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It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access (which we also refer to as cognitive spectrum access) networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign: 1) portions of the spectrum and 2) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this paper, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems: 1) spectrum management through power allocation with given relay selection strategy; and 2) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a newly designed centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the impact of information-theoretic secrecy constraint on the capacity and delay of mobile ad hoc networks (MANETs) with mobile legitimate nodes and static eavesdroppers whose location and channel state information (CSI) are both unknown. We assume n legitimate nodes move according to the fast i.i.d. mobility pattern and each desires to communicate with one randomly selected destination node. There are also n^\nu static eavesdroppers located uniformly in the network and we assume the number of eavesdroppers is much larger than that of legitimate nodes, i.e., \nu>1. We propose a novel simple secure communication model, i.e., the secure protocol model, and prove its equivalence to the widely accepted secure physical model under a few technical assumptions. Based on the proposed model, a framework of analyzing the secrecy capacity and delay in MANETs is established. Given a delay constraint D, we find that the optimal secrecy throughput capacity is \widetilde\Theta(W((D/n))^(2/3)), where W is the data rate of each link. We observe that: 1) the capacity-delay tradeoff is independent of the number of eavesdroppers, which indicates that adding more eavesdroppers will not degenerate the performance of the legitimate network as long as \nu>1; 2) the capacity-delay tradeoff of our paper outperforms the previous result \Theta((1/n\psie)) in refid="ref11"/, where \psie=n^{\nu-1}=\omega(1) is the density of the eavesdroppers. Throughout this paper, for functions f(n) and g(n), we denote f(n)=o(g(n)) if \limn\rightarrow\infty(f(n)/g(n))=0; f(n)=\omega(g(n)) if g(n)=o(f(n)); f(n)=O(g(n)) if there is a positive constant c such that f(n)\leq cg(n) for sufficiently large n; f(n)=\Omega(g(n)) if g(n)=O(f(n)); f(n)=\Theta(g(n)) if both f(n)=O(g(n)) and f(n)=\Omega(g(n)) hold. Besides, the order notation \widetilde\Theta omits the polylogarithmic factors for better readability. [ABSTRACT FROM AUTHOR]

The three-tier spectrum sharing framework (3-TSF) is a spectrum sharing model adopted by the Federal Communications Commission. According to this model, under-utilized federal spectrum like the Citizens Broadband Radio Service band is released for shared use where the highest preference is given to Tier-1 followed by Tier-2 (T2) and then Tier-3 (T3). In this paper, we study how a wireless operator, who is interested in maximizing its profit, can strategically operate as a T2 and/or a T3 user. T2 is characterized by paid but ”almost” guaranteed and interference-free channel access while T3 access is free but has the lesser guarantee and also faces channel interference. So the operator has to optimally decide between paid but better channel quality and free but uncertain channel quality. Also, the operator has to make these decisions without knowing future market variables like customer demand or channel availability. The main contribution of this paper is a deterministic online algorithm for leasing channels that has finite competitive ratio, low time complexity, and that does not rely on the knowledge of market statistics. Such algorithms are desirable in the early stages of the deployment of 3-TSF because the knowledge of market statistics may be rather inaccurate. We use tools from the ski-rental literature to design the online algorithm. The online optimization problem for leasing channels is a novel generalization of the ski-rental problem. We, therefore, make fundamental contributions to the ski-rental literature, the applications of which extend beyond this paper. We also conduct simulations using synthetic traces to compare our online algorithm with the benchmark and state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of spectrum sharing in mmWave cellular networks, where multiple network operators are granted access to the same spectrum resources. Prior spectrum sharing schemes for mmWave cellular networks have two limitations: high coordination overhead and high computational complexity. In this paper, we propose two spectrum sharing schemes for the mobile scenario and the stationary scenario, respectively. In the mobile scenario, we propose a Spectrum sharing scheme using Clustering Algorithms to find the Optimal Positions of Mobile BSes (SCA-OPM). In the stationary scenario, we propose a Spectrum sharing scheme using Clustering Algorithms to Select the most appropriate Subset from all BSes (SCA-SSB). Moreover, for each newly-arriving mobile terminal (MT), we propose two online spectrum sharing schemes based on our SCA-OPM scheme and SCA-SSB scheme, which effectively saves the overall overhead and complexity. Our experimental results show that the SCA-OPM scheme has the best performance, while the SCA-SSB scheme has the same performance as that of the prior scheme but with lower overhead and lower complexity. When the MT density is 200 MTs/km2, for the sum-rate with 10−2 bits/s/Hz, the performance gap between the SCA-OPM scheme and the remaining schemes achieves about 19 dB. Moreover, our online schemes have the same performance as those of their counterpart schemes but with lower overhead and lower complexity for the scenario of a few newly-arriving MTs. [ABSTRACT FROM AUTHOR]

Degree-of-freedom (DoF) based models have become prevalent in studying MIMO-based wireless networks. However, most existing DoF-based models assume the channel matrix is of full-rank. Such a simplifying assumption has gradually become problematic, particularly when the number of antennas increases and the propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for the DoF-based model under general channel rank conditions. We start with a fundamental understanding on how MIMO’s DoFs are consumed at each node for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank-deficient channels. Based on this understanding, we develop a DoF model that can be used for identifying the DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks under general channel rank conditions. Specifically, we find that for IC, shared DoF consumption at both transmit and receive nodes is critical for efficient DoF allocation. Further, we show that DoF consumption under the existing full-rank assumption is a special case of our generalized DoF model. Based on case studies, we show that the general IC model can achieve larger feasible DoF regions or improved objective values than existing unilateral IC models. The findings of this paper pave the way for future research of many-antenna networks under general channel rank conditions. [ABSTRACT FROM AUTHOR]

