A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> Over the last few years, we have experienced a variety of access technologies being deployed. While 2G cellular systems evolve into 3G systems such as UMTS or cdma2000, providing worldwide coverage, wireless LAN solutions have been extensively deployed to provide hotspot high-bandwidth Internet access in airports, hotels, and conference centers. At the same time, fixed access such as DSL and cable modem tied to wireless LANs appear in home and office environments. The always best connected (ABC) concept allows a person connectivity to applications using the devices and access technologies that best suit his or her needs, thereby combining the features of access technologies such as DSL, Bluetooth, and WLAN with cellular systems to provide an enhanced user experience for 2.5G, 3G, and beyond. An always best connected scenario, where a person is allowed to choose the best available access networks and devices at any point in time, generates great complexity and a number of requirements, not only for the technical solutions, but also in terms of business relationships between operators and service providers, and in subscription handling. This article describes the concept of being always best connected, discusses the user experience and business relationships in an ABC environment, and outlines the different aspects of an ABC solution that will broaden the technology and business base of 3G. <s> BIB001 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> In the next generation of wireless networks, mobile users can move between heterogeneous networks, using terminals with multiple access interfaces and non-real-time or real-time services. The most important issue in such environment is the Always Best Connected (ABC) concept allowing the best connectivity to applications anywhere at anytime. To answer ABC requirement, various vertical handover decision strategies have been proposed in the literature recently, using advanced tools and proven concepts. In this paper, we give an overview of the most interesting and recent strategies. We classify it into five categories for which we present their main characteristics. We also compare each one with the others in order to introduce our vertical handover decision approach. <s> BIB002 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> In this paper we develop a tractable framework for SINR analysis in downlink heterogeneous cellular networks (HCNs) with flexible cell association. The HCN is modeled as a multi-tier cellular network where each tier's base stations (BSs) are randomly located and have a unique transmit power, path loss exponent, spatial density, and bias towards admitting users. We implicitly assume every BS has full queues. From this model, we derive the outage probability of a typical user in the network, which can be viewed as a spatial average of SINR over all users in the network. We observe that deploying more or less BSs does not change the outage probability in interference-limited HCN with unbiased cell association, and observe how biasing affects the metric. <s> BIB003 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> In this work, we consider a heterogeneous network consisting in several macro nodes and pico nodes. Our goal is to associate users, belonging to this network, to one of the nodes, while maximizing the sum rate of all users. We also want to analyze the load balancing achieved by this association. Therefore, we develop a new theoretical framework to study cell association for the downlink of multi-cell networks and derive an upper bound on the achievable sum rate. We propose a dynamic cell association heuristic, which achieves performance close to optimal. Finally, we verify our results through numerical evaluations and implement the proposed heuristic in an LTE simulator to demonstrate its viability. <s> BIB004 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> Current wireless networks are becoming increasingly heterogeneous both in terms of the radio access technologies (RATs) and the base station capabilities. Downlink rate or throughput in such heterogeneous networks is a crucial metric and has been primarily studied using system level simulations. To derive the distribution of rate analytically we develop a fairly general tractable model that consists of M different RATs, each deploying up to K different classes of access points (APs), where each class differs in transmit power, path loss exponent, and deployment density. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. Using a weighted association strategy, the distribution of rate over the entire network is derived. Further, it is shown that in a two-RAT setting there exists an optimum fraction of traffic that should be associated with each RAT to maximize rate coverage, defined as the fraction of users achieving a given rate. <s> BIB005 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> To minimize battery drain due to background communication in cellular-connected devices such as smartphones, the duration for which the cellular radio is kept active should be minimized. This, in turn, calls for scheduling the background communication so as to maximize the throughput. It has been recognized in prior work that a key determinant of throughput is the wireless link quality. However, as we show here, another key factor is the load in the cell, arising from the communication of other nodes. Unlike link quality, the only way, thus far, for a cellular client to obtain a measure of load has been to perform active