Watermarking for large language models (LLMs) offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection but ignores identification. We present StealthInk, a stealthy multi-bit watermarking scheme that preserves the original text distribution while enabling the embedding of provenance data, such as userID, TimeStamp, and modelID, within LLM-generated text. This enhances fast traceability without requiring access to the language model’s API or prompts. We derive a lower bound on the number of tokens necessary for watermark detection at a fixed equal error rate, which provides insights on how to enhance the capacity. Comprehensive empirical evaluations across diverse tasks highlight the stealthiness, detectability, and resilience of StealthInk, establishing it as an effective solution for LLM watermarking applications.

We propose an energy-efficient power allocation algorithm for the multi-user millimeter-wave (mmWave) rate-splitting multiple access (RSMA) downlink with hybrid precoding and quality of service (QoS) constraints. The proposed scheme is applicable to the physical layer design of future wireless networks, such as the 6G cellular downlink, in which a transmitter equipped with multiple antennas must communicate unicast messages to multiple receivers simultaneously. First, we use a low-complexity design to define the analog and digital precoders in closed form. Second, we define an energy efficiency (EE) maximization problem to jointly optimize the power allocation among streams and the common stream rate allocation among users. We then solve the problem using a combination of Dinkelbach’s algorithm and difference of convex functions (DC) programming methods. Simulation results show that the proposed RSMA scheme offers EE improvements over a comparable space division multiple access (SDMA) power allocation scheme in scenarios with perfect and imperfect channel state information at the transmitter. Lastly, we present extensive numerical experiments that suggest that the computational complexity of the proposed RSMA energy-efficient power allocation algorithm can be reduced using the interior-point method such that the computational efficiency of RSMA is comparable to that of SDMA.

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks.

Wireless device pairing is a critical security mechanism to bootstrap the secure communication between two devices without a pre-shared secret. It has been widely used in many Internet of Things (IoT) applications, such as smarthome and smarthealth. Most existing device pairing mechanisms are based on out-of-band channels, e.g., extra sensors or hardware, to validate the location proximity of pairing devices. However, out-of-band channels are not universal on all wireless devices, thus this type of scheme is limited to certain application scenarios or conditions. On the other hand, in-band channel-based device pairing aims at universal applicability by only relying on wireless interfaces. Existing in-band channel-based pairing schemes either require multiple antennas separated in a good distance on one pairing devices which is not applicable in certain scenarios, or require users to repeat multiple sweeps which is not optimal in terms of usability. Therefore, an in-band wireless device pairing scheme providing high security while maintaining good usability (simple pairing process and user interaction) is highly desired. In this work, we propose an easy-to-use mutual authentication device pairing scheme, named Swipe2Pair, based on location proximity of pairing devices and wireless transmission power randomization. We conduct extensive security analysis and collect considerable experimental data under various settings in different environments. Experimental results show that Swipe2Pair achieves high security and usability. It only takes less than one second to complete the pairing process with a simple swipe of one device in front of the other.

Abstract Network tomography aims at estimating source–destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for Poisson traffic over networks operating under deterministic as well as random routing regimes. In this article, we expand Vardi's second‐order moment matching rate estimation approach to higher‐order cumulant matching with the goal of increasing the column rank of the mapping and consequently improving the rate estimation accuracy. We develop a systematic set of linear cumulant matching equations and express them compactly in terms of the Khatri–Rao product. Both least squares estimation and iterative minimum I‐divergence estimation are considered. We develop an upper bound on the mean squared error (MSE) in least squares rate estimation from empirical cumulants. We demonstrate that supplementing Vardi's approach with the third‐order empirical cumulant reduces its minimum averaged normalized MSE in rate estimation by almost 20% when iterative minimum I‐divergence estimation was used.

Stochastic network calculus involves the use of a traffic bound or envelope to make admission control and resource allocation decisions for providing end-to-end quality-of-service guarantees. To apply network calculus in practice, the traffic envelope should: (i) be readily determined for an arbitrary traffic source, (ii) be enforceable by traffic regulation, and (iii) yield statistical multiplexing gain. Existing traffic envelopes typically satisfy at most two of these properties. A well-known traffic envelope based on the moment generating function (MGF) of the arrival process satisfies only the third property. We propose a new traffic envelope based on the MGF of the workload process obtained from offering the traffic to a constant service rate queue. We show that this traffic workload envelope can achieve all three properties and leads to a framework for a network service that provides stochastic delay guarantees. We demonstrate the performance of the traffic workload envelope with two bursty traffic models: Markov on-off fluid and Markov modulated Poisson Process (MMPP).