In wireless networks, the design of radio resource sharing mechanisms is complicated by the complex interference constraints among the various links. In their seminal paper (IEEE Trans. Autom. Control, vol. 37, no. 12, pp. 1936–1948), Tassiulas and Ephremides introduced Maximum Weighted Scheduling, a centralized resource sharing algorithm, and proved its optimality. Since then, there have been extensive research efforts to devise distributed implementations of this algorithm. Recently, distributed adaptive CSMA scheduling schemes have been proposed and shown to be optimal, without the need of message passing among transmitters. However, their analysis relies on the assumption that interference can be accurately modeled by a simple interference graph. In this paper, we consider the more realistic and challenging signal-to-interference-plus-noise ratio (SINR) interference model. We present distributed scheduling algorithms that: 1) are optimal under the SINR interference model; and 2) do not require any message passing. These algorithms are based on a combination of a simple and efficient power allocation strategy referred to as Power Packing and randomization techniques. The optimality of our algorithms is illustrated in various traffic scenarios using numerical experiments. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel scheme for the design of topology-transparent scheduling (TTS) in mobile ad hoc networks (MANETs), based on the Chinese remainder theorem (CRT). TTS can provide each node with guaranteed success in each schedule without any detailed topology information and yields a guaranteed upper bound on the transmission delay of each packet at every node in a MANET. In general, TTS requires two global constraints on the number of nodes in the MANET and the maximum nodal degree of the graph representing connectivity of the MANET. Due to the inherent mobility of MANETs, the maximum nodal degree, however, cannot be available or easily estimated. To eliminate the requirement for the maximum nodal degree, this paper proposes TTS via the CRT. By the redundancy property of the Chinese remainder representation, the proposed CRT-based scheme not only preserves the advantages of providing guaranteed success in each schedule with only the global constraint on the number of nodes in the MANET, but also offers flexibility in constructing TTS. To have a better transmission delay bound for a node with lower interference, this paper also introduces two threaded counterparts of the proposed CRT-based scheme. This paper provides performance analyses for the proposed CRT-based scheme and its threaded counterparts. Numerical results demonstrate that TTS via the CRT can outperform existing schemes, especially in scenarios with harsh interference, and is a versatile approach for the design of TTS. [ABSTRACT FROM AUTHOR]

We present a distributed stable protocol for local broadcast in multi-hop wireless networks, where packets are injected to the nodes continuously, and each node needs to quickly disseminate the injected packets to all its neighbors within a given communication range $R$. We investigate the maximum packet injection rate and the minimum packet latency that can be achieved in a stable protocol. This paper assumes the signal-to-interference-plus-noise-ratio (SINR) interference model, which reflects more accurately the physical characteristics of the wireless interference, such as fading and signal accumulation, than conventional local interference models, e.g., graph-based models. More specifically, we present a stable protocol that can handle both stochastic and adversarial injection patterns. The protocol is asymptotically optimal in terms of both injection rate and packet latency. To the best of our knowledge, this paper is the first one studying the properties of stable protocols for the basic primitive of local broadcast in a multi-hop setting under SINR. Our proposed protocol utilizes a static local broadcast algorithm as a subroutine. This static algorithm is of independent interest, and it closes the $O(\log n)$ gap between the upper and lower bounds for static local broadcast. Simulation results indicate that our proposed algorithms can perform well in realistic environments. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to assess the performance in terms of end-to-end throughput of an ad hoc multi-hop wireless network, where each link is affected by interference coming from other multi-hop paths nearby. The approach captures the mutual impact of each path on all others. It can be applied to both, contention-based and scheduled, medium access control (MAC) protocols. Sources have data to send to destination nodes through $n$ relays. Nodes are assumed to be uniformly and randomly distributed in the 2-D infinite plane. The model shows the impact on the end-to-end throughput of $n$ ; it also captures the influence of node density, traffic generated, number of retransmissions, and other MAC parameters. Finally, the model provides the throughput-delay tradeoff. Unlike most previous approaches, the mathematical tool proposed appears to be scalable, allowing easy extension to any number of hops. Comparison with simulation results is provided to prove that the impact of the approximations introduced in the analysis is almost negligible. [ABSTRACT FROM PUBLISHER]

Link scheduling is an appealing solution for ensuring the reliability and latency requirements of Internet of Things (IoT). Most existing results on the link scheduling problem were based on the graph or SINR (Signal-to-Interference-plus-Noise-Ratio) models, which ignored the impact of the random fading gain of the signals strength. In this paper, we address the link scheduling problem under the Rayleigh fading model. Both Shortest Link Scheduling (SLS) and Maximum Link Scheduling (MLS) problems are studied. In particular, we show that a set of links can be activated simultaneously under Rayleigh fading model if all link SINR constraints are satisfied. Based on the analysis of previous Link Diversity Partition (LDP) algorithm, we propose an Improved LDP (ILDP) algorithm and a centralized algorithm by localizing the global interference (denoted by CLT), building on which we design a distributed CLT algorithm (denoted by RCRDCLT) that converges to a constant approximation factor of the optimum with the time complexity of $O(\ln n)$ , where $n$ is the number of links. Furthermore, executing repeatedly RCRDCLT can solve the SLS with an approximation factor of $\Theta (\ln n)$. Extensive simulations indicate that CLT is more effective than previous six popular link scheduling algorithms, and RCRDCLT has the lowest time complexity while only losses a constant fraction of the optimum schedule. [ABSTRACT FROM AUTHOR]