probing, which defeats the goal of minimizing the active duration of the radio. In this paper, we address the above dilemma by making the following contributions. First, we show experimentally that to obtain good throughput, considering link quality alone is insufficient, and that cellular load must also be factored in. Second, we present a novel technique called LoadSense for a cellular client to obtain a measure of the cellular load, locally and passively, that allows the client to determine the ideal times for communication when available throughput to the client is likely to be high. Finally, we present the Peek-n-Sneak protocol, which enables a cellular client to "peek" into the channel and "sneak" in with its background communication when the conditions are suitable. When multiple clients in a cell perform Peen-n-Sneak, it enables them to coordinate their communications, implicitly and in an entirely distributed manner, akin to CSMA in wireless LANs, helping improve throughput (and reduce energy drain) for all. Our experimental evaluation shows overall device energy savings of 20-60% even when Peek-n-Sneak is deployed incrementally. <s> BIB006 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> In heterogeneous wireless networks, an important task for mobile terminals is to select the best network for various communications at any time anywhere, usually called network selection. In recent years, this topic has been widely studied by using various mathematical theories. The employed theory decides the objective of optimization, complexity and performance, so it is a must to understand the potential mathematical theories and choose the appropriate one for obtaining the best result. Therefore, this paper systematically studies the most important mathematical theories used for modeling the network selection problem in the literature. With a carefully designed unified scenario, we compare the schemes of various mathematical theories and discuss the ways to benefit from combining multiple of them together. Furthermore, an integrated scheme using multiple attribute decision making as the core of the selection procedure is proposed. <s> BIB007 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> We study the dynamics of network selection in heterogeneous wireless networks (HetNets). Users in such networks selfishly select the best radio access technology (RAT) with the objective of maximizing their own throughputs. We propose two general classes of throughput models that capture the basic properties of random access (e.g., Wi-Fi) and scheduled access (e.g., WiMAX, LTE, 3G) networks. Next, we formulate the problem as a non-cooperative game, and study its convergence, efficiency, and practicality. Our results reveal that: (i) Singleclass RAT selection games converge to Nash equilibria, while an improvement path can be repeated infinitely with a mixture of classes. We next introduce a hysteresis mechanism in RAT selection games, and prove that with appropriate hysteresis policies, convergence can still be guaranteed; (ii) We analyze the Pareto-efficiency of the Nash equilibria of these games. We derive the conditions under which Nash equilibria are Paretooptimal, and we quantify the distance of Nash equilibria with respect to the set of Pareto-dominant points when the conditions are not satisfied; (iii) Finally, with extensive measurement-driven simulations we show that RAT selection games converge to Nash equilibria in a small number of steps, and hence are amenable to practical implementation. We also investigate the impact of noisy throughput measurements, and propose solutions to handle them. <s> BIB008 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> In a two-tier heterogeneous network (HetNet) where femto access points (FAPs) with lower transmission power coexist with macro base stations (BSs) with higher transmission power, the FAPs may suffer significant performance degradation due to inter-tier interference. Introducing cognition into the FAPs through the spectrum sensing (or carrier sensing) capability helps them avoiding severe interference from the macro BSs and enhance their performance. In this paper, we use stochastic geometry to model and analyze performance of HetNets composed of macro BSs and cognitive FAPs in a multichannel environment. The proposed model explicitly accounts for the spatial distribution of the macro BSs, FAPs, and users in a Rayleigh fading environment. We quantify the performance gain in outage probability obtained by introducing cognition into the femto-tier, provide design guidelines, and show the existence of an optimal spectrum sensing threshold for the cognitive FAPs, which depends on the HetNet parameters. We also show that looking into the overall performance of the HetNets is quite misleading in the scenarios where the majority of users are served by the macro BSs. Therefore, the performance of femto-tier needs to be explicitly accounted for and optimized. <s> BIB009 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> The joint user association and spectrum allocation problem is studied for multi-tier heterogeneous networks (HetNets) in both downlink and uplink in the interference-limited regime. Users are associated with base-stations (BSs) based on the biased downlink received power. Spectrum is either shared or orthogonally partitioned among the tiers. This paper models the placement of BSs in different tiers as spatial point processes and adopts stochastic geometry to derive the theoretical mean proportionally fair utility of the network based on the coverage rate. By