Advanced DSP Algorithms For Modern Wireless Communication Transceivers

A higher network throughput, a minimized delay and reliable communications are some of many goals that wireless communication standards, such as the fifthgeneration (5G) standard and beyond, intend to guarantee for its customers. Hence, many key innovations are currently being proposed and investigated by researchers in the academic and industry circles to fulfill these goals. This dissertation investigates some of the proposed techniques that aim at increasing the spectral efficiency, enhancing the energy efficiency, and enabling low latency wireless communications systems. The contributions lay in the evaluation of the performance of several proposed receiver architectures as well as proposing novel digital signal processing (DSP) algorithms to enhance the performance of radio transceivers. Particularly, the effects of several radio frequency (RF) impairments on the functionality of a new class of wireless transceivers, the full-duplex transceivers, are thoroughly investigated. These transceivers are then designed to operate in a relaying scenario, where relay selection and beamforming are applied in a relaying network to increase its spectral efficiency. The dissertation then investigates the use of greedy algorithms in recovering orthogonal frequency division multiplexing (OFDM) signals by using sparse equalizers, which carry out the equalization in a more efficient manner when the low-complexity single tap OFDM equalizer can no longer recover the received signal due to severe interferences. The proposed sparse equalizers are shown to perform close to conventional optimal and dense equalizers when the OFDM signals are impaired by interferences caused by the insertion of an insufficient cyclic prefix and RF impairments

A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Single-Frequency Network Terrestrial Broadcasting with 5GNR Numerology

L'abstract è presente nell'allegato / the abstract is in the attachmen

Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes

Low-complexity iterative receiver algorithms for multiple-input multiple-output underwater wireless communications

This dissertation proposes three low-complexity iterative receiver algorithms for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. First is a bidirectional soft-decision feedback Turbo equalizer (Bi-SDFE) which harvests the time-reverse diversity in severe multipath MIMO channels. The Bi-SDFE outperforms the original soft-decision feedback Turbo equalizer (SDFE) while keeping its total computational complexity similar to that of the SDFE. Second, this dissertation proposes an efficient direct adaptation Turbo equalizer for MIMO UWA communications. Benefiting from the usage of soft-decision reference symbols for parameter adaptation as well as the iterative processing inside the adaptive equalizer, the proposed algorithm is efficient in four aspects: robust performance in tough channels, high spectral efficiency with short training overhead, time efficient with fast convergence and low complexity in hardware implementation. Third, a frequency-domain soft-decision block iterative equalizer combined with iterative channel estimation is proposed for the uncoded single carrier MIMO systems with high data efficiency. All the three new algorithms are evaluated by data recorded in real world ocean experiment or pool experiment. Finally, this dissertation also compares several Turbo equalizers in single-input single-output (SISO) UWA channels. Experimental results show that the channel estimation based Turbo equalizers are robust in SISO underwater transmission under harsh channel conditions --Abstract, page iv

Adaptive relay techniques for OFDM-based cooperative communication systems

Cooperative communication has been considered as a cost-effective manner to exploit the spatial diversity, improve the quality-of-service and extend transmission coverage. However, there are many challenges faced by cooperative systems which use relays to forward signals to the destination, such as the accumulation of multipath channels, complex resource allocation with the bidirectional asymmetric traffic and reduction of transmission efficiency caused by additional relay overhead. In this thesis, we aim to address the above challenges of cooperative communications, and design the efficient relay systems. Starting with the channel accumulation problem in the amplify-and-forward relay system, we proposed two adaptive schemes for single/multiple-relay networks respectively. These schemes exploit an adaptive guard interval (GI) technique to cover the accumulated delay spread and enhance the transmission efficiency by limiting the overhead. The proposed GI scheme can be implemented without any extra control signal. Extending the adaptive GI scheme to multiple-relay systems, we propose a relay selection strategy which achieves the trade-off between the transmission reliability and overhead by considering both the channel gain and the accumulated delay spread. We then consider resource allocation problem in the two-way decode-and-forward relay system with asymmetric traffic loads. Two allocation algorithms are respectively investigated for time-division and frequency-division relay systems to maximize the end-to-end capacity of the two-way system under a capacity ratio constraint. For the frequency-division systems, a balanced end-to-end capacity is defined as the objective function which combines the requirements of maximizing the end-to-end capacity and achieving the capacity ratio. A suboptimal algorithm is proposed for the frequency-division systems which separates subcarrier allocation and time/power allocation. It can achieve the similar performance with the optimal one with reduced complexity. In order to further enhance the transmission reliability and maintaining low processing delay, we propose an equalize-and-forward (EF) relay scheme. The EF relay equalizes the channel between source and relay to eliminate the channel accumulation without signal regeneration. To reduce the processing time, an efficient parallel structure is applied in the EF relay. Numerical results show that the EF relay exhibits low outage probability at the same data rate as compared to AF and DF schemes