In this paper, we consider the Max-Weight protocol for routing and scheduling in wireless networks under an adversarial model. This protocol has received a significant amount of attention dating back to the papers of Tassiulas and Ephremides. In particular, this protocol is known to be throughput-optimal whenever the traffic patterns and propagation conditions are governed by a stationary stochastic process. However, the standard proof of throughput optimality (which is based on the negative drift of a quadratic potential function) does not hold when the traffic patterns and the edge capacity changes over time are governed by an arbitrary adversarial process. Such an environment appears frequently in many practical wireless scenarios when the assumption that channel conditions are governed by a stationary stochastic process does not readily apply. In this paper, we prove that even in the above adversarial setting, the Max-Weight protocol keeps the queues in the network stable (i.e., keeps the queue sizes bounded) whenever this is feasible by some routing and scheduling algorithm. However, the proof is somewhat more complex than the negative potential drift argument that applied in the stationary case. Our proof holds for any arbitrary interference relationships among edges. We also prove the same stability of \varepsilon-approximate Max-Weight under the adversarial model. We conclude the paper with a discussion of queue sizes in the adversarial model as well as a set of simulation results. [ABSTRACT FROM PUBLISHER]

This paper addresses fundamental issues in a shared channel where the users have different priority levels. In particular, we study a two-user cognitive shared channel consisting of a primary (higher-priority) and a secondary user, operating in the cognitive underlay fashion, but in a novel way where interference suffered by the primary user is compensated by requiring the secondary user to cooperatively relay some of the primary's packets. We start by analyzing the case of no node cooperation, where nodes transmit their own packets to their respective destinations. We then extend the analysis to a system in which the secondary node acts as a relay for the primary user, in addition to serving its own packets. Specifically, in the cognitive cooperation case, the secondary node forwards those packets to the primary destination that it receives successfully from the primary source. In such cognitive shared channels, a tradeoff arises in terms of activating the secondary along with the primary so that both transmissions may be successful, but with a lower probability, compared to the case of the secondary node staying idle when the primary user transmits. Results show the benefits of relaying for both the primary as well as the secondary nodes in terms of the stable-throughput region. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio. This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants. [ABSTRACT FROM AUTHOR]

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allows users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental test beds and simulations, where we compare the performance of EMIT with a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT and reveal significant gains over CSMA strategies in the case of IoT traffic. [ABSTRACT FROM AUTHOR]

To tackle the contradiction between the computation intensive applications and the resource-hungry mobile user equipments (UEs), mobile edge computing (MEC) has been provisioned as a promising solution, which enables the UEs to offload the tasks to the MEC servers. Considering the characteristics of small cell networks (SCNs), integrating MEC into SCNs is natural. But in terms of the high interference, multi-access property, and limited resources of small cell base stations (SBSs), an efficient computation offloading scheme is essential. However, there still lack comprehensive studies on this problem in the densely deployed SCNs. In this paper, we study the energy-efficient computation offloading management scheme in the MEC system with SCNs. The aim of this paper is to minimize the energy consumption of all UEs via jointly optimizing computation offloading decision making, spectrum, power, and computation resource allocation. Specially, the UEs need not only to decide whether to offload but also to determine where to offload. First, we present the computation offloading model and formulate this problem as a mix integer non-linear programming problem, which is NP-hard. Taking advantages of genetic algorithm (GA) and particle swarm optimization (PSO), we design a suboptimal algorithm named as hierarchical GA and PSO-based computation algorithm to solve this problem. Finally, the convergence of this algorithm is studied by simulation, and the performance of the proposed algorithm is verified by comparing with the other baseline algorithms. [ABSTRACT FROM AUTHOR]

We consider the multiple-unicast problem with three source–terminal pairs over directed acyclic networks with unit-capacity edges. The three s_i\–t_i pairs wish to communicate at unit-rate via network coding. The connectivity between the s_i\–t_i pairs is quantified by means of a connectivity-level vector, [k_1\ k_2\ k_3] such that there exist k_i edge-disjoint paths between s_i and t_i. In this paper, we attempt to classify networks based on the connectivity level. It can be observed that unit-rate transmission can be supported by routing if k_i \geq 3, for all i = 1, \ldots, 3. In this paper, we consider connectivity-level vectors such that \mini = 1, \ldots, 3 k_i < 3. We present either a constructive linear network coding scheme or an instance of a network that cannot support the desired unit-rate requirement, for all such connectivity-level vectors except the vector [1 2 4] (and its permutations). The benefits of our schemes extend to networks with higher and potentially different edge capacities. Specifically, our experimental results indicate that for networks where the different source–terminal paths have a significant overlap, our constructive unit-rate schemes can be packed along with routing to provide higher throughput as compared to a pure routing approach. [ABSTRACT FROM AUTHOR]

Recent work demonstrates that IEEE 802.11 networks are vulnerable to cascading DoS attacks, wherein a single node can remotely and suddenly congest an entire network. In this paper, we propose, analyze, simulate, and experimentally verify a counter-measure against such attacks. Our main idea is to optimize the duration of packet transmissions in order to weaken coupling effects between neighboring pairs of nodes. Toward that end, we propose a new theoretical model that relates the utilization of neighboring pairs of nodes using a sequence of iterative equations. The model captures important specifications of the IEEE 802.11 MAC layer. Through a fixed point analysis of the sequence, we show how to optimally set the packet duration so that, on one hand, cascading DoS attacks are avoided and, on the other hand, throughput is maximized. We validate the analysis through extensive ns-3 simulations and demonstrate the effectiveness of the mitigation through experiments with real Wi-Fi cards. A key insight is that IEEE 802.11 networks with relatively large MAC overhead are less susceptible to cascading DoS attacks than networks with smaller MAC overhead. [ABSTRACT FROM AUTHOR]

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched; therefore, they must make distributed decisions. In this paper, we pose the following question: If every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers that are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced PHY-layer design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a constructive distributed algorithm that achieves rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. We compare our new aggressive algorithm to a conservative algorithm we have presented in a 2013 conference paper. Both algorithms schedule subnetworks such that each subnetwork can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection, and scheduling of subnetworks, especially when subnetworks are larger than a single link. [ABSTRACT FROM AUTHOR]