Device-to-device (D2D) communications is widely adopted as the underlay to cellular primary networks. By reusing the licensed spectrum and directly transmitting to other nearby devices, D2D users can significantly enhance system spectrum efficiency. However, D2D communications usually faces the restrictions of short transmission distance and limited energy supply. Mutual interference among different devices increases energy consumption and exacerbates communication quality-of-service degradation. In this paper, we consider a dense D2D network where the D2D devices communicate using cooperative relaying. We propose a hierarchical game framework consisting of a Stackelberg game to model intra-tier interactions within each D2D link and a mean field game to model inter-tier interactions among different D2D links. A finite difference method is used to derive an optimal power control scheme, and numerical results are presented to demonstrate the performance of the proposed hierarchical power control scheme. We also make comparisons to a proportional power control scheme, where the experimental result shows clear advantage of the proposed hierarchical scheme.

The increased proliferation of IoT devices and the emergence of 5G networks have necessitated increased security of data storage and communication in such connected devices. Thus, cryptography is used in IoT environments to provide secrecy and integrity to the data as well as both authentication and anonymity to the communications across the IoT network. However, IoT devices are resource-constrained devices; have limited memory, network bandwidth, power, and compute units. Since most of the existing cryptographic algorithms were designed to run on resource powerful devices (e.g., desktops or servers), many of these algorithms may not fit into resource-constrained devices. Therefore, in this work, we present a practical performance analysis of different implementations of the Advanced Encryption Standard (AES), which is the most widely used symmetric-key cryptosystem in the IoT environment. Specifically, we explore execution times, energy consumption, and memory usage of the different AES implementations across 4 different public libraries. Furthermore, our analysis is done using various modes, key sizes, plaintext sizes, and microprocessor-based IoT devices. Our results show that for the same combination of inputs and a given algorithm, different crypto library implementations give results with widely varying relative differences. As per the obtained results, the PyCryptodome library seems to be the most suitable one in terms of both execution time and energy on a resource-constrained IoT device and has the most efficient memory usage.

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Since deterministic bounds are based on the worst-case traffic behavior, various frameworks for stochastic network calculus have been proposed to provide less conservative, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We design and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Two alternative implementations of the stochastic regulator are developed and compared. Numerical results are provided to demonstrate the performance of the proposed stochastic traffic regulator.

In a wireless network with dynamic spectrum sharing, tracking temporal spectrum holes across a wide spectrum band is a challenging task. We consider a scenario in which the spectrum is divided into a large number of bands or channels, each of which has the potential to provide dynamic spectrum access opportunities. The occupancy times of each band by primary users are generally non-exponentially distributed. We develop an approach to determine and parameterize a small selected subset of the bands with good spectrum access opportunities, using limited computational resources under noisy measurements. We model the noisy measurements of the received signal in each band as a bivariate Markov modulated Gaussian process, which can be viewed as a continuous-time bivariate Markov chain observed through Gaussian noise. The underlying bivariate Markov process allows for the characterization of non-exponentially distributed state sojourn times. The proposed scheme combines an online expectation-maximization algorithm for parameter estimation with a computing budget allocation algorithm. Observation time is allocated across the bands to determine the subset of G*out of G frequency bands with the largest mean idle times for dynamic spectrum access and at the same time to obtain accurate parameter estimates for this subset of bands. Our simulation results show that when channel holding times are non-exponential, the proposed scheme achieves a substantial improvement in the probability of correct selection of the best subset of bands compared to an approach based on a (univariate) Markov modulated Gaussian process model.

