A Survey of Client-Controlled HetNets for 5G <s> E. Wi-Fi OFFLOADING <s> Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage. To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications. <s> BIB001 </s> A Survey of Client-Controlled HetNets for 5G <s> E. Wi-Fi OFFLOADING <s> We investigate if WiFi access can be used to augment 3G capacity in mobile environments. We rst conduct a detailed study of 3G and WiFi access from moving vehicles, in three different cities. We find that the average 3G and WiFi availability across the cities is 87% and 11%, respectively. WiFi throughput is lower than 3G through-put, and WiFi loss rates are higher. We then design a system, called Wiffler, to augments mobile 3G capacity. It uses two key ideas leveraging delay tolerance and fast switching -- to overcome the poor availability and performance of WiFi. For delay tolerant applications, Wiffler uses a simple model of the environment to predict WiFi connectivity. It uses these predictions to delays transfers to offload more data on WiFi, but only if delaying reduces 3G usage and the transfers can be completed within the application's tolerance threshold. For applications that are extremely sensitive to delay or loss (e.g., VoIP), Wiffler quickly switches to 3G if WiFi is unable to successfully transmit the packet within a small time window. We implement and deploy Wiffler in our vehicular testbed. Our experiments show that Wiffler significantly reduces 3G usage. For a realistic workload, the reduction is 45% for a delay tolerance of 60 seconds. <s> BIB002 </s> A Survey of Client-Controlled HetNets for 5G <s> E. Wi-Fi OFFLOADING <s> The increase in data consumed by smartphones is becoming a huge problem for mobile operators. In three years, mobile data traffic in ATT it is also assisted by predictions made by the operator. We evaluate both algorithms using a large data set, obtained from a major mobile operator and a realistic application similar to Apple's Ping music social network. The metrics address the amount of offloading, delay and mobile energy efficiency. We find that both solutions succeed in offloading a significant amount of traffic, with a positive impact on user battery lifetime. Surprisingly, we also find that all the benefit obtained from the operator with the MixZones algorithm (i.e with ad hoc exchanges between users) can be achieved with the HotZones algorithm and a small investment in Wi-Fi access points. Note that the latter is considerably less complex to deploy than the former. <s> BIB003 </s> A Survey of Client-Controlled HetNets for 5G <s> E. Wi-Fi OFFLOADING <s> Cellular networks are currently facing the challenges of mobile data explosion. High-end mobile phones and laptops double their mobile data traffic every year and this trend is expected to continue given the rapid development of mobile social applications. It is imperative that novel architectures be developed to handle such voluminous mobile data. In this paper, we propose and evaluate an integrated architecture exploiting the opportunistic networking paradigm to migrate data traffic from cellular networks to metropolitan WiFi access points (APs). To quantify the benefits of deploying such an architecture, we consider the case of bulk file transfer and video streaming over 3G networks and simulate data delivery using real mobility data set of 500 taxis in an urban area. We are the first to quantitatively evaluate the gains of citywide WiFi offloading using large scale real traces. Our results give the numbers of APs needed for different requirements of quality of service for data delivery in large metropolitan area. We show that even with a sparse WiFi network the delivery performance can be significantly improved. This effort serves as an important feasibility study and provides guidelines for operators to evaluate the possibility and cost of this solution. Keywords-Cellular traffic offloading, delay tolerant, WiFi access points, trace-driven simulation. <s> BIB004 </s> A Survey of Client-Controlled HetNets for 5G <s> E. Wi-Fi OFFLOADING <s> This paper presents a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited 97 iPhone users from metropolitan areas and collected statistics on their WiFi connectivity during a two-and-a-half-week period in February 2010. Our trace-driven simulation using the acquired whole-day traces indicates that WiFi already offloads about 65% of the total mobile data traffic and saves 55% of battery power without using any delayed transmission. If data transfers can be delayed with some deadline until users enter a WiFi zone, substantial gains can be achieved only when the deadline is fairly larger than tens of minutes. With 100-s delays, the achievable gain is less than only 2%-3%, whereas with 1 h or longer deadlines, traffic and energy saving gains increase beyond 29% and 20%, respectively. These results are in contrast to the substantial gain (20%-33%) reported by the existing work even for 100-s delayed transmission using traces taken from transit buses or war-driving. In addition, a distribution model-based simulator and a theoretical framework that enable analytical studies of the average performance of offloading are proposed. These tools are useful for network providers to obtain a rough estimate on the average performance of offloading for a given WiFi deployment condition. <s> BIB005
Opportunistic client-centric switching based on local availability of non-cellular networks in HetNets has already been well-studied in the context of 802.11 Wi-Fi over different timescales (sub-second to tens of seconds and more) BIB005 - BIB001 , and much of it remains applicable to HetNets in a 5G environment. Wi-Fi networks have been shown to be a high capacity option for offloading traffic from cellular networks, accepting 65% of total mobile client traffic while saving 55% of the battery by only offloading data to Wi-Fi when the network is available BIB005 . The Wiffler system BIB002 has also shown up to a 45% reduction in cellular workload for data with a delay tolerance of up to 60 seconds. By leveraging delay tolerance of different types of data and fast switching, Wiffler is able to route data from one RAT to another if the first BS is unable to satisfy the traffic delay requirements. The system is able to opportunistically leverage the offloading capacity of Wi-Fi networks (if present), and fall back upon other traditional cellular networks if no Wi-Fi networks that can satisfy data delay requirements is within reach. The authors in BIB001 have gone further, developing clientstored models of daily mobility in order to perform predictive forecasts for the mobile client's radio environment. By leveraging the habitual behavior of people taking similar paths during their daily lives and combining past wireless measurements, a system for determining typical Wi-Fi BS quality and client location can generate connectivity forecasts of which Wi-Fi BSs can be available to in the future, and can assist in opportunistic client-centric data offloading, though the system requires some training. Many works also address Wi-Fi offloading in the presence of some network prediction. This has been studied for integration of cellular-and-WLAN networks  , which supports network discovery and selection to help clients discover non-cellular networks. In BIB003 , HotZones, uses prediction to download delay-tolerant content when close to Wi-Fi BSs. It creates a rank-ordered list of most frequently visited BS based on past client behavior. This profile is shared with the network operator, and the total aggregate list is broadcast to all clients-in effect, bypassing the high overhead of opportunistic scanning for higher-throughput Wi-Fi RATs. Clients may then connect to various Wi-Fi BSs as they wish with the added knowledge of a measure of load on those BSs, and are found to be able to offload up to 70% of their cellular traffic to those BSs. Large-scale city traces of mobile clients over the course of 30 days were used in MADNet BIB004 to evaluate the gains of citywide (San Francisco) Wi-Fi offloading using metropolitan BSs. It allows the cellular network to reduce load by signaling over cellular, but performing download/uploading over Wi-Fi based on explicitly defined client preferences. More than half of cellular traffic was offloaded, and file transfer delay was reduced by more than 50% in the majority of requests. Many of these client-centric approaches remain valid for HetNets with 5G, as the ubiquitous deployment of Wi-Fi for both indoor and outdoor coverage and offloading is not expected to be replaced anytime soon. These techniques may also be extended for unmanaged 5G RATs deployed as small cells in both licensed and unlicensed spectrum, however these techniques must be updated so that they function on the faster timescale of 5G mmWave RATs (e.g. order of milliseconds) in an efficient way. Opportunistic use of these RATs in the home or office environment within a single room or a floor has the potential to maintain the benefits of load balancing and coverage of Wi-Fi offloading, but also to exploit the higher throughput potential of 5G technologies such as mmWave.