This paper considers the problem of scheduling real-time traffic in wireless networks. We consider ad hoc wireless networks with general conflict graph-based interference model and single-hop traffic. Each packet is associated with a deadline and will be dropped if it is not transmitted before the deadline. The number of packet arrivals in each time-slot and the maximum delay before the deadline are independent and identically distributed across time. We require a minimum fraction of packets to be delivered. At each link, we assume the link keeps track of the difference between the minimum number of packets that need to be delivered so far and the number of packets that are actually delivered, which we call the deficit. The largest-deficit-first (LDF) policy schedules links in descending order according to their deficit values, which is a variation of the longest-queue-first (LQF) policy for non-real-time traffic. We prove that the efficiency ratio of LDF, which is the fraction of the throughput region that LDF can achieve for given traffic distributions, can be lower-bounded by a quantity that we call the real-time local-pooling factor (R-LPF). We further prove that a lower bound on the R-LPF can be related to the weighted sum of the service rates, with a special case of 1/(\beta+1) by considering the uniform weight, where \beta is the interference degree of the conflict graph. We also propose a heuristic consensus algorithm that can be used to obtain a good weight vector for such lower bounds for given network topology. [ABSTRACT FROM AUTHOR]

Cellular V2X, the “Vehicle to Everything” standard, defines a framework for information exchange among vehicles and other network entities. In one of the main modes, LTE V2X relies on a central scheduler to minimize the consumed resources in a conflict-free manner. This NP-hard problem leads to a scheduling strategy that assigns separate resources to the conflicting links and can enjoy from opportunistic resource reuse. In this paper, a novel polynomial-time heuristic, MUCS, is introduced, which models this scheduling as a Vehicle Routing Problem. The existing conflict-free schedulers face major incompetence in satisfying the LTE and 5G V2X requirements mainly due to their reliance on simplifications that are unnatural to the V2X environment. On the contrary, MUCS can flexibly accommodate general Device-to-Device topologies as the basis of V2X networks without imposing any packet segmentation. This way, MUCS minimizes the control information overhead in the cellular V2X standard. Due to scalability and a high quality resource utilization (near-optimal in certain conditions), compared to the existing literature, MUCS is desirable for LTE V2X and the 5G networks. [ABSTRACT FROM AUTHOR]

Millimeter-wave (mmWave) communication is a promising technology to cope with the exponential increase in 5G data traffic. Such networks typically require a very dense deployment of base stations. A subset of those, so-called macro base stations, feature high-bandwidth connection to the core network, while relay base stations are connected wirelessly. To reduce cost and increase flexibility, wireless backhauling is needed to connect both macro to relay as well as relay to relay base stations. The characteristics of mmWave communication mandates new paradigms for routing and scheduling. The paper investigates scheduling algorithms under different interference models. To showcase the scheduling methods, we study the maximum throughput fair scheduling problem. Yet the proposed algorithms can be easily extended to other problems. For a full-duplex network under the no interference model, we propose an efficient polynomial-time scheduling method, the schedule-oriented optimization. Further, we prove that the problem is NP-hard if we assume pairwise link interference model or half-duplex radios. Fractional weighted coloring based approximation algorithms are proposed for these NP-hard cases. Moreover, the approximation algorithm parallel data stream scheduling is proposed for the case of half-duplex network under the no interference model. It has better approximation ratio than the fractional weighted coloring based algorithms and even attains the optimal solution for the special case of uniform orthogonal backhaul networks. [ABSTRACT FROM AUTHOR]

In an effort to meet growing demands on the radio frequency spectrum, regulators are exploring methods to enable band sharing among a diverse set of user devices. Proposed spectrum access systems would dynamically assign spectrum resources to users, maintaining databases of spectrum use information. While these systems are anticipated to increase the efficiency of spectrum sharing, incumbent users have raised concerns about exposing details of their operations and have questioned whether their privacy can be protected. In this paper, we explore whether primary users can retain a critical level of privacy in a spectrum access system setting, where they must reveal some information to enable dynamic access to the spectrum by other users. Under a variety of operational scenarios and user models, we examine adversary techniques to exploit the spectrum access system and obfuscation strategies to protect user privacy. We develop analytical methods to quantify the resulting privacy and validate our results through simulation. To the best of our knowledge, this is the first paper that considers inference attacks on primary users in the setting of a highly dynamic spectrum access system. Privacy analysis of this kind will help to enable the adoption of shared spectrum access systems by allowing incumbent users to quantify and mitigate risks to their privacy. [ABSTRACT FROM AUTHOR]

Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobility-induced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent on-board sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensor-estimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of $1.24\times $ and $1.28\times $ compared with the bestknown RA algorithm for 2- and 3-antenna APs, respectively. [ABSTRACT FROM AUTHOR]

The greedy maximal scheduling (GMS) and maximal scheduling (MS) algorithms are well-known low-complexity scheduling policies with guaranteed capacity region in the context of single-radio single-channel (SR-SC) wireless networks. However, how to design maximal scheduling algorithms for multiradio multichannel (MR-MC) wireless networks and the associated capacity analysis are not well understood yet. In this paper, we develop a new model by transforming an MR-MC network node to multiple node-radio-channel (NRC) tuples. Such a framework facilitates the derivation of a tuple-based back-pressure algorithm for throughput-optimal control in MR-MC wireless networks and enables the tuple-based GMS and MS scheduling as low-complexity approximation algorithms with guaranteed performance. An important existing work on GMS and MS for MR-MC networks is that of Lin and Rasool (IEEE/ACM Trans. Networking, vol. 17, no. 6, 1874–1887, Dec. 2009), where link-based algorithms are developed. Compared to the link-based algorithms, the tuple-based modeling has significant advantages in enabling a fully decomposable cross-layer control framework. Another theoretical contribution in this paper is that we, for the first time, extend the local-pooling factor analysis to study the capacity efficiency ratio of the tuple-based GMS in MR-MC networks and obtain a lower bound that is much tighter than those known in the literature. Moreover, we analyze the communications and computation overhead in implementing the distributed MS algorithm and present simulation results to demonstrate the performance of the tuple-based maximal scheduling algorithms. [ABSTRACT FROM PUBLISHER]

