Heterogeneity in wireless network architectures (i.e., the coexistence of 3G, LTE, 5G, WiFi, etc.) has become a key component of current and future generation cellular networks. Simultaneous aggregation of each client's traffic across multiple such radio access technologies (RATs) base stations (BSs) can significantly increase the system throughput, and has become an important feature of cellular standards on multi-RAT integration. Distributed algorithms that can realize the full potential of this aggregation are thus of great importance to operators. In this paper, we study the problem of resource allocation for multi-RAT traffic aggregation in HetNets (heterogeneous networks). Our goal is to ensure that the resources at each BS are allocated so that the aggregate throughput achieved by each client across its RATs satisfies a proportional fairness (PF) criterion. In particular, we provide a simple distributed algorithm for resource allocation at each BS that extends the PF allocation algorithm for a single BS. Despite its simplicity and lack of coordination across the BSs, we show that our algorithm converges to the desired PF solution and provide (tight) bounds on its convergence speed. We also study the characteristics of the optimal solution and use its properties to prove the optimality of our algorithm's outcomes.
Single-RAT Multi-BS Communication. Prior works have studied the problem of traffic aggregation when a client can simultaneously communicate with multiple same technology BSs. For example, @cite_26 uses game theory to model selfish traffic splitting by each client in WLANs. On the other hand, the resource allocation problem in HetNets is primarily addressed at the BS side. Similarly, @cite_17 proposes an approximation algorithm to address the problem of client association and traffic splitting in LTE DC. Our algorithm (AFRA) goes beyond this and other related work by guaranteeing optimal resource allocation for any number of RATs and BSs . Other works have developed centralized client association algorithms to achieve max-min @cite_21 and proportional fairness @cite_2 in multi-rate WLANs. In contrast, the problem of resource allocation in HetNets needs to be solved in a fully distributed manner.
@cite_26 @cite_2 @cite_21 @cite_17
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Massive multiple-input–multiple-output (MIMO) and small cell are both recognized as key technologies for the future fifth-generation wireless systems. In this paper, we investigate the problem of user association in a heterogeneous network (HetNet) with massive MIMO and small cells, where the macro base station (BS) is equipped with a massive MIMO, and the picocell BSs are equipped with regular MIMOs. We first develop centralized user association algorithms with proven optimality, considering various objectives such as rate maximization, proportional fairness, and joint user association and resource allocation. We then develop a repeated game model, which leads to distributed user association algorithms with proven convergence to the Nash equilibrium. We demonstrate the efficacy of these optimal schemes by comparing with several greedy algorithms through simulations. We consider the problem of LTE network self organization and optimization of resource allocation. One particular challenge for LTE systems is that, by applying OFDMA, a transmission may use multiple resource blocks scheduled over the frequency and time. There are three key components involved in the resource allocation and network optimization: resource block scheduling, power control, and client association. We propose a distributed protocol that aims to achieve weighted proportional fairness (WPF) among clients by jointly consider them. The cross-layer design includes: (i) an optimal online policy for resource block scheduling, (ii) a heuristic for transmit power control, and (iii) a selfish strategy for client association. The proposed scheme only requires limited local information exchange and thus can be easily implemented for large networks. Simulation results have shown its effectiveness in both the system throughput and user fairness. Millimeter-wave communications in the 60-GHz band are considered one of the key technologies for enabling multigigabit wireless access. However, the special characteristics of such a band pose major obstacles to the optimal utilization of the wireless resources, where the problem of efficient client association to access points (APs) is of vital importance. In this paper, the client association in 60-GHz wireless access networks is investigated. The AP utilization and the quality of the rapidly vanishing communication links are the control parameters. Because of the tricky nonconvex and combinatorial nature of the client association optimization problem, a novel solution method is developed to guarantee balanced and fair resource allocation. A new distributed, lightweight, and easy-to-implement association algorithm, based on Lagrangian duality theory and subgradient methods, is proposed. It is shown that the algorithm is asymptotically optimal, that is, the relative duality gap diminishes to zero as the number of clients increases . In this paper, we consider resource allocation optimization problem in the fourth generation