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This paper focuses on the problem of broadcasting information in the most efficient manner in a large 2-D ad hoc wireless network at low SNR and under line-of-sight propagation. A new communication scheme is proposed, where source nodes first broadcast their data to the entire network, despite the lack of sufficient available power. The signal’s power is then reinforced via successive back-and-forth beamforming transmissions between different groups of nodes in the network, so that all nodes are able to decode the transmitted information at the end. This scheme is shown to achieve asymptotically the broadcast capacity of the network, which is expressed in terms of the largest singular value of the matrix of fading coefficients between the nodes in the network. A detailed mathematical analysis is then presented to evaluate the asymptotic behavior of this largest singular value. [ABSTRACT FROM PUBLISHER]

This paper studies the asymptotic properties of average area spectral efficiency (ASE) of a downlink cellular network in the limit of very dense base station (BS) and user densities. This asymptotic analysis relies on three assumptions: 1) interference is treated as noise; 2) the BS locations are drawn from a Poisson point process; and 3) the path loss function is bounded above satisfying mild regularity conditions. We consider three possible definitions of the average ASE, all of which give units of bits per second per unit bandwidth per unit area. When there is no constraint on the minimum operational signal-to-interference-plus-noise ratio (SINR) and instantaneous full channel state information (CSI) is available at the transmitter, the average ASE is proven to saturate to a constant, which we derive in a closed form. For the other two ASE definitions, wherein either a minimum SINR is enforced or CSI is not available, the average ASE is instead shown to collapse to zero at high BS density. We provide several familiar case studies for the class of considered path loss models, and demonstrate that our results cover most previous models and results on ultradense networks as special cases. [ABSTRACT FROM AUTHOR]

In this paper, we examine the benefits of multiple antenna communication in random wireless networks, the topology of which is modeled by stochastic geometry. The setting is the Poisson bipolar model introduced in [1], which is a natural model for ad-hoc and device-to-device networks. The primary finding is that, with the knowledge of channel state information between a receiver and its associated transmitter, by zero-forcing successive interference cancellation, and for appropriate antenna configurations, the ergodic spectral efficiency can be made to scale linearly with both: 1) the minimum of the number of transmit and receive antennas and 2) the density of nodes. This scaling law is achieved by using the multiple transmit antennas to send multiple data streams (e.g., through an open-loop transmission method) and by exploiting the receive antennas to cancel interference. Furthermore, when a receiver is able to learn channel state information from a certain number of near interferers, higher scaling gains can be achieved when a successive interference cancellation method is used. Both results require rich scattering environments. A major implication of the derived scaling laws is that, under this scattering assumption, spatial multiplexing transmission methods are essential for obtaining better and eventually optimal scaling laws in random wireless networks with multiple antennas. [ABSTRACT FROM AUTHOR]

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes and assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication with end-to-end encryption for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that information-theoretic secrecy can be achieved asymptotically if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose a practical scheme that achieves a good balance of network and channel rates with information-theoretic secrecy guarantee. This work has a wide range of potential applications for which strong secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks. [ABSTRACT FROM AUTHOR]

This paper studies the delay constrained multicast capacity of large-scale mobile ad hoc networks (MANETs). We consider a MANET that consists of ns multicast sessions. Each multicast session has one source and p destinations. Each source sends identical information to the p destinations in its multicast session, and the information is required to be delivered to all the p destinations within D .1 We then propose a joint coding/scheduling algorithm achieving a throughput of \Theta (\min \{1,{({D}/{n_{s}})}^{1/2}\}) . Our simulation results suggest that the same scaling law also holds under random walk and random waypoint models.1

Given non-negative functions f(n)$ and g(n)$ : f(n)=O(g(n))$ means there exist positive constants c$ and m$ such that f(n) \leq cg(n)$ for all n\geq m;~f(n)=\Omega (g(n))$ means there exist positive constants c$ and m$ such that f(n)\geq cg(n)$ for all n\geq m;~f(n)=\Theta (g(n)) ; and f(n)=\omega (g(n))$ means that \lim _{n\rightarrow \infty } g(n)/f(n)=0 . [ABSTRACT FROM PUBLISHER]

We investigate the benefits of channel-aware (opportunistic) scheduling of transmissions in ad hoc networks. The key challenge in optimizing the performance of such systems is finding a good compromise among three interdependent quantities: 1) the density of scheduled transmitters; 2) the quality of transmissions; and 3) the long term fairness among nodes. We propose two new channel-aware slotted CSMA protocols opportunistic CSMA and quantile-based CSMA (QT-CSMA) and develop new stochastic geometric models to quantify their performance in terms of spatial reuse and spatial fairness. When properly optimized, these protocols offer substantial improvements in performance relative to CSMA—particularly, when the density of nodes is moderate to high. In addition, we show that a simple version of QT-CSMA can achieve robust performance gains without requiring careful parameter optimization. The quantitative results in this paper suggest that channel-aware scheduling in ad hoc networks can provide substantial benefits which might far outweigh the associated implementation overheads. [ABSTRACT FROM AUTHOR]

It is well known that cooperation between users in a communication network can lead to significant performance gains. A common assumption in past works is that all the users are aware of the resources available for cooperation, and know exactly to what extent these resources can be used. Unfortunately, in many modern communication networks, the availability of cooperation links cannot be guaranteed a priori, due to the dynamic nature of the network. In this paper, a family of models is suggested where the cooperation links may or may not be present. Coding schemes are devised that exploit the cooperation links if they are present, and can still operate (although at reduced rates) if cooperation is not possible. [ABSTRACT FROM AUTHOR]

