A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> We study the negative consequences of selfish behavior in a congested network and economic means of influencing such behavior. We consider a model of selfish routing in which the latency experienced by network traffic on an edge of the network is a function of the edge congestion, and network users are assumed to selfishly route traffic on minimum-latency paths. The quality of a routing of traffic is measured by the sum of travel times (the total latency).It is well known that the outcome of selfish routing (a Nash equilibrium) does not minimize the total latency. An ancient strategy for improving the selfish solution is the principle of marginal cost pricing, which asserts that on each edge of the network, each network user on the edge should pay a tax offsetting the congestion effects caused by its presence. By pricing network edges according to this principle, the inefficiency of selfish routing can always be eradicated.This result, while fundamental, assumes a very strong homogeneity property: all network users are assumed to trade off time and money in an identical way. The guarantee also ignores both the algorithmic aspects of edge pricing and the unfortunate possibility that an efficient routing of traffic might only be achieved with exorbitant taxes. Motivated by these shortcomings, we extend this classical work on edge pricing in several different directions and prove the following results.We prove that the edges of a single-commodity network can always be priced so that an optimal routing of traffic arises as a Nash equilibrium, even for very general heterogeneous populations of network users.When there are only finitely many different types of network users and all edge latency functions are convex, we show how to compute such edge prices efficiently.We prove that an easy-to-check mathematical condition on the population of heterogeneous network users is both necessary and sufficient for the existence of edge prices that induce an optimal routing while requiring only moderate taxes. <s> BIB001 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> Recent mobile equipment (as well as the norm IEEE 802.21) offers the possibility for users to switch from one technology to another (vertical handover). This allows flexibility in resource assignments and, consequently, increases the potential throughput allocated to each user. In this paper, we design a fully distributed algorithm based on trial and error mechanisms that exploits the benefits of vertical handover by finding fair and efficient assignment schemes. On the one hand, mobiles gradually update the fraction of data packets they send to each network based on the rewards they receive from the stations. On the other hand, network stations send rewards to each mobile that represent the impact each mobile has on the cell throughput. This reward function is closely related to the concept of marginal cost in the pricing literature. Both the station and the mobile algorithms are simple enough to be implemented in current standard equipment. Based on tools from evolutionary games, potential games and replicator dynamics, we analytically show the convergence of the algorithm to fair and efficient solutions. Moreover, we show that after convergence, each user is connected to a single network cell which avoids costly repeated vertical handovers. To achieve fast convergence, several simple heuristics based on this algorithm are proposed and tested. Indeed, for implementation purposes, the number of iterations should remain in the order of a few tens. <s> BIB002 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> There are two emerging trends in the mobile data world. First, mobile data is exploding at a rapid rate with analysts predicting 25-50X growth by the year 2015. The second trend is that users are demanding greater degree of flexibility in selecting their operators at fine timescales. Across Asia, dual-SIM phones have become popular, while Apple is rumored to be designing a Universal SIM that will allow iPhone users to toggle between different operators. This latter trend points towards an impending disruption in wireless service models which could also be the need of the hour from the spectrum shortage perspective. This points towards a new service model where users can choose an operator based on application needs. However, if users make this choice greedily without network assistance, it can exacerbate spectrum scarcity and degrade user experience. In this work, we consider user devices with multiple network interfaces (3G, LTE etc.) that can be simultaneously active and each running multiple applications. We propose the MOTA service model to enable users to associate each interface with the operator of choice at fine time scales. Under the MOTA service model, through concise signalling information, operators provide information about their own network, so that each user can (i) choose a suitable operator for each interface, and (ii) choose an interface for each active application. We make the following contributions in this paper. First, we propose concise network signalling that assists users to make informed choices even under mobility. Second, we develop user-choice algorithms that maximize a suitable notion of user satisfaction while using spectrum resources efficiently. Third, we perform extensive evaluation over actual base station deployment in a city coupled with real signal propagation maps. Our results with two operators show that, MOTA service model provides capacity gain in the range 2.5-4X over the current existing service model. Finally, we argue that our solution is practically implementable by combining appropriate IEEE standards and IETF proposals. <s> BIB003 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association. <s> BIB004 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> In this paper, the problem of uplink user association in small cell networks, which involves interactions between users, small cell base stations, and macro-cell stations, having often conflicting objectives, is considered. The problem is formulated as a college admissions game with transfers in which a number of colleges, i.e., small cell and macro-cell stations seek to recruit a number of students, i.e., users. In this game, the users and access points (small cells and macro-cells) rank one another based on preference functions that capture the users' need to optimize their utilities which are functions of packet success rate (PSR) and delay as well as the small cells' incentive to extend the macro-cell coverage (e.g., via cell biasing/range expansion) while maintaining the users' quality-of-service. A distributed algorithm that combines notions from matching theory and coalitional games is proposed to solve the game. The convergence of the algorithm is shown and the properties of the resulting assignments are discussed. Simulation results show that the proposed approach yields a performance improvement, in terms of the average utility per user, reaching up to 23% relative to a conventional, best-PSR algorithm. I. INTRODUCTION Meeting the stringent quality-of-service (QoS) requirements of emerging wireless services warrants substantial changes in current cellular infrastructure. In this respect, the introduction of small cell base stations (SCBSs) (picocells, microcells, fem- <s> BIB005 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <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> BIB006 