Providing end-to-end network delay guarantees in packet-switched networks such as the Internet is highly desirable for mission-critical and delay-sensitive data transmission, yet it remains a challenging open problem. Due to the looseness of the deterministic bounds, various frameworks for stochastic network calculus have been proposed to provide tighter, probabilistic bounds on network delay, at least in theory. However, little attention has been devoted to the problem of regulating traffic according to stochastic burstiness bounds, which is necessary in order to guarantee the delay bounds in practice. We propose and analyze a stochastic traffic regulator that can be used in conjunction with results from stochastic network calculus to provide probabilistic guarantees on end-to-end network delay. Numerical results are provided to demonstrate the performance of the proposed traffic regulator.11This work was supported in part by the U.S. National Science Foundation under Grant No. 1717033.

Under a dynamic spectrum access paradigm, a set of L spectrum bands licensed to primary users provide opportunities for an unlicensed secondary user to gain access to spectrum left idle by a primary user. We model the received noisy signal measurements on each band as a continuous-time Markov chain observed through a discrete-time Gaussian channel. Based on this model, we develop a scheme for estimating the parameters of the subset of L* <; L bands that offer the “best” opportunities for dynamic spectrum access in the sense of largest mean idle periods. Our approach consists of a Markov modulated Gaussian process model, an associated expectation-maximization algorithm, and a computing budget allocation scheme for allocating sensing effort across the spectrum bands over a sequence of observation intervals. The sensing effort allocation scheme maximizes the probability that the L* best bands will be determined from their parameter stimates obtained in the next observation interval. Simulation results are presented to demonstrate the performance of the proposed scheme.

We propose a novel Stackelberg game-theoretic framework to jointly manage spectrum resources and coordinate secondary users in a cognitive radio network with decode-and-forward cooperative relaying capability to extend coverage. The primary users (PUs) and secondary users (SUs) are mapped into leader-follower pairs in which the SUs purchase spectrum resources from their corresponding PU leaders. An optimal SU transmit strategy incorporating cooperative relaying is derived, and a hybrid scheduling algorithm incorporating both direct transmission and relay transmission is proposed. Experimental results show that the proposed Stackelberg game framework can achieve significantly better system performance with cooperative relaying compared to an SU direct transmission scheme.

Spectrum sensing enables secondary users in a cognitive radio network to opportunistically access portions of the spectrum left idle by primary users. Tracking spectrum holes jointly in time and frequency over a wide spectrum band is a challenging task. In one approach to wideband temporal sensing, the spectrum band is partitioned into narrowband subchannels of fixed bandwidth, which are then characterized via hidden Markov modeling using average power or energy measurements as observation data. Adjacent, correlated subchannels are recursively aggregated into channels of variable bandwidths, corresponding to the primary user signals. Thus, wideband temporal sensing is transformed into a multiband sensing scenario by identifying the primary user channels in the spectrum band. However, future changes in the configuration of the primary user channels in the multiband setup cannot generally be detected using an energy detector front end for spectrum sensing. We propose the use of a cepstral feature vector to detect changes in the spectrum envelope of a primary user channel. Our numerical results show that the cepstrum-based spectrum envelope detector performs well under moderate to high signal-to-noise ratio conditions.

Wideband spectrum sensing techniques determine which portions of a given spectrum band are occupied or idle in the frequency domain. The idle portions represent spectrum holes that can potentially be exploited by secondary or unlicensed users. Existing methods for wideband sensing, however, do not take into account the temporal activity of the primary or licensed users within the spectrum band. We propose an algorithm that identifies primary user activity over a wide spectrum band and provides a statistical characterization of the primary user signals in the band. The algorithm applies hidden Markov modeling to a hierarchically partitioned representation of the spectrum band, together with a recursive tree search. Different from existing wideband sensing algorithms, the proposed wideband temporal sensing method is able to accurately detect spectrum holes even in the presence of bursting primary user signals. Moreover, the hidden Markov modeling of the primary user signals enables the accurate detection and the prediction of primary user activity over time. Numerical results demonstrate the significant performance gain of the proposed algorithm over existing wideband spectrum sensing algorithms, particularly in the presence of low duty-cycle primary user signals.