We formulate adaptation of antenna tilt angle as a utility fair optimization task. This optimization problem is nonconvex, but in this paper we show that, under reasonable conditions, it can be reformulated as a convex optimization. Using this insight, we develop a lightweight method for finding the optimal antenna tilt angles, making use of measurements that are already available at base stations, and suited to distributed implementation. [ABSTRACT FROM PUBLISHER]

In modern broadband cellular networks, the omnidirectional antenna at each cell is replaced by three or six directional antennas, one in every sector. While every sector can run its own scheduling algorithm, bandwidth utilization can be significantly increased if a joint scheduler makes these decisions for all the sectors. This gives rise to a new problem, referred to as “joint scheduling,” addressed in this paper for the first time. The problem is proven to be NP-hard, but we propose efficient algorithms with a worst-case performance guarantee for solving it. We then show that the proposed algorithms indeed substantially increase the network throughput. [ABSTRACT FROM PUBLISHER]

In cellular networks, a recent trend in research is to make spectrum access dynamic in the spatial and temporal dimensions for the sake of efficient utilization of spectrum. In one such model, the spectrum is divided into channels and periodically allocated to competing base stations using an auction-based market mechanism. An “efficient” auction mechanism is essential to the success of such a dynamic spectrum access model. A key objective in designing an auction mechanism is “truthfulness.” Combining this objective with an optimization of some social choice function (such as the social-welfare or the generated revenue) is highly desirable. In this paper, we design polynomial-time spectrum auction mechanisms that are truthful and yield an allocation with O(1)-approximate social-welfare or revenue. Our mechanisms generalize to general interference models. To the best of our knowledge, ours is the first work to design polynomial-time truthful spectrum auction mechanisms with a constant-factor approximation of either the expected revenue or the social-welfare. We demonstrate the performance of our designed mechanism through simulations. [ABSTRACT FROM PUBLISHER]

This paper investigates the benefits of cooperation and proposes a relay activation strategy for a large wireless network with multiple transmitters. In this framework, some nodes cooperate with a nearby node that acts as a relay, using the decode-and-forward protocol, and others use direct transmission. The network is modeled as an independently marked Poisson point process, and the source nodes may choose their relays from the set of inactive nodes. Although cooperation can potentially lead to significant improvements in the performance of a communication pair, relaying causes additional interference in the network, increasing the average noise that other nodes see. We investigate how source nodes should balance cooperation versus interference to obtain reliable transmissions, and for this purpose, we study and optimize a relay activation strategy with respect to the outage probability. Surprisingly, in the high reliability regime, the optimized strategy consists on the activation of all the relays or none at all, depending on network parameters. We provide a simple closed-form expression that indicates when the relays should be active, and we introduce closed-form expressions that quantify the performance gains of this scheme with respect to a network that only uses direct transmission. [ABSTRACT FROM PUBLISHER]

Since wireless channel is vulnerable to eavesdroppers, the secrecy during message delivery is a major concern in many applications such as commercial, governmental, and military networks. This paper investigates information-theoretic secrecy in large-scale networks and studies how capacity is affected by the secrecy constraint where the locations and channel state information (CSI) of eavesdroppers are both unknown. We consider two scenarios: 1) noncolluding case where eavesdroppers can only decode messages individually; and 2) colluding case where eavesdroppers can collude to decode a message. For the noncolluding case, we show that the network secrecy capacity is not affected in order-sense by the presence of eavesdroppers. For the colluding case, the per-node secrecy capacity of \Theta(1 \over \sqrtn) can be achieved when the eavesdropper density \psi_e(n) is O(n^-\beta), for any constant \beta > 0 and decreases monotonously as the density of eavesdroppers increases. The upper bounds on network secrecy capacity are derived for both cases and shown to be achievable by our scheme when \psi_e(n)=O(n^-\beta) or \psi_e(n)=\Omega(\log^\alpha-2 \over \alphan), where \alpha is the path-loss gain. We show that there is a clear tradeoff between the security constraints and the achievable capacity. Furthermore, we also investigate the impact of secrecy constraint on the capacity of dense network, the impact of active attacks and other traffic patterns, as well as mobility models in the context. [ABSTRACT FROM AUTHOR]

Full-duplex (FD) communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of FD are still not well understood. In this paper, we characterize the FD rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference (SI) and signal-to-noise ratio values. We also provide a sufficient condition under which the sum of uplink and downlink rates on an FD channel is biconcave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a compact (e.g., smartphone) FD receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum SI suppression, where the objective is to maximize the sum of the rates over uplink and downlink orthogonal frequency division multiplexing channels. We develop a polynomial time algorithm, which is nearly optimal, in practice, under very mild restrictions. To reduce the running time, we develop an efficient nearly optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in different use cases and obtain insights into the impact of the remaining SI and wireless channel states on the performance. [ABSTRACT FROM PUBLISHER]

WiFi networks have been globally deployed and most mobile devices are currently WiFi-enabled. While WiFi has been proposed for multimedia content distribution, its lack of adequate support for multicast services hinders its ability to provide multimedia content distribution to a large number of devices. In this paper, we present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast services. AMuSe is based on accurate receiver feedback and incurs a small control overhead. In particular, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes, which periodically send information about the channel quality to the multicast sender. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. AMuSe does not require any changes to the standards or any modifications to the WiFi devices. We implemented AMuSe on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi devices, both with and without interference sources. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. [ABSTRACT FROM PUBLISHER]