This paper presents a systematic approach for solving wireless max–min utility fairness optimization problems in multiuser wireless networks with general monotonic constraints. These problems are often challenging to solve due to their nonconvexity. By establishing a connection between this class of optimization problems and the class of conditional eigenvalue problems that can be addressed by a generalized nonlinear Perron–Frobenius theory, we show how these problems can be solved optimally using an iterative algorithm that converges geometrically fast. The mathematical development in this paper unifies previous work and allows us to handle a broader class of competitive utility functions with general nonlinear monotonic constraints. Several representative applications illustrate the effectiveness of the proposed framework, including the max–min quality-of-service subject to robust interference temperature constraints in cognitive radio networks, the min–max weighted mean-square error subject to signal-to-interference-and-noise ratio constraints in multiuser downlink systems, the max–min throughput subject to nonlinear power constraints in energyefficient wireless networks, the max–min sigmoid utility in multimedia wireless networks, and the min–max outage probability subject to outage constraints in heterogeneous wireless networks. Numerical results are presented to demonstrate the fast-convergence behavior of the algorithms to the optimal fixed-point solution characterized by our generalized nonlinear Perron–Frobenius theoretic framework. [ABSTRACT FROM PUBLISHER]

The performance of existing coded caching schemes is sensitive to the worst channel quality, a problem which is exacerbated when communicating over fading channels. In this paper, we address this limitation in the following manner: in short-term, we allow transmissions to subsets of users with good channel quality, avoiding users with fades, while in long-term we ensure fairness among users. Our online scheme combines (i) the classical decentralized coded caching scheme with (ii) joint scheduling and power control for the fading broadcast channel, as well as (iii) congestion control for ensuring the optimal long-term average performance. We prove that our online delivery scheme maximizes the alpha-fair utility among all schemes restricted to decentralized placement. By tuning the value of alpha, the proposed scheme can achieve different operating points on the average delivery rate region and tune performance according to an operator’s choice. We demonstrate via simulations that our scheme outperforms two baseline schemes: (a) standard coded caching with multicast transmission, limited by the worst channel user yet exploiting the global caching gain; (b) opportunistic scheduling with unicast transmissions exploiting the fading diversity but limited to local caching gain. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of rate/distortion with side information available only at the decoder. For the case of jointly Gaussian source X and side information Y, and mean-squared error distortion, Wyner proved in 1976 that the rate/distortion function for this problem is identical to the conditional rate/distortion function RX∣Y, assuming the side information Y is available at the encoder. In this paper we construct a structured class of asymptotically optimal quantizers for this problem: under the assumption of high correlation between source X and side information Y, we show there exist quantizers within our class whose performance comes arbitrarily close to Wyner's bound. As an application illustrating the relevance of the high-correlation asymptotics, we also explore the use of these quantizers in the context of a problem of data compression for sensor networks, in a setup involving a large number of devices collecting highly correlated measurements within a confined area. An important feature of our formulation is that, although the per-node throughput of the network tends to zero as network size increases, so does the amount of information generated by each transmitter. This is a situation likely to be encountered often in practice, which allows us to cast under new—and more "optimistic"—light some negative results on the transport capacity of large-scale wireless networks. [ABSTRACT FROM AUTHOR]

This paper considers large random wireless networks, where transmit-and-receive node pairs communicate within a certain range while sharing a common spectrum. By modeling the spatial locations of nodes as a Poisson point process, analytical expressions for the ergodic spectral efficiency of a typical node pair are derived as a function of the channel state information available at a receiver (CSIR) in terms of relevant system parameters: the density of communication links, the number of receive antennas, the path loss exponent, and the operating signal-to-noise ratio. One key finding is that when the receiver only exploits CSIR for the direct link, the sum spectral efficiency increases linearly with the density, provided the number of receive antennas increases as a certain superlinear function of the density. When each receiver exploits CSIR for a set of dominant interfering links in addition to that of the direct link, the sum spectral efficiency increases linearly with both the density and the path loss exponent if the number of antennas is a linear function of the density. This observation demonstrates that having CSIR for dominant interfering links provides an order gain in the scaling law. It is also shown that this linear scaling holds for direct CSIR when incorporating the effect of the receive antenna correlation, provided that the rank of the spatial correlation matrix scales superlinearly with the density. These scaling laws are derived from integral representations of the distribution of the signal to interference and noise ratio, which are of independent interest and which in turn derived from stochastic geometry and more precisely from the theory of shot noise fields. Simulation results back the scaling laws and the integral representations. [ABSTRACT FROM AUTHOR]

The capacity scaling laws of two overlaid networks are investigated, which are located in the same area sharing the same wireless resources with different priorities. The primary network can be regarded as an existing communication system operated in a licensed band and, therefore, is assumed to operate in an order-optimal fashion to achieve its standalone capacity scaling law. The secondary cognitive network must keep its interference to the primary network below a certain threshold while at the same time maximizing its own throughput scaling law based on cognition information. The existing scaling results for cognitive networks inherently assume multihop communication, which is a restricted coding model. By contrast, in this paper, a general coding model is considered without any specific physical layer coding assumptions. The capacity scaling exponents for both networks are analyzed when the numbers of primary nodes n , primary base stations l , which support the communication between primary nodes, and secondary nodes m increase with the relations , \beta >1 , and l=n^\gamma , 0\leq \gamma \leq 1 . For the extended network model, the capacity scaling exponents are completely characterized as \max \2-\alpha /2,1/2,\gamma \ and \max \2-\alpha /2,1/2\ for the primary and secondary networks respectively, where \alpha >2 denotes the path-loss exponent. That is, the capacity scaling laws for the primary and secondary networks are represented, respectively, by n^{\max \{2-\alpha /2,1/2,\gamma \}\pm \epsilon } and m^{\max \{2-\alpha /2,1/2\}\pm \epsilon }$ for \epsilon >0$ arbitrarily small. For the dense network model, when the primary network achieves its standalone capacity scaling exponent of 1, the secondary network is shown to achieve a scaling exponent of 1-{1}/({2\beta }) , which improves the previous scaling exponent of 1/2 achieved by multihop. For both models, it turns out that the conventional multihop approach is in general quite suboptimal. [ABSTRACT FROM AUTHOR]

