scholarly journals A Submodular Optimization Framework for Outage-Aware Cell Association in Heterogeneous Cellular Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Gongchao Su ◽  
Bin Chen ◽  
Xiaohui Lin ◽  
Hui Wang ◽  
Lemin Li
Keyword(s):  
Load Balancing ◽  
Small Cell ◽  
Base Stations ◽  
Maximization Problem ◽  
Proportional Fairness ◽  
Submodular Optimization ◽  
Outage Performance ◽  
Cell Association ◽  
Outage Probabilities ◽  
Utility Maximization Problem

In cellular heterogeneous networks (HetNets), offloading users to small cell base stations (SBSs) leads to a degradation in signal to interference plus noise ratio (SINR) and results in high outage probabilities for offloaded users. In this paper, we propose a novel framework to solve the cell association problem with the intention of improving user outage performance while achieving load balancing across different tiers of BSs. We formulate a combinatorial utility maximization problem with weighted BS loads that achieves proportional fairness among users and also takes into account user outage performance. A formulation of the weighting parameters is proposed to discourage assigning users to BSs with high outage probabilities. In addition, we show that the combinatorial optimization problem can be reformulated as a monotone submodular maximization problem and it can be readily solved via a greedy algorithm with lazy evaluations. The obtained solution offers a constant performance guarantee to the cell association problem. Simulation results show that our proposed approach leads to over 30% reduction in outage probabilities for offloaded users and achieves load balancing across macrocell and small cell BSs.

scholarly journals Energy Efficiency Maximization of Dynamic CoMP-JT Algorithm in Dense Small Cell Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Xuefei Peng ◽  
Jiandong Li ◽  
Yifei Xu
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Optimal Solution ◽  
Small Cell ◽  
Base Stations ◽  
Maximization Problem ◽  
Second Phase ◽  
User Association ◽  
Coordinated Multipoint ◽  
Convex Problem

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP-hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP-JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP-JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.

scholarly journals Harvested Energy Maximization of SWIPT System with Popularity Cache Scheme in Dense Small Cell Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Xuefei Peng ◽  
Jiandong Li
Keyword(s):  
Small Cell ◽  
Base Stations ◽  
Maximization Problem ◽  
Power Splitting ◽  
Small Cell Networks ◽  
Splitting Ratio ◽  
Energy Maximization ◽  
Cache Scheme ◽  
Cell Networks

In this paper, we propose a harvested energy maximization problem of simultaneous wireless information and power transfer (SWIPT) system with popularity cache scheme in dense small cell networks. Firstly, network model, content request, and popularity cache schemes are provided in the system model. Then, we establish a harvested energy maximization problem of SWIPT system with popularity cache scheme in dense small cell networks, where maximum transmit power of small cell base stations (SBSs), minimum rate requirement, i.e., quality of service (QoS) of user terminals (UTs), and power splitting ratio are considered. Further, an iterative power splitting ratio and power allocation optimization (IPSPA) algorithm is proposed to solve the formulated problem. Finally, the better performance of our proposed method is demonstrated through a number of simulations. These results are of significance for maximizing harvesting energy of UTs and reducing consumption of backhaul resources and energy.

scholarly journals Power-Availability-Aware Cell Association for Energy-Harvesting Small-Cell Base Stations

2017 ◽  
Vol 16 (4) ◽  
pp. 2409-2422 ◽  
Author(s):  
Fanny Parzysz ◽  
Marco Di Renzo ◽  
Christos Verikoukis
Keyword(s):  
Energy Harvesting ◽  
Small Cell ◽  
Base Stations ◽  
Cell Association ◽  
Cell Base

scholarly journals On the Performance of UAV-Aided Content Caching in Small-Cell Networks with Joint Transmission

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1040
Author(s):  
Menghan Wei ◽  
Youjia Chen ◽  
Ming Ding
Keyword(s):  
Transmission Rate ◽  
High Mobility ◽  
Limited Resource ◽  
Small Cell ◽  
Base Stations ◽  
Cell Content ◽  
Small Cell Networks ◽  
Content Caching ◽  
Cell Networks ◽  
Joint Transmission

