During the last decade, the number of devices connected to the Internet by Wi-Fi has grown significantly. A high density of both the client devices and the hot spots posed new challenges related to providing the desired quality of service in the current and emerging scenarios. To cope with the negative effects caused by network densification, modern Wi-Fi is becoming more and more centralized. To improve network efficiency, today many new Wi-Fi deployments are under control of management systems that optimize network parameters in a centralized manner. In the paper, for such a cloud management system, we develop an algorithm which aims at maximizing energy efficiency and also keeps fairness among clients. For that, we design an objective function and solve an optimization problem using the branch and bound approach. To evaluate the efficiency of the developed solution, we implement it in the NS-3 simulator and compare with existing solutions and legacy behavior.
In the modern world, a high density of wireless networks and huge interference between them make centralized coordination of the networks more and more popular. It allows optimizing network performance and thus increasing total efficiency. While today's wireless networks are mainly optimized to provide high throughput, the growing OPEX of network operators including the payments for energy consumption may shift the paradigm in the near future. Because of the very high number of base stations and access points energy consumption becomes an essential issue for wireless networks. To improve energy efficiency, various approaches can be used, including energy harvesting, improving hardware, network planning, and resource allocation @cite_5 .
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2093917343 2100998721 2130891087 2104286156
Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design. The spread of mobile connectivity is generating major social and economic benefits around the world, while along with the rapid growth of new telecommunication technologies like mobile broadband communication and M2M (Machine-to-Machine) networks, larger number of various base stations will be employed into the network, which will greatly increase the power expense and CO2 emission. In order to degrade the system power expense, variety of researches on new energy and novel transmission technology are put in agenda. In this paper, instead of reducing the absolute power expense, the research focuses on guiding more power consumption into green source energy, which implying that the UEs (User Equipment), especially the cell edge UEs, will have preferential access to the BSs (Base Station) with natural energy supply. To realize the tendentious connection, two detailed approaches are proposed, the HO (Hand Over) parameter tuning for target cell selection and power control for coverage optimization. The system evaluation shows that, by proper setting of parameters in HO and power control, both of the two approaches can achieve good balance between energy saving effect and system throughput impact. With the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever-increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. One potential solution to address these issues is to overlay small cell networks with macrocell networks as a means to provide higher network capacity and better coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of such multi-tier networks. Another technique to improve energy efficiency in cellular networks is to introduce active sleep (on off) modes in macrocell base stations. In this paper, we investigate the design and the associated tradeoffs of energy efficient cellular networks through the deployment of sleeping strategies and small cells. Using a stochastic geometry based model, we derive the success probability and energy efficiency in homogeneous macrocell (single-tier) and heterogeneous K-tier wireless networks under different sleeping policies. In addition, we formulate the power consumption minimization and energy efficiency maximization problems, and determine the optimal operating regimes for macrocell base stations. Numerical results confirm the effectiveness of switching off base stations in homogeneous macrocell networks. Nevertheless, the gains in terms of energy efficiency depend on the type of sleeping strategy used. In addition, the deployment of small cells generally leads to higher energy efficiency but this gain saturates as the density of small cells increases. In a nutshell, our proposed framework provides an essential understanding on the deployment of future green heterogeneous networks. The last ten years have witnessed explosive growth in the number of subscribers for mobile telephony. The technology has evolved from early voice only services to today's mobile wireless broadband (Internet) data delivery. The increasing use of wireless connectivity via smartphones and laptops has led to an exponential surge in network traffic. Meeting traffic demands will cause a significant increase in operator energy cost as an enlarged network of radio base stations will be needed to support mobile broadband effectively and maintain operational competitiveness. This article explores approaches that will assist in delivering significant energy efficiency gains in future wireless networks, easing the burden on network operators. It investigates three approaches to saving energy in future wireless networks. These include sleep mode techniques to switch off radio transmissions whenever possible; femtocell and relay deployments; and multiple antenna wireless systems. The impact of these approaches on achieving energy-efficient wireless communication systems is discussed.