Decentralized binary detection problem in ad-hoc sensor networks where a link between two sensors is on with a certain probability is considered in this paper. We propose a consensus based detection scheme where sensors exchange their local decisions, update their own decisions based on the exchanges and finally reach a consensus about the state of nature. We analyze the error probability and convergence of this decision consensus scheme. We show that with our scheme, the detection performance in ad-hoc networks is asymptotically equivalent to that of a parallel sensor network where all the local decisions are processed by a central node (fusion center) in the sense that the error exponents are the same. The probability distribution of the consensus time is also studied. Simulation and numerical results are given to verify the theoretical results. [ABSTRACT FROM PUBLISHER]

In this paper, we characterize the information-theoretic capacity scaling of wireless ad hoc networks with n randomly distributed nodes. By using an exact channel model from Maxwell's equations, we successfully resolve the conflict in the literature between the linear capacity scaling by Özgür and the degrees of freedom limit given as the ratio of the network diameter and the wavelength \lambda by Franceschetti In dense networks where the network area is fixed, the capacity scaling is given as the minimum of n and the degrees of freedom limit \lambda^-1 to within an arbitrarily small exponent. In extended networks where the network area is linear in n, the capacity scaling is given as the minimum of n and the degrees of freedom limit \sqrtn\lambda^-1 to within an arbitrarily small exponent. Hence, we recover the linear capacity scaling by Özgür if \lambda=O(n^-1) in dense networks and if \lambda=O(n^-1/2) in extended networks. Otherwise, the capacity scaling is given as the degrees of freedom limit characterized by Franceschetti For achievability, a modified hierarchical cooperation is proposed based on a lower bound on the capacity of multiple-input multiple-output channel between two node clusters using our channel model. [ABSTRACT FROM AUTHOR]

Interference between nodes is a critical impairment in mobile ad hoc networks. This paper studies the role of multiple antennas in mitigating such interference. Specifically, a network is studied in which receivers apply zero-forcing beamforming to cancel the strongest interferers. Assuming a network with Poisson-distributed transmitters and independent Rayleigh fading channels, the transmission capacity is derived, which gives the maximum number of successful transmissions per unit area. Mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information (CSI). It is shown that, if each node cancels L interferers, the transmission capacity decreases as \Theta \big (\epsilon ^ 1\over L+1\big) as the outage probability \epsilon vanishes. For fixed \epsilon , as L grows, the transmission capacity increases as \Theta \big (L^1- 2\over \alpha \big) where \alpha is the path-loss exponent. Moreover, CSI inaccuracy is shown to have no effect on the transmission capacity scaling as \epsilon vanishes, provided that the CSI training sequence has an appropriate length, which we derive. Numerical results suggest that canceling merely one interferer by each node may increase the transmission capacity by an order of magnitude or more, even when the CSI is imperfect. [ABSTRACT FROM AUTHOR]

The transmission capacity of an ad-hoc network is the maximum density of active transmitters per unit area, given an outage constraint at each receiver for a fixed rate of transmission. Assuming that the transmitter locations are distributed as a Poisson point process, this paper derives upper and lower bounds on the transmission capacity of an ad-hoc network when each node is equipped with multiple antennas. The transmitter either uses eigen multi-mode beamforming or a subset of its antennas without channel information to transmit multiple data streams, while the receiver uses partial zero forcing to cancel certain interferers using some of its spatial receive degrees of freedom (SRDOF). The receiver either cancels the nearest interferers or those interferers that maximize the post-cancellation signal-to-interference ratio. Using the obtained bounds, the optimal number of data streams to transmit, and the optimal SRDOF to use for interference cancellation are derived that provide the best scaling of the transmission capacity with the number of antennas. With beamforming, single data stream transmission together with using all but one SRDOF for interference cancellation is optimal, while without beamforming, single data stream transmission together with using a fraction of the total SRDOF for interference cancellation is optimal. [ABSTRACT FROM AUTHOR]

In this paper, a lower bound on the capacity of wireless ad hoc erasure networks is derived in closed form in the case where n nodes are uniformly and independently distributed in the unit area square. It holds almost surely and is asymptotically tight. Nodes are assumed to have fixed transmit power; hence, two nodes should be within a specified distance rn to overcome noise. With interference determining outages, each transmitter-receiver pair is modeled as an erasure channel with a broadcast constraint, i.e., each node can transmit only one signal across all its outgoing links. A lower bound of \Theta (n rn) for the network capacity is derived when erasures across distinct links are independent, with constant erasure probabilities. When erasures are correlated, the lower bound \Theta \left ( 1\over rn\right ) is proved. If the broadcast constraint is relaxed, the gain is a function of rn and the link erasure probabilities, and is at most a constant if the erasure probabilities grow sufficiently large with n. Finally, the case where the erasure probabilities are random variables, for example due to randomness in geometry or channels, is analyzed. In this setting, it is shown somewhat surprisingly that variability in erasure probabilities increases network capacity. [ABSTRACT FROM AUTHOR]

In this paper, we study distributed opportunistic scheduling (DOS) in an ad hoc network, where many links contend for the same channel using random access. In such a network, DOS involves a process of joint channel probing and distributed scheduling. Due to channel fading, the link condition corresponding to a successful channel probing could be either good or poor. In the latter case, further channel probing, although at the cost of additional delay, may lead to better channel conditions and hence yield higher throughput. The desired tradeoff boils down to judiciously choosing the optimal stopping rule for channel probing and distributed scheduling. In this paper, we pursue a rigorous characterization of the optimal strategies from two perspectives, namely, a network-centric perspective and a user-centric perspective. We first consider DOS from a network-centric point of view, where links cooperate to maximize the overall network throughput. Using optimal stopping theory, we show that the optimal scheme for DOS turns out to be a pure threshold policy, where the rate threshold can be obtained by solving a fixed-point equation. We further devise iterative algorithms for computing the threshold. We also generalize the studies to take into account fairness requirements. Next, we explore DOS from a user-centric perspective, where each link seeks to maximize its own throughput. We treat the problem of threshold selection across different links as a noncooperative game. We explore the existence and uniqueness of the Nash equilibrium, and show that the Nash equilibrium can be approached by the best response strategy. Since the best response strategy requires message passing from neighboring nodes, we then develop an online stochastic iterative algorithm based on local observations only, and establish its convergence to the Nash equilibrium. Because there is an efficiency loss at the Nash equilibrium, we then study pricing-based mechanisms to mitigate the loss. Our results reveal that rich physical layer/MAC layer (PHY/MAC) diversities are available for exploitation in ad hoc networks. We believe that these initial steps open a new avenue for channel-aware distributed scheduling. [ABSTRACT FROM AUTHOR]

