Wireless backhaul resource allocation and user‐centric clustering in ultra‐dense wireless networks

Hua, Cunqing; Luo, Yi‐Feng; Liu, Huibo

doi:10.1049/iet-com.2016.0119

Cited by 18 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [25], joint access and backhaul links optimization is considered to minimize the total network power consumption. In [26], the authors study joint wireless backhaul and the access links resource allocation optimization. The goal is to maximize the sum rate subject to the backhaul and access constraints.…”

Section: Joint Backhaul and Access Resource Allocationmentioning

confidence: 99%

“…The objective function in (26) is non-convex. The form of (26) can be classified into the nonlinear fractional programming [45].…”

Section: Fractional Programmingmentioning

confidence: 99%

“…The objective function in (26) is non-convex. The form of (26) can be classified into the nonlinear fractional programming [45]. Therefore, after replacing nonconvex constraints by convex constraints using the D.C. method in the previous section, the Dinkelbach's algorithm use to solve convex fractional programming.…”

Section: Fractional Programmingmentioning

confidence: 99%

See 2 more Smart Citations

Single or Multiple Frames Content Delivery for Next-Generation Networks?

et al. 2019

View full text Add to dashboard Cite

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 nextgeneration networks. We also show that the proposed caching strategy, MFCD and joint solutions have 43%, 9.4% and %51.3 performance gain compared to no cahcing, SFCD and disjoint solutions, respectively.

show abstract

Section: Joint Backhaul and Access Resource Allocationmentioning

confidence: 99%

“…The objective function in (26) is non-convex. The form of (26) can be classified into the nonlinear fractional programming [45].…”

Section: Fractional Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Single or Multiple Frames Content Delivery for Next-Generation Networks?

et al. 2019

View full text Add to dashboard Cite

show abstract

“…For instance, in [24], an orthogonal training resource allocation scheme was developed based on the user-centric overlapped clustering model with the objective of minimizing the overall training overhead. The joint user-centric overlapped clustering and resource allocation problem of UDNs was investigated in [35] and [36]. More specifically, Liu et al [35] considered a joint user-centric overlapped clustering and resource allocation problem in UDNs from a rather different perspective.…”

Section: B Related Workmentioning

confidence: 99%

“…Additionally, the resource allocation subproblem did not consider the limited number of RBs, as a constraint. Hua et al [36] studied the resource allocation problem in the context of wireless back-haul links as well as joint user-centric overlapped clustering and beamforming in the access links in order to maximize the weighted sum rate under specific back-haul resource constraints in UDNs. Nevertheless, Hua et al have not considered traffic-load-aware clustering neither have they dealt with the limited availability of orthogonal RBs in UDNs, which constitute the main focus of our paper.…”

Section: B Related Workmentioning

confidence: 99%

Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks

Lin

Zhang

et al. 2018

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Abstract-With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultra dense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each UE. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency (SE). To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph-theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs' traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate (PAAR) and user rate.

show abstract