To protect primary users from interference caused by secondary users (SUs) in a cognitive radio network, a geographic area called an exclusion zone can be defined in which SUs are prohibited from transmitting using a specified spectrum band. We propose a Gaussian Random Field Model (GRFM) framework for determining an exclusion zone with the desired properties in practical scenarios where analytical specifications may not be available. Based on the GRFM, we derive the radius of a disk determining the exclusion zone, assuming that the SUs are distributed geographically over a planar coverage area. Using measurement data obtained from SUs, the GRFM is applied to approximate the equivalent received signal power and aggregate interference at specified locations. Simulation results show that the GRFM approximation yields an accurate characterization of the exclusion zone.1

In dynamic or opportunistic spectrum access, the primary user (PU) alternates between an idle and an active state, and a secondary user (SU) may access the channel during the idle periods. In multiband spectrum sensing, an SU tracks the PU state on a given set of channels to determine spectrum access opportunities. In this context, we address the following problem: Given amp;#924; channels, determine the best subset of amp;#925; amp;#8804; amp;#924; channels with respect to spectrum access opportunities and, at the same time, estimate the parameter of the PU state process for each channel within the selected subset. Specifically, we model the PU state on the given set of channels by amp;#924; independent, two-state continuous-time Markov chains. Over a given interval of time, our goal is to determine, with high probability, the amp;#925; channels with the largest mean idle periods and, at the same time, to accurately estimate the parameter of each channel in the selected subset. We adapt the optimal computing budget allocation (OCBA) methodology from the field of simulation optimization to allocate the total time budget for sensing the $N$ channels in order to perform the channel subset selection and parameter estimation. Simulation results are presented to demonstrate the performance of the proposed algorithm.

Estimation of link delay densities in a computer network, from source-destination delay measurements, is of great importance in analyzing and improving the operation of the network. In this paper, we develop a general approach for estimating the density of the delay in any link of the network, based on continuous-time bivariate Markov chain modeling. The proposed approach also provides the estimates of the packet routing probability at each node, and the probability of each source-destination path in the network. In this approach, the states of one process of the bivariate Markov chain are associated with nodes of the network, while the other process serves as an underlying process that affects statistical properties of the node process. The node process is not Markov, and the sojourn time in each of its states is phase-type. Phase-type densities are dense in the set of densities with non-negative support. Hence, they can be used to approximate arbitrarily well any sojourn time distribution. Furthermore, the class of phase-type densities is closed under convolution and mixture operations. We adopt the expectation-maximization (EM) algorithm of Asmussen, Nerman, and Olsson for estimating the parameter of the bivariate Markov chain. We demonstrate the performance of the approach in a numerical study.

In wideband spectrum sensing, an unlicensed user determines which portions of a given band have been left idle by the licensed users. A historical deficiency of wideband spectrum sensing, the inability to detect signals with low duty cycle, was addressed in a recent paper, where wideband temporal spectrum sensing was introduced. We propose an algorithm for reliable detection of low duty cycle signals in noisy environments. We leverage this recent advance in wideband spectrum sensing, and apply a well-known edge detection algorithm to determine channel boundaries. Numerical results are presented which show performance improvements over the original wideband temporal spectrum sensing algorithm, particularly in low signal-to-noise ratio scenarios.

Collaborative spectrum sensing exploits multiuser diversity by combining spectrum sensing information from multiple secondary users to make joint decisions about spectrum occupancy. In hard fusion schemes, each secondary user makes a hard decision on spectrum occupancy and a fusion center makes a final decision by combining the individual hard decisions according to a fusion rule. In soft fusion schemes, each secondary user provides a signal power measurement to the fusion center, which performs further processing on the collection of all observations to make a final decision. In this paper, we propose hard and soft fusion collaborative spectrum sensing schemes based on the online hidden bivariate Markov chain modeling of the signals received by secondary users. Compared with prior collaborative sensing schemes, the proposed model-based schemes do not rely on precomputed thresholds or weights, and achieve superior performance. The online estimation of hidden bivariate Markov models provides predictive information that can be used to improve the performance of the dynamic spectrum access. Numerical results are presented to demonstrate the performance and communication overhead tradeoffs of the proposed collaborative spectrum sensing schemes.

We develop a computationally efficient online parameter estimation algorithm for temporal spectrum sensing of a cognitive radio channel using a hidden bivariate Markov model. The online estimator is based on a block-recursive parameter estimation algorithm developed by Rydén for hidden Markov models. This approach requires the score function only. We develop an efficient method for computing the score function recursively and extend Rydén's approach to hidden bivariate Markov models. The advantage of the hidden bivariate Markov model over the hidden Markov model is its ability to characterize non-geometric state sojourn time distributions, which can be crucial in spectrum sensing. Based on the hidden bivariate Markov model, an estimate of the future state of the primary user can be obtained, which can be used to reduce harmful interference and improve channel utilization. Moreover, the online estimator can adapt to changes in the statistical characteristics of the primary user. We present numerical results that demonstrate the performance of temporal spectrum sensing using the proposed online parameter estimator.