This paper addresses three issues in the field of ad hoc network capacity: the impact of i) channel fading, ii) channel inversion power control, and iii) threshold-based scheduling on capacity. Channel inversion and threshold scheduling may be viewed as simple ways to exploit channel state information (CSI) without requiring cooperation across transmitters. We use the transmission capacity (TC) as our metric, defined as the maximum spatial intensity of successful simultaneous transmissions subject to a constraint on the outage probability (OP). By assuming the nodes are located on the infinite plane according to a Poisson process, we are able to employ tools from stochastic geometry to obtain asymptotically tight bounds on the distribution of the signal-to-interference (SIR) level, yielding in turn tight bounds on the OP (relative to a given SIR threshold) and the TC. We demonstrate that in the absence of CSI, fading can significantly reduce the TC and somewhat surprisingly, channel inversion only makes matters worse. We develop a threshold-based transmission rule where transmitters are active only if the channel to their receiver is acceptably strong, obtain expressions for the optimal threshold, and show that this simple, fully distributed scheme can significantly reduce the effect of fading. [ABSTRACT FROM AUTHOR]

Wireless planar networks have been used to model wireless networks in a tradition that dates back to 1961 to the work of E. N. Gilbert. Indeed, the study of connected components in wireless networks was the motivation for his pioneering work that spawned the modern field of continuum percolation theory. Given that node locations in wireless networks are not known, random planar modeling can be used to provide preliminary assessments of important quantities such as range, number of neighbors, power consumption, and connectivity, and issues such as spatial reuse and capacity. In this paper, the problem of connectivity based on nearest neighbors is addressed. The exact threshold function for θ-coverage is found for wireless networks modeled as n points uniformly distributed in a unit square, with every node connecting to its ϕn nearest neighbors. A network is called θ-covered if every node, except those near the boundary, can find one of its ϕn nearest neighbors in any sector of angle θ. For all θ ∊ (0, 2π), if ϕn = (1 + δ) log(Multiple line equation(s) cannot be represented in ASCII text) n, it is shown that the probability of θ-coverage goes to one as n goes to infinity, for any δ > 0; on the other hand, if ϕn = (1 - δ) log(Multiple line equation(s) cannot be represented in ASCII text) n, the probability of θ-coverage goes to zero. This sharp characterization of θ-coverage is used to show, via further geometric arguments, that the network will be connected with probability approaching one if ϕn = (1 + δ) log2 n. Connections between these results and the performance analysis of wireless networks, especially for routing and topology control algorithms, are discussed. [ABSTRACT FROM AUTHOR]

In this paper, upper and lower bounds on the transmission capacity of spread-spectrum (SS) wireless ad hoc networks are derived. We define transmission capacity as the product of the maximum density of successful transmissions multiplied by their data rate, given an outage constraint. Assuming that the nodes are randomly distributed in space according to a Poisson point process, we derive upper and lower bounds for frequency hopping (FH-CDMA) and direct sequence (DS-CDMA) SS networks, which incorporate traditional modulation types (no spreading) as a special case. These bounds cleanly summarize how ad hoc network capacity is affected by the outage probability, spreading factor, transmission power, target signal-to-noise ratio (SNR), and other system parameters. Using these bounds, it can be shown that FH-CDMA obtains a higher transmission capacity than DS-CDMA on the order of M..., where M is the spreading factor and α > 2 is the path loss exponent. A tangential contribution is an (apparently) novel technique for obtaining tight bounds on tail probabilities of additive functionals of homogeneous Poisson point processes. [ABSTRACT FROM AUTHOR]

We analyze a novel architecture for caching popular video content to enable wireless device-to-device (D2D) collaboration. We focus on the asymptotic scaling characteristics and show how they depend on video content popularity statistics. We identify a fundamental conflict between collaboration distance and interference and show how to optimize the transmission power to maximize frequency reuse. Our main result is a closed form expression of the optimal collaboration distance as a function of the model parameters. Under the common assumption of a Zipf distribution for content reuse, we show that if the Zipf exponent is greater than 1, it is possible to have a number of D2D interference-free collaboration pairs that scales linearly in the number of nodes. If the Zipf exponent is smaller than 1, we identify the best possible scaling in the number of D2D collaborating links. Surprisingly, a very simple distributed caching policy achieves the optimal scaling behavior. [ABSTRACT FROM AUTHOR]

We consider the problem of distributed channel allocation in large networks under the frequency-selective interference channel. Performance is measured by the weighted sum of achievable rates. Our proposed algorithm is a modified Fictitious Play algorithm that can be implemented distributedly, and its stable points are the pure Nash equilibria of a given game. Our goal is to design a utility function for a non-cooperative game, such that all of its pure Nash equilibria have close to optimal global performance. This will make the algorithm close to optimal while requiring no communication between users. We propose a novel technique to analyze the Nash equilibria of a random interference game, determined by the random channel gains. Our analysis is asymptotic in the number of users. First, we present a natural non-cooperative game where the utility of each user is his achievable rate. It is shown that, asymptotically in the number of users and for strong enough interference, this game exhibits many bad equilibria. Then, we propose a novel non-cooperative M frequency-selective interference channel game as a slight modification of the former, where the utility of each user is artificially limited. We prove that even its worst equilibrium has asymptotically optimal weighted sum rate for any interference regime and even for correlated channels. This is based on an order statistics analysis of the fading channels that is valid for a broad class of fading distributions (including Rayleigh, Rician, m-Nakagami, and more). We carry out simulations that show fast convergence of our algorithm to the proven asymptotically optimal pure Nash equilibria. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of open-loop multi-antenna point-to-point links in ad hoc networks with slotted ALOHA medium access control (MAC). We consider spatial multiplexing transmission with linear maximum ratio combining and zero forcing receivers, as well as orthogonal space time block coded transmission. New closed-form expressions are derived for the outage probability, throughput and transmission capacity. Our results demonstrate that both the best performing scheme and the optimum number of transmit antennas depend on different network parameters, such as the node intensity and the signal-to-interference-and-noise ratio operating value. We then compare the performance to a network consisting of single-antenna devices and an idealized fully centrally coordinated MAC. These results show that multi-antenna schemes with a simple decentralized slotted ALOHA MAC can outperform even idealized single-antenna networks in various practical scenarios. [ABSTRACT FROM AUTHOR]

