Resource allocation for relay based green communication systems

The relay based cooperative network is one of the promising techniques for next generation wireless communications, which can help extend the cell coverage and enhance the diversity. To deploy relays efficiently with limited power and bandwidth under certain performance requirements, resource allocation (RA) plays an increasingly important role in the system design. In recent years, with the fast growth of the number of mobile phone users, great portion of CO2 emission is contributed by wireless communication systems. The combination of relay techniques and RA schemes reveals the solution to green communications, which aims to provide high data rate with low power consumption. In this thesis, RA is investigated for next generation relay based green wireless systems, including the long-range cellular systems, and the short-range point-to-point (P2P) systems. In the first contribution, an optimal asymmetric resource allocation (ARA) scheme is proposed for the decode-and-forward (DF) dual-hop multi-relay OFDMA cellular systems in the downlink. With this scheme, the time slots for the two hops via each of the relays are designed to be asymmetric, i.e., with K relays in a cell, a total of 2K time slots may be of different durations, which enhances the degree of freedom over the previous work. Also, a destination may be served by multiple relays at the same time to enhance the transmission diversity. Moreover, closed-form results for optimal resource allocation are derived, which require only limited amount of feedback information. Numerical results show that, due to the multi-time and multi-relay diversities, the proposed ARA scheme can provide a much better performance than the scheme with symmetric time allocation, as well as the scheme with asymmetric time allocation for a cell composed of independent single-relay sub-systems, especially when the relays are relatively close to the source. As a result, with the optimal relay location, the system can achieve high throughput in downlink with limited transmit power. In the second contribution, the power consumption in relay based 60 GHz cooperative networks is studied, which is based on three-terminal diversity amplify-and-forward (DAF) and diversity DF (DDF) relaying strategies. A total power consumption model including drive power, decoding power, and power consumption of power amplifier (PA) is proposed, excluding the transmit power, as it is relatively small compared to decoding power and PA power in the indoor environment. This model is formulated as a function of drive power, which gives an easy access to the system level power allocation. To minimise the system total power consumption, the optimal drive power can be allocated to the source node by numerical searching method while satisfying the data rate requirement. The impact of relay locations on the total power consumption is also investigated. It is shown that, with the same data rate requirement, in the small source-relay separation case, DAF consumes slightly less power than DDF; while with larger source-relay separation, DAF consumes much more power than DDF. In the future work, multiuser relay-based short-range communication systems will be considered for the 60 GHz communication in the fading channel scenario, which extends the proposed power consumption model in a more practical way. The power consumption model of other components, such as analog-to-digital converter, data buffer, modulation/demodulation could also be considered to provide more details about green P2P communications

Full-Duplex Versus Half-Duplex Amplify-and-Forward Relaying: Which is More Energy Efficient in 60-GHz Dual-Hop Indoor Wireless Systems?

We provide a comprehensive energy efficiency (EE) analysis of the full-duplex (FD) and half-duplex (HD) amplify-and-forward (AF) relay-assisted 60-GHz dual-hop indoor wireless systems, aiming to answer the question of which relaying mode is greener (more energy efficient) and to address the issue of EE optimization. We develop an opportunistic relaying mode selection scheme, where FD relaying with one-stage self-interference cancellation (passive suppression) or two-stage self-interference cancellation (passive suppression + analog cancellation) or HD relaying is opportunistically selected, together with transmission power adaptation, to maximize the EE with given channel gains. A low-complexity joint mode selection and EE optimization algorithm are proposed. We show a counter-intuitive finding that with a relatively loose maximum transmission power constraint, FD relaying with two-stage self-interference cancellation is preferable to both FD relaying with one-stage self-interference cancellation and HD relaying, resulting in a higher optimized EE. A full range of power consumption sources is considered to rationalize our analysis. The effects of imperfect self-interference cancellation at relay, drain efficiency, and static circuit power on EE are investigated. Simulation results verify our theoretical analysis

CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training

Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training (CIF-PT). It relies on a simple but effective frame-to-token alignment: continuous integrate-and-fire (CIF) to bridge the representations between speech and text. It jointly performs speech-to-text training and language model distillation through CIF as the pre-training (PT). Evaluated on SLU benchmark SLURP dataset, CIF-PT outperforms the state-of-the-art model by 1.94% of accuracy and 2.71% of SLU-F1 on the tasks of intent classification and slot filling, respectively. We also observe the cross-modal representation extracted by CIF-PT obtains better performance than other neural interfaces for the tasks of SLU, including the dominant speech representation learned from self-supervised pre-training.Comment: Accepted by ACL 2023 Finding

Language-specific Acoustic Boundary Learning for Mandarin-English Code-switching Speech Recognition

Code-switching speech recognition (CSSR) transcribes speech that switches between multiple languages or dialects within a single sentence. The main challenge in this task is that different languages often have similar pronunciations, making it difficult for models to distinguish between them. In this paper, we propose a method for solving the CSSR task from the perspective of language-specific acoustic boundary learning. We introduce language-specific weight estimators (LSWE) to model acoustic boundary learning in different languages separately. Additionally, a non-autoregressive (NAR) decoder and a language change detection (LCD) module are employed to assist in training. Evaluated on the SEAME corpus, our method achieves a state-of-the-art mixed error rate (MER) of 16.29% and 22.81% on the test_man and test_sge sets. We also demonstrate the effectiveness of our method on a 9000-hour in-house meeting code-switching dataset, where our method achieves a relatively 7.9% MER reduction