QoS-Oriented joint optimization of resource allocation and concurrent scheduling in 5G millimeter-wave network

With the increase of vehicular broadband services, network slicing is regarded as a promising technology to meet the various requirements. In the Internet of Vehicles (IoV) enabled by network slicing, both the resource allocation and task scheduling algorithms have received extensive attentions. However, most of the existing works on the joint optimization of inter-slice resource allocation and intra-slice task scheduling ignore the power consumption. In this regard, this paper further considers the queuing latency and the power consumption in the uplink transmission. Three sub Markov decision processes (MDP) are modeled and a layered deep reinforcement learning (DRL) based algorithm is designed for the joint optimization. The simulation consists of two parts, firstly the cumulative reward of different task scheduling algorithms are compared and analyzed, then the performance of all solutions are evaluated under different task densities. The results show that the proposed algorithm can significantly reduce the queuing latency while the power consumption is comparable to that of second best algorithm.

With the successful demonstration of in-band full-duplex (IBFD) transceivers, a new research dimension has been added to wireless networks. This paper proposes a use case of this capability for IBFD self-backhauling heterogeneous networks (HetNets). IBFD self-backhauling in a HetNet refers to IBFD-enabled small cells backhauling themselves with macro cells over the wireless channel. Owing to their IBFD capability, the small cells simultaneously communicate over the access and backhaul links, using the same frequency band. The idea is doubly advantageous, as it obviates the need for fiber backhauling small cells every hundred meters and allows the access spectrum to be reused for backhauling at no extra cost. This paper considers the case of a two-tier cellular network with IBFD-enabled small cells, wirelessly backhauling themselves with conventional macro cells. For clear exposition, the case considered is that of the Frequency Division Duplexing (FDD) network, where within access and backhaul links, the downlink (DL) and uplink are frequency duplexed (f1 , f2 respectively), while the total frequency spectrum used at access and backhaul (f1+f2 ) is the same. Analytical expressions for coverage and average DL rate in such a network are derived using tools from the field of stochastic geometry. It is shown that DL rate in such networks could be close to double that of a conventional TDD/FDD self-backhauling network, at the expense of reduced coverage due to higher interference in IBFD networks. For the proposed IBFD network, the conflicting aspects of increased interference on one side and high spectral efficiency on the other are captured into a mathematical model. The mathematical model introduces an end-to-end joint analysis of backhaul (or fronthaul) and access links, in contrast to the largely available access-centric studies.

Facing the ever-increasing demand for high quality video streaming and the dramatic variations of wireless network environment, adaptive bitrate video streaming (ABS) emerges as a prominent video quality adaptation technique in improving users' quality of experience (QoE). Reinforcement learning (RL)-based ABS and network-assisted ABS can improve users' QoE through resource utilization efficiency enhancement. However, limited network capacity is still the bottleneck of QoE improvement. Considering the potential capacity gain of non-orthogonal multiple access (NOMA), we study network-assisted ABS in NOMA networks. Based on a synchronous video streaming model and the knowledge of channel state information for next segment, a joint optimization problem of resource allocation and bitrate adaptation is formulated to maximize users' QoE and minimize energy consumption. The original problem is NP-hard and intractable due to the interference coordination problem introduced by NOMA and the multi-slot sum rate constraint. We decompose the original problem into the subproblem of joint subchannel assignment and power allocation (JSAPA), and the subproblem of bitrate adaptation (BA) with Lagrange relaxation method. A joint resource allocation and bitrate adaptation (JRABA) algorithm is further proposed to solve the JSAPA and BA subproblems iteratively. Specifically, the JSAPA subproblem is solved by an extended Gale-Shapley matching algorithm and differ of convex (DC) programming method iteratively. For the BA subproblem, appropriate video bitrate is explored with subgradient method. Numerical results validate the effectiveness of the proposed algorithm and reveal that network-assisted ABS in NOMA networks benefits users in achieving higher QoE with less energy consumption, especially when radio resources are insufficient or with large segment size.

In this study, the authors adopt the non‐orthogonal multiple access (NOMA) technique to improve the spectrum efficiency in the wireless backhaul networks, whereby the downlink and uplink NOMA techniques are applied for the backhaul and access links, respectively. Due to the coupling between the backhaul and access transmission stages, the transmission power should be carefully allocated in both stages so that the overall throughput can be maximised. They start with the single user equipment (UE) case and consider different scenarios and analyse the tradeoff between the access and backhaul links, and the optimal power allocation solutions are obtained accordingly. They then extend the analysis to the multi‐UE case and formulate the optimal power allocation problem, which is solved using the Lagrangian dual decomposition algorithm. Simulation results demonstrate that the proposed schemes are effective in improving the throughput and outperforms the conventional orthogonal multiple access technique under different network settings.

