Managing interference in a network of macrocells underlaid with femtocells presents an important, yet challenging problem. A majority of spatial (frequency time) reuse based approaches partition the users based on coloring the interference graph, which is shown to be suboptimal. Some spatial time reuse based approaches schedule the maximal independent sets (MISs) in a cyclic, (weighted) round-robin fashion, which is inefficient for delay-sensitive applications. Our proposed policies schedule the MISs in a non-cyclic fashion, which aim to optimize any given network performance criterion for delay-sensitive applications while fulfilling minimum throughput requirements of the users. Importantly, we do not take the interference graph as given as in existing works; we propose an optimal construction of the interference graph. We prove that under certain conditions, the proposed policy achieves the optimal network performance. For large networks, we propose a low-complexity algorithm for computing the proposed policy. We show that the policy computed achieves a constant competitive ratio (with respect to the optimal network performance), which is independent of the network size, under wide range of deployment scenarios. The policy can be implemented in a decentralized manner by the users. Compared to the existing policies, our proposed policies can achieve improvement of up to 130 in large-scale deployments.
Some policies based on spatial time reuse, partition the UEs based on the coloring of the interference graph h @cite_30 @cite_29 @cite_0 @cite_40 @cite_41 , which is not efficient. In general, a set of UEs with the same color (i.e. the UEs who can transmit simultaneously) may not be maximal (See Fig. 1 a), in the sense that there may be UEs who do not interfere but have different colors (we will also show this in the motivating example in Subsection ). In this case, it is more efficient to also let those non-interfering UEs to transmit simultaneously, although they have different colors. Hence, the partitioning based on coloring the interference graph is not efficient, because the average number of active UEs (i.e. the average cardinality of the subsets of UEs with the same color) is low.
@cite_30 @cite_41 @cite_29 @cite_0 @cite_40
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We address interference avoidance by resource partitioning in densely deployed femtocell networks. The main objective is to protect user equipments (UEs) that are located near the cell boundary of two or more femtocells from detrimental downlink interference. The available frequency bands are divided into subbands that are distributed among femtocells in a way that directly adjacent cells do not occupy the same subbands. For this purpose, a novel centrally controlled resource partitioning method is developed based on graph coloring that assigns subbands in terms of resource efficiency. The proposed algorithm strikes a balance between interference protection and spatial frequency reuse of subbands, and is well suited for randomly deployed wireless networks. System-level simulations reveal that cell edge capacities are significantly boosted without causing a degradation in average system throughput. Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρ o . Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρ o . We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs P u , and ρ o . That is, when P u is a tight operational constraint with respect to (w.r.t.) λ and ρ o , the uplink outage probability and spectral efficiency highly depend on the values of λ and ρ o . In this case, there exists an optimal cutoff threshold ρ o *, which depends on the system parameters, that minimizes the outage probability. On the other hand, when P u is not a binding operational constraint w.r.t. λ and ρ o , the uplink outage probability and spectral efficiency become independent of λ and ρ o . We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with MEC. In this integrated framework, we formulate the computation offloading decision, physical resource block (PRB) allocation, and MEC computation resource allocation as optimization problems. The MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using the graph coloring method. The outcomes of the offloading decision and PRB allocation are then used to distribute the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters. The massive multiple-input multiple-output (MIMO) technology has great potential to manage the rapid growth of wireless data traffic. Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing many tens of user equipments (UEs). These gains are only achieved in practice if many more UEs can connect efficiently to the network than today. As the number of UEs increases, while each UE intermittently accesses the network, the random access functionality becomes essential to share the limited number of pilots among the UEs. In this paper, we revisit the random access problem in the Massive MIMO context and develop a reengineered protocol, termed strongest-user collision resolution (SUCRe). An accessing UE asks for a dedicated pilot by sending an uncoordinated random access pilot, with a risk that other UEs send the same pilot. The favorable propagation of massive MIMO channels is utilized to enable distributed collision detection at each UE, thereby determining the strength of the contenders’ signals and deciding to repeat the pilot if the UE judges that its signal at the receiver is the strongest. The SUCRe protocol resolves the vast majority of all pilot collisions in crowded urban scenarios and continues to admit UEs efficiently in overloaded networks. In this paper, we consider resource allocation optimization problem in cellular networks for different types of users running multiple applications simultaneously. In our proposed model, each user application is assigned a utility function that represents the application type running on the user equipment (UE). The network operators assign a subscription weight to each UE based on its subscription. Each UE assigns an application weight to each of its applications based on the instantaneous usage percentage of the application. Additionally, UEs with higher priority assign applications target rates to their applications. Our objective is to allocate the resources optimally among the UEs and their applications from a single evolved node B (eNodeB) based on a utility proportional fairness policy with priority to realtime application users. A minimum quality of service (QoS) is guaranteed to each UE application based on the UE subscription weight, the UE application weight and the UE application target rate. We propose a two-stage rate allocation algorithm to allocate the eNodeB resources among users and their applications. Finally, we present simulation results for the performance of our rate allocation algorithm. A recent way to model and analyze downlink cellular networks is by using random spatial models. Assuming user equipment (UE) distribution to be uniform, the analysis is performed at a typical UE located at the origin. At least one shortcoming of this approach is its inability to model non-uniform UE distributions, especially when there is dependence in the UE and the base station (BS) locations. To facilitate analysis in such cases, we propose a new tractable method of sampling UEs by conditionally thinning the BS point process and show that the resulting framework can be used as a tractable generative model to study current capacity-centric deployments, where the UEs are more likely to lie closer to the BSs. In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration exploitation, myopic foresighted behavior and complete incomplete information. Very recent studies showed that in a fully loaded dense small cell network (SCN), the coverage probability performance will continuously decrease with the network densification. Such new results were captured in IEEE ComSoc Technology News with an alarming title of "Will Densification Be the Death of 5G?". In this paper, we revisit this issue from more practical views of realistic network deployment, such as a finite number of active base stations (BSs) and user equipments (UEs), a decreasing BS transmission power with the network densification, etc. Particularly, in dense SCNs, due to an oversupply of BSs with respect to UEs, a large number of BSs can be put into idle modes without signal transmission, if there is no active UE within their coverage areas. Setting those BSs into idle modes mitigates unnecessary inter-cell interference and reduces energy consumption. In this paper, we investigate the performance impact of such BS idle mode capability (IMC) on dense SCNs. Different from existing work, we consider a realistic path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Moreover, we obtain analytical results for the coverage probability, the area spectral efficiency (ASE) and the energy efficiency (EE) performance for SCNs with the BS IMC and show that the performance impact of the IMC on dense SCNs is significant. As the BS density surpasses the UE density in dense SCNs, the coverage probability will continuously increase toward one, addressing previous concerns on "the death of 5G". Finally, the performance improvement in terms of the EE performance is also investigated for dense SCNs using practical energy models developed in the Green-Touch project. In a wireless powered communication network, where user equipments (UEs) harvest radio frequency energy from an access point (AP) and send data to the AP, there exists the near-far problem with respect to energy harvesting efficiency due to UEs’ random locations. In this paper, we introduce the concept of delay-aware energy balancing by minimizing the average transmission delay while taking into account the issue of unbalanced harvested energy distribution. In particular, we propose an adaptive harvest-then-cooperate protocol, where every UE first harvests the energy emitted by the AP and then sends data to the AP directly or via other UEs acting as relays in a time-division multiplexing manner. In this protocol, the AP selects the combination of transmission power and routing topology by matching load and energy distributions in the network while minimizing the average transmission delay. Furthermore, we develop a method generating scheduling schemes for this protocol to