Statistical Delay QoS Driven Energy Efficiency and Effective Capacity Tradeoff for Uplink Multi-User Multi-Carrier Systems

In this paper, the total system effective capacity (EC) maximization problem for the uplink transmission, in a multi-user multi-carrier OFDMA system, is formulated as a combinatorial integer programming problem, subject to each user?s link-layer energy efficiency (EE) requirement as well as the individual?s average transmission power limit. To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier linklayer EE-EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE-EC tradeoff problem for each user is formulated and solved by using Karush-Kuhn-Tucker (KKT) conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user?s EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. Simulation results confirm our proofs and design intentions, and further show the effects of delay quality-of-service (QoS) exponent, the total number of users and the number of subcarriers on the system tradeoff performance

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Statistical delay QoS driven resource allocation and performance analysis for wireless communication networks

Delay quality-of-service (QoS) guarantees play a critical role in enabling delay-sensitive wireless applications. By applying the theory of effective capacity (EC), the maximum arrival rate with a guaranteed delay-outage probability constraint, is analyzed and investigated in terms of delayconstrained resource allocation and link-layer throughput analysis. Firstly, a joint optimization problem of link-layer energy efficiency (EE) and EC in a single-user single-carrier communication system, is proposed and investigated, under a delay violation probability requirement and an average transmit power constraint. Formulated as a normalized multiobjective optimization problem (MOP), the problem is transformed into a weighted single-objective optimization problem (SOP), and then solved. The proposed optimal power value is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Secondly, a total EC maximization problem subject to the individual linklayer EE requirement as well as the per-user average transmit power limit, in a multi-user multi-carrier orthogonal frequency-division multiple access (OFDMA) system, is proposed and analyzed. Formulated as a combinatorial integer programming problem, the problem is decoupled into a frequency provisioning problem and an independent per-user multi-carrier EE-EC tradeoff problem. A low-complexity heuristic algorithm is proposed to obtain the subcarrier assignment solution coupled with a per-user optimal power allocation strategy, across frequency and time domains. Finally, the achievable link-layer rate under the per-user delay QoS requirements is studied for a downlink M-user non-orthogonal multiple access (NOMA) network. The impact of the transmit signal-to-noise ratio (SNR) and the delay QoS requirement on the per-user achievable EC and the total link-layer rate is investigated and compared between NOMA and orthogonal multiple access (OMA) networks. All theoretical conclusions and closed-form expressions are confirmed with Monte Carlo results

Energy-Efficient NOMA Enabled Heterogeneous Cloud Radio Access Networks

Heterogeneous cloud radio access networks (H-CRANs) are envisioned to be promising in the fifth generation (5G) wireless networks. H-CRANs enable users to enjoy diverse services with high energy efficiency, high spectral efficiency, and low-cost operation, which are achieved by using cloud computing and virtualization techniques. However, H-CRANs face many technical challenges due to massive user connectivity, increasingly severe spectrum scarcity and energy-constrained devices. These challenges may significantly decrease the quality of service of users if not properly tackled. Non-orthogonal multiple access (NOMA) schemes exploit non-orthogonal resources to provide services for multiple users and are receiving increasing attention for their potential of improving spectral and energy efficiency in 5G networks. In this article a framework for energy-efficient NOMA H-CRANs is presented. The enabling technologies for NOMA H-CRANs are surveyed. Challenges to implement these technologies and open issues are discussed. This article also presents the performance evaluation on energy efficiency of H-CRANs with NOMA.Comment: This work has been accepted by IEEE Network. Pages 18, Figure