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 @cite_15 @cite_33 @cite_13 @cite_21 partition the UEs based on the MISs of the interference graph, which is more efficient, because we cannot add any more UEs to an MIS without creating strong interference. However, they are still inefficient compared to our proposed policies for delay-sensitive applications. Specifically, they schedule different MISs in a cyclic and (weighted) round-robin manner, in which each UE transmits at a fixed position in each cycle. For delay-sensitive applications, earlier positions in the cycle are more desirable because they enhance the chances of transmitting prior to delay deadlines. Hence, a cyclic schedule is not fair to the UEs allocated to later positions. In contrast, our proposed policies schedule the MISs in an efficient, nonstationary manner for delay-sensitive applications.
@cite_15 @cite_21 @cite_13 @cite_33
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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 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. 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. 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. An uplink multisecondary user (SU) cognitive radio system having average delay constraints as well as an interference constraint to the primary user (PU) is considered. If the interference channels between the SUs and the PU are statistically heterogeneous due to the different physical locations of the different SUs, the SUs will experience different delay performances. This is because SUs located closer to the PU transmit with lower power levels. Two dynamic scheduling-and-power-allocation policies that can provide the required average delay guarantees to all SUs irrespective of their locations are proposed. The first policy solves the problem when the interference constraint is an instantaneous one, while the second is for problems with long-term average interference constraints. We show that although the average interference problem is an extension to the instantaneous interference one, the solution is totally different. The two policies, derived using the Lyapunov optimization technique, are shown to be asymptotically delay optimal while satisfying the delay and interference constraints. Our findings are supported by extensive system simulations and shown to outperform the existing policies as well as shown to be robust to channel estimation errors. We consider the question of obtaining tight delay guarantees for throughout-optimal link scheduling in arbitrary topology wireless ad-hoc networks. We consider two classes of scheduling policies: 1) a maximum queue-length weighted independent set scheduling policy, and 2) a randomized independent set scheduling policy where the independent set scheduling probabilities are selected optimally. Both policies stabilize all queues for any set of feasible packet arrival rates, and are therefore throughput-optimal. For these policies and i.i.d. packet arrivals, we show that the average packet delay is bounded by a constant that depends on the chromatic number of the interference graph, and the overall load on the network. We also prove that this upper bound is asymptotically tight in the sense that there exist classes of topologies where the expected delay attained by any scheduling policy is lower bounded by the same constant. Through simulations we examine the scaling of the average packet delay with respect to the overall load on the network, and the chromatic number of the link interference graph. Providing packet-level quality of service (QoS) is critical to support both rate-sensitive and delay-sensitive applications in bandwidth-constrained, shared-channel, multihop wireless networks. Packet scheduling has been a very popular paradigm to ensure minimum throughput and bounded delay access for packet flows. This work describes a packet scheduling approach to QoS provisioning in multihop wireless networks. Besides minimum throughput and delay bounds for each flow, our scheduling disciplines seek to achieve fair and maximum allocation of the shared wireless channel bandwidth. However, these two criteria can potentially be in conflict in a generictopology multihop wireless network where a single logical channel is shared among multiple contending flows and spatial reuse of the channel bandwidth is possible. In this paper, we propose a new scheduling model that addresses this conflict. The main results of this paper are the following: (a) a two-tier service model that provides a minimum "fair" allocation of the channel bandwidth for each packet flow and additionally maximizes spatial reuse of bandwidth, (b) an ideal centralized packet scheduling algorithm that realizes the above service model, and (c) a practical distributed backoff-based channel contention mechanism that approximates the ideal service within the framework of the CSMA CA protocol. Traditionally, scheduling policies have been optimized to perform well on metrics such as throughput, delay and fairness. In the context of shared event schedulers, where a common processor is shared among multiple users, one also has to consider the privacy offered by the scheduling policy. The privacy offered by a scheduling policy measures how much information about the usage pattern of one user of the system can be learnt by another as a consequence of sharing the scheduler. In [1], we introduced an estimation error based metric to quantify this privacy. We showed that the most commonly deployed scheduling policy, the first-come-first-served (FCFS) offers very little privacy to its users. We also proposed a parametric non-work-conserving policy which traded off delay for improved privacy. In this work, we ask the question, is a trade-off between delay and privacy fundamental to the design to scheduling policies? In particular, is there a work-conserving, possibly randomized, scheduling policy that scores high on the privacy metric? Answering the first question, we show that there does exist a fundamental limit on the privacy performance of a work-conserving scheduling policy. We quantify this limit. Furthermore, answering the second question, we demonstrate that the round-robin scheduling policy (a deterministic policy) is privacy optimal within the class of work-conserving policies. Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC. We analyze the delay performance of a multi-hop wireless network with a fixed route between each source-destination pair. There are arbitrary interference constraints on the set of links that can be served simultaneously at any given time. These interference constraints impose a fundamental lower bound on the delay performance of any scheduling policy for the system. We present a methodology to derive such lower bounds. For the tandem queue network, where the delay optimal policy is known, the expected delay of the optimal policy numerically coincides with the lower bound. We conduct extensive numerical studies to suggest that the average delay of the back-pressure scheduling policy can be made close to the lower bound by using appropriate functions of queue length. Delay-sensitive traffic services, such as live streaming video, voice over IP and multimedia teleconferencing, requires low end-to-end delay in order to maintain its interactive and streaming nature. In recent years, the popularity of delay-sensitive applications has been rapidly growing. In this study, the authors consider the delay-sensitive user scheduling and power control design for heterogeneous networks (HetNets). They model the problem as an infinite-horizon average cost Markov decision process MDP. Based on the dynamics of channel state information (CSI) and queue state information (QSI), they focus on finding an adaptive user scheduling and power control policy to minimise the average delay of network subjected to the power constraints of heterogeneous base stations. In order to reduce the computational complexity and facilitate the distributive implementation, the authors derive a distributive stochastic learning algorithm which only requires local CSI and local QSI to determine the optimal user scheduling and power control policy. The simulation results confirm that significant delay performance gain can be achieved compared with various baseline schemes. We consider the question of obtaining tight delay guarantees for throughout-optimal link scheduling in arbitrary topology wireless ad-hoc networks. Two classes of scheduling policies are considered: 1) a maximum queue-length weighted independent set scheduling policy and 2) a randomized independent set scheduling policy where the set scheduling probabilities are selected optimally. Both policies stabilize all queues for any set of feasible packet arrival rates, and are therefore throughput-optimal. For these policies, we show that the average packet delay is bounded by a constant that depends on the chromatic number of the interference graph, and the arrival slack in the system-a metric representing the overall load on the network. We prove that this upper bound is asymptotically tight in the sense that there exist classes of topologies where the expected delay attained by any scheduling policy is lower bounded by the same constant. We extend our upper bounds to the case of multi-hop sessions. Through simulations, we study how our analysis compares with actual delays computed for i.i.d., Markovian, and trace-driven packet arrival processes. In this paper, we introduce an approach for application-aware resource block scheduling of elastic and inelastic adaptive real-time traffic in fourth generation Long Term Evolution (LTE) systems. The users are assigned to resource blocks. A transmission may use multiple resource blocks scheduled over frequency and time. 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 an optimal 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. Every user subscribing for the mobile service should have a minimum quality-of-service (QoS) with a priority criterion. We prove that our scheduling policy exists and achieves the maximum. Therefore the optimal solution is tractable. We present a centralized scheduling algorithm to allocate evolved NodeB (eNodeB) resources optimally with a priority criterion. Finally, we present simulation results for the performance of our scheduling algorithm and compare our results with conventional proportional fairness approaches. The results show that the user satisfaction is higher with our proposed method. An important issue in IP-based QoS networks is the effective management of packets at the router level. Specifically, if the arriving packets cannot all be stored in a buffer, or if the packets have deadlines by which they must be delivered, the router needs to identify the packets that should be dropped. In recent work, [6] propose a model, called buffer management with bounded delay, which can be thought of as an online scheduling problem on a single machine: packets arrive at a network switch and are stored in a buffer of size B. Each packet has a positive weight and a deadline, with the weight representing the value of transmitting the packet by its deadline. At each integer time step, exactly one packet can be transmitted, and the objective is to maximize the total weight of the transmitted packets. If B = ∞, this is the online version of the scheduling problem 1| p j = 1, r j , d j |Σ w j U j . (We assume that r j and d j are integers.) In this paper, we consider CSMA policies for scheduling packet transmissions in multihop wireless networks with one-hop traffic. The main contribution of the paper is to propose a novel CSMA policy, called Unlocking CSMA (U-CSMA), that enables to obtain both high throughput and low packet delays in large wireless networks. More precisely, we show that for torus interference graph topologies with one-hop traffic, U-CSMA is throughput optimal and achieves order-optimal delay. For one-hop traffic, the delay performance is defined to be order-optimal if the delay stays bounded as the network-size increases. Simulations that we conducted suggest that (a) U-CSMA is throughput-optimal and achieves order-optimal delay for general geometric interference graphs and (b) that U-CSMA can be combined with congestion control algorithms to maximize the network-wide utility and obtain order-optimal delay. To the best of our knowledge, this is the first time that a simple distributed scheduling policy has been proposed that is both throughput utility optimal and achieves order-optimal delay. We consider a communication network and study the problem of designing a high-throughput and low-delay scheduling policy that only requires a polynomial amount of computation at each time step. The well-known maximum weight scheduling policy, proposed by Tassiulas and Ephremides (1992), has favorable performance in terms of throughput and delay but, for general networks, it can be computationally very expensive. A related randomized policy proposed by Tassiulas (1998) provides maximal throughput with only a small amount of computation per step, but seems to induce exponentially large average delay. These considerations raise some natural questions. Is it possible to design a policy with low complexity, high throughput, and