avoid data overflow in the UE relay. To determine the combination with minimum delay, we approximate the average delay as a Markov decision process and propose a low-complexity sample path-based algorithm to obtain a near-optimal solution. Simulation results demonstrate that the proposed protocol is able to balance the energy distribution while minimizing the transmission delay. Anticipating multimedia file requests via caching at the small cell base stations (SBSs) of a cellular network has emerged as a promising technique for optimizing the quality of service (QoS) of wireless user equipments (UEs). However, developing efficient caching strategies must properly account for specific small cell constraints, such as backhaul congestion and limited storage capacity. In this paper, we address the problem of devising a usercell association, in which the SBSs exploit caching capabilities to overcome the backhaul capacity limitations and enhance the users’ QoS. In the proposed approach, the SBSs individually decide on which UEs to service based on both content availability and on the data rates they can deliver, given the interference and backhaul capacity limitations. We formulate the problem as a oneto-many matching game between SBSs and UEs. To solve this game, we propose a distributed algorithm, based on the deferred acceptance scheme, that enables the players (i.e., UEs and SBSs) to self-organize into a stable matching, in a reasonable number of algorithm iterations. Simulation results show that the proposed cell association scheme yields significant gains, reaching up to 21 improvement compared to a traditional cell association techniques with no caching considerations. In commercial-off-the-shelf (COTS) multi-core systems, the execution times of tasks become hard to predict because of contention on shared resources in the memory hierarchy. In particular, a task running in one processor core can delay the execution of another task running in another processor core. This is due to the fact that tasks can access data in the same cache set shared among processor cores or in the same memory bank in the DRAM memory (or both). Such cache and bank interference effects have motivated the need to create isolation mechanisms for resources accessed by more than one task. One popular isolation mechanism is cache coloring that divides the cache into multiple partitions. With cache coloring, each task can be assigned exclusive cache partitions, thereby preventing cache interference from other tasks. Similarly, bank coloring allows assigning exclusive bank partitions to tasks. While cache coloring and some bank coloring mechanisms have been studied separately, interactions between the two schemes have not been studied. Specifically, while memory accesses to two different bank colors do not interfere with each other at the bank level, they may interact at the cache level. Similarly, two different cache colors avoid cache interference but may not prevent bank interference. Therefore it is necessary to coordinate cache and bank coloring approaches. In this paper, we present a coordinated cache and bank coloring scheme that is designed to prevent cache and bank interference simultaneously. We also developed color allocation algorithms for configuring a virtual memory system to support our scheme which has been implemented in the Linux kernel. In our experiments, we observed that the execution time can increase by 60 due to inter-task interference when we use only cache coloring. Our coordinated approach can reduce this figure down to 12 (an 80 reduction). There is an increasing interest in a new machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), and each UE contributes to the learning model by independently computing the gradient based on its local training data. Federated Learning has several benefits of data privacy and potentially a large amount of UE participants with modern powerful processors and low-delay mobile-edge networks. While most of the existing work focused on designing learning algorithms with provable convergence time, other issues such as uncertainty of wireless channels and UEs with heterogeneous power constraints and local data size, are under-explored. These issues especially affect to various trade-offs: (i) between computation and communication latencies determined by learning accuracy level, and thus (ii) between the Federated Learning time and UE energy consumption. We fill this gap by formulating a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs. Even though FEDL is non-convex, we exploit the problem structure to decompose and transform it to three convex sub-problems. We also obtain the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDL learning time, accuracy level, and UE energy cost. Our theoretical analysis is also illustrated by extensive numerical results. For an arbitrarily-located user terminal (UE) in a multi-tier heterogeneous cellular wireless network, the joint distribution of the downlink SINR at the UE from the candidate serving base stations (BSs) in the accessible tiers of the network has been derived in closed form for the cases where the candidate serving BS from each accessible tier is