scholarly journals Cross-Layer Resource Allocation for Multihop V2X Communications

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yanhua He ◽  
Liangrui Tang ◽  
Yun Ren ◽  
Jonathan Rodriguez ◽  
Shahid Mumtaz

Inspired by the increasingly mature vehicle-to-everything (V2X) communication technology, we propose a multihop V2X downlink transmission system to improve users’ quality of experience (QoE) in hot spots. Specifically, we develop a cross-layer resource allocation algorithm to optimize the long-term system performance while guaranteeing the stability of data queues. Lyapunov optimization is employed to transform the long-term optimization problem into a series of instantaneous subproblems, which involves the joint optimization of rate control, power allocation, and mobile relay selection at each time slot. On one hand, the optimization of rate control is decoupled and carried out independently. On the other hand, a low-complexity pricing-based stable matching algorithm is proposed to solve the joint power allocation and mobile relay selection problem. Finally, simulation results demonstrate that the proposed algorithm can achieve superior performance and simultaneously guarantee queue stability.

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 80
Author(s):  
Qiuqi Han ◽  
Guangyuan Zheng ◽  
Chen Xu

Device-to-Device (D2D) communications, which enable direct communication between nearby user devices over the licensed spectrum, have been considered a key technique to improve spectral efficiency and system throughput in cellular networks (CNs). However, the limited spectrum resources cannot be sufficient to support more cellular users (CUs) and D2D users to meet the growth of the traffic data in future wireless networks. Therefore, Long-Term Evolution-Unlicensed (LTE-U) and D2D-Unlicensed (D2D-U) technologies have been proposed to further enhance system capacity by extending the CUs and D2D users on the unlicensed spectrum for communications. In this paper, we consider an LTE network where the CUs and D2D users are allowed to share the unlicensed spectrum with Wi-Fi users. To maximize the sum rate of all users while guaranteeing each user’s quality of service (QoS), we jointly consider user access and resource allocation. To tackle the formulated problem, we propose a matching-iteration-based joint user access and resource allocation algorithm. Simulation results show that the proposed algorithm can significantly improve system throughput compared to the other benchmark algorithms.


2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Yang ◽  
Guan Gui ◽  
Song Guo

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of the most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.


Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 105 ◽  
Author(s):  
Ankur Vora ◽  
Kyoung-Don Kang

In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline.


2010 ◽  
Vol 33 (5) ◽  
pp. 571-582 ◽  
Author(s):  
Pablo Ameigeiras ◽  
Juan J. Ramos-Munoz ◽  
Jorge Navarro-Ortiz ◽  
Preben Mogensen ◽  
Juan M. Lopez-Soler

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