Collaborative computation offloading and wireless charging scheduling in multi-UAV-assisted MEC networks: A TD3-based approach

This paper provides the system performance for the type 1 relay that 3GPP LTE-Advanced supports. Since type 1 relay is operated in half duplex mode to avoid self-interference, the overall relay system performance highly depends on the backhaul link performance and the resource allocation between backhaul and access link. In this paper, we applied the dual-layer beamforming(DLBF) in the backhaul link to increase the backhaul link performance by adopting spatial multiplexing with the multiple relays. Also, we optimized the resource allocation to maximize the relay system performance.

Recently, distributed resource allocation for relay enhanced cellular networks has gained increasing attention. However, it poses a new challenge for the backhaul link scheduling. In this paper, we propose a queue-status-based resource allocation scheme for the backhaul link in a distributed manner in relay enhanced networks. By applying this scheme, the evolved Node B (eNB) adjusts the data rate for each user equipment (UE) in the backhaul link according to its queue status at the relay node (RN). It can guarantee the rate match between the backhaul and access links while minimizing feedback signaling. Simulation results show that the proposed scheme can achieve a good tradeoff between the spectral efficiency and the signaling overhead.

The integrated access and backhaul (IAB) has demonstrated tremendous potential in the fifth-generation (5G) network, acting as an economical and sustainable alternative to conventional fiber-like architectures. Although promising, how to efficiently and effectively allocate bandwidth and power resource to the backhaul and access links is a challenging issue for IAB. In this paper, we propose a novel scheme to jointly allocate the spectrum and transmitting power of the IAB donor and IAB nodes, so as to serve their respective access links and backhaul links. With the objective of maximizing the downlink data rate, we formulate the spectrum allocation and power management as a mix-integer and non-linear programming problem. Then, we propose to use an advanced deep reinforcement learning (DRL), i.e., double deep Q-learning, to achieve an efficient policy for joint spectrum allocation and power management, namely SAPM-DDQN. We perform the proposed SAPM-DDQN in a decentralized manner, which can significantly reduce the transmission overhead, compared to the centralized one. To the best of the authors’ knowledge, this is the first attempt on the joint spectrum allocation and power management in a decentralized manner for IAB, leveraging the property of DRL techniques. The proposed SAPM-DDQN does not require any prior information from other units for optimization, which is suitable for practical deployment. The extensive simulation results are presented to verify the effectiveness of the proposed scheme for joint spectrum allocation and power management.

Network behaviors such as power control and transmission duration allocation significantly affect network performance. The existing related works explored network performance improvement by balancing radio resources between backhaul and access links of each millimeter wave (mmWave) small cell in the fifth generation (5G) cellular systems, but they only focused on network throughput instead of network energy efficiency. We address the design and analysis of network behaviors for optimizing network energy efficiency, where each small cell is allowed to adjust not only its individual transmission duration but also its backhaul transmission power, and also each access user is allowed to select an out-band Device-to-Device (D2D) relay if its mmWave access link is blocked. Unlike the existing related works, our scheme allows both backhaul and access powers to be adjusted, which makes the being addressed problem more challenging. Therefore, we formulate it as the Stackelberg game model, which makes the large solution space problem be decomposed into the several interrelated small solution space sub-problems. Thus, the solving difficulty of the challenging problem is reduced. The simulation results demonstrate that the proposed scheme outperforms the comparison algorithms in terms of network energy efficiency while keeping network throughput and convergence speed at acceptable levels.

In-band full-duplex (IBFD) self-backhauling is promising to provide cost-effective and flexible backhaul connectivity in ultra-dense wireless networks. In this paper, we propose a user-centric joint access-backhaul transmission framework for performance optimization in IBFD-enabled self- backhauled cooperative wireless networks. In the access link, user-centric clustering is adopted so that each user is cooperatively served by a cluster of small base stations (SBSs) via joint beamforming. In the backhaul link, user-centric multicast transmission is proposed so that the macro base station (MBS) treats each user's data as a multicast message and sends it to its serving SBS cluster. We formulate an optimization problem to maximize the end-to-end weighted sum rate of all users under power constraints through joint design of multicast beamforming in the backhaul link as well as SBS clustering and beamforming in the access link. We first tackle the joint access- backhaul beamforming problem under given SBS clustering by transforming it into a manifold optimization problem, for which a stationary solution is obtained using the Riemannian optimization technique. We then develop a heuristic algorithm to determine the SBS clustering. Simulation results demonstrate the effectiveness of the proposed algorithms.