long-term evolution (4G-LTE) with elastic and inelastic real-time traffic. Mobile users are running either delay-tolerant or real-time applications. The users applications are approximated by logarithmic or sigmoidal-like utility functions. Our objective is to allocate resources according to the utility proportional fairness policy. Prior utility proportional fairness resource allocation algorithms fail to converge for high-traffic situations. We present a robust algorithm that solves the drawbacks in prior algorithms for the utility proportional fairness policy. Our robust optimal algorithm allocates the optimal rates for both high-traffic and low-traffic situations. It prevents fluctuation in the resource allocation process. In addition, we show that our algorithm provides traffic-dependent pricing for network providers. This pricing could be used to flatten the network traffic and decrease the cost per bandwidth for the users. Finally, numerical results are presented on the performance of the proposed algorithm. A challenging problem in multi-band multi-cell self-organized wireless systems, such as multi-channel Wi-Fi networks, femto pico cells in 3G 4G cellular networks, and more recent wireless networks over TV white spaces, is of distributed resource allocation. This involves four components: channel selection, client association, channel access, and client scheduling. In this paper, we present a unified framework for jointly addressing the four components with the global system objective of maximizing the clients throughput in a proportionally fair manner. Our formulation allows a natural dissociation of the problem into two sub-parts. We show that the first part, involving channel access and client scheduling, is convex and derive a distributed adaptation procedure for achieving Pareto-optimal solution. For the second part, involving channel selection and client association, we develop a Gibbs-sampler based approach for local adaptation to achieve the global objective, as well as derive fast greedy algorithms from it that achieve good solutions. Spectrum sharing is a promising solution for the problem of spectrum congestion. We consider a spectrum sharing scenario between a multiple-input multiple-output (MIMO) radar and Long Term Evolution (LTE) Advanced cellular system. In this paper, we consider resource allocation optimization problem with carrier aggregation. The LTE Advanced system has N BS base stations (BS) which it operates in the radar band on a sharing basis. Our objective is to allocate resources from the LTE Advanced carrier and the MIMO radar carrier to each user equipment (UE) in an LTE Advanced cell based on the running application of UE. Each user application is assigned a utility function based on the type of application. We propose a carrier aggregation resource allocation algorithm to allocate the LTE Advanced and the radar carriers' resources optimally among users based on the type of user application. The algorithm gives priority to users running inelastic traffic when allocating resources. Finally we present simulation results on the performance of the proposed carrier aggregation resource allocation algorithm. Drone base stations (DBSs) can enhance network coverage and area capacity by moving supply towards demand when required. This degree of freedom could be especially useful for future applications with extreme demands, such as ultra reliable and low latency communications (uRLLC). However, deployment of DBSs can face several challenges. One issue is finding the 3D placement of such BSs to satisfy dynamic requirements of the system. Second, the availability of reliable wireless backhaul links and the related resource allocation are principal issues that should be considered. Finally, association of the users with BSs becomes an involved problem due to mobility of DBSs. In this paper, we consider a macro-BS (MBS) and several DBSs that rely on the wireless links to the MBS for backhauling. Considering regular and uRLLC users, we propose an algorithm to find efficient 3D locations of DBSs in addition to the user-BS associations and wireless backhaul bandwidth allocations to maximize the sum logarithmic rate of the users. To this end, a decomposition method is employed to first find the user-BS association and bandwidth allocations. Then DBS locations are updated using a heuristic particle swarm optimization algorithm. Simulation results show the effectiveness of the proposed method and provide useful insights on the effects of traffic distributions and antenna beamwidth. Long Term Evolution-Advanced (LTE-A) networks exploit the Carrier Aggregation (CA) technique to achieve a higher data rate by allowing user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs). Moreover, MIMO technologies have become increasingly mature and been adopted as a default choice of the 4G standards. However, most of existing studies on resource allocation with carrier aggregation do not consider the MIMO capability of UEs. In this paper, we address the spectrum resource allocation problem with consideration of UEs' MIMO capability as well as modulation and coding schemes (MCSs) selection in carrier aggregation based LTE-A systems. We formulate the problem under both backlogged and finite queue traffic models as an