The performance of a fixed size averaging estimator in case of fast time-varying channels is subject to the buffering time. When the buffering time is smaller or equal to a portion of the coherence time the averaging process offers better performance than the conventional estimation, but when the buffering time exceeds this portion of the coherence time the performance of the averaging process degrades fast. So, an extension has been made to the averaging estimator that estimates the Doppler shift and thus the coherence time, where the channel could be assumed as stationary. The improved estimator called Adaptive Averaging Channel Estimator (AACE) is capable to adjust its buffer size and thus to average only successive Orthogonal Frequency Division Multiplexing (OFDM) symbols that have the same channel distortions. The OFDM is a transmission method where instead of transmitting the data stream using only on carrier, the stream is divided into parallel sub-streams where the subcarriers conveying the sub-streams are orthogonal to each other. The use of the OFDM increases the symbol duration making it more robust against Inter-Symbol Interference (ISI), which the interference among successive transmitted symbols, and also divides the channel bandwidth into small sub-bandwidths preventing frequency selectivity because of the multipath nature of the radio channel. Simulations using the Rayleigh channel model were performed and the results clearly demonstrate the benefits of the AACE in the channel estimation process. The performance of the combination of AACE with Least Square estimation (AACE-LS) is superior to the conventional Least Square estimation especially for low Doppler shifts and it is close to the Linear Minimum Mean Square Error (LMMSE) estimation performance. Consequently, if the receiver has low computational resources and/or the channel statistics are unknown, then the AACE-LS estimator is a valid choice for modern radio receivers. Moreover, the proposed adaptive averaging process could be used in any OFDM system based on pilot aided channel estimation. In order to verify the superiority of the AACE algorithm, quantitative results are provided in terms of BER vs SNR. It is demonstrated that AACE-LS is 7dB more sensitive than the LS estimator

Enabling Technology and Algorithm Design for Location-Aware Communications

Location-awareness is emerging as a promising technique for future-generation wire­ less network to adaptively enhance and optimize its overall performance through location-enabled technologies such as location-assisted transceiver reconfiguration and routing. The availability of accurate location information of mobile users becomes the essential prerequisite for the design of such location-aware networks. Motivated by the low locationing accuracy of the Global Positioning System (GPS) in dense multipath environments, which is commonly used for acquiring location information in most of the existing wireless networks, wireless communication system-based po­sitioning systems have been investigated as alternatives to fill the gap of the GPS in coverage. Distance-based location techniques using time-of-arrival (TOA) mea­surements are commonly preferred by broadband wireless communications where the arrival time of the signal component of the First Arriving Path (FAP) can be con­verted to the distance between the receiver and the transmitter with known location. With at least three transmitters, the location of the receiver can be determined via trilatération method. However, identification of the FAP’s signal component in dense multipath scenarios is quite challenging due to the significantly weaker power of the FAP as compared with the Later Arriving Paths (LAPs) from scattering, reflection and refraction, and the superposition of these random arrival LAPs’ signal compo­ nents will become large interference to detect the FAP. In this thesis, a robust FAP detection scheme based on multipath interference cancellation is proposed to im­ prove the accuracy of location estimation in dense multipath environments. In the proposed algorithm, the signal components of LAPs is reconstructed based on the estimated channel and data with the assist of the communication receiver, and sub­ sequently removed from the received signal. Accurate FAP detection results are then achieved with the cross-correlation between the interference-suppressed signal and an augmented preamble which is the combination of the original preamble for com­ munications and the demodulated data sequences. Therefore, more precise distance estimation (hence location estimation) can be obtained with the proposed algorithm for further reliable network optimization strategy design. On the other hand, multiceli cooperative communication is another emerging technique to substantially improve the coverage and throughput of traditional cellular networks. Location-awareness also plays an important role in the design and imple­mentation of multiceli cooperation technique. With accurate location information of mobile users, the complexity of multiceli cooperation algorithm design can be dra­matically reduced by location-assisted applications, e.g., automatic cooperative base station (BS) determination and signal synchronization. Therefore, potential latency aroused by cooperative processing will be minimized. Furthermore, the cooperative BSs require the sharing of certain information, e.g., channel state information (CSI), user data and transmission parameters to perform coordination in their signaling strategies. The BSs need to have the capabilities to exchange available information with each other to follow up with the time-varying communication environment. As most of broadband wireless communication systems are already orthogonal frequency division multiplexing (OFDM)-based, a Multi-Layered OFDM System, which is spe­cially tailored for multiceli cooperation is investigated to provide parallel robust, efficient and flexible signaling links for BS coordination purposes. These layers are overlaid with data-carrying OFDM signals in both time and frequency domains and therefore, no dedicated radio resources are required for multiceli cooperative networks. In the final aspect of this thesis, an enhanced channel estimation through itera­ tive decision-directed method is investigated for OFDM system, which aims to provide more accurate estimation results with the aid of the demodulated OFDM data. The performance of traditional training sequence-based channel estimation is often lim­ ited by the length of the training. To achieve acceptable estimation performance, a long sequence has to be used which dramatically reduces the transmission efficiency of data communication. In this proposed method, the restriction of the training se­quence length can be removed and high channel estimation accuracy can be achieved with high transmission efficiency, and therefore it particular fits in multiceli coopera­tive networks. On the other hand, as the performance of the proposed FAP detection scheme also relies on the accuracy of channel estimation and data detection results, the proposed method can be combined with the FAP detection scheme to further optimize the accuracy of multipath interference cancellation and FAP detection