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> Heterogeneous cellular networks use small base stations, such as femtocells and WiFi APs, to offload traffic from macrocells. While network operators wish to globally balance the traffic, users may selfishly select the nearest base stations and make some base stations overcrowded. In this paper, we propose to use an auction-based algorithm — Femto-Matching, to achieve both load balancing among base stations and fairness among users. Femto-Matching optimally solves the global proportional fairness problem in polynomial time by transforming it into an equivalent matching problem. Furthermore, it can efficiently utilize the capacity of randomly deployed small cells. Our trace-driven simulations show Femto-Matching can reduce the load of macrocells by more than 30% compared to non-cooperative game based strategies. <s> BIB007 </s> A Survey of Client-Controlled HetNets for 5G <s> III. EXTENSIONS OF CLIENT-CENTRIC RAT SELECTION A. HYBRID CONTROL <s> We model and analyze a User-Equipment (UE) based wireless network selection method where individuals act on their stochastic knowledge of the expected behavior off their available networks. In particular, we focus on networks with millimeter-wave (mmWave) radio. Modeling mmWave radio access technologies (RATs) as a stochastic 3-state process based on their physical layer characteristics in Line-of-Sight (LOS), Non-Line-of-Sight (NLOS), and Outage states, we make the realistic assumption that users have no knowledge of the statistics of the RATs and must learn these while maximizing the throughput obtained. We develop an online learning-based approach to access network selection: a user-centric Multi-Armed Bandit Problem that incorporates the cost of switching access networks. We develop an online learning policy that groups network access to minimize costs for RAT selection, analyze the regret (loss due to uncertainty) of our algorithm. We also show that our algorithm obtains optimal regret and in numerical examples achieves 24% increase in total throughput compared to existing techniques for high throughput mmWave RATs that vary over a fast timescale. <s> BIB008
Hybrid control extends the concept of client-centric control of RAT selection in HetNets to allow for network-assistance in the RAT selection process. This type of shared control preserves the client's right to make the final decision on when and where to switch, but gives the network some input in the switching itself, such as providing information on network metrics such as load balancing. This added information is useful because the purely-local view of the network observed by fully-distributed client-centric RAT selection is not guaranteed to be accurate. Operating under this limited perspective can lead to suboptimal behavior when a client simply ignores the effect of its presence upon other users. Several works address this by allowing the network to inform clients with some global knowledge such as BIB006 - BIB002 for example. A broadcast technique to inform clients making local decisions of network conditions is used in several approaches BIB003 , BIB004 . In BIB003 , the authors designed a BS association system for HetNets in which base stations broadcast both their current weighted load and price. These parameters are then used by clients to select and associate to the base station that best satisfies their utility. Under this service model, clients are assisted to make the best decision even under mobility, and can even assign individual applications to different interfaces. This is taken one step further in BIB004 where the authors develop a low-complexity distributed algorithm that uses gradient descent dual-decomposition to split the multi-homed joint RAT association problem into two distinct subproblems of identifying the optimal BS at the client, and updating a BS-specific multiplier to be broadcast after each iteration at the BS. The Lagrangian multiplier acts as the price of the BS determined by its load, and performs a sort of load-balancing in the network itself. The authors in BIB002 develop a distributed algorithm in which the clients do not directly compete to maximize throughput-instead, they associate to BSs in a way to maximize the total reward obtained from the BS in order to prevent selfish behavior. The reward assigned by each BS is dependent on the loss of throughput incurred for other users due to association based on marginal cost pricing BIB001 . Theoretical results for generalized distributed clientcentric RAT selection in HetNets with prioritized service were analyzed in BIB006 . Both a purely client-centric and the hybrid association model were analyzed, and showed that purely client-centric association with generic weights can result in infinite oscillations; but under several specific classes of weighted priorities, convergence can be guaranteed for the system. Tight polynomial and linear bounds are found for the client-centric model, and that the proper selection of a potential function by the network can guarantee convergence of the system. Assignment problem approaches for traffic offloading in HetNets for femto have also been proposed BIB007 , BIB005 that find optimal association. Compared to the above works, matching schemes explicitly allow for indirect negotiation between clients and BSs in allocating network resources. Instead of providing a price parameter to clients, these approaches directly rank clients for each BS in terms of how well a potential BS may maximize the client or network metric. In BIB007 , the problem of achieving proportional-fair throughput for client-RAT association in HetNets is transformed into an equivalent matching problem, which can be solved in polynomial time. This exchange, where the BSs iteratively announce their price of association, and clients submit bids for resources, results in a global reduction for macrocellular traffic of up to 30% compared to several noncooperative game-based strategies. A preference list approach is used in BIB005 to find a stable matching between clients and femtocells for uplink. In this setup, both clients and BSs rank each other based on preference functions that capture clients' utilities which depend on packet success rate, delay, and small cells' incentive to extend macrocell coverage. The game is solved using two phases involving admission games that allow transfers between BSs, followed by data transmission, with performance improvements of up to 23% compared to a best packet success rate algorithm. Although the network may provide some additional information, the ultimate decision to switch still rests on the client itself-and in the absence of perfect, global knowledge, the client will need to discover how it should associate with the BSs to meet its objective. Online learning techniques such as reinforcement learning  and multi-armed bandits BIB008 are among some of the methods used to explore and exploit the spectrum resources that are accessible to a client. Q-learning is used in  to learn the client-specific bias values for received power in order to perform cell-range expansion for HetNets. By using these individualized bias values over a common bias over all UEs, the system is a multi-agent system that leverages distributed learning where information is never shared. The costs are reported back to the clients from the BSs and are used to update the Q-values for future ranking of RATs for association.