optimization model, and prove its NP-hardness. As a result, a 1 2-approximation algorithm is proposed to find a suboptimal solution of resource allocation. Simulation results show that the proposed algorithm outperforms the existing schemes, and performs fairly close to the optimal solution under the small-scale scenarios. We consider network utility maximization problems over heterogeneous cellular networks (HetNets) that permit dual connectivity. Dual connectivity (DC) is a feature that targets emerging practical HetNet deployments that will comprise of non-ideal (higher latency) connections between transmission nodes, and has been recently introduced to the LTE-Advanced standard. DC allows for a user to be simultaneously served by a macro node as well as one other (typically micro or pico) node and requires relatively coarser level coordination among serving nodes. For such a DC enabled HetNet we comprehensively analyze the problem of determining an optimal user association that maximizes the weighted sum rate system utility subject to per-user rate constraints, over all feasible associations. Here, in any feasible association each user can be associated with (i.e., configured to receive data from) any one macro node (in a given set of macro nodes) and any one pico node that lies in the chosen macro node's coverage area. We show that, remarkably, this problem can be cast as a non-monotone submodular set function maximization problem, which allows us to construct a constant-factor approximation algorithm. We then consider the proportional fairness (PF) system utility and characterize the PF optimal resource allocation. This enables us to construct an efficient algorithm to determine an association that is optimal up-to an additive constant. We then validate the performance of our algorithms via numerical results. System bottlenecks, namely those resources which are subjected to high contention, constrain system performance. Hence effective resource management should be done by focusing on the bottleneck resources and allocating them to the most deserving clients. It has been shown that for any combination of entitlements and requests a fair allocation of bottleneck resources can be found, using an off-line algorithm that is given full information in advance regarding the needs of each client. We extend this result to the on-line case with no prior information. To this end we introduce a simple greedy algorithm. In essence, when a scheduling decision needs to be made, this algorithm selects the client that has the largest minimal gap between its entitlement and its current allocation among all the bottleneck resources. Importantly, this algorithm takes a global view of the system, and assigns each client a single priority based on his usage of all the resources; this single priority is then used to make coordinated scheduling decisions on all the resources. Extensive simulations show that this algorithm achieves fair allocations according to the desired entitlements for a wide range of conditions, without using any prior information regarding resource requirements. It also follows shifting usage patterns, including situations where the bottlenecks change with time. We consider a multi-cell wireless network with a large number of users. Each user selfishly chooses the Base Station (BS) that gives it the best throughput (utility), and each BS allocates its resource by some simple scheduling policy. First we consider two cases: (1) BS allocates the same time to its users; (2) BS allocates the same throughput to its users. It turns out that, combined with users' selfish behavior, case (1) results in a single Nash Equilibrium (NE), which achieves system-wide Proportional Fairness. On the other hand, case (2) results in many possible Nash Equilibria, some of which are very inefficient. Next, we extend (1) to the case where the users have general concave utility functions. It is shown that the if each BS performs intra- cell optimization, the total utility of all users is maximized at NE. This suggests that under our model, the task of joining the ";correct"; BS can be left to individual users, leading to a distributed solution. In a densely deployed heterogeneous network (HetNet), the number of pico micro base stations (BS) can be comparable with the number of the users. To reduce the operational overhead of the HetNet, proper identification of the set of serving BSs becomes an important design issue. In this work, we show that by jointly optimizing the transceivers and determining the active set of BSs, high system resource utilization can be achieved with only a small number of BSs. In particular, we provide formulations and efficient algorithms for such joint optimization problem, under the following two common design criteria: i) minimization of the total power consumption at the BSs, and ii) maximization of the system spectrum efficiency. In both cases, we introduce a nonsmooth regularizer to facilitate the activation of the most appropriate BSs. We illustrate the efficiency and the efficacy of the proposed algorithms via extensive numerical simulations. A regional central manager is employed to set aside, for the regional (or back-bone) network that it manages, for each flow class, communications capacity resources for a