Estimation and detection techniques for doubly-selective channels in wireless communications

A fundamental problem in communications is the estimation of the channel. The signal transmitted through a communications channel undergoes distortions so that it is often received in an unrecognizable form at the receiver. The receiver must expend significant signal processing effort in order to be able to decode the transmit signal from this received signal. This signal processing requires knowledge of how the channel distorts the transmit signal, i.e. channel knowledge. To maintain a reliable link, the channel must be estimated and tracked by the receiver. The estimation of the channel at the receiver often proceeds by transmission of a signal called the 'pilot' which is known a priori to the receiver. The receiver forms its estimate of the transmitted signal based on how this known signal is distorted by the channel, i.e. it estimates the channel from the received signal and the pilot. This design of the pilot is a function of the modulation, the type of training and the channel. [Continues.

Joint detection and channel estimation for MIMO systems with SC-FDE modulations

SC modulation (Single-Carrier) with FDE (Frequency-Domain Equalization) allows excellent performance in severely time-dispersive channels, provided that accurate channel estimates are available at the receiver. For this purpose, pilot symbols and/or training sequences are usually multiplexed with data symbols, which lead to spectral degradation. As an alternative, we can use implicit pilots (i.e., pilots superimposed to data). In this paper we consider MIMO SC-FDE systems where the channel estimation is based on either explicit or implicit pilots, for comparison purposes. An iterative receiver with joint equalization, turbo decoding and channel estimation was employed for optimum results, and to reduce the high interference levels between data and pilots (for the implicit pilots). The main differences between the different schemes are discussed and the performance results show that the use of the proposed techniques for channel estimation yield excellent results.info:eu-repo/semantics/acceptedVersio

Pilot Design for Enhanced Channel Estimation in Doubly Selective Channels

This paper investigates pilot design for enhanced channel estimation in single carrier communication systems over doubly-selective channels (DSC). Our contribution is twofold: first, we propose to use Huffman sequences as pilot clusters with low peak-to-average power ratio (PAPR), yet with good channel estimation performance when periodic pilot placement is adopted; second, we propose a low-complexity pilot placement strategy based on the analysis of the complex-exponential basis expansion model (CE-BEM) of the DSC. The latter leads to improved channel estimation performance with useful insights for pilot placement

Channel Estimation and ICI Cancelation in Vehicular Channels of OFDM Wireless Communication Systems

Orthogonal frequency division multiplexing (OFDM) scheme increases bandwidth efficiency (BE) of data transmission and eliminates inter symbol interference (ISI). As a result, it has been widely used for wideband communication systems that have been developed during the past two decades and it can be a good candidate for the emerging communication systems such as fifth generation (5G) cellular networks with high carrier frequency and communication systems of high speed vehicles such as high speed trains (HSTs) and supersonic unmanned aircraft vehicles (UAVs). However, the employment of OFDM for those upcoming systems is challenging because of high Doppler shifts. High Doppler shift makes the wideband communication channel to be both frequency selective and time selective, doubly selective (DS), causes inter carrier interference (ICI) and destroys the orthogonality between the subcarriers of OFDM signal. In order to demodulate the signal in OFDM systems and mitigate ICIs, channel state information (CSI) is required. In this work, we deal with channel estimation (CE) and ICI cancellation in DS vehicular channels. The digitized model of the DS channels can be short and dense, or long and sparse. CE methods that perform well for short and dense channels are highly inefficient for long and sparse channels. As a result, for the latter type of channels, we proposed the employment of compressed sensing (CS) based schemes for estimating the channel. In addition, we extended our CE methods for multiple input multiple output (MIMO) scenarios. We evaluated the CE accuracy and data demodulation fidelity, along with the BE and computational complexity of our methods and compared the results with the previous CE procedures in different environments. The simulation results indicate that our proposed CE methods perform considerably better than the conventional CE schemes

High mobility in OFDM based wireless communication systems

Orthogonal Frequency Division Multiplexing (OFDM) has been adopted as the transmission scheme in most of the wireless systems we use on a daily basis. It brings with it several inherent advantages that make it an ideal waveform candidate in the physical layer. However, OFDM based wireless systems are severely affected in High Mobility scenarios. In this thesis, we investigate the effects of mobility on OFDM based wireless systems and develop novel techniques to estimate the channel and compensate its effects at the receiver. Compressed Sensing (CS) based channel estimation techniques like the Rake Matching Pursuit (RMP) and the Gradient Rake Matching Pursuit (GRMP) are developed to estimate the channel in a precise, robust and computationally efficient manner. In addition to this, a Cognitive Framework that can detect the mobility in the channel and configure an optimal estimation scheme is also developed and tested. The Cognitive Framework ensures a computationally optimal channel estimation scheme in all channel conditions. We also demonstrate that the proposed schemes can be adapted to other wireless standards easily. Accordingly, evaluation is done for three current broadcast, broadband and cellular standards. The results show the clear benefit of the proposed schemes in enabling high mobility in OFDM based wireless communication systems.Orthogonal Frequency Division Multiplexing (OFDM) wurde als Übertragungsschema in die meisten drahtlosen Systemen, die wir täglich verwenden, übernommen. Es bringt mehrere inhärente Vorteile mit sich, die es zu einem idealen Waveform-Kandidaten in der Bitübertragungsschicht (Physical Layer) machen. Allerdings sind OFDM-basierte drahtlose Systeme in Szenarien mit hoher Mobilität stark beeinträchtigt. In dieser Arbeit untersuchen wir die Auswirkungen der Mobilität auf OFDM-basierte drahtlose Systeme und entwickeln neuartige Techniken, um das Verhalten des Kanals abzuschätzen und seine Auswirkungen am Empfänger zu kompensieren. Auf Compressed Sensing (CS) basierende Kanalschätzverfahren wie das Rake Matching Pursuit (RMP) und das Gradient Rake Matching Pursuit (GRMP) werden entwickelt, um den Kanal präzise, robust und rechnerisch effizient abzuschätzen. Darüber hinaus wird ein Cognitive Framework entwickelt und getestet, das die Mobilität im Kanal erkennt und ein optimales Schätzungsschema konfiguriert. Das Cognitive Framework gewährleistet ein rechnerisch optimales Kanalschätzungsschema für alle möglichen Kanalbedingungen. Wir zeigen außerdem, dass die vorgeschlagenen Schemata auch leicht an andere Funkstandards angepasst werden können. Dementsprechend wird eine Evaluierung für drei aktuelle Rundfunk-, Breitband- und Mobilfunkstandards durchgeführt. Die Ergebnisse zeigen den klaren Vorteil der vorgeschlagenen Schemata bei der Ermöglichung hoher Mobilität in OFDM-basierten drahtlosen Kommunikationssystemen