A new statistical model, in the form of a hidden bivariate Markov chain observed through a Gaussian channel, is developed and applied to spectrum sensing for cognitive radio. We focus on temporal spectrum sensing in a single narrowband channel in which a primary transmitter is either in an idle or an active state. The main advantage of the proposed model, compared to a standard hidden Markov model (HMM), is that it allows a phase-type dwell time distribution for the process in each state. This distribution significantly generalizes the geometric dwell time distribution of a standard HMM. Measurements taken from real data confirm that the geometric dwell time distribution characteristic of the HMM is not adequate for this application. The Baum algorithm is used to estimate the parameter of the proposed model and a forward recursion is applied to online estimation and prediction of the state of the cognitive radio channel. The performance of the proposed model and spectrum sensing approach are demonstrated using numerical results derived from real spectrum measurement data.

We propose an integrated scheme for tracking the mobility of a user based on autoregressive models that accurately capture the characteristics of realistic user movements in wireless networks. The mobility parameters are obtained from training data by computing Minimum Mean Squared Error (MMSE) estimates. Estimation of the mobility state, which incorporates the position, velocity, and acceleration of the mobile station, is accomplished via an extended Kalman filter using signal measurements from the wireless network. By combining mobility parameter and state estimation in an integrated framework, we obtain an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. We consider two variants of an autoregressive mobility model in our study and validate the proposed mobility tracking scheme using mobile trajectories collected from drive test data. Our simulation results validate the accuracy of the proposed tracking scheme even when only a small number of data samples is available for initial training.

Cognitive radios hold tremendous promise for increasing spectral efficiency in wireless systems. In cognitive radio networks, secondary users equipped with frequency-agile cognitive radios communicate with one another via spectrum that is not being used by the primary, licensed users of the spectrum. We consider a multichannel cognitive radio network scenario in which a secondary transmitter can switch to different channels for opportunistic communications. Multichannel diversity can be achieved by dynamically switching to different channels during transmission. Our numerical results show that even a simple randomized channel switching scheme can significantly reduce the average symbol error probability. We also propose a scheduling algorithm based on maximizing signal-to-noise ratio to further improve the performance of cognitive transmission.

The Internet was designed as a packet-switched network in the 1960's and 1970's, with the explicit intent of sacrificing quality-of-service guarantees for an individual application in order to optimize channel usage and provide optimal median service for all applications. This approach was successful, since the application mix of the Internet heretofore has been dominated by applications with low quality-of-service needs: primarily bulk data transfer and low-bandwidth text- based interactive applications. As the Internet absorbs other networks and applications with strong quality-of-service requirements (television, voice over IP) , this tradeoff changes. We are faced with the problem of introducing the quality- of-service guarantees of circuit-switching into packet-switched networks. Fundamental change to the lower layers of the Internet stack have proven infeasible; even strongly-motivated, well-designed modifications which made transition a first-class design consideration have had difficult introductions. ATM and IPv6 are two recent examples. One effective transition strategy for new networking techniques has been the use of overlays. In this paper, we introduce the concept of a service overlay network, to offer circuit-switched behavior on legacy IP networks, and establish the requirements on the underlying IP network to make this strategy effective.

Automated geolocation of IP addresses has important applications to targeted delivery of local news, advertising and other content over the Internet. Previous measurement-based approaches to geolocation employ active probing to measure delays among a set of landmark nodes with known locations. The location of a target IP address can be approximated by that of the nearest landmark, as determined by the delay measurements. To improve geolocation accuracy, a variation of this approach uses multilateration with geographic distance constraints to obtain a continuous location space rather than the discrete set of landmark locations. Since the previous approaches are fundamentally deterministic, they can only provide relatively loose bounds on the true location of an IP address. We develop a statistical geolocation scheme based on applying kernel density estimation to delay measurements among a set of landmarks. An estimate of the target location is then obtained by maximizing the likelihood of the distances from the target to the landmarks, given the measured delays. This is achieved by an algorithm which combines gradient ascent and force-directed methods. We present experimental results on PlanetLab to demonstrate the superior accuracy of the proposed geolocation scheme compared to previous methods.