specific future time horizon. In the context of such a traffic management operation, a longer temporal scale is involved in controlling the admission and distribution of flows across the network. For management scal-ability purposes, flows are aggregated into flow classes. Furthermore, we consider a network operation under which multiple simultaneously activated routes are employed, across possibly distinct segments, to distribute traffic between identified source-destination pairs. We aim to ensure that the utility assigned to each class is as high as feasibly possible while striving to raise the utility gained by all classes in a maxmin fair manner. In doing so, we incorporate the communications capacity constraints that are imposed by the underlying hybrid of directional and or multiple-access wireline and wireless communications media employed across the network system. We develop and present in this paper an optimal algorithm for solving such a traffic management problem. It yields multi-utility-based max-min fair distributions of flow rates, per each class, across the specified multitude of simultaneously activated multi-segment routes. To guarantee that admitted flows are granted their desired capacity resources (and targeted corresponding utility levels), the selection of optimal flow distributions across the network routes is combined with the use of a flow admission control scheme that serves to optimally limit the aggregate rate of flows admitted for each flow class. As illustrative examples, we demonstrate the effectiveness of our solution in comparing its performance with that obtained under the use of a traffic regulation scheme that is not overlaid with a traffic management mechanism that serves to set aside resources for the support of flow classes. We also illustrate the use of our optimal algorithm for determining the optimal placement of unmanned aerial vehicle platforms that serve to supplement a terrestrial transport segment with a space-based one. In many IEEE 802.11 WLAN deployments, wireless clients have a choice of access points (AP) to connect to. In current systems, clients associate with the access point with the strongest signal to noise ratio. However, such an association mechanism can lead to unequal load sharing, resulting in diminished system performance. In this paper, we first provide a numerical approach based on stochastic dynamic programming to find the optimal client-AP association algorithm for a small topology consisting of two access points. Using the value iteration algorithm, we determine the optimal association rule for the two-AP topology. Next, utilizing the insights obtained from the optimal association rule for the two-AP case, we propose a near-optimal heuristic that we call RAT. We test the efficacy of RAT by considering more realistic arrival patterns and a larger topology. Our results show that RAT performs very well in these scenarios as well. Moreover, RAT lends itself to a fairly simple implementation. In multi-rate wireless LANs, throughput-based fair bandwidth allocation can lead to drastically reduced aggregate throughput. To balance aggregate throughput while serving users in a fair manner, proportional fair or time-based fair scheduling has been proposed to apply at each access point (AP). However, since a realistic deployment of wireless LANs can consist of a network of APs, this paper considers proportional fairness in this much wider setting. Our technique is to intelligently associate users with APs to achieve optimal proportional fairness in a network of APs. We propose two approximation algorithms for periodical offline optimization. Our algorithms are the first approximation algorithms in the literature with a tight worst-case guarantee for the NP-hard problem. Our simulation results demonstrate that our algorithms can obtain an aggregate throughput which can be as much as 2.3 times more than that of the max-min fair allocation in 802.11b. While maintaining aggregate throughput, our approximation algorithms outperform the default user-AP association method in the 802.11b standard significantly in terms of fairness. In this paper, we consider a resource allocation optimization problem with carrier aggregation in fourth generation long term evolution (4G-LTE). In our proposed model, each user equipment (UE) is assigned a utility function that represents the application type running on the UE. Our objective is to allocate the resources from two carriers to each user based on its application that is represented by the utility function assigned to that user. We consider two groups of users, one with elastic traffic and the other with inelastic traffic. Each user is guaranteed a minimum resource allocation. In addition, a priority resource allocation is given to the UEs running adaptive real time applications. We prove that the optimal rate allocated to each UE by the single carrier resource allocation optimization problem is equivalent to the aggregated optimal rates allocated to the same user by the primary and secondary carriers when their total resources is equivalent to the single carrier resources. Our goal is to guarantee a minimum quality of service (QoS) that varies based on the user application type. We present a carrier aggregation rate allocation