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

Low-Complexity Algorithms for Channel Estimation in Optimised Pilot-Assisted Wireless OFDM Systems

Orthogonal frequency division multiplexing (OFDM) has recently become a dominant transmission technology considered for the next generation fixed and mobile broadband wireless communication systems. OFDM has an advantage of lessening the severe effects of the frequency-selective (multipath) fading due to the band splitting into relatively flat fading subchannels, and allows for low-complexity transceiver implementation based on the fast Fourier transform algorithms. Combining OFDM modulation with multilevel frequency-domain symbol mapping (e.g., QAM) and spatial multiplexing (SM) over the multiple-input multiple-output (MIMO) channels, can theoretically achieve near Shannon capacity of the communication link. However, the high-rate and spectrumefficient system implementation requires coherent detection at the receiving end that is possible only when accurate channel state information (CSI) is available. Since in practice, the response of the wireless channel is unknown and is subject to random variation with time, the receiver typically employs a channel estimator for CSI acquisition. The channel response information retrieved by the estimator is then used by the data detector and can also be fed back to the transmitter by means of in-band or out-of-band signalling, so the latter could adapt power loading, modulation and coding parameters according to the channel conditions. Thus, design of an accurate and robust channel estimator is a crucial requirement for reliable communication through the channel, which is selective in time and frequency. In a MIMO configuration, a separate channel estimator has to be associated with each transmit/receive antenna pair, making the estimation algorithm complexity a primary concern. Pilot-assisted methods, relying on the insertion of reference symbols in certain frequencies and time slots, have been found attractive for identification of the doubly-selective radio channels from both the complexity and performance standpoint. In this dissertation, a family of the reduced-complexity estimators for the single and multiple-antenna OFDM systems is developed. The estimators are based on the transform-domain processing and have the same order of computational complexity, irrespective of the number of pilot subcarriers and their positioning. The common estimator structure represents a cascade of successive small-dimension filtering modules. The number of modules, as well as their order inside the cascade, is determined by the class of the estimator (one or two-dimensional) and availability of the channel statistics (correlation and signal-to-noise power ratio). For fine precision estimation in the multipath channels with statistics not known a priori, we propose recursive design of the filtering modules. Simulation results show that in the steady state, performance of the recursive estimators approaches that of their theoretical counterparts, which are optimal in the minimum mean square error (MMSE) sense. In contrast to the majority of the channel estimators developed so far, our modular-type architectures are suitable for the reconfigurable OFDM transceivers where the actual channel conditions influence the decision of what class of filtering algorithm to use, and how to allot pilot subcarrier positions in the band. In the pilot-assisted transmissions, channel estimation and detection are performed separately from each other over the distinct subcarrier sets. The estimator output is used only to construct the detector transform, but not as the detector input. Since performance of both channel estimation and detection depends on the signal-to-noise power vi ratio (SNR) at the corresponding subcarriers, there is a dilemma of the optimal power allocation between the data and the pilot symbols as these are conflicting requirements under the total transmit power constraint. The problem is exacerbated by the variety of channel estimators. Each kind of estimation algorithm is characterised by its own SNR gain, which in general can vary depending on the channel correlation. In this dissertation, we optimise pilot-data power allocation for the case of developed low-complexity one and two-dimensional MMSE channel estimators. The resultant contribution is manifested by the closed-form analytical expressions of the upper bound (suboptimal approximate value) on the optimal pilot-to-data power ratio (PDR) as a function of a number of design parameters (number of subcarriers, number of pilots, number of transmit antennas, effective order of the channel model, maximum Doppler shift, SNR, etc.). The resultant PDR equations can be applied to the MIMO-OFDM systems with arbitrary arrangement of the pilot subcarriers, operating in an arbitrary multipath fading channel. These properties and relatively simple functional representation of the derived analytical PDR expressions are designated to alleviate the challenging task of on-the-fly optimisation of the adaptive SM-MIMO-OFDM system, which is capable of adjusting transmit signal configuration (e.g., block length, number of pilot subcarriers or antennas) according to the established channel conditions