chosen as either the one nearest to the UE [1] or the one that is received strongest (equivalently, with maximum SINR) at the UE [2], when the locations of the BSs in the tiers are modeled by independent Poisson Point Processes, and the fading on all links is assumed independent identically distributed (iid) and Rayleigh. The actual serving BS for the UE is chosen as the nearest strongest max-SINR candidate serving BS after imposing selection bias across the tiers. The above joint distributions can be used to yield the distribution of the actual SINR at the UE (i.e., when receiving from this serving BS) when no selection bias exists across tiers. However, for the practically important case of selection bias, analytical calculation of the distribution of the actual SINR presents significant challenges. This work derives and summarizes the distribution of actual downlink SINR for all the above criteria for selection of the serving BS accounting for selection bias. We then explore some implications of these results for design and operation of a heterogeneous network. Millimeter-wave (mm-wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6-GHz band. Communication at mm-waves is, however, quite challenging due to the severe propagation pathloss incurred by conventional isotropic antennas. To cope with this problem, directional beamforming both at the base station (BS) side and at the user equipment (UE) side is necessary in order to establish a strong path conveying enough signal power. Finding such beamforming directions is referred to as beam alignment (BA). This paper presents a new scheme for efficient BA. Our scheme finds a strong propagation path identified by an angle-of-arrival (AoA) and angle-of-departure (AoD) pair, by exploring the AoA–AoD domain through pseudo-random multi-finger beam patterns and constructing an estimate of the resulting second-order statistics (namely, the average received power for each pseudo-random beam configuration). The resulting under-determined system of equations is efficiently solved using non-negative constrained least-squares, yielding naturally a sparse non-negative vector solution whose maximum component identifies the optimal path. As a result, our scheme is highly robust to variations of the channel time dynamics compared with alternative concurrent approaches based on the estimation of the instantaneous channel coefficients, rather than of their second-order statistics. In the proposed scheme, the BS probes the channel in the downlink and trains simultaneously an arbitrarily large number of UEs. Thus, “beam refinement,” with multiple interactive rounds of downlink uplink transmissions, is not needed. This results in a scalable BA protocol, where the protocol overhead is virtually independent of the number of UEs, since all the UEs run the BA procedure at the same time. Extensive simulation results illustrate that our approach is superior to the state-of-the-art BA schemes proposed in the literature in terms of training overhead in multi-user scenarios and robustness to variations in the channel dynamics. Device to device (D2D) relaying and multi-beam reflection are two effective approaches to deal with the blockage problem in millimeter-wave (mmWave) communication, each with its own limitations when serving a large number of user equipments (UE). A combination of D2D relaying and multibeam reflection is expected to enhance the performance, but the selection of UEs to be served by each approache remains a challenge. In this paper, we consider adaptive mode selection between D2D relaying and multi-beam reflection in a time division duplex (TDD) mmWave network. We formulate a joint mode selection and resource sharing problem with the objective of maximizing the sum logarithm rate, and propose a two-stage solution algorithm. In the first stage, we derive the optimal resource sharing solution under the case that all UEs are served by D2D relaying. In the second stage, an adaptive algorithm is proposed to determine the set of UEs that switch from D2D relaying to multi-beam reflection. Simulation results demonstrate that the proposed scheme achieves considerable performance gain compared to several benchmark schemes. The notions of user equilibrium (UE) and system optimum (SO) often allude to the literature together with the well-known principle of marginal-cost pricing in traffic network analyses. This pricing principle states that the UE flow pattern on a network can be driven to an SO in the sense of total travel cost minimization by charging a toll on each link equal to the difference between marginal social cost and marginal private cost. In reality, users do not always behave in a UE manner, typically when there exist oligopoly Cournot-Nash (CN) firms. Users in a CN firm cooperate among themselves to minimize total cost of the firm and compete against others. In the presence of such UE-CN mixed equilibrium behaviors, we are interested in whether an SO flow pattern remains attainable by meaningful link tolls. In this paper we show that in a network with both UE and CN users, applying the traditional marginal-cost pricing for a system optimum requires that link tolls be differentiated