Improving energy and spectrum efficiency of joint DBS placement and radio resource allocation in 5G cellular networks

An efficient radio resource allocation scheme is crucial for guaranteeing the quality of service (QoS) requirements and fully utilizing the scarce radio resources in wireless mobile networks. Most of previous studies of radio resource allocation in traditional wireless networks concentrates on network layer connection blocking probability QoS. In this paper, we show that physical layer techniques and QoS have significant impacts on network layer QoS. We use a concept of cross-layer effective bandwidth to measure the unified radio resource usage taking into account both physical layer linear minimum-mean square error (LMMSE) receivers and varying statistical characteristics of the packet traffic in code devision multiple access (CDMA) networks. We demonstrate the similarity between traditional circuit-switched networks and packet CDMA networks, which enables rich theories developed in traditional wireless mobile networks to be used in packet CDMA networks. Moreover, since both physical layer signal-to-interference ratio (SIR) QoS and network layer connection blocking probability QoS are considered simultaneously, we can explore the tradeoff between physical layer QoS and network layer QoS in packet CDMA networks.

Due to the popularity of mobile user devices, D2D communications become a key technology to improve local services in the next generation cellular networks. However, the benefits count on efficient radio resource allocation between cellular links and D2D links, which poses critical challenges. By exploiting the network knowledge extracted from underlying mobile social networks (MSNs), this article proposes a socially aware D2D communication scheme to improve the spectrum and energy efficiency in cellular networks. First, by jointly considering the social and network knowledge, we propose a cluster formation scheme to categorize a group of UEs into multiple D2D clusters. Within each cluster, highly spectrum-efficient D2D multicasting (from the cluster head, i.e., a D2D transmitter UE to multiple cluster member UEs) is enabled. Next, a half-duplex scheme and a full-duplex scheme are proposed to coordinate the channel sharing between the cellular links and the D2D links. Simulation results show that the proposed schemes can form D2D clusters rationally among a group of UEs, and therefore largely increase the spectrum and energy efficiency of the network.

Vehicular networks are facing an overwhelming growth in data traffic demands recently. However, radio resources in wireless networks infrastructures have not been fully exploited, resulting in low quality of services for vehicle users. As a result, efficient radio resource allocation schemes for HetVNETs are in urgent demand. In this chapter, we first present a brief overview on radio resource allocation in vehicular networks. Then, Sect. 4.2 presents a new content-based resource scheduling mechanism. A Bipartite graph (BG)-based cooperative scheduling scheme is also studied in Sect. 4.3, followed by concluding remarks of the chapter in Sect. 4.4.

To obtain better bandwidth and performance, the fifth generation (5G) cellular network is proposed to implement new-generation cellular mobile communications for new applications such as the internet of things, big data, smart city and so on. However, due to multiple/dense cellular network structures and high data rate, the 5G cellular network holds high inter-cell interference (ICI) and lower energy efficiency. The soft frequency reuse (SFR) is introduced to reduce the inter-cell interference in multiple cellular networks (such as 5G cellular networks) with the orthogonal frequency division multiplexing in base stations. Then, we investigate the energy-efficient resource allocation problem in the 5G cellular network with SFR. To coordinate the ICI among adjacent cells, we introduce the interference pricing factor into the utility function. The energy-efficient resource allocation problem is described as a Stackelberg game model. Because the sub-carrier assignment in the optimization process is an integer program which is very hard to be solved, we make a relaxation for the integer variable in the model and propose an iteration algorithm to obtain the Stackelberg game equilibrium solution. Simulation results show that the proposed method is feasible and promising.

Editor’s note: Fifth-generation (5G) cellular network will significantly enhance the amount of mobile data traffic. This article presents various resource allocation and power management problems in 5G cellular networks. It highlights multiple techniques that maximize the energy efficiency while meeting performance and QoS requirements. —Partha Pratim Pande, Washington State University

Special issue on energy‐efficient wireless communication networks with QoS

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

To meet the ever-increasing requirements of high data rate, extremely low latency, and ubiquitous connectivity for the fifth generation (5G) and beyond 5G (B5G) wireless communications, there is imperious demands for advanced communication system design. Particularly, efficient resource allocation is regarded as the fundamental challenge whereas an effective way to improve system performance. The term ”resource” refers to scare quantities such as limited bandwidth, power and time in wireless communications. Moreover, the development of wireless communication systems is accompanied by the innovation of applied technologies. Motivated by the above observations, efficient resource allocation strategies for several promising 5G and B5G technologies in terms of non-orthogonal multiple access (NOMA), mobile edge computing (MEC) and Long Range (LoRa) are addressed and investigated in this thesis. Firstly, the strong user’s data rate maximization problem for simultaneous wireless information and power transfer (SWIPT)-enabled cooperative NOMA system, considering the presence of channnel uncertainties, is proposed and investigated. Two major channel uncertainty design criteria in terms of the outage-based constraint design and the worst-case based optimization are adopted. In addition to the high-complexity optimal two-dimensional exhaustive search method, the low-complexity suboptimal solution is further proposed. The advantages of SWIPT-enabled cooperation in robust NOMA are confirmed with simulations. Secondly, considering the application of NOMA and user cooperation (UC) in a wireless powered MEC under the non-linear energy harvesting model, a computation efficiency maximization problem subject to the quality of service (QoS) and power budget constraint, is studied and analyzed. The formulated problem is nonconvex, which is challenging to solve. The semidefinite relaxation (SDR) approach is first applied, then the sequential convex approximation (SCA)-based solution is further proposed to maximize the system computation efficiency. Finally, taking into consideration the aspect of energy efficiency (EE), this thesis investigates the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. The energy efficient resource allocation is formulated as NP-hard problems. A low-complexity user scheduling scheme based on matching theory is proposed to allocate users to channels, then the heuristic SF assignment solution is designed for LoRa users scheduled on the same channel. The optimal power allocation strategy is further proposed to maximize the corresponding EE.