The capacity scaling of extended two-dimensional wireless networks is known in the high-attenuation regime, i.e., when the power path loss exponent n is greater than 4. This has been accomplished by deriving information-theoretic upper bounds for this regime that match the corresponding lower bounds. On the contrary, not much is known in the so-called low-attenuation regime when 2 ≤ a ≤ 4. (For one-dimensional networks, the uncharacterized regime is 1 ≤ a ≤ 2.5) The dichotomy is due to the fact that while communication is highly power-limited in the first case and power-based arguments suffice to get tight upper bounds, the study of the low-attenuation regime requires a more precise analysis of the degrees of freedom involved. In this paper, we study the capacity scaling of extended wireless networks with an emphasis on the low-attenuation regime and show that in the absence of small scale fading, the low attenuation regime does not behave significantly different from the high attenuation regime. [ABSTRACT FROM AUTHOR]

The transmission capacity (TC) of a wireless ad hoc network is defined as the maximum spatial intensity of successful transmissions such that the outage probability does not exceed some specified threshold. This work studies the improvement in TC obtainable with successive interference cancellation (SIC), an important receiver technique that has been shown to achieve the capacity of several classes of multiuser channels, but has not been carefully evaluated in the context of ad hoc wireless networks. This paper develops closed-form upper bounds and easily computable lower bounds for the TC of ad hoc networks with SIC receivers, for both perfect and imperfect SIC. The analysis applies to any multiuser receiver that cancels the K strongest interfering signals by a factor z ∊ [0, 1]. In addition to providing the first closed-form capacity results for SIC in ad hoc networks, design-relevant insights are made possible. In particular, it is shown that SIC should be used with direct sequence spread spectrum. Also, any imperfections in the interference cancellation rapidly degrade its usefulness. More encouragingly, only a few—often just one—interfering nodes need to be canceled in order to get the vast majority of the available performance gain. [ABSTRACT FROM AUTHOR]

We develop a dynamic control strategy for minimizing energy expenditure in a time-varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates or channel statistics, and yields average power that is arbitrarily close to the minimum possible value achieved by an algorithm optimized with complete knowledge of future events. Proximity to this optimal solution is shown to be inversely proportional to network delay. We then present a similar algorithm that solves the related problem of maximizing network throughput subject to peak and average power constraints. The techniques used in this paper are novel and establish a foundation for stochastic network optimization. [ABSTRACT FROM AUTHOR]

We consider an energy harvesting multiple access channel (MAC) where the transmitters are powered by an exogenous stochastic energy harvesting process and equipped with finite batteries. We characterize the capacity region of this channel as $n$ -letter mutual information rate and develop inner and outer bounds that differ by a constant gap. An interesting conclusion that emerges from our results is that the sum-capacity approaches that of a standard additive white Gaussian noise MAC (with only an average constraint on the transmitted power), as the number of users in the MAC becomes large. [ABSTRACT FROM AUTHOR]

We consider a wireless device-to-device network, where $n$ nodes are uniformly distributed at random over the network area. We let each node caches M$ files from a library of size m\geq M$ . Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions, including Zipf distributions with exponent less than one. In the parameter regime of interest, i.e., m=o(nM) for heavy-tailed popularity distributions. This scaling is constant with $n$ , thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is $\Theta (M/m)$ . The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as $n$ increases. [ABSTRACT FROM PUBLISHER]

We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple packet streams (commodities) when only the average channel state information is present, and that only locally. The proposed algorithms are the modifications of the diversity backpressure (DIVBAR) algorithm, under which the packet whose commodity has the largest “backpressure metric” is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially received packet in a separate “partial packet queue”, thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-renewal mutual information accumulation (RMIA), under which all the receiving nodes clear the received partial information of a packet once one or more receiving nodes firstly decode the packet; and DIVBAR-full mutual information accumulation (FMIA), under which all the receiving nodes retain the partial information of a packet until the packet has reached its destination. We characterize the network capacity region with the RMIA transmission scheme and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimal among the policies with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR with respect to the achievable throughput. Moreover, we prove that DIVBAR-FMIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results. [ABSTRACT FROM PUBLISHER]

We consider a wireless device-to-device (D2D) network where the nodes have precached information from a library of available files. Nodes request files at random. If the requested file is not in the on-board cache, then it is downloaded from some neighboring node via one-hop local communication. An outage event occurs when a requested file is not found in the neighborhood of the requesting node, or if the network admission control policy decides not to serve the request. We characterize the optimal throughput-outage tradeoff in terms of tight scaling laws for various regimes of the system parameters, when both the number of nodes and the number of files in the library grow to infinity. Our analysis is based on Gupta and Kumar protocol model for the underlying D2D wireless network, widely used in the literature on capacity scaling laws of wireless networks without caching. Our results show that the combination of D2D spectrum reuse and caching at the user nodes yields a per-user throughput independent of the number of users, for any fixed outage probability in (0, 1). This implies that the D2D caching network is scalable: even though the number of users increases, each user achieves constant throughput. This behavior is very different from the classical Gupta and Kumar result on ad hoc wireless networks, for which the per-user throughput vanishes as the number of users increases. Furthermore, we show that the user throughput is directly proportional to the fraction of cached information over the whole file library size. Therefore, we can conclude that D2D caching networks can turn memory into bandwidth (i.e., doubling the on-board cache memory on the user devices yields a 100% increase of the user throughout). [ABSTRACT FROM AUTHOR]

