Femtocells, despite their name, pose a potentially large disruption to the carefully planned cellular networks that now connect a majority of the planet's citizens to the Internet and with each other. Femtocells - which by the end of 2010 already outnumbered traditional base stations and at the time of publication are being deployed at a rate of about five million a year - both enhance and interfere with this network in ways that are not yet well understood. Will femtocells be crucial for offloading data and video from the creaking traditional network? Or will femtocells prove more trouble than they are worth, undermining decades of careful base station deployment with unpredictable interference while delivering only limited gains? Or possibly neither: are femtocells just a "flash in the pan"; an exciting but short-lived stage of network evolution that will be rendered obsolete by improved WiFi offloading, new backhaul regulations and/or pricing, or other unforeseen technological developments? This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology. In the course of the article, we also position and introduce the articles that headline this special issue.

/pdf/femtocells-past-present-and-future-di96bi1e7j.pdf

Femtocells: Past, Present, and Future

For more than three decades, stochastic geometry has been used to model large-scale ad hoc wireless networks, and it has succeeded to develop tractable models to characterize and better understand the performance of these networks. Recently, stochastic geometry models have been shown to provide tractable yet accurate performance bounds for multi-tier and cognitive cellular wireless networks. Given the need for interference characterization in multi-tier cellular networks, stochastic geometry models provide high potential to simplify their modeling and provide insights into their design. Hence, a new research area dealing with the modeling and analysis of multi-tier and cognitive cellular wireless networks is increasingly attracting the attention of the research community. In this article, we present a comprehensive survey on the literature related to stochastic geometry models for single-tier as well as multi-tier and cognitive cellular wireless networks. A taxonomy based on the target network model, the point process used, and the performance evaluation technique is also presented. To conclude, we discuss the open research challenges and future research directions.

https://www3.nd.edu/~mhaenggi/pubs/tut13.pdf

Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey

This paper attempts to clarify the difference between visible light communication (VLC) and light-fidelity (LiFi). In particular, it will show how LiFi takes VLC further by using light emitting diodes (LEDs) to realise fully networked wireless systems. Synergies are harnessed as luminaries become LiFi attocells resulting in enhanced wireless capacity providing the necessary connectivity to realise the Internet-of-Things, and contributing to the key performance indicators for the fifth generation of cellular systems (5G) and beyond. It covers all of the key research areas from LiFi components to hybrid LiFi/wireless fidelity (WiFi) networks to illustrate that LiFi attocells are not a theoretical concept any more, but at the point of real-world deployment.

What is LiFi

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.

Energy Efficient Heterogeneous Cellular Networks

Light-Fidelity (LiFi) takes visible light communication (VLC) further by using light emitting diodes (LEDs) to realise fully networked wireless systems. Synergies are harnessed as lights become LiFi attocells resulting in enhanced wireless capacity for the Internet-of-Things (IoT), 5G and beyond.

http://www.homepages.ed.ac.uk/hxh/Li-Fi_PAPERS/15_cheng_ecoc_invited.pdf

The deployment of femtocells in a macrocell network is an economical and effective way to increase network capacity and coverage. Nevertheless, such deployment is challenging due to the presence of inter-tier and intra-tier interference, and the ad hoc operation of femtocells. Motivated by the flexible subchannel allocation capability of OFDMA, we investigate the effect of spectrum allocation in two-tier networks, where the macrocells employ closed access policy and the femtocells can operate in either open or closed access. By introducing a tractable model, we derive the success probability for each tier under different spectrum allocation and femtocell access policies. In particular, we consider joint subchannel allocation, in which the whole spectrum is shared by both tiers, as well as disjoint subchannel allocation, whereby disjoint sets of subchannels are assigned to both tiers. We formulate the throughput maximization problem subject to quality of service constraints in terms of success probabilities and per-tier minimum rates, and provide insights into the optimal spectrum allocation. Our results indicate that with closed access femtocells, the optimized joint and disjoint subchannel allocations provide the highest throughput among all schemes in sparse and dense femtocell networks, respectively. With open access femtocells, the optimized joint subchannel allocation provides the highest possible throughput for all femtocell densities.

Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks

In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst case regret, i.e., the expected total revenue loss compared to a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of O(log^(m) T), or m iterations of the logarithm. We further show that this regret is the smallest possible up to a constant factor. Our analysis provides important structural insights into optimal pricing strategies. Finally, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2952097_code2241348.pdf?abstractid=2457296

Dynamic Pricing and Demand Learning with Limited Price Experimentation

Perfect state transfer in cubelike graphs

With the exponential increase in data traffic driven by a new generation of wireless devices, data is expected to overwhelm cellular network capacity in the next few years. To address this issue, heterogeneous networks have recently being considered as a comprehensive approach to provide high cellular network capacity and coverage. However, the dense and random deployment of small cells and their uncoordinated operation raise important questions about the energy efficiency implications of multi-tier networks. In this paper, we analyze the energy efficiency of downlink two-tier heterogeneous networks, i.e. a network of macrocell base stations overlaid by randomly distributed picocell base stations, where each tier serves a disjoint set of users. Using tools from stochastic geometry, we first derive analytical expressions of success probabilities for each tier when a disjoint set of subchannels is employed. In addition, we evaluate the performance of two-tier networks in terms of energy efficiency and fairness of resource allocation. Numerical results confirm that there exists an optimal pico-macro density ratio that maximizes the overall energy efficiency of such a two-tier network. This work provides essential understanding for successful deployment of green heterogeneous networks.

Energy Efficiency Analysis of Two-Tier Heterogeneous Networks

In a dynamic pricing problem where the demand function is not known a priori, price experimentation can be used as a demand learning tool. Existing literature usually assumes no constraint on price changes, but in practice, sellers often face business constraints that prevent them from conducting extensive experimentation. We consider a dynamic pricing model where the demand function is unknown but belongs to a known finite set. The seller is allowed to make at most m price changes during T periods. The objective is to minimize the worst-case regret-i.e., the expected total revenue loss compared with a clairvoyant who knows the demand distribution in advance. We demonstrate a pricing policy that incurs a regret of OlogmT, or m iterations of the logarithm. Furthermore, we describe an implementation of this pricing policy at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings.

The e-companion is available at https://doi.org/10.1287/opre.2017.1629 .

/pdf/technical-note-dynamic-pricing-and-demand-learning-with-1uz9lydatd.pdf

Wang Chi Cheung

Papers

Throughput Optimization, Spectrum Allocation, and Access Control in Two-Tier Femtocell Networks

Dynamic Pricing and Demand Learning with Limited Price Experimentation

Perfect state transfer in cubelike graphs

Energy Efficiency Analysis of Two-Tier Heterogeneous Networks

Technical Note-Dynamic Pricing and Demand Learning with Limited Price Experimentation