Cognitive radios have the ability to sense the radio spectrum environment and to switch dynamically to available frequency ranges. Mobile WiMAX is an emerging wireless networking standard that could potentially benefit from cognitive radio technology. We develop a framework for applying cognitive radio technology to mobile WiMAX networks to increase capacity and simplify network operations. In the proposed cognitive WiMAX architecture, base stations are equipped with sensitive detectors and assign channels to subscriber stations dynamically based on spectrum availability. Power control is employed to increase frequency reuse in conjunction with spectrum sensing. Using computer simulation, we evaluate the performance of “cognitive channel assignment” relative to conventional dynamic channel assignment. Our numerical results show that cognitive radios can substantially increase the capacity of emerging WiMAX networks by exploiting inherent spectrum hole opportunities. The key performance parameters determining the achievable capacity of cognitive WiMAX networks are the detection and interference range, which depend in turn on characteristics of the radio propagation environment.

We consider a scenario in which frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme for a group of frequency agile radios to estimate the maximum power at which they can transmit on a given frequency channel, without causing harmful interference to the primary receivers. The proposed scheme relies on signal strength measurements taken by a group of frequency agile radios, which are then used by a target node to characterize the spatial size of its perceived spectrum hole in terms of the maximum permissible transmit power. We derive an approximation to the maximum interference-free transmit power using the Cramer-Rao bound on localization accuracy. We present numerical results to demonstrate the effectiveness of the proposed scheme under a variety of scenarios.

We present a distributed, collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple cochannel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issues of how to perform collaborative spectrum sensing in the presence of multiple cochannel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. Simulation results are provided to validate the feasibility and performance of the proposed scheme.

We analyze the performance of a wireless system consisting of a set of secondary users opportunistically sharing bandwidth with a set of primary users over a coverage area. The secondary users employ spectrum sensing to detect channels that are unused by the primary users and hence make use of the idle channels. If an active secondary user detects the presence of a primary user on a given channel, it releases the channel and switches to another idle channel, if one is available. In the event that no channel is available, the call waits in a buffer until either a channel becomes available or a maximum waiting time is reached. Spectrum sensing errors on the part of a secondary user cause false alarm and mis-detection events, which can potentially degrade the quality-of-service experienced by primary users. We derive system performance metrics of interest such as blocking probabilities. Our results suggest that opportunistic spectrum sharing can significantly improve spectrum efficiency and system capacity, even under unreliable spectrum detection. The proposed model and analysis method can be used to evaluate the performance of future opportunistic spectrum sharing systems.

We develop a general framework for analyzing the performance of an opportunistic spectrum sharing (OSS) wireless system at the session level with Markovian arrivals and phasetype service times. The OSS system consists of primary or licensed users of the spectrum and secondary users that sense the channel status and opportunistically share the spectrum resources with the primary users in a coverage area. When a secondary user with an active session detects an arrival of a primary session in its current channel, the secondary user leaves the channel quickly and switches to an idle channel, if one is available, to continue the session. Otherwise, the secondary session is preempted and moved to a preemption queue. The OSS system is modeled by a multi-dimensional Markov process. We derive explicit expressions for the related transition rate matrices using matrix-analytic methods. We also obtain expressions for several performance measures of interest, and present both analytic and simulation results in terms of these performance measures. The proposed OSS model encompasses a large class of specific models as special cases, and should be useful for modeling and performance evaluation of future opportunistic spectrum sharing systems.

To improve the performance of VPN connections we investigate how the bandwidth of multiple access links can be aggregated with inverse multiplexing to create a single, higher capacity logical communication link. But achieving the maximum possible aggregated TCP throughput becomes extremely challenging if the underlying links either use different technologies (e.g., DSL, cable modem) or suffer different or time-varying communication characteristics (e.g., available bandwidth, packet loss rate). To maximize VPN throughput we have constructed a system that combines two distinct innovations. First, we continuously measure the communication characteristics of the underlying component links in our aggregate and dynamically assign packets to each link in proportion to its available capacity. Second, we modify TCP congestion control across the inverse-multiplexed access hop to avoid rate decreases normally initiated by the delayed acknowledgments often triggered when using legacy TCP on multiple heterogeneous paths. We describe the systempsilas implementation, the test environment we built on Emulab, and show that when access links form the communication bottleneck in the end-to-end connection we can significantly increase VPN performance over conventional approaches.