formulating and solving the network utility maximization problem, the optimal user association bias factors and spectrum partition ratios are analytically obtained for the multi-tier network. The resulting analysis reveals that the downlink and uplink user associations do not have to be symmetric. For uplink under spectrum sharing, if all tiers have the same target signal-to-interference ratio (SIR), distance-based user association is shown to be optimal under a variety of path loss and power control settings. For both downlink and uplink, under orthogonal spectrum partition, it is shown that the optimal proportion of spectrum allocated to each tier should match the proportion of users associated with that tier. Simulations validate the analytical results. Under typical system parameters, simulation results suggest that spectrum partition performs better for downlink in terms of utility, while spectrum sharing performs better for uplink with power control. <s> BIB010 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> Massive MIMO is expected to play a key role in coping with the predicted mobile-data traffic explosion. Indeed, in combination with small cells and TDD operation, it promises large throughputs per unit area with low latency. In this paper we focus on the problem of balancing the load across networks with massive MIMO base-stations (BSs). The need for load balancing arises from variations in the user population density and is more pronounced in small cells due to the large variability in coverage area. We consider methods for load balancing over networks with small and large massive MIMO BSs. As we show, the distinct operation and properties of massive MIMO enable practical resource-efficient load-balancing methods with near-optimal performance. <s> BIB011 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> We study the convergence properties of distributed network selection in HetNets with priority-based service. Clients in such networks have different priority weights (e.g., QoS requirements, scheduling policies, etc.) for different access networks and act selfishly to maximize their own throughput. We formulate the problem as a non-cooperative game, and study its convergence for two models: (i) A purely client-centric model where each client uses its own preference to select a network, and (ii) a hybrid client-network model that uses a combination of client and network preferences to arrive at pairings. Our results reveal that: (a) Pure client-centric network selection with generic weights can result in infinite oscillations for any improvement path (i.e., shows strongly cyclic behavior). However, we show that under several classes of practical priority weights (e.g., weights that achieve different notions of fairness) or under additional client-side policies, convergence can be guaranteed; (b) We study convergence time under client-centric model and provide tight polynomial and linear bounds; (c) We show that applying a minimal amount of network control in the hybrid model, guarantees convergence for clients with generic weights. We also introduce a controllable knob that network controller can employ to balance between convergence time and its network-wide objective with predictable tradeoff. <s> BIB012 </s> A Survey of Client-Controlled HetNets for 5G <s> A. HetNet SELECTION <s> The use of a very large number of antennas at each base station site (referred to as"Massive MIMO") is one of the most promising approaches to cope with the predicted wireless data traffic explosion. In combination with Time Division Duplex and with simple per-cell processing, it achieves large throughput per cell, low latency, and attractive power efficiency performance. Following the current wireless technology trend of moving to higher frequency bands and denser small cell deployments, a large number of antennas can be implemented within a small form factor even in small cell base stations. In a heterogeneous network formed by large (macro) and small cell BSs, a key system optimization problem consists of"load balancing", that is, associating users to BSs in order to avoid congested hot-spots and/or under-utilized infrastructure. In this paper, we consider the user-BS association problem for a massive MIMO heterogeneous network. We formulate the problem as a network utility maximization, and provide a centralized solution in terms of the fraction of transmission resources (time-frequency slots) over which each user is served by a given BS. Furthermore, we show that such a solution is physically realizable, i.e., there exists a sequence of integer scheduling configurations realizing (by time-sharing) the optimal fractions. While this solution is optimal, it requires centralized computation. Then, we also consider decentralized user-centric schemes, formulated as non-cooperative games where each user makes individual selfish association decisions based only on its local information. We identify a class of schemes such that their Nash equilibrium is very close to the global centralized optimum. Hence, these user-centric algorithms are attractive not only for their simplicity and fully decentralized implementation, but also because they operate near the system"social"optimum. <s> BIB013