across user classes. Because users differ from one another in an unobservable way, it is impossible to introduce discriminatory tolling on a network in a mixed behaviour equilibrium. We then seek alternative meaningful tolls by establishing the existence of nonnegative anonymous link tolls to decentralize the SO into a UE-CN mixed behavior equilibrium with resort to a rigorous mathematical programming approach. In this paper, we consider resource allocation optimization problem in fourth generation long term evolution (4G-LTE) with users running multiple applications. Each mobile user can run both delay-tolerant and real-time applications. In every user equipment (UE), each application has a application-status differentiation from other applications depending on its instantaneous usage percentage. In addition, the network operators provide subscriber differentiation by assigning each UE a subscription weight relative to its subscription. The objective is to optimally allocate the resources with a utility proportional fairness policy. We propose an algorithm to allocate the resources in two-stages. In the first-stage, the UEs collaborate with the evolved node B (eNodeB) that allocates the optimal rates to users according to that policy. In the second-stage, each user allocates its assigned rate internally to its applications according to their usage percentage. We prove that the two-stage resource allocation algorithm allocates the optimal rates without eNodeB knowledge of the UEs utilities. Finally, numerical results on the performance of the proposed algorithm are presented. In this paper, we present an efficient resource allocation with user discrimination framework for 5G Wireless Systems to allocate multiple carriers resources among users with elastic and inelastic traffic. Each application running on the user equipment (UE) is assigned an application utility function. In the proposed model, different classes of user groups are considered and users are partitioned into different groups based on the carriers coverage area. Each user has a minimum required application rate based on its class and the type of its application. Our objective is to allocate multiple carriers resources optimally among users, that belong to different classes, located within the carriers’ coverage area. We use a utility proportional fairness approach in the utility percentage of the application running on the UE. Each user is guaranteed a minimum quality of service with a priority criterion that is based on user’s class and the type of application running on the UE. In addition, we prove the existence of optimal solutions for the proposed resource allocation optimization problem and present a multi-carrier resource allocation with user discrimination algorithm. Finally, we present simulation results for the performance of the proposed algorithm. Device-to-Device (D2D) communication is an important feature for many kinds of mobile networks and in particular for the UMTS Long Term Evolution Advanced (LTE-A) cellular technology, where an underlay network can be created between User Equipments (UEs) without the support of an evolved Node B (eNB). The possibility to allow direct links between UEs plays an important role when the commercial LTE infrastructure is subject to failures or becomes unavailable after a disaster. Project ABSOLUTE, in scenarios for public safety, proposes raising in the sky a balloon with attached a 4G eNB (AeNB) to restore temporarily connectivity. In all cases in which UEs are out-of-coverage of the AeNB like in indoors, D2D becomes crucial. In this work, we propose to establish direct links employing a communication protocol that relies on the transmission of beacon frames sent in broadcast mode by selected UEs. We delve security aspects of this D2D protocol suitable for Public Safety (PS) users with out-of-coverage UEs based on sharing encryption keys. We pursue a network connectivity analysis of the secure protocol whereby we are able to show the existence of tradeoff points between connectivity and the increased overhead added by security for different values of the system parameters. Graph coloring problems, in which one would like to color the vertices of a given graph with a small number of colors so that no two adjacent vertices receive the same color, arise in many applications, including various scheduling and partitioning problems. In this paper the complexity and performance of algorithms which construct such colorings are investigated. For a graph G , let k( G ) denote the minimum possible number of colors required to color G and, for any graph coloring algorithm A , let A ( G ) denote the number of colors used by A when applied to G . Since the graph coloring problem is known to be “NP-complete,” it is considered unlikely that any efficient algorithm can guarantee A ( G ) = k( G ) for all input graphs. In this paper it is proved that even coming close to khgr;( G ) with a fast algorithm is hard. Specifically, it is shown that if for some constant r d there exists a polynomial-time algorithm A which guarantees A ( G ) ≤ r ·k( G ) + d , then there also exists a polynomial-time algorithm A which guarantees A ( G ) = k( G ).