Discounted-rate utility maximization (DRUM): A framework for delay-sensitive fair resource allocation

2017 ◽  
Author(s):  
Atilla Eryilmaz ◽  
Irem Koprulu
Keyword(s):  
Resource Allocation ◽  
Utility Maximization ◽  
Delay Sensitive

Stable-Matching-Based Energy-Efficient Context-Aware Resource Allocation for Ultra-Dense Small Cells

2017 ◽  
pp. 29-57
Author(s):  
Zhenyu Zhou ◽  
Zheng Chang ◽  
Chen Xu ◽  
Tapani Ristaniemi
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Small Cells ◽  
Context Aware ◽  
Delay Sensitive ◽  
Optimum Energy ◽  
Significant Performance ◽  
Promising Solution ◽  
Nonlinear Fractional Programming ◽  
Efficient Matching

Implementing caching to ultra-densely deployed small cells provides a promising solution for satisfying the stringent quality of service (QoS) requirements of delay-sensitive applications with limited backhaul capacity. With the rapidly increasing energy consumption, in this chapter, the authors investigate the NP-hard energy-efficient context-aware resource allocation problem and formulate it as a one-to-one matching problem. The preference lists in the matching are modeled based on the optimum energy efficiency (EE) under specified matching, which can be obtained by using an iterative power allocation algorithm based on nonlinear fractional programming and Lagrange dual decomposition. Next, on account of the Gale-Shapley algorithm, an energy-efficient matching algorithm is proposed. Some properties of the proposed algorithm are discussed and analyzed in detail. Moreover, the authors extend the algorithm to the matching with indifferent and incomplete preference lists. Finally, the significant performance gain of the proposed algorithm is demonstrated through simulation results.

scholarly journals Resource Allocation for Green Cognitive Radios: Energy Efficiency Maximization

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhou Yang ◽  
Wenqian Jiang ◽  
Gang Li
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Cognitive Radio ◽  
Cognitive Radios ◽  
Maximum Energy ◽  
Secondary User ◽  
Power Constraint ◽  
Transmit Power ◽  
Delay Sensitive ◽  
Allocation Strategies

Green cognitive radios are promising in future wireless communications due to high energy efficiency. Energy efficiency maximization problems are formulated in delay-insensitive green cognitive radio and delay-sensitive green cognitive radio. The optimal resource allocation strategies for delay-insensitive green cognitive radio and delay-sensitive green cognitive radio are designed to maximize the energy efficiency of the secondary user. The peak interference power and the average/peak transmit power constraints are considered. Two algorithms based on the proposed resource allocation strategies are proposed to solve the formulated problems. Simulation results show that the maximum energy efficiency of the secondary user achieved under the average transmit power constraint is higher than that achieved under the peak transmit power constraint. It is shown that the design of green cognitive radio should take the tradeoff between its complexity and its achievable maximum energy efficiency into consideration.

scholarly journals Utility Maximization for Load Optimization in Cellular/WLAN Interworking Network Based on Generalized Benders Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Fanqin Zhou ◽  
Lei Feng ◽  
Peng Yu ◽  
Wenjing Li ◽  
Luoming Meng
Keyword(s):  
Resource Allocation ◽  
Utility Maximization ◽  
Benders Decomposition ◽  
Mixed Integer ◽  
User Association ◽  
Generalized Benders Decomposition ◽  
Approximation Techniques ◽  
Load Optimization ◽  
System Utilization ◽  
User Throughput

Load steering is widely accepted as a key SON function in cellular/WLAN interworking network. To investigate load optimizing from a perspective of system utilization maximization more than just offloading to improve APs’ usage, a utility maximization (UTMAX) optimization model and an ASRAO algorithm based on generalized Benders Decomposition are proposed in this paper. UTMAX is to maximize the sum of logarithmic utility functions of user data rate by jointly optimizing user association and resource allocation. To maintain the flexibility of resource allocation, a parameter β is added to the utility function, where smaller β means more resources can be allocated to edge users. As a result, it reflects a tradeoff between improvements in user throughput fairness and system total throughput. UTMAX turns out to be a mixed integer nonlinear programming, which is intractable intuitively. So ASRAO is proposed to solve it optimally and effectively, and an optional phase for expediting ASRAO is proposed by using relaxation and approximation techniques, which reduces nearly 10% iterations and time needed by normal ASRAO from simulation results. The results also show UTMAX’s good effects on improving WLAN usage and edge user throughput.

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