There have been many different approaches to modeling the RAT selection problem in HetNets BIB002 - BIB009 , where clients are allowed to switch radio interfaces for data transmission in order to improve some metric (Fig. 2a) . These approaches have attempted to ask and answer the questions of where in the wireless network that RAT selection should take place, and how RAT selection should take place: specifically, what algorithm should be used to determine which RATs to connect to. Traditionally, solutions to these questions have been primarily network-centric, setting where to be inside the network core, and how to be some centralized agent running an optimization algorithm that makes a global clientto-Base-Station/eNode-B association decision for all clients in the network at the same time. The advantages of such an architecture, where all decisions are made on the network side, are that the RAT selection algorithm can act upon a big-picture view of the network (can poll clients for their local channel conditions and have loading information on the RATs themselves) and converge to and achieve a globallyoptimal result with respect to some network metric relatively quickly. This approach is generally favored by organizations such as cellular service providers that seek to maintain control of network operation, reflecting the view that the network should optimize for some network-wide metric that the service provider wishes to focus on. In network-controlled HetNets, these decisions are made in the following way ( Fig. 2b) : first, the centralized agent that performs the decision-making polls all clients seeking to connect to one or more of the RATs controlled by the agent, VOLUME 5, 2017 FIGURE 2. (a) Client with access to 3 Heterogeneous RATs; (b) Network-controlled HetNets: (1) The client reports information on its accessible RATs to the centralized agent, which (2) computes the globally optimal client-RAT association back to the client and (3) switches RATs; (c) Client-controlled HetNets: (1) The client gathers information on its accessible RATs and makes a local decision for its own client-RAT association decision and (2) switches RATs. requesting information on the client's local channel conditions. Based on this information, it calculates the optimal association of clients to RATs, and then transmits these associations back to each client. Once the client receives these instructions (by way of the RAT it is currently using), it then changes its RAT association as directed. The exact optimization performed is unique to each network. Depending on the type of network and the network operator, different networks may be designed to optimize for different metrics depending on their business needs: • Maximize aggregate throughput and fairness of resource allocation BIB010 , BIB013 , BIB004 • Load balancing BIB005 - BIB011 , BIB006 • Minimize outage probability BIB003 , BIB009 • Maximize a measure of Quality of Service A variety of techniques are used to achieve these goals such as cost functions, utility maximization, stochastic geometry and combinatorial optimization to determine the ''best" BIB001 network to associate with BIB007 and  . However, there are downsides to centralized networkcontrolled HetNets. Chief among these are the issues of timeliness of switching, scalability of the system to multiple clients with multiple interfaces, and the issue of how to obtain global control of client-RAT association. Any centralized scheme would have to obtain client inputs (e.g., channel conditions, battery life, number and type of interfaces on the device, application type, etc.) in order to optimally allocate resources. Such polling would incur significant delay in each calculation due to latency between client and controller. Furthermore, all clients need to convey this information to the network-depending on the geographical area that the centralized agent controls, the sheer amount of required overhead traffic simply for passing clients' parameters to the network could become a significant portion of data that the network transports, further limiting the efficiency of the HetNet. Finally, different RATs belonging to different business entities may not be willing to pool resources and allow outside control of proprietary networks. For example, to achieve true centralized network-controlled HetNets, companies such as Verizon and Boingo must be willing to hand off their existing customer traffic to each other when instructed to do so-raising questions about fairness of traffic allocation between their respective cellular and Wi-Fi networks, how to agree upon a method of traffic allocation, and even customer privacy. In contrast to centralized network-controlled HetNets, client-controlled HetNets focuses solely on the clients' perspective. Based on local observations at the client itself, the client must make a decision to associate with the RAT that provides some optimal metric (e.g., a utility function, throughput, etc.), evaluated locally (Fig. 2c) . Although this form of distributed optimization run on individual clients may not obtain a client-RAT association with as good a metric as the centralized network-controlled case BIB008 , BIB012 , there are several advantages. First, the timeliness problem of the centralized case is avoided: all measurements do not suffer from network latency, as all measurements are locally available on the client. Second, scalability of RAT selection in HetNets is maintained as each client only needs to calculate its optimal association. Third, client privacy is preserved as it no longer needs to transfer information about its traffic (data type, utility function, battery power, etc.) to agents in the network. Finally, the different business entities needing to share control of their networks is avoided-as long as each client has permission to associate with a RAT, each network only need allow the client to connect when requested.