An efficient radio resource allocation scheme is crucial for guaranteeing the quality of service (QoS) requirements and fully utilizing the scarce radio resources in wireless multimedia networks. Most of previous work of radio resource allocation in traditional wireless networks concentrates on network layer connection blocking probability QoS. In this paper, we show that physical layer techniques and QoS have significant impacts on network layer QoS. We use a concept of cross-layer effective bandwidth to measure the unified radio resource usage taking into account both physical layer receivers and network layer traffic in code devision multiple access (CDMA) networks. Based on this concept, we can use rich theories developed in traditional wireless mobile networks for packet multimedia wireless networks. Moreover, since both physical layer QoS and network layer QoS are considered simultaneously, we can explore the tradeoff between physical layer QoS and network layer QoS in packet multimedia wireless networks.

The fifth-generation of wireless communication (5G) is a promising paradigm toward massive interconnectivity within Internet-of-Things (IoT) networks. However, because the data traffic throughput sharply increases with the number of IoT devices, a tremendous burden on the backhaul links and core networks results. With this in mind, mobile edge caching is an effective method that can relieve stress of the backhaul links, while decreasing the service latency. The purpose of this study is to analyze the problem of jointly optimizing cooperative edge caching and radio resource allocation in 5G-enabled massive IoT networks. For that, a joint optimization long-term nonlinear integer programming problem is posed. This class of problems is known to be NP-hard; thus, to reduce the problem complexity, a divide and conquer scheme will be applied—the task at hand will be divided into two subproblems: 1) cooperative edge caching and 2) radio resource allocation. The cooperative edge caching subproblem is formulated as a constrained Markov decision process. Herein, a deep reinforcement learning method to optimize the caching decisions for all the edge nodes. Then, based on the resulting optimal caching decisions, the radio resource allocation subproblem for each edge node is posed as an NLIP problem, and an improved branch-and-bound method is proposed to yield the optimal radio resource allocation decisions for each edge node. Extensive simulations were performed to confirm that the proposed methods have the capability of enhancing the content caching hit ratio, while lessening the content retrieving delays for 5G-enabled massive IoT networks—improving over various baseline algorithms.

This paper considered multiuser orthogonal frequency multiple access (OFDMA) scheme, which has been proposed as the transmission technique for 4th Generation (4G) cellular network. An optimum and dynamic radio resource (subcarrier and power) allocation scheme is proposed for OFDMA multiuser system. The proposed algorithm has considered a structured degree of flexibility among the services that can be provided from the network. The performance of the algorithm is evaluated through computer simulation, by applying it to multipath frequency selective fading channel with different number of users, SNR and channels conditions. The goal is to dynamically assign subcarrier and power while minimizing the total transmitted power under the constraint of guaranteed QoS to the users. The results shows that it outperform Static OFDM subcarrier and power allocation scheme achieves 3 dB SNR gain for a bit error rate of 10−2. Notable amount of reduction in system capacity outage probability has been observed, while exploiting the proposed dynamic algorithm compared with the existing algorithm due to its more flexible nature in resource allocation.

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

This research compares different methods for optimizing and monitoring Kubernetes clusters. Three referenced journals are analyzed: "Kubernetes cluster optimization using hybrid shared-state scheduling framework" by Oana-Mihaela Ungureanu, Călin Vlădeanu, Robert Kooij; "Monitoring Kubernetes Clusters Using Prometheus and Grafana" by Salma Rachman Dira, Muhammad Arif Fadhly Ridha; and "Cluster Frameworks for Efficient Scheduling and Resource Allocation in Data Center Networks: A Survey" by Kun Wang, Qihua Zhou, Song Guo, and Jiangtao Luo. These journals explore various approaches to optimizing and monitoring Kubernetes clusters. This review concludes that selecting appropriate technologies for optimizing and monitoring Kubernetes clusters can enhance performance and resource management efficiency in data centre networks. The research addresses the problem of improving Kubernetes cluster performance through optimization and efficient monitoring. The required methods include utilizing hybrid state-sharing scheduling frameworks, implementing Prometheus and Grafana for monitoring, and employing efficient cluster frameworks. The study's findings demonstrate that adopting a hybrid shared-state scheduling framework can improve Kubernetes cluster performance. Additionally, leveraging Prometheus and Grafana as monitoring tools offer valuable insights into cluster health and performance. The survey also reveals various cluster frameworks that enable efficient scheduling and resource allocation in data centre networks. In conclusion, this research emphasizes the significance of employing suitable technologies to optimize and monitor Kubernetes clusters, leading to enhanced performance and efficient resource management in data centre networks. By leveraging appropriate scheduling frameworks and monitoring tools, organizations can optimize their utilization of Kubernetes clusters and ensure efficient resource allocation

3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.

There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high call blocking probability whereas high-capability mobile terminals (such as quad-mode terminals) experience very low call blocking probability, in the same heterogeneous wireless network. This paper proposes a Terminal-Modality-Based Joint Call Admission Control (TJCAC) algorithm to reduce this problem of unfairness. The proposed TJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed TJCAC algorithm are to reduce call blocking/dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. An analytical model is developed to evaluate the performance of the proposed TJCAC scheme in terms of call blocking/dropping probability in a heterogeneous wireless network. The performance of the proposed TJCAC algorithm is compared with that of other JCAC algorithms. Results show that the proposed algorithm reduces call blocking/dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.