We present a collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple co-channel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issue of how to perform collaborative spectrum sensing in the presence of multiple co-channel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. We provide some simulation results to validate the feasibility of our approach.

We propose an adaptive spectrum detection mechanism for cognitive radios in a dynamic traffic environment. Cognitive radios generate secondary calls, which opportunistically make use of channels left idle by primary traffic generated by the licensed radios in the system. Spectrum detection for the cognitive radios is formulated as a hypothesis testing problem based on the Bayes criterion to minimize average cost. The maximum likelihood estimates of the prior probabilities for the hypothesis test are obtained from the dynamics of both traffic types of traffic using a Markovian model of the system channel occupancy. The spectrum detection scheme is extended to incorporate cooperation among multiple secondary users. Our numerical results suggest that the adaptive spectrum detection scheme can achieve significantly better error performance than a static scheme that ignores the prior probabilities, especially under light primary traffic conditions. Cooperative spectrum detection among multiple users can further reduce the false alarm and misdetection probabilities.

We consider a scenario in which frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme for frequency agile radios to estimate the maximum power at which they can transmit, without causing harmful interference to the primary receivers. The proposed scheme relies on signal strength measurements, which are used to localize primary transmitters. An approximation to the maximum interference-free transmit power is derived using the Cramer-Rao lower bound on localization accuracy. We present numerical results to demonstrate the effectiveness of the proposed scheme under a variety of scenarios.

In an opportunistic spectrum sharing system, secondary users equipped with cognitive radios opportunistically access spectrum that is not being used by the primary users, i.e., the licensed spectrum users, without causing harmful interference to the primary users. We present an analytical performance model of opportunistic spectrum access in a military environment consisting of a group of secondary users sharing a set of channels with primary users in a coverage area. A secondary user occupying a given channel detects when a primary user accesses the channel and then either moves to another idle channel or is placed in a virtual queue where it waits until either a channel becomes available or a maximum waiting time is reached. Using a two-dimensional Markov model, we derive expressions for the blocking probabilities and reconnection probability and evaluate the performance metrics under a range of parameter settings.

In an opportunistic spectrum sharing (OSS) wireless network there are two types of users: primary users and secondary users. The primary users own the license for the system bandwidth, while the secondary users opportunistically share the spectrum resources with the primary users. When a secondary user detects a call arrival from a primary user in its current channel, the secondary user leaves the channel immediately and switches to an idle channel, if one is available, to continue the call. Otherwise, the secondary user is preempted. Call arrivals from primary users and secondary users in the OSS system are modeled by a Markovian arrival process (MAP) which captures correlation in the aggregate arrival process consisting of the two types of call arrivals. We derive the stationary probability vector using matrix-analytic methods and obtain expressions for a set of key performance measures. We present numerical results for a sample scenario.

We introduce a new system performance measure due to handoff called handoff interference, which characterizes the additional interference noise induced by the handoff process. The handoff interference experienced by a mobile unit is determined by the parameters of the handoff algorithm. We present an exact analysis of the handoff interference and the outage probability and develop a discrete-time method to efficiently and accurately compute these performance metrics along arbitrary trajectories in a cellular network. Our numerical results reveal critical tradeoffs among the critical handoff performance metrics, which should be taken into account in dimensioning the handoff parameters to optimize system performance. We discuss an example handoff design problem, which illustrates how to dimension the handoff parameters to optimize this tradeoff using the proposed handoff analysis.

We analyze the performance of a wireless system that allows opportunistic spectrum sharing. The system consists of a set of primary users sharing a set of channels over a coverage area. The resources allocated to the primary users are shared opportunistically with a set of secondary users. The secondary users are capable of detecting channels that are unused by the primary users and then making use of the idle channels. If no channel is available for a secondary call, the call waits in a buffer until either a channel becomes available or a maximum waiting time is reached. We compute the blocking probabilities, mean reconnection probability, channel utilization, and total carried traffic in the system. Our results suggest that opportunistic spectrum sharing can significantly improve the efficiency of a wireless system, without negatively impacting the performance seen by the primary users.