We study the user association problem in the context of dense networks, where standard adaptive algorithms become ineffective. This paper proposes a novel data-driven technique leveraging the theory of robust optimization. The main idea is to predict future traffic fluctuations, and use the predictions to design association maps before the actual arrival of traffic. Although, the actual playout of the map is random due to prediction error, the maps are robustly designed to handle uncertainty, preventing constraint violations, and maximizing the expectation of a convex utility function, which is used to accurately balance base station loads. We propose a generalized iterative algorithm, referred to as GRMA, which is shown to converge to the optimal robust map. The optimal maps have the intriguing property that they jointly optimize the predicted load and the variance of the prediction error. We validate our robust maps in Milano-area traces, with dense coverage and find that we can reduce violations from 25% (inflicted by a baseline adaptive algorithm) down to almost zero. [ABSTRACT FROM AUTHOR]

This paper studies the important sensory data collection problem in the sensor-augmented RFID systems, which is to quickly and accurately collect sensory data from a predefined set of target tags with the coexistence of unexpected tags. The existing RFID data collection schemes suffer from either low time-efficiency due to tag-collisions or serious data corruption issue due to interference of unexpected tags. To overcome these limitations, we propose the hierarchical-hashing data collection (HDC) protocol, which can not only significantly improve the utilization of RFID wireless communication channel by establishing bijective mapping between $k$ target tags and the first $k$ slots in time frame, but also effectively filter out the serious interference of unexpected tags. Although HDC has attractive advantages, the theoretical analysis reveals that the computation cost involved in it is as huge as $\mathcal {O}(k2^{k})$ , where $k$ is normally large in practice. By making some modifications to the basic HDC protocol, we propose the multi-framed hierarchical-hashing data collection (MHDC) protocol to effectively reduce the involved computation complexity. Unlike HDC that only issues a single time frame, MHDC uses multiple time frames to collaboratively collect sensory data from the $k$ target tags. It can be understood as that a big computation task is disintegrated into multiple small pieces and then shared by multiple time frames. As a result, the computation cost involved in MHDC is reduced to $\mathcal {O}(k2^{n})$ , where $n\ll k$ is the expected number of target tags that each time frame handles. Theoretical analysis is given to jointly consider the communication cost and computation cost thereby maximizing the overall time-efficiency of MHDC. Extensive simulation results reveal that the proposed MHDC protocol can correctly collect all sensory data and is always about more than $2\times $ faster than the state-of-the-art RFID sensory data collection protocols. [ABSTRACT FROM AUTHOR]

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel semi-distributed and practical ICIC scheme based on the Almost Blank SubFrame (ABSF) approach specified by 3GPP. We define two mathematical programming problems for the cases of guaranteed and best-effort traffic, and use game theory to study the properties of the derived ICIC distributed schemes, which are compared in detail against unaffordable centralized schemes. Based on the analysis of the proposed models, we define Distributed Multi-traffic Scheduling (DMS), a unified distributed framework for adaptive interference-aware scheduling of base stations in future cellular networks, which accounts for both guaranteed and best-effort traffic. DMS follows a two-tier approach, consisting of local ABSF schedulers, which perform the resource distribution between the guaranteed and best effort traffic, and a light-weight local supervisor, which coordinates ABSF local decisions. As a result of such a two-tier design, DMS requires very light signaling to drive the local schedulers to globally efficient operating points. As shown by means of numerical results, DMS allows to: (i) maximize radio resources resue; (ii) provide requested quality for guaranteed traffic; (iii) minimize the time dedicated to guaranteed traffic to leave room for best-effort traffic; and (iv) maximize resource utilization efficiency for the best-effort traffic. [ABSTRACT FROM AUTHOR]

In this paper, to exploit the challenges and potential offered by the simultaneous use of non-orthogonal multiple access (NOMA) and orthogonal frequency division multiple access (OFDMA) transmission options in future 5G wireless systems, we aim at the proper modeling and transformation of the uplink power allocation problem. In particular, in this setting, each user has two degrees of freedom in the decision making process, namely its overall transmission power level, and the corresponding power investment to the OFDMA and/or NOMA based transmissions. The resulting multi-variable power allocation problem is treated and solved under three different perspectives, namely: 1) Games in Normal Form and Nash Equilibrium (NE); 2) Optimization techniques targeting system social welfare through a centralized optimal solution; and 3) Games in Satisfaction Form and Efficient Satisfaction Equilibrium (ESE). Based on these approaches, different solutions and stable operation points are identified and their properties are analyzed. An in depth evaluation and comparison of the various obtained outcomes is achieved, via modeling and simulations. The focus is placed on the impact and the interplay of the NOMA specific features, including the potential over-exploitation of the available bandwidth, the fairness in accessing it, and the interference treatment. It is also shown that, using the satisfaction form games for the users to converge to the ESE, provides an efficient and promising user-centric modeling approach to the power allocation problem, as the system adapts to the users’ application needs, while at the same time eliminates a significant amount of interference. [ABSTRACT FROM AUTHOR]

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol). [ABSTRACT FROM AUTHOR]

In this paper, we investigate distributed broadcasting in dynamic networks, where the topology changes continually over time. We propose a network model that captures the dynamicity caused by both churn and mobility of nodes. In contrast to existing work on dynamic networks, our model defines the dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a local dynamic model suits distributed algorithms better than a global one. The proposed dynamic model uses the more realistic SINR model to depict wireless interference, instead of oversimplified graph-based models adopted in most existing work. We consider the fundamental communication primitive of global broadcast, which is to disseminate a message from a source node to the whole network. Specifically, we present a randomized distributed algorithm that can accomplish dynamic broadcasting in an asymptotically optimal running time of $O(D_{T})$ with a high probability guarantee, under the assumption of reasonably constant dynamicity rate, where $D_{T}$ is the dynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. We believe our local dynamic model can greatly facilitate distributed algorithm studies in mobile and dynamic wireless networks. [ABSTRACT FROM AUTHOR]

Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim’s transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim’s throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam. [ABSTRACT FROM AUTHOR]

We investigate the problem of minimizing the sum of the queue lengths of all the nodes in a wireless network with a tree topology. Nodes send their packets to the tree's root (sink). We consider a time-slotted system and a K-hop interference model. We characterize the existence of causal sample-path optimal scheduling policies in these networks, i.e., we wish to find a policy such that at each time-slot, for any traffic arrival pattern, the sum of the queue lengths of all the nodes is minimum among all policies. We provide an algorithm that takes any tree and K as inputs, and outputs whether a causal sample-path optimal policy exists for this tree under the K-hop interference model. We show that when this algorithm returns FALSE, there exists a traffic arrival pattern for which no causal sample-path optimal policy exists for the given tree structure. We further show that for certain tree structures, even noncausal sample-path optimal policies do not exist. We provide causal sample-path optimal policies for those tree structures for which the algorithm returns TRUE. Thus, we completely characterize the existence of such policies for all trees under the K-hop interference model. The nonexistence of sample-path optimal policies in a large class of tree structures implies that we need to study other (relatively) weaker metrics for this problem. [ABSTRACT FROM PUBLISHER]

Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes. However, they do not analyze the cause of packet losses. In this paper, we ask: 1) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission? 2) How can these parameters be used to perform a forensic analysis of the reason for the losses? Toward answering the above questions, we first build an analytical framework that computes the likelihood that evidence (we call this transmission evidence, or TE for short) exists with respect to transmissions, in terms of a set of network parameters. We validate our analytical framework via both simulations as well as real-world experiments on two different wireless testbeds. The analytical framework is then used as a basis for a protocol within a forensic analyzer to assess the cause of packet losses and determine the likelihood of forwarding misbehaviors. Through simulations, we find that our assessments are close to the ground truth in all examined cases, with an average deviation of 2.3% from the ground truth and a worst case deviation of 15.0%. [ABSTRACT FROM PUBLISHER]

Radio access network (RAN) slicing is a virtualization technology that partitions radio resources into multiple autonomous virtual networks. Since RAN slicing can be tailored to provide diverse performance requirements, it will be pivotal to achieve the high-throughput and low-latency communications that next-generation (5G) systems have long yearned for. To this end, effective RAN slicing algorithms must (i) partition radio resources so as to leverage coordination among multiple base stations and thus boost network throughput; and (ii) reduce interference across different slices to guarantee slice isolation and avoid performance degradation. The ultimate goal of this paper is to design RAN slicing algorithms that address the above two requirements. First, we show that the RAN slicing problem can be formulated as a 0–1 Quadratic Programming problem, and we prove its NP-hardness. Second, we propose an optimal solution for small-scale 5G network deployments, and we present three approximation algorithms to make the optimization problem tractable when the network size increases. We first analyze the performance of our algorithms through simulations, and then demonstrate their performance through experiments on a standard-compliant LTE testbed with 2 base stations and 6 smartphones. Our results show that not only do our algorithms efficiently partition RAN resources, but also improve network throughput by 27% and increase by 2× the signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

The use of wirelessly internetworked miniaturized biomedical devices is promising a significant leap forward in medical treatment of many pervasive diseases. Recognizing the limitations of traditional radio-frequency wireless communications in interconnecting devices within the human body, in this paper, we propose for the first time to develop network protocols for implantable devices based on ultrasonic transmissions. We start off by assessing the theoretical feasibility of using ultrasonic waves in human tissues and by deriving an accurate channel model for ultrasonic intrabody communications. Then, we propose a new ultrasonic transmission and multiple access technique, which we refer to as Ultrasonic WideBand (UsWB). UsWB is based on the idea of transmitting information bits spread over very short pulses following a time-hopping pattern. The short impulse duration results in limited reflection and scattering effects, and the low duty cycle reduces the impact of thermal and mechanical effects, which may be detrimental for human health. We then develop a multiple access technique with distributed control to enable efficient simultaneous access by mutually interfering devices based on minimal and localized information exchange and on measurements at the receiver only. Finally, we demonstrate the performance of UsWB through a multiscale simulator that models the proposed communication system at the acoustic wave level, at the physical (bit) level, and at the network (packet) level. We also validate the simulation results by comparing them to experimental results obtained with a software-defined testbed. [ABSTRACT FROM PUBLISHER]

In cognitive radio networks (CRNs), secondary users (SUs) can flexibly access primary users' (PUs') idle spectrum bands, but such spectrum opportunities are dynamic due to PUs' uncertain activity patterns. In a multihop CRN consisting of SUs as relays, such spectrum dynamics will further cause the invalidity of predetermined routes. In this paper, we investigate spectrum-mobility-incurred route-switching problems in both spatial and frequency domains for CRNs, where spatial switching determines which relays and links should be reselected and frequency switching decides which channels ought to be reassigned to the spatial routes. The proposed route-switching scheme not only avoids conflicts with PUs but also mitigates spectrum congestion. Meanwhile, tradeoffs between routing costs and channel switching costs are achieved. We further formulate the route-switching problem as the Route-Switching Game, which is shown to be a potential game and has a pure Nash equilibrium (NE). Accordingly, efficient algorithms for finding the NE and the \epsilon –NE are proposed. Then, we extend the proposed game to the incomplete-information scenario and provide a method to compute the Bayesian NE. Finally, we prove that the price of anarchy of the proposed game has a deterministic upper bound. [ABSTRACT FROM PUBLISHER]

