Rate, Reliability and Secrecy Performance Analysis and Optimization for Millimeter WaveCommunications
Time: Wed 2020-12-09 15.00
Subject area: Electrical Engineering
Doctoral student: Shaocheng Huang , Teknisk informationsvetenskap
Opponent: Professor Marco Renzo,
Supervisor: Ming Xiao, Teknisk informationsvetenskap; Mikael Skoglund, Signaler, sensorer och system, Teknisk informationsvetenskap
With the fast development of electronic devices and computer science, various emerging applications (e.g., virtual and augmented reality, ultra-high-definition three-dimensional video, autonomous driving, big data analysis, etc.) have created an explosive growth of mobile data traffic and caused growing demands for higher communication rates, more reliable and secure connectivity in the future wireless communications, e.g., the fifth-generation (5G) and beyond mobile communications. In recent years, millimeter wave (mmWave) communication, as a promising candidate to meet the aforementioned demands, has attracted extensive research attention, and is regarded as one of the key enablers for the 5G and beyond mobile communications. The main features of mmWave communications include: abundant spectral resources, high penetration loss, severe pathloss, and narrow antenna beams, and these particular features make the potential challenges and solutions with mmWave significantly different from those in the conventional sub-6 GHz systems.
It is known that beamforming is a crucial stage of mmWave communication to support high antenna gains and suppress inter-channel interference. However, the related research on beamforming design is fairly recent and insufficient. Motivated by the urgent needs for further development, in this thesis, we investigate beamforming optimization and the reliability and secrecy performance for mmWave communications. Our main research regarding beamforming design and secrecy performance can be categorized into the following three aspects:
1) Hybrid beamforming (HBF) for mmWave systems with learning machines: We propose two low-complexity and robust learning schemes to design HBF for mmWave full-duplex systems and multi-user multi-input and multi-output (MU-MIMO) systems i.e., extreme learning machine based HBF and convolutional neural networks based HBF. To provide accurate labels for off-line training, effective HBF algorithms are proposed to achieve joint self-interference cancellation and HBF optimization for mmWave full-duplex systems and to achieve joint transmitting and receiving HBF optimization for mmWave MU-MIMO systems. The convergence of the proposed algorithms is proven and the computation complexity is analyzed. Results show that the proposed learning based methods can achieve higher spectral efficiency, lower complexity, and more robust HBF performance than conventional optimization based methods.
2) Decentralized beamforming for intelligent reflecting surface (IRS)-enhanced cell-free networks: To avoid the centralized computation and inspired by the cost-effective IRS technique, we propose a fully decentralized design framework for cooperative beamforming in IRS-aided cell-free networks, in which transmitting digital beamformers and IRS-based analog beamformers are jointly optimized. We first derive a closed-form expression of each updating variable and then propose a fully decentralized beamforming scheme based on the alternating direction method of multipliers to incrementally and locally update the beamformers. Results reveal that the new scheme outperforms existing decentralized methods and IRSs can significantly improve the system sum-rate.
3) Physical layer security of mmWave non-orthogonal multiple access (NOMA) networks: Considering the limited scattering characteristics of mmWave channels and imperfect successive interference cancellation at receivers, we develop an analytic framework on the secrecy outage probability for mmWave NOMA networks. Based on the directional transmission property of mmWave signals, we propose a minimal angle-difference user pairing scheme to reduce the secrecy outage probability of users. Considering the spatial correlation between the selected user pair and eavesdroppers, we develop two maximum ratio transmission beamforming schemes to further enhance the secrecy performance of mmWave NOMA networks. Closed-form secrecy outage probability for the paired users with different eavesdropper detection capacities is derived
4) Achievable rates and reliability analysis of mmWave channels: We leverage random coding error exponent to investigate the achievable rate of mmWave channels under reliability and packet duration (finite blocklength) constraints. Under the assumption of perfect and imperfect channel state information at the receiver (CSIR), exact and approximate analytical expressions of achievable rates are derived to capture the relationship of rate, latency, and reliability. Furthermore, we show that the achievable rate always increases as the bandwidth increases with perfect CSIR. However, there exists a critical bandwidth that maximizes the achievable rate for non-line-of-sight mmWave signals with imperfect CSIR, beyond which the achievable rate will decrease with increasing bandwidth. For imperfect CSIR, the optimal training symbol length and power allocation factor at the training phase are investigated and closed-form expressions for special cases are derived.