low delay for a general network? Does Tassiulas' randomized policy result in low average delay? In this paper, we answer both of these questions negatively. We consider a wireless network operating under two alternative interference models: (a) a combinatorial model involving independent set constraints and (b) the standard SINR (signal to interference noise ratio) model. We show that unless NP ⊆ BPP (or P = NP for the case of determistic arrivals and deterministic policies), and even if the required throughput is a very small fraction of the network's capacity, there does not exist a low-delay policy whose computation per time step scales polynomially with the number of queues. In particular, the average delay of Tassiulas' randomized algorithm must grow super-polynomially. To establish our results, we employ a clever graph transformation introduced by Lund and Yannakakis (1994). We consider packet scheduling in a network providing differentiated services, where each packet is assigned a value. We study various queueing models for supporting QoS (Quality of Service). In the nonpreemptive model, packets accepted to the queue will be transmitted eventually and cannot be dropped. The FIFO preemptive model allows packets accepted to the queue to be preempted (dropped) prior to their departure, while ensuring that transmitted packets are sent in the order of arrival. In the bounded delay model, packets must be transmitted before a certain deadline, otherwise it is lost (while transmission ordering is allowed to be arbitrary). In all models the goal of the buffer policy is to maximize the total value of the accepted packets.Let α be the ratio between the maximal and minimal value. For the non-preemptive model we derive a Θ(log &alpha) competitive ratio, both exhibiting a buffer policy and a general lower bound. For the interesting case of two distinct values, we give an 2α--1 α competitive buffer policy, which exactly matches the lower bound. We also analyze a RED-like policy and derive its competitive ratio, which is approximately 2α--0.5 α for two values and Θ(log α) for multiple values. In addition we improve the previous known lower and upper bounds of the Fixed Partition and Flexible Partition policies.For the FIFO preemptive model, we improve the general lower bound and show a tight bound for the special case of queue size 2. We prove that the bounded delay model with uniform delay 2 is equivalent to a modified FIFO preemptive model with queue size 2. We then give improved upper and lower bounds on the 2-uniform bounded delay model. We also give lower bound for the 2-variable bounded delay model, which matches the previously known upper bound. Next-generation cellular wireless communication networks aim to provide a variety of quality-of-service (QoS)-sensitive packet-based services to downlink users. Included among these are real-time multimedia services, which have stringent delay requirements. Downlink packet scheduling at the base station plays a key role in efficiently allocating system resources to meet the desired level of QoS for various users. In this paper, we employ dynamic programming (DP) to study the design of a downlink packet scheduler capable of supporting real-time multimedia applications. Under well-justified modeling reductions, we extensively characterize structural properties of the optimal control associated with the DP problem. We leverage intuition gained from these properties to propose a heuristic scheduling policy, namely, Channel-Aware Earliest Due Date (CA-EDD), which is based on a "quasi- static" approach to scheduling. The per-time-slot implementation complexity of CA-EDD is only O(K) for a system with K downlink users. Experimental results show that CA-EDD delivers up to 50 percent of performance gains over benchmark schedulers. CA-EDD achieves these performance gains by using channel and deadline information in conjunction with application layer information (relative importance of packets) in a systematic and unified way for scheduling. The Internet-of-Things (IoT) is promising to inter-connect physical objects and machines in an intelligent network. In this context, scalable and resilient Machine-to-Machine (M2M) communication protocols are required, and the new Device-to-Device (D2D) feature of the 4G system constitutes an appealing solution. Standardization activities are still in progress, but the rapid diffusion of both LTE and IoT devices and applications leads to development of experimental proposals aimed at merging these two technologies. We contribute in this work with a novel D2D communication protocol, which enables user equipments (UEs) to become the hubs of machine-type traffic by means of a suitable radio interface and upload data in the Web via the mobile network, thus opening to new cloud services. The protocol we propose is designed for multi-hop communications between D2D-enabled terminals and it is equipped with a light-weight security mechanism. It is meant to address the communication needs of UEs inside mobile network coverage, and those of UEs that suffer from scarce radio coverage. In terms of performance, we analyse connectivity and security in the multi-hop D2D network, taking into account the interference created by the transmission of beacon signals during the discovery phase of the wide network. We resort to stochastic geometry to model the end-to-end delay and communication resilience of the multi-hop D2D network. This paper presents a dangerous low-cost traffic analysis attack in packet-based networks, such as the Internet. The attack is mountable in any scenario where a shared routing resource exists among users. A real-world attack successfully compromised the privacy of a user without requiring significant resources in terms of access, memory, or computational power. The effectiveness of our attack is demonstrated in a scenario where the user's DSL router uses FCFS scheduling policy. Specifically, we show that by using a low-rate sequence of probes, a remote attacker can obtain significant traffic-timing and volume information about a particular user, just by observing the round trip time of the probes. We also observe that even when the scheduling policy is changed to round-robin, while the correlation reduces significantly, the attacker can still reliably deduce user's traffic pattern. Most of the router scheduling policies designed to date are evaluated mostly on the metrics of throughput, delay and fairness. Our work is aimed to demonstrate a need for considering an additional metric that quantifies the information leak between the individual traffic flows through the router.