The influence of node mobility on the convergence time of averaging gossip algorithms in networks is studied. It is shown that a small number of fully mobile nodes can yield a significant decrease in convergence time. A method is developed for deriving lower bounds on the convergence time by merging nodes according to their mobility pattern. This method is used to show that if the agents have 1-D mobility in the same direction, the convergence time is improved by at most a constant. Upper bounds on the convergence time are obtained using techniques from the theory of Markov chains and show that simple models of mobility can dramatically accelerate gossip as long as the mobility paths overlap significantly. Simulations verify that different mobility patterns can have significantly different effects on the convergence of distributed algorithms. [ABSTRACT FROM AUTHOR]

The secrecy throughput of mobile ad hoc networks (MANETs) with malicious nodes is investigated. The MANET consists of n legitimate mobile nodes and m malicious nodes. Transmissions between legitimate nodes are subject to a delay constraint D. A model under passive attack is first studied, in which the malicious nodes are assumed to be eavesdroppers that only listen to transmission without actively injecting signals. An information-theoretic approach for security is applied to achieve secure communication among legitimate nodes in MANETs with transmissions being kept perfectly secure from eavesdroppers. A critical threshold on the number of malicious nodes (m) is identified such that when m=o(\sqrt nD), i.e., \lim n\rightarrow \infty m/\sqrt {nD}=0, the optimal secrecy throughput equals that of MANETs without malicious nodes, i.e., the impact of the presence of malicious nodes on the network throughput is negligible; and when m=\Omega \left (\sqrt nDpoly(n)\right ), i.e., \lim n\rightarrow \infty m/\left (\sqrt {nD}poly(n)\right ) \geq c for a positive constant c, the optimal secrecy throughput is limited by the number of malicious nodes. A model under active attack is further studied, in which the malicious nodes actively attack the network by transmitting modified packets to the destination nodes. It is shown that to guarantee the same throughput as the model under passive attack, the model under active attack needs to satisfy more stringent condition on the number of malicious nodes. [ABSTRACT FROM AUTHOR]

This work studies the throughput scaling laws of ad hoc wireless networks in the limit of a large number of nodes. A random connections model is assumed in which the channel connections between the nodes are drawn independently from a common distribution. Transmitting nodes are subject to an on--off strategy, and receiving nodes employ conventional single-user decoding. The following results are proven: 1) for a class of connection models with finite mean and variance, the throughput scaling is upper-bounded by O(n1/3) for single-hop schemes, and O(n½) for two-hop (and multihop) schemes; 2) the Θ (n½) throughput scaling is achievable for a specific connection model by a two-hop opportunistic relaying scheme, which employs full, but only local channel state information (CSI) at the receivers, and partial CSI at the transmitters; 3) by relaxing the constraints of finite mean and variance of the connection model, linear throughput scaling Θ (n) is achievable with Pareto-type fading models. [ABSTRACT FROM AUTHOR]

It is shown that the capacity scaling of wireless networks is subject to a fundamental limitation which is independent of power attenuation and fading models. It is a degrees of freedom limitation which is due to the laws of physics. By distributing uniformly an order of n users wishing to establish pairwise independent communications at fixed wavelength inside a two-dimensional domain of size of the order of n, there are an order of n communication requests originating from the central half of the domain to its outer half. Physics dictates that the number of independent information channels across these two regions is only of the order of √n, so the per-user information capacity must follow an inverse square-root of n law. This result shows that information-theoretic limits of wireless communication problems can be rigorously obtained without relying on stochastic fading channel models, but studying their physical geometric structure. [ABSTRACT FROM AUTHOR]

Decentralized medium access control schemes for wireless networks based on CSMA/CA, such as the IEEE 802.11 protocol, are known to be unfair. In multihop networks, they can even favor some links to such an extent that the others suffer from virtually complete starvation. This observation has been reported in quite a few works, but the factors causing it are still not well understood. We find that the capture effect and the relative values of the receive and carrier sensing ranges play a crucial role in the performance of these protocols. Using a simple Markovian model, we show that an idealized CSMA/CA protocol suffers from starvation when the receiving and sensing ranges are equal, but quite surprisingly that this unfairness is reduced or even disappears when these two ranges are sufficiently different. We also show that starvation has a positive counterpart, namely organization. When its access intensity is large the protocol organizes the transmissions in space in such a way that it maximizes the number of concurrent successful transmissions. We obtain exact formula for the so-called spatial reuse of the protocol on large line networks. [ABSTRACT FROM AUTHOR]

In this work we present a new framework for the analysis and design of randomized gossip algorithms for solving the average consensus problem. We show how classical randomized iterative methods for solving linear systems can be interpreted as gossip algorithms when applied to special systems encoding the underlying network and explain in detail their decentralized nature. Our general framework recovers a comprehensive array of well-known gossip algorithms as special cases, including the pairwise randomized gossip algorithm and path averaging gossip, and allows for the development of provably faster variants. The flexibility of the new approach enables the design of a number of new specific gossip methods. For instance, we propose and analyze novel block and the first provably accelerated randomized gossip protocols, and dual randomized gossip algorithms. From a numerical analysis viewpoint, our work is the first that explores in depth the decentralized nature of randomized iterative methods for linear systems and proposes them as methods for solving the average consensus problem. We evaluate the performance of the proposed gossip protocols by performing extensive experimental testing on typical wireless network topologies. [ABSTRACT FROM AUTHOR]

