Optimized Resource Allocation in Mobile Edge Communication, 5G Data Multiplexing, and WiFi Channel Access- [electronic resource]
Optimized Resource Allocation in Mobile Edge Communication, 5G Data Multiplexing, and WiFi Channel Access- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101159
- ISBN
- 9798379911966
- DDC
- 621.3
- 서명/저자
- Optimized Resource Allocation in Mobile Edge Communication, 5G Data Multiplexing, and WiFi Channel Access - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(123 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Roy, Sumit.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Optimizing bandwidth utilization, transmission power and transmission latency while guaranteeing the quality of service (Qos) is oftentimes the objectives of interests in communication networks. For example, modern mobile traffic such as online gaming, mobile virtual/augmented reality (VR/AR) requires low latency, transmission power, and occupied bandwidth for better user experience and less utilized network frequency bandwidth. In this thesis, a novel bidirectional computation task model is proposed as an important use case in mobile edge communication networks, e.g., interactive AR/VR gaming service needs to render the live scene by jointly computing user features such as 3D positions and video data stored at the cloud server. In the bidirectional computation task model, each task is served via three mechanisms, i.e., local computing with local caching, local computing without local caching, and computing at the mobile edge computing server. To minimize the average utilize bandwidth, we formulate and optimize the joint caching and computing optimization problem under the latency, cache size and average power constraints. The proposed Lagrangian Relaxation (LR) plus concave-convex procedure is shown to outperform the baselines such as greedy algorithm and LR algorithm with simulations.The obtained allocation policy reduces the communication bandwidth. However, which resource blocks (RBs) in orthogonal frequency-division multiple access (OFDMA) are occupied by such allocated bandwidth when multiple devices coexist is not yet answered by this policy, which is currently a challenging topic in both 5G and WiFi. Hence, we are further motivated to consider the enhanced channel accessing mechanism in the next-generation WiFi networks and the RB scheduling problem in 5G with ultra-reliability-low-latency communication (URLLC) requirement. Standardization for recently started IEEE 802.11be (Wi-Fi 7) Working Groups has focused on significant medium access control layer changes that emphasize the role of the access point (AP) for coordinating channel access due to the high collision probability with the distributed coordination function (DCF), especially in dense overlapping Wi-Fi networks. We propose a novel multi-AP coordination system architecture aided by a centralized AP controller (APC). Meanwhile, a deep reinforcement learning channel access (DLCA) protocol is developed to enhance the network throughput by enabling the coordination of APs. On the other hand, 5G New Radio (NR) is envisioned to efficiently support both enhanced mobile broadband (eMBB) and URLLC. In 5G NR, URLLC represents the task files in the application of online gaming and VR/AR with demanding QoS constraints. From the media access control (MAC) layer scheduling perspective, URLLC traffic has higher priority than eMBB traffic because URLLC traffic can pre-emptively puncture the existing eMBB traffic on the time scale of mini-slot to obtain the strict requirements of URLLC (achieve 99.999\\% reliability within 1 ms latency). The multiplexing of eMBB and URLLC traffic in 5G downlink transmission is investigated with the dual objectives of maximizing eMBB utility like proportional fairness for eMBB users while satisfying URLLC constraints. The resource allocation problem in each mini-slot is formulated as an integer programming (IP) with two solutions: 1) convex relaxation; 2) greedy algorithm. The simulation results show that our algorithms have a higher utility of eMBB users while satisfying the URLLC users' latency and reliability requirements than using the basic round robin in 5G standard.
- 일반주제명
- Electrical engineering.
- 일반주제명
- Communication.
- 일반주제명
- Computer engineering.
- 키워드
- Deep learning
- 키워드
- Mobile traffic
- 키워드
- Virtual reality
- 기타저자
- University of Washington Electrical and Computer Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
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