Energy efficient green wireless communication systems with imperfect CSI and data outage

Modern applications involve green communication technologies motivating well optimisation in the power–limited regime. In comparison to most of existing related work that assumes perfect channel state information (CSI) is always available, which is unfortunately not true in reality, this work focuses on an optimal energy efficient solution for resource allocation in multiuser orthogonal frequency division multiple access (OFDMA) networks in the presence of imperfect CSI and data outage conditions. Particularly, in view that wireless channel conditions, circuit power consumptions and users’ quality–of–service (QoS) requirements are heterogeneous in nature, we enable attractive tuning options by letting energy efficiency optimisation objective to assign weights to each allocation link. Also, we interpret effects of data outage due to imperfect CSI using a profound insight on the monotonicity of noncentral chi-squared inverse distribution function, which reveals that our design complies with expected physics and mechanics of conventional energy efficiency approach and that it can be successfully degenerated to the energy efficiency model with perfect CSI. Furthermore, we formulate a mixed combinatorial problem towards maximising the energy efficiency subject to a minimum QoS requirement, channel interference and transmitting power constraints. The problem is transformed into an equivalent quasiconcave problem with respect to power, and concave problem with respect to the subcarrier indexing coefficients using the concept of subcarrier time–sharing. We optimise through a simple and versatile methodology, which uses standard–Lagrangian optimisation technique to obtain joint dynamic subcarrier and adaptive power allocations by means of final formulas. We also examine key properties of the introduced optimal solution in terms of implementation convergence and complexity, level of optimality, and impact of imperfect CSI coefficients and circuit power on network performance. The simulation results demonstrate the effectiveness of our allocation scheme for achieving higher energy efficiency performance with the guaranteed QoS support and lower complexity than existing approaches especially when perfect CSI is not available

セルラーシステムにおける周波数利用とエネルギー利用効率改善に関する研究

早大学位記番号:新7583早稲田大

Joint QoS-Aware Scheduling and Precoding for Massive MIMO Systems via Deep Reinforcement Learning

The rapid development of mobile networks proliferates the demands of high data rate, low latency, and high-reliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multiple-input-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional cross-layer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a long-term system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios

Optimizing communication performance of low-resolution ADC systems with hybrid beamforming

Low-resolution analog-to-digital converter (ADC) systems and hybrid analog-and-digital beamforming systems have drawn extensive attention as a promising receiver architecture for millimeter wave (mmWave) communications by reducing hardware cost and power consumption. In this dissertation, hybrid beamforming systems that employ low-resolution ADCs are considered to achieve a better trade-off between communication performance and power consumption. Due to non-negligible quantization errors, however, existing state-of-the-art hybrid beamforming techniques cannot be directly applied to such systems as they ignore the impact of the quantization error. In this regard, I propose new receiver architectures and algorithms for hybrid beamforming with low-resolution ADC systems to enhance spectral efficiency under coarse quantization in different layers of the network stack, and provide subsequent analyses. First, problems of optimizing the number of ADC bits and designing analog combiners with fixed-resolution ADCs are tackled to design an energy-efficient receiver architecture with phase shifter-based hybrid beamforming. A hybrid receiver architecture with resolution-adaptive ADCs for mmWave communications is proposed to optimize the power distribution over ADCs. For the proposed architecture, a near-optimal bit-allocation solution is derived in closed form. In addition, the performance lower bound of the proposed receiver architecture is derived in ergodic rate. For a fixed-resolution ADC system, a new analog combining architecture is proposed for mmWave communications. The proposed analog combiner consists of two consecutive analog combiners that maximize channel gain and minimize effective quantization error. An approximated ergodic rate of the proposed receiver is also derived in closed form. Next, considering switch-based analog beamforming, antenna selection at a base station is investigated for low-resolution ADC systems. Unlike downlink transmit antenna selection problems, a quantization-aware antenna selection criterion is necessary and derived to incorporate quantization error for uplink receive antenna selection problems. Leveraging the criterion, a quantization-aware antenna selection algorithm is proposed and analyzed for uplink. Last, in a higher layer of the network stack, a user scheduling problem is investigated for hybrid beamforming systems with low-resolution ADCs. New user scheduling criteria are derived to maximize scheduling gain under coarse quantization and efficient scheduling algorithms are proposed accordingly. Subsequent analysis for the proposed algorithm provides closed-form ergodic ratesElectrical and Computer Engineerin

Analysis and Design of Non-Orthogonal Multiple Access (NOMA) Techniques for Next Generation Wireless Communication Systems

The current surge in wireless connectivity, anticipated to amplify significantly in future wireless technologies, brings a new wave of users. Given the impracticality of an endlessly expanding bandwidth, there’s a pressing need for communication techniques that efficiently serve this burgeoning user base with limited resources. Multiple Access (MA) techniques, notably Orthogonal Multiple Access (OMA), have long addressed bandwidth constraints. However, with escalating user numbers, OMA’s orthogonality becomes limiting for emerging wireless technologies. Non-Orthogonal Multiple Access (NOMA), employing superposition coding, serves more users within the same bandwidth as OMA by allocating different power levels to users whose signals can then be detected using the gap between them, thus offering superior spectral efficiency and massive connectivity. This thesis examines the integration of NOMA techniques with cooperative relaying, EXtrinsic Information Transfer (EXIT) chart analysis, and deep learning for enhancing 6G and beyond communication systems. The adopted methodology aims to optimize the systems’ performance, spanning from bit-error rate (BER) versus signal to noise ratio (SNR) to overall system efficiency and data rates. The primary focus of this thesis is the investigation of the integration of NOMA with cooperative relaying, EXIT chart analysis, and deep learning techniques. In the cooperative relaying context, NOMA notably improved diversity gains, thereby proving the superiority of combining NOMA with cooperative relaying over just NOMA. With EXIT chart analysis, NOMA achieved low BER at mid-range SNR as well as achieved optimal user fairness in the power allocation stage. Additionally, employing a trained neural network enhanced signal detection for NOMA in the deep learning scenario, thereby producing a simpler signal detection for NOMA which addresses NOMAs’ complex receiver problem