Unmanned aerial vehicles (UAVs), featured by the high-mobility and high-quality propagation environment, have shown great potential in wireless communication applications. In this paper, a novel UAV-aided small-cell content caching network is proposed and analyzed, where joint transmission (JT) is considered in the dense small-cell networks and mobile UAVs are employed to shorten the serving distance. The system performance is evaluated in terms of the average cache hit probability and the ergodic transmission rate. From the analytical results, we find that (i) the proposed UAV-aided small-cell network shows superior caching performance and, even with a small density of UAVs the system’s cache hit probability, can be improved significantly; (ii) the content’s optimal caching probability to maximize the cache hit probability is proportional to the (K+1)-th root of its request probability, where K is the number of small-cell base stations that serve each user by JT; (iii) caching the most popular content in UAVs may lead to a low transmission rate due to the limited resource offered by the low-density UAVs. Simulation results are presented to validate the theoretical results and the performance gain achieved by the optimal caching strategy.

scholarly journals A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1493
Author(s):  
Ayesha Ayub ◽  
Sobia Jangsher ◽  
M. Majid Butt ◽  
Abdur Rahman Maud ◽  
Farrukh A. Bhatti
Keyword(s):  
Small Cell ◽  
Base Stations ◽  
Small Cells ◽  
Effective Capacity ◽  
Rate Enhancement ◽  
Cell Network ◽  
Small Cell Network ◽  
User Throughput ◽  
Average User ◽  
Cell Cooperation

Small cells deliver cost-effective capacity and coverage enhancement in a cellular network. In this work, we present the interplay of two technologies, namely Wi-Fi offloading and small-cell cooperation that help in achieving this goal. Both these technologies are also being considered for 5G and B5G (Beyond 5G). We simultaneously consider Wi-Fi offloading and small-cell cooperation to maximize average user throughput in the small-cell network. We propose two heuristic methods, namely Sequential Cooperative Rate Enhancement (SCRE) and Sequential Offloading Rate Enhancement (SORE) to demonstrate cooperation and Wi-Fi offloading, respectively. SCRE is based on cooperative communication in which a user data rate requirement is satisfied through association with multiple small-cell base stations (SBSs). However, SORE is based on Wi-Fi offloading, in which users are offloaded to the nearest Wi-Fi Access Point and use its leftover capacity when they are unable to satisfy their rate constraint from a single SBS. Moreover, we propose an algorithm to switch between the two schemes (cooperation and Wi-Fi offloading) to ensure maximum average user throughput in the network. This is called the Switching between Cooperation and Offloading (SCO) algorithm and it switches depending upon the network conditions. We analyze these algorithms under varying requirements of rate threshold, number of resource blocks and user density in the network. The results indicate that SCRE is more beneficial for a sparse network where it also delivers relatively higher average data rates to cell-edge users. On the other hand, SORE is more advantageous in a dense network provided sufficient leftover Wi-Fi capacity is available and more users are present in the Wi-Fi coverage area.

scholarly journals UTILITY THEORY FRONT TO BACK — INFERRING UTILITY FROM AGENTS' CHOICES

2014 ◽  
Vol 17 (03) ◽  
pp. 1450018 ◽  
Author(s):  
ALEXANDER M. G. COX ◽  
DAVID HOBSON ◽  
JAN OBłÓJ
Keyword(s):  
Utility Function ◽  
Utility Theory ◽  
Consistency Condition ◽  
Maximization Problem ◽  
Consumption Pattern ◽  
Investment Strategies ◽  
Inverse Approach ◽  
To Come ◽  
Utility Maximization Problem ◽  
Stochastic Setting

We pursue an inverse approach to utility theory and associated consumption and investment problems. Instead of specifying a utility function and deriving the actions of an agent, we assume that we observe the actions of the agent (i.e. consumption and investment strategies) and ask if it is possible to derive a utility function for which the observed behavior is optimal. We work in continuous time both in a deterministic and stochastic setting. In the deterministic setup, we find that there are infinitely many utility functions generating a given consumption pattern. In the stochastic setting of a geometric Brownian motion market it turns out that the consumption and investment strategies have to satisfy a consistency condition (PDE) if they are to come from a classical utility maximization problem. We show further that important characteristics of the agent such as risk attitudes (e.g., DARA) can be deduced directly from the agent's consumption and investment choices.

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