Joint power allocation and bit loading for enhanced SVC video downlink transmissions over SDMA/OFDMA networks

2011 ◽  
Author(s):  
Maodong Li ◽  
Zhenzhong Chen ◽  
Yap-Peng Tan
Keyword(s):  
Power Allocation ◽  
Joint Power ◽  
Bit Loading ◽  
Ofdma Networks

scholarly journals Joint Power Allocation and Bit-Loading for Multicarrier Systems with Discrete Modulation

2021 ◽  
Vol 8 (4) ◽  
pp. 626-634
Author(s):  
Abdul-Nasser Nofal ◽  
Abdel-Nasser Assimi ◽  
Yasser M. Jaamour
Keyword(s):  
Power Allocation ◽  
Lagrangian Relaxation ◽  
Optimal Solution ◽  
Low Complexity ◽  
Discrete Optimization Problem ◽  
Joint Power ◽  
Multicarrier Systems ◽  
Bit Loading ◽  
Suboptimal Solution ◽  
Lagrangian Relaxation Algorithm

In this paper, we propose two algorithms for joint power allocation and bit-loading in multicarrier systems using discrete modulations. The objective is to maximize the data rate under the constraint of a suitable Bit Error Rate per subcarrier. The first algorithm is based on the Lagrangian Relaxation of the discrete optimization problem in order to find an initial solution. A discrete solution is found by bit truncation followed by an iterative modulation adjustment. The second algorithm is based on Discrete Coordinate Ascent framework with iterative modulation increment of one selected subcarrier at each iteration. A simple cost function related to the power increment per bit is used for subcarrier selection. A sub-optimal low complexity Discrete Coordinate Ascent algorithm is proposed that overcome the limitations of the Hughes-Hartogs algorithm. The Lagrangian Relaxation algorithm provides a suboptimal solution for non-coded system using M-QAM modulations, whereas the low complexity Discrete Coordinate Ascent algorithm provides a near optimal solution for coded as well as for non-coded system using an arbitrary modulation set. Numerical results show the efficiency of the proposed algorithms in comparison with traditional methods.

scholarly journals Joint Power Allocation and Passive Beamforming Design for IRS-Assisted Physical-Layer Service Integration

2021 ◽  
pp. 1-1
Author(s):  
Boyu Ning ◽  
Zhi Chen ◽  
Zhongbao Tian ◽  
Xiaomei Wang ◽  
Cunhua Pan ◽  
...  
Keyword(s):  
Power Allocation ◽  
Service Integration ◽  
Physical Layer ◽  
Joint Power

scholarly journals Joint power allocation and subcarrier assignment for device to device in 5G time division duplex networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881109 ◽  
Author(s):  
Pan Zhao ◽  
Lei Feng ◽  
Peng Yu ◽  
Wenjing Li ◽  
Xuesong Qiu
Keyword(s):  
Quality Of Service ◽  
Power Allocation ◽  
Base Station ◽  
Joint Power ◽  
Time Division Duplex ◽  
Device To Device ◽  
Minlp Problem ◽  
Device To Device Communication ◽  
Time Division

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

scholarly journals Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Yang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

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.

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