Recent studies in wireless scheduling have shown that carrier-sense multiple access (CSMA) can be made throughput optimal by optimizing over activation rates. However, those throughput optimal CSMA algorithms were found to suffer from poor delay performance, especially at high throughputs where the delay can potentially grow exponentially in the size of the network. Motivated by these shortcomings, in this paper we propose a node-based version of the throughput optimal CSMA (NB-CSMA) as opposed to traditional link-based CSMA algorithms, where links were treated as separate entities. Our algorithm is fully distributed and corresponds to Glauber dynamics with “Block updates”. We show analytically and via simulations that NB-CSMA outperforms conventional link-based CSMA in terms of delay for any fixed-size network. We also characterize the fraction of the capacity region for which the average queue lengths (and the average delay) grow polynomially in the size of the network, for networks with bounded-degree conflict graphs. This fraction is no smaller than the fraction known for link-based CSMA, and is significantly larger for many instances of practical wireless ad-hoc networks. Finally, we restrict our focus to the special case of collocated networks, analyze the mean starvation time using a Markov chain with rewards framework and use the results to quantitatively demonstrate the improvement of NB-CSMA over the baseline link-based algorithm. [ABSTRACT FROM AUTHOR]

Multi-user multiple input multiple output (MU-MIMO) enables a multi-antenna access point to serve multiple users simultaneously, and has been adopted as the IEEE 802.11ac standard. While several PHY-MAC designs have recently been proposed to improve the throughput performance of a MU-MIMO WLAN, they, however, usually assume that all the concurrent streams are of roughly equal length. In reality, users usually have frames with heterogeneous lengths even after aggregation, leading to different lengths of a transmission time. Hence, the concurrent transmission opportunities might not always be fully utilized when some streams finish earlier than the others in a transmission opportunity. To resolve this inefficiency, this paper presents full degree-of-freedom (FDoF), a PHY-MAC design that exploits a novel power allocation scheme to reduce the idle channel time and further leverages frame padding to better utilize the spatial multiplexing gain. Unlike traditional MIMO power allocation, which aims at maximizing the theoretical sum-rate, FDoF’s power allocation explicitly considers heterogeneous frame lengths and minimizes the channel time required to finish concurrent frames, as a result improving the effective throughput. FDoF’s padding protocol then identifies proper users to reuse the remaining idle channel time, while preventing this padding from harming all the ongoing streams. Our evaluation via large-scale trace-driven simulations demonstrates that FDoF’s improves the throughput by up to $2.83\times $ , or by $1.36\times $ on average, as compared to the conventional 802.11ac. By combining FDoF’s power allocation with frame padding, the average throughput gain can be further increased to $1.75\times $ . [ABSTRACT FROM PUBLISHER]

With the prevalence of broadband network and wireless mobile network accesses, distributed interactive applications (DIAs) such as online gaming have attracted a vast number of users over the Internet. The deployment of these systems, however, comes with peculiar hardware/software requirements on the user consoles. Recently, such industrial pioneers as Gaikai, Onlive, and Ciinow have offered a new generation of cloud-based DIAs (CDIAs), which shifts the necessary computing loads to cloud platforms and largely relieves the pressure on individual user’s consoles. In this paper, we aim to understand the existing CDIA framework and highlight its design challenges. Our measurement reveals the inside structures as well as the operations of real CDIA systems and identifies the critical role of cloud proxies. While its design makes effective use of cloud resources to mitigate client’s workloads, it may also significantly increase the interaction latency among clients if not carefully handled. Besides the extra network latency caused by the cloud proxy involvement, we find that computation-intensive tasks (e.g., game video encoding) and bandwidth-intensive tasks (e.g., streaming the game screens to clients) together create a severe bottleneck in CDIA. Our experiment indicates that when the cloud proxies are virtual machines (VMs) in the cloud, the computation-intensive and bandwidth-intensive tasks may seriously interfere with each other. We accordingly capture this feature in our model and present an interference-aware solution. This solution not only smartly allocates workloads but also dynamically assigns capacities across VMs based on their arrival/departure patterns. [ABSTRACT FROM PUBLISHER]

In this paper, we present the first algorithm to precisely implement the abstract MAC (absMAC) layer under the physical SINR model in dynamic networks. The absMac layer, first presented by (Kuhn et al., 2009), provides reliable local broadcast communications, with timing guarantees stated in terms of a collection of abstract delay functions, based on which high-level algorithms can be designed, independent of specific channel behaviors. The implementation of absMAC requires the design of a distributed algorithm for the local broadcast communication primitives over a particular communication model that defines concrete channel behaviors, and the objective is to minimize the bounds of the abstract delay functions. Halldórsson et al. (2015) showed that under the standard SINR model (synchronous communications without physical carrier sensing or location information), there exist no efficient exact implementations. In this work, we demonstrate that physical carrier sensing, a commonly seen function performed by wireless devices, can help get efficient exact implementation algorithms. Specifically, we propose an algorithm that precisely implements the absMAC layer under the SINR model in dynamic networks. The algorithm provides asymptotically optimal bounds for both acknowledgement and progress functions defined in the absMAC layer. Our algorithm leads to many new faster algorithms for solving high-level problems under the SINR model in dynamic networks. We demonstrate this by exemplifying problems of Consensus, Multi-Message Broadcast, and Single-Message Broadcast. It deserves to point out that our implementation algorithm is designed based on an optimal algorithm for a General Local Broadcast (GLB) problem, which takes the number of distinct messages into consideration for the first time. The GLB algorithm can handle many communication scenarios apart from those defined in the absMAC layer. Simulation results show that our proposed algorithms perform well in reality. [ABSTRACT FROM AUTHOR]