We study the scaling properties of a georouting scheme in a wireless multi-hop network of n mobile nodes. Our aim is to increase the network capacity quasi-linearly with n , while keeping the average delay bounded. In our model, we consider mobile nodes moving according to an independent identically distributed random walk with velocity v and transmitting packets to randomly chosen fixed and known destinations. The average packet delivery delay of our scheme is of order 1/v . This shows a practical throughput-delay tradeoff, in particular when compared with the seminal result of Gupta and Kumar, which shows network capacity of order {(n/\log n)}^{1/2} and negligible delay and the groundbreaking result of Grossglauser and Tse, which achieves network capacity of order n$ but with an average delay of order \sqrt {n}/v . The foundation of our improved capacity and delay tradeoff relies on the fact that we use a mobility model that contains straight-line segments, a model that we consider more realistic than classic Brownian motions. We confirm the generality of our analytical results using simulations under various interference models. [ABSTRACT FROM AUTHOR]

We investigate optimal resource allocation for delay-limited cooperative communication in time varying wireless networks. Motivated by real-time applications that have stringent delay constraints, we develop a dynamic cooperation strategy that makes optimal use of network resources to achieve a target outage probability (reliability) for each user subject to average power constraints. Using the technique of Lyapunov optimization, we first present a general framework to solve this problem and then derive quasi-closed form solutions for several cooperative protocols proposed in the literature. Unlike earlier works, our scheme does not require prior knowledge of the statistical description of the packet arrival, channel state, and node mobility processes and can be implemented in an online fashion. [ABSTRACT FROM AUTHOR]

Communication between two neighboring nodes is a very basic operation in wireless networks. Yet very little research has focused on the local delay in networks with randomly placed nodes, defined as the mean time it takes a node to connect to its nearest neighbor. We study this problem for Poisson networks, first considering interference only, then noise only, and finally and briefly, interference plus noise. In the noiseless case, we analyze four different types of nearest-neighbor communication and compare the extreme cases of high mobility, where a new Poisson process is drawn in each time slot, and no mobility, where only a single realization exists and nodes stay put forever. It turns out that the local delay behaves rather differently in the two cases. We also provide the low- and high-rate asymptotic behavior of the minimum achievable delay in each case. In the cases with noise, power control is essential to keep the delay finite, and randomized power control can drastically reduce the required (mean) power for finite local delay. [ABSTRACT FROM AUTHOR]

An asymptotic technique is presented for finding the spectral efficiency of multiantenna links in spatially distributed wireless networks where transmitters have channel-state-information (CSI) corresponding to their target receiver. Transmitters are assumed to transmit independent data streams on a limited number of channel modes which limits the rank of transmit covariance matrices. An approximation for the spectral efficiency in the interference-limited regime as a function of link-length, interferer density, number of antennas per receiver and transmitter, number of transmit streams, and path-loss exponent is derived. It is found that targeted-receiver CSI, which can be acquired with low overhead in duplex systems with reciprocity, can increase spectral efficiency several fold, particularly when link lengths are large, node density is high, or both. Additionally, the per-link spectral efficiency is found to be a function of the ratio of node density to the number of receiver antennas, and it can often be improved if nodes transmit using fewer streams. These results are validated for finite-sized systems by Monte-Carlo simulation and are asymptotic in the regime where the number of users and antennas per receiver approach infinity. [ABSTRACT FROM PUBLISHER]

We analyze the capacity scaling laws of clustered ad hoc networks comprising significant inhomogeneities in the node spatial distribution over the area. In particular, we consider the class of networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process, which allows to model a wide variety of inhomogeneous topologies. For this class of networks, we derive information theoretic upper-bounds to the capacity, identifying six operational regions. We also provide constructive lower bounds by devising, for each region, an optimal communication strategy to achieve the maximum network throughput. The performance of our communication schemes match, in terms of scaling exponent, the theoretical upper-bounds. [ABSTRACT FROM AUTHOR]

We address the percolation-based connectivity of large-scale ad hoc heterogeneous wireless networks, where secondary users exploit channels temporarily unused by primary users and the existence of a communication link between two secondary users depends on not only the distance between them but also the transmitting and receiving activities of nearby primary users. We introduce the concept of connectivity region defined as the set of density pairs—the density of secondary users and the density of primary transmitters — under which the secondary network is connected. Using theories and techniques from continuum percolation, we analytically characterize the connectivity region of the secondary network and reveal the tradeoff between proximity (the number of neighbors) and the occurrence of spectrum opportunities. Specifically, we establish three basic properties of the connectivity region—contiguity, monotonicity of the boundary and uniqueness of the infinite connected component, where the uniqueness implies the occurrence of a phase transition phenomenon in terms of the almost sure existence of either zero or one infinite connected component; we identify and analyze two critical densities which jointly specify the profile as well as an outer bound on the connectivity region; we study the impacts of secondary users' transmission power on the connectivity region and the conditional average degree of a secondary user and demonstrate that matching the interference ranges of the primary and the secondary networks maximizes the tolerance of the secondary network to the primary traffic load. Furthermore, we establish a necessary condition and a sufficient condition for connectivity, which lead to an outer bound and an inner bound on the connectivity region. [ABSTRACT FROM AUTHOR]

A Wireless network is considered where a source is communicating With a remote destination, and where a relay terminal is occasionally present in close proximity to the source but Without the source's knowledge. The channel source-relay is assumed fixed due to the short distance, while the channels source-destination and relay-destination are affected by block flat Rayleigh fading, where channel state information is known only to respective receivers. Oblivious cooperative protocols are addressed which improve performance when the relay is present and do not degrade it when the relay is absent--and all this while the source' is uninformed of the actual topology. Using the expected throughput as a performance measure, several such protocols based on decode-and-forward and quantize-and-forward .are proposed for this network. It turns out that block Markov decode-and-forward (BMDF) with appropriate decoding is in fact an oblivious cooperative protocol. The optimal correlation between the transmissions of the source and the relay in decode- and-forward and the corresponding optimum performance are characterized. Finally, quantize-and-forward is considered for rates which preclude the usage of decode-and-forward, and several schemes are proposed for incorporating partial side information in the relay's quantization. [ABSTRACT FROM AUTHOR]