algorithm to allocate two carriers resources optimally among users. Finally we present simulation results with the carrier aggregation rate allocation algorithm. The traffic load of wireless LANs is often unevenly distributed among the access points (APs), which results in unfair bandwidth allocation among users. We argue that the load imbalance and consequent unfair bandwidth allocation can be greatly reduced by intelligent association control. In this paper, we present an efficient solution to determine the user-AP associations for max-min fair bandwidth allocation. We show the strong correlation between fairness and load balancing, which enables us to use load balancing techniques for obtaining optimal max-min fair bandwidth allocation. As this problem is NP-hard, we devise algorithms that achieve constant-factor approximation. In our algorithms, we first compute a fractional association solution, in which users can be associated with multiple APs simultaneously. This solution guarantees the fairest bandwidth allocation in terms of max-min fairness. Then, by utilizing a rounding method, we obtain the integral solution from the fractional solution. We also consider time fairness and present a polynomial-time algorithm for optimal integral solution. We further extend our schemes for the on-line case where users may join and leave dynamically. Our simulations demonstrate that the proposed algorithms achieve close to optimal load balancing (i.e., max-min fairness) and they outperform commonly used heuristics. In this paper, we introduce an approach for resource allocation of elastic and inelastic adaptive real-time traffic in fourth generation long term evolution (4G-LTE) system. In our model, we use logarithmic and sigmoidal-like utility functions to represent the users applications running on different user equipments (UE)s. We present a resource allocation optimization problem with utility proportional fairness policy, where the fairness among users is in utility percentage (i.e user satisfaction with the service) of the corresponding applications. Our objective is to allocate the resources to the users with priority given to the adaptive real-time application users. In addition, a minimum resource allocation for users with elastic and inelastic traffic should be guaranteed. Our goal is that every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our resource allocation optimization problem is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our rate allocation algorithm. The basic idea of device-to-device (D2D) communication is that pairs of suitably selected wireless devices reuse the cellular spectrum to establish direct communication links, provided that the adverse effects of D2D communication on cellular users are minimized and that cellular users are given higher priority in using limited wireless resources. Despite its great potential in terms of coverage and capacity performance, implementing this new concept poses some challenges, particularly with respect to radio resource management. The main challenges arise from a strong need for distributed D2D solutions that operate in the absence of precise channel and network knowledge. To address this challenge, this paper studies a resource allocation problem in a single-cell wireless network with multiple D2D users sharing the available radio frequency channels with cellular users. We consider a realistic scenario where the base station (BS) is provided with strictly limited channel knowledge, whereas D2D and cellular users have no information. We prove a lower bound for the cellular aggregate utility in the downlink with fixed BS power, which allows for decoupling the channel allocation and D2D power control problems. An efficient graph-theoretical approach is proposed to perform channel allocation, which offers flexibility with respect to allocation criteria (aggregate utility maximization, fairness, and quality-of-service (QoS) guarantee). We model the power control problem as a multiagent learning game. We show that the game is an exact potential game with noisy rewards, which is defined on a discrete strategy set, and characterize the set of Nash equilibria. Q-learning better-reply dynamics is then used to achieve equilibrium. Cellular systems in general suffer from co-channel interference, when simultaneous transmissions in other cells use the same physical resources. In order to mitigate such co-channel interference cooperating Base Stations (BSs) can perform joint multi-antenna signal processing across cell borders. This paper describes a concept of distributed cooperation, where BSs communicate directly via a BS-BS interface without central control. A serving BS can serve its terminals on its own or it can request cooperation from one or more supporting BSs. By collecting IQ samples from the supporting BSs' antenna elements, the serving BS can virtually increase its number of receive antennas. Exchanging additional parameters allows applying advanced receiver algorithms, e.g., interference rejection or cancelation. Performance evaluations by means of simulation show the capability of BS cooperation applied to 3GPP LTE in terms of cell and user throughput but it also shows the trade-off in terms of increased backhaul requirement due to BS-BS communication.