Wireless communication, sensing, and REM: A security perspective

The diverse requirements of next-generation communication systems necessitate awareness, flexibility, and intelligence as essential building blocks of future wireless networks. The awareness can be obtained from the radio signals in the environment using wireless sensing and radio environment mapping (REM) methods. This is, however, accompanied by threats such as eavesdropping, manipulation, and disruption posed by malicious attackers. To this end, this work analyzes the wireless sensing and radio environment awareness mechanisms, highlighting their vulnerabilities and provides solutions for mitigating them. As an example, the different threats to REM and its consequences in a vehicular communication scenario are described. Furthermore, the use of REM for securing communications is discussed and future directions regarding sensing/REM security are highlighted

On Efficient Signal Processing Algorithms for Signal Detection and PAPR Reduction in OFDM Systems

The driving force of the study is susceptibility of LS algorithm to noise. As LS algorithm is simple to implement, hence it’s performance improvement can contribute a lot to the wireless technology that are especially deals with high computation. Cascading of AdaBoost algorithm with LS greatly influences the OFDM system performance. Performance of Adaptive Boosting based symbol recovery was investigated on the performance of LS, MMSE, BLUE were also compared with the performance of AdaBoost algorithm and MMSE has been found the higher computational complexity. Furthermore, MMSE also requires apriori channel statistics and computational complexity O(5N3) of the MMSE increases exponentially as the number of carrier increases. For the Adaboost case the computational complexity calculation is little different.Therefore, in the training stage of the AdaBoost algorithm, the computational complexity is only O(nT M) Furthermore, as it is a classification algorithm so in the receiver side we will require a separate de-mapper (or decoder) to get the desired data bits, i.e., a. SAS aided DCT based PAPR reduction 1326 and b. SAS aided DCT based PAPR reduction. A successive addition subtraction preprocessed DCT based PAPR reduction technique was proposed. Here, the performance of proposed method was compared with other preexisting techniques like SLM and PTS and the performance of the proposed method was seen to outperform specially in low PAPR region. In the proposed PAPR reduction method, the receiver is aware of the transmitted signal processing, this enables a reverse operation at the receiver to extract the transmit data. Hence the requirement of sending extra information through extra subcarrier is eliminated. The proposed method is also seen to be spectrally efficient. In the case of PTS and SLM it is inevitable to send the side information to retrieve the transmit signal. Hence, these two methods are spectrally inefficient. Successive addition subtraction based PAPR reduction method was also applied to MIMO systems. The performance of the SAS based PAPR reduction method also showed better performance as compared to other technique. An extensive simulation of MIMO OFDM PAPR reduction was carried out by varying the number of subcarriers and number of transmitter antennas. A detailed computational complexity analysis was also carried out. BATE aided SDMA multi user detection. A detailed study of SDMA system was carried out with it’s mathematical analysis.Many linear and non linear detectors like ML, MMSE, PIC, SIC have been proposed in literature for multiuser detection of SDMA system. However, except MMSE every receivers other are computational extensive. So as to enhance the performance of the MMSE MUD a meta heuristic Bat algorithm was incorporated in cascade with MMSE

Orthogonal frequency division multiplexing for next generation optical networks

Next generation optical networks will be required to provide increased data throughput on a greater number of optical channels and will also have to facilitate network flexibility in order to adapt to dynamic traffic patterns. Furthermore, the potentially wide deployment of optical Access and Metropolitan networks in particular require that these challenges are met in a cost effect manner. This thesis examines the use of Orthogonal Frequency Division Multiplexing (OFDM) as a means of helping to meet these requirements for next generation optical systems with a high market volume. OFDM is a multi–carrier modulation technique which exhibits high spectral efficiency and a tolerance to chromatic dispersion making it an excellent candidate for use in next generation optical networks. The work presented in this thesis shows how the use of OFDM in conjunction with novel laser devices and direct detection can be used to construct cost effective, low footprint optical systems. These systems are capable of providing >10Gb/s per optical channel and are suitable for implementation as optical access networks. Furthermore, OFDM is shown to be a realistic candidate for use in an optical switching environment where external modulation is employed and, as such, can be considered for use in next generation metropolitan networks

Superimposed training for single carrier transmission in future mobile communications

The amount of wireless devices and wireless traffic has been increasing exponentially for the last ten years. It is forecasted that the exponential growth will continue without saturation till 2020 and probably further. So far, network vendors and operators have tackled the problem by introducing new evolutions of cellular macro networks, where each evolution has increased the physical layer spectral efficiency. Unfortunately, the spectral efficiency of the physical layer is achieving the Shannon-Hartley limit and does not provide much room for improvement anymore. However, considering the overhead due to synchronization and channel estimation reference symbols in the context of physical layer spectral efficiency, we believe that there is room for improvement. In this thesis, we will study the potentiality of superimposed training methods, especially data-dependent superimposed training, to boost the spectral efficiency of wideband single carrier communications even further. The main idea is that with superimposed training we can transmit more data symbols in the same time duration as compared to traditional time domain multiplexed training. In theory, more data symbols means more data bits which indicates higher throughput for the end user. In practice, nothing is free. With superimposed training we encounter self-interference between the training signal and the data signal. Therefore, we have to look for iterative receiver structures to separate these two or to estimate both, the desired data signal and the interfering component. In this thesis, we initiate the studies to find out if we truly can improve the existing systems by introducing the superimposed training scheme. We show that in certain scenarios we can achieve higher spectral efficiency, which maps directly to higher user throughput, but with the cost of higher signal processing burden in the receiver. In addition, we provide analytical tools for estimating the symbol or bit error ratio in the receiver with a given parametrization. The discussion leads us to the conclusion that there still remains several open topics for further study when looking for new ways of optimizing the overhead of reference symbols in wireless communications. Superimposed training with data-dependent components may prove to provide extra throughput gain. Furthermore, the superimposed component may be used for, e.g., improved synchronization, low bit-rate signaling or continuous tracking of neighbor cells. We believe that the current systems could be improved by using the superimposed training collectively with time domain multiplexed training