This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of next-generation networks. On the one hand, our proposed caching strategy increases slightly the computational complexity by 3%, 1.1%, and 2% compared to no caching, SFCD, and disjoint solution, respectively. On the other hand, the proposed caching scheme achieves a performance gain of 43%, 9.4%, and 51.3% compared to the three state-of-the-art schemes.

The main objective of this study was to assess the effect of resource management on performance of selected international compassion projects in Rwanda while the following are specific objectives: to assess the effect of resource planning on performance of International Compassion projects in Kinyinya Parish, Rwanda, to assess the effect of resource scheduling on performance of International Compassion projects in Kinyinya Parish, Rwanda, to assess the effect of resource allocation on performance of International Compassion projects in Kinyinya Parish, Rwanda, and to assess the effect of resource monitoring on performance of International Compassion projects in Kinyinya Parish. The stratified sample of 132 individuals, using Yamane’s formula, is expected to be used and was selected among 197 staffs engaged in projects financed by International Compassion in ADPR where all parishes of Gasabo were considered The SPSS Version 23 was used to perform the analysis while the questionnaire was used as the main tool for data collection.  The study revealed that a mixed survey design, being both descriptive and correlative, was used in this study where this study involved gathering and analyzing data to describe the current state of resource management and correlate it with the project performance within compassion projects, in Gasabo district. The findings on model summary indicate R-value of 0.407 indicates a moderate positive correlation between the combined predictors (Resource monitoring, Resource allocation, and Resource scheduling) and project performance (Performance of Projects). However, the relatively low R Square value of 0.166 suggests that only approximately 16.6% of the variability in project performance can be explained by these predictors, with a substantial portion of variability remaining unaccounted for. Analysis of Variance (ANOVA) results demonstrating the statistical significance of the regression model, encompassing the predictors "Report monitoring, Resource allocation, Resource planning, and Resource scheduling," in explaining variations in the dependent variable "Performance of Projects". Importantly, the associated p-value (Sig.) of 0.002 (denoted as "b") falls below the conventional significance threshold of 0.05, indicating the model's statistical significance. Therefore, it was recommended, encourage continuous improvement in resource planning adequacy to minimize restructuring needs. Key terms: Resource planning, Resource scheduling, Resource allocation, Resource monitoring and performance

In this paper, channel adaptive resource allocation and user scheduling problem for cognitive radio multiple-inputmultiple-output orthogonal frequency-division multiple access systems is investigated. Furthermore, stochastic approximation is employed to develop an on-line algorithm which is capable of adaptively learning the underlying channel distribution. The NP-hard combinatorial resource allocation and user scheduling problem is then solved through via the Lagrangian relaxation approach. Numerical results demonstrate the effectiveness of our proposed scheme.

With the widespread application of cloud computing technology, the energy consumption problem of data centers is becoming increasingly severe. This research is dedicated to presenting a highly efficient and smart solution for optimizing energy usage in cloud computing systems, especially since effectively managing energy consumption through resource allocation and task scheduling has now become a critical challenge in the field. In view of this, the study first constructs a resource allocation model based on redundant resource recovery to dynamically identify and recover redundant resources. Second, a task scheduling optimization method based on dual deep reinforcement learning is established, which utilizes directed acyclic graphs to model task dependencies and optimizes task execution order and resource allocation strategies through reinforcement learning agents. In the testing of the resource allocation optimization model, when the number of tasks is 500, the redundant resource recovery rate is 87.0%, and the computational complexity is 16.7 s. The response time in the regional resource environment is 2.28 s, and the load adaptability is 95.8%. In the task scheduling method test, the proposed method has a memory occupancy of 85 MB and a scheduling delay of 0.13 s in a high-bandwidth environment of 300 Mbps. The results show that the proposed method outperforms traditional scheduling strategies in terms of resource utilization and energy consumption, and contributes to achieving the goal of green cloud computing. The research aims to provide an efficient and intelligent solution for energy optimization in cloud computing systems.

This paper presents how to design fiber-optic relay node considering interferences between backhaul and access links. The way of proper arrangement of two different antennas is described as parameters of optical fiber length and radio frequency. Theoretical DUR due to the interference is provided relating to CNR performance mainly determined by the optical link. Provided that the radio frequency is 3.4 GHz and the antenna facing to eNB is positioned at high-SNR environments, optical fiber length (antenna separation) of 200 meters is enough to meet DUR of 30 dB. This will provide a solution of relaying without interference canceller for in-band relaying.