As a promising technique for next-generation wireless networks, femtocells expand the coverage of cellular networks, provide high data rate for users, decrease the transmission power of user equipments, and increase the spectrum efficiency. In a few years, the number of deployed femtocell base stations (FBSs) will reach hundreds of millions. This huge deployment will bring a lot of challenges in terms of interference management, resource scheduling, and energy consumption. In recent years, more and more attention has been paid to energy-efficient communications. The huge number of deployed FBSs will aggravate energy consumption. In this article, we comprehensively survey the related work on energy efficiency issues in femtocell networks, including energy efficiency metrics, energy consumption models, deployments of femtocells, and energy-efficient schemes. Then a simple sleeping scheme, fixed time sleeping, is presented as a case study for saving the energy of FBSs. Some interesting results are also presented to show that fixed time sleeping makes a good trade-off among energy efficiency, actual waiting time, and call loss.
To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.
Due to the increasing number of wireless devices, power consumption and energy efficiency (EE) have become important performance indicators of the cellular network. It is an effective method to reduce energy consumption by switching off some underutilized base stations (BSs). The user equipments (UEs) left by the sleeping BS can be serviced by the nearby active BSs. And through Coordinated Multi-Point technology (CoMP), it does not need to increase transmit power of the active BSs to ensure the coverage of the network. The method combining sleep mode with static clustering is only applied to the scenario during off-peak time and uniform user distributed. Static clustering is fixed once it formed, which does not change with the movement of the user's position. In order to solve this problem, we propose an algorithm combining BS sleeping with dynamic clustering (SMDC) to maximize EE. The simulation results show that the proposed method can greatly improve the EE of the whole system. Besides, we use system level simulation to estimate the performance of this method.
With the ever-increasing requirement of WLAN to support real-time services, it is becoming important to study the delay properties of WLAN protocols. This paper constructs a new model to analyze the channel access delay and delay jitter of IEEE 802.11 DCF in saturation traffic condition. Based on this analytical model, average channel access delay and delay jitter are derived for both basic access and RTS/CTS-based access scheme. The accuracy of the analytical model is validated by simulations and furthermore we discuss the impact of initial contention window, maximal backoff stage, and packet size on channel access delay and delay jitter of 802.11 DCF using the proposed model.
The Probability Distribution of Slot Selection (PDoSS) of IEEE 802.11 DCF is extremely uneven, which makes the packet collision probability very high. In this paper, we propose a novel RWBO+BEB backoff algorithm for 802.11 DCF to make the PDoSS even and thus decrease the packet collision probability. A Markov model is built for analyzing RWBO+BEB's PDoSS and saturation throughput. The model's correctness is validated by simulation. The performance of RWBO+BEB is also evaluated by simulation in terms of PDoSS, saturation throughput, packet collision probability and packet delay. The simulation results indicate that RWBO+BEB can decrease the packet collision probability to a large extent, utilize the channel more efficiently, and make the packet delay jitter much lower comparing to 802.11 DCF. Moreover, we analyze the relation of saturation throughput and packet collision probability to walking probability ( p d ) and contention windows (w), respectively. The analysis indicates that RWBO+BEB has a remarkable feature: its saturation throughput keeps high, and packet collision probability keeps very low (which under 0.1) in a large range of p d and w, this allows us to configure p d and w more flexibly.
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