Relay networks having n source-to-destination pairs and m half-duplex relays, all operating in the same frequency band and in the presence of block fading, are analyzed. This setup has attracted significant attention, and several relaying protocols have been reported in the literature. However, most of the proposed solutions require either centrally coordinated scheduling or detailed channel state information (CSI) at the transmitter side. Here, an opportunistic relaying scheme is proposed that alleviates these limitations, without sacrificing the system throughput scaling in the regime of large n. The scheme entails a two-hop communication protocol, in which sources communicate with destinations only through half-duplex relays. All nodes operate in a completely distributed fashion, with no cooperation. The key idea is to schedule at each hop only a subset of nodes that can benefit from multiuser diversity. To select the source and destination nodes for each hop, CSI is required at receivers (relays for the first hop, and destination nodes for the second hop), and an index-valued CSI feedback at the transmitters. For the case when n is large and m is fixed, it is shown that the proposed scheme achieves a system throughput of m/2 bits/s/Hz. In contrast, the information-theoretic upper bound of (m/2) log log n bits/s/Hz is achievable only with more demanding CSI assumptions and cooperation between the relays. Furthermore, it is shown that, under the condition that the product of block duration and system bandwidth scales faster than log n log log n, the achievable throughput of the proposed scheme scales as θ (log n). Notably, this is proven to be the optimal throughput scaling even if centralized scheduling is allowed, thus proving the optimality of the proposed scheme in the scaling law sense. Simulation results indicate a rather fast convergence to the asymptotic limits with the system's size, demonstrating the practical importance of the scaling results. [ABSTRACT FROM AUTHOR]

For a wireless network with n nodes distributed in an area A, and with n source-destination pairs communicating with each other at some common rate, the hierarchical cooperation scheme proposed in (Ozgur, Leveque, and Tse, 2007) is analyzed and optimized by choosing the number of hierarchical stages and the corresponding cluster sizes that maximize the total throughput. It turns out that increasing the number of stages does not necessarily improve the throughput, and the closed-form solutions for the optimization problem can be explicitly obtained. Based on the expression of the maximum achievable throughput, it is found that the hierarchical scheme achieves a scaling with the exponent depending on n. In addition, to apply the hierarchical cooperation scheme to random networks, a clustering algorithm is developed, which divides the whole network into quadrilateral clusters, each with exactly the number of nodes required. [ABSTRACT FROM AUTHOR]

We consider the problem of determining asymptotic bounds on the capacity of a random ad hoc network. Previous approaches assumed a link layer model in which if a transmitter-receiver pair can communicate with each other, i.e., the signal to interference and noise ratio (SINR) is above a certain threshold, then the transmitted packet is received error-free by the receiver thereby. Using this model, the per node capacity of the network was shown to be ⊙ (1/¿nlogn). In reality, for any finite link SINR, there is a nonzero probability of erroneous reception of the packet. We show that in a large network, as the packet travels an asymptotically large number of hops from source to destination, the cumulative impact of packet losses over intermediate links results in a per-node throughput of only O (1/n) under the previously proposed routing and scheduling strategy. We then propose a new scheduling scheme to counter this effect. The proposed scheme provides tight guarantees on end-to-end packet loss probability, and improves the per-node throughput to Ο [This equation can not converted into ASCII.txt] where Ω > 2 is the path loss exponent. [ABSTRACT FROM AUTHOR]

A two-hop relay fading channel is considered, where only decoders possess perfect channel state information (CSI). Various relaying protocols and broadcasting strategies are studied. The main focus of this work is on simple relay transmission scheduling schemes. For decode-and-forward (DF) relaying, the simple relay cannot buffer multiple packets, nor can it reschedule retransmissions. This gives rise to consideration of other relaying techniques, such as amplify-and-forward (AF), where a maximal broadcasting achievable rate is analytically derived. A quantize-and-forward (QF) relay, coupled with a single-level code at the source, uses codebooks matched to the received signal power and performs optimal quantization. This is simplified by a hybrid amplify-QF (AQF) relay, which performs scaling, and single codebook quantization on the input. It is shown that the latter is optimal by means of throughput on the relay-destination link, while maintaining a lower coding complexity than the QF setting. A further extension of the AQF allows the relay to perform successive refinement, coupled with a matched multilevel code. Numerical results show that for high signal-to-noise ratios (SNRs), the broadcast approach over AF relay may achieve higher throughput gains than other relaying protocols that are numerically tractable. [ABSTRACT FROM AUTHOR]

The throughput of wireless networks is known to scale poorly when the number of users grows. The rate at which an arbitrary pair of nodes can communicate must decrease to zero as the number of users tends to infinity, under various assumptions. One of them is the requirement that the network is fully connected: the computed rate must hold for any pair of nodes of the network. We show that this requirement can be responsible for the lack of throughput scalability. We consider a two-dimensional (2-D) network of extending area with only one active source-destination pair at any given time, and all remaining nodes acting only as possible relays. Allowing an arbitrary small fraction of the nodes to be disconnected, we show that the per-node throughput remains constant as the network size increases. As a converse bound, we show that communications occurring at a fixed nonzero rate imply a fraction of the nodes to be disconnected. Our results are of information theoretic flavor, as they hold without assumptions on the communication strategies employed by the network nodes. [ABSTRACT FROM AUTHOR]

We consider the problem of establishing minimum-cost multicast connections over coded packet networks, i.e., packet networks where the contents of outgoing packets are arbitrary, causal functions of the contents of received packets. We consider both wireline and wireless packet networks as well as both static multicast (where membership of the multicast group remains constant for the duration of the connection) and dynamic multicast (where membership of the multicast group changes in time, with nodes joining and leaving the group). For static multicast, we reduce the problem to a polynomial-time solvable optimization problem, and we present decentralized algorithms for solving it. These algorithms, when coupled with existing decentralized schemes for constructing network codes, yield a fully decentralized approach for achieving minimum-cost multicast. By contrast, establishing minimum-cost static multicast connections over routed packet networks is a very difficult problem even using centralized computation, except in the special cases of unicast and broadcast connections. For dynamic multicast, we reduce the problem to a dynamic programming problem and apply the theory of dynamic programming to suggest how it may be solved. [ABSTRACT FROM AUTHOR]