In mobile ad hoc networks (MANETs), a source node must rely on other nodes to forward its packets on multi-hop routes to the destination. Secure and reliable handling of packets by the intermediate nodes is difficult to ensure in an ad hoc environment. We propose a trust establishment scheme for MA NETs, which aims to improve the reliability of packet forwarding over multi-hop routes in the presence of potentially malicious nodes. Using a Bayesian framework, each node assigns a “trustworthiness” value to each of its neighbor nodes based on direct observations of packet forwarding behavior. More generally, each node forms an “opinion” about each of the other nodes in the network, based on the set of trustworthiness values computed in the network. The opinion metric can be incorporated into ad hoc routing protocols to achieve reliable packet delivery even when a portion of the network exhibits malicious behavior. We present numerical results, which demonstrate the effectiveness of the proposed trust establishment scheme.

We propose a local averaging technique for processing the received pilot signal strength, which can significantly improve handoff performance in cellular networks. In handoff algorithms, the received pilot signal strength is typically averaged to diminish the undesirable effect of the fast fading component. Unfortunately, the averaging process can substantially alter the characteristics of path loss and shadowing components, causing increased handoff delay. The proposed local averaging method provides significant improvement for handoff delay performance, especially in the non-line-of-sight case, when the mobile station turns around a corner. An important feature of local averaging is that the handoff performance is insensitive to the speed of the mobile station, such that velocity estimation is not needed. We develop efficient numerical procedures to compute the handoff performance metrics under local averaging

We consider the problem of allocating bandwidth to a set of traffic flows at a statistical multiplexer to provide both quality-of-service (QoS) and resistance to a class of denial- of-service (DoS) attacks. The target application is QoS signaling in a mobile ad hoc network (MANET) environment where the channel bandwidth is variable and the mobile device handles bandwidth requests arriving from multi-hop flows. An admission controller maintains a reserved rate to limit the aggregate traffic rate and to make admission decisions. We analyze the behavior of a rate adjustment scheme based on a Markov Modulated Poisson Process (MMPP) model, which captures the flow-level and burst-level characteristics of variable bit rate traffic. We propose a scheme for adjusting the reserved rate using traffic measurements and an MMPP parameter estimation applied to a reduced MMPP model. Finally, we develop a scheme to estimate parameters for a heuristic rate adjustment scheme that can be executed in real-time. We present numerical results that illustrate the modeling approach and demonstrate the effectiveness of the proposed bandwidth allocation schemes.

We present a multipath routing scheme that is designed to increase throughput and alleviate congestion in networks employing shortest path routing. The multipath routing scheme consists of an algorithm to determine a set of multiple disjoint or partially disjoint paths and a mechanism for distributing traffic over a multipath route to reduce the traffic load on a congested link. The algorithm for finding multipath routes is based on shortest path routing and does not require pre-establishment of paths or support for source routing. The mechanism for multipath traffic distribution is triggered at a node when the average load on an outgoing link exceeds a threshold. Our simulation results demonstrate that the proposed congestion-triggered multipath routing scheme can effectively improve network performance by exploiting routing redundancies inherent in the network topology.

Mobile ad hoc networks are especially susceptible to denial-of-service attacks due to the lack of infrastructure, the imperfections of the wireless channel, and the limitations of the mobile devices. In principle, providing quality-of-service and resistance against flooding attacks can be achieved with per-flow management. However, a per-flow management scheme makes a mobile device vulnerable to state table exhaustion attacks. To avoid such attacks some degree of flow aggregation is necessary, but such aggregation tends to have a negative impact on flow fairness. We introduce a quantitative metric for the fairness experienced by a flow in the presence of flooding attacks and develop a model to study the impact of flow aggregation on the fairness experienced by a flow in the presence of flooding attacks. We propose a dynamic 3-level flow aggregation scheme, which is able to maintain a high degree of flow fairness even with a relatively small state table size. Our simulation results quantify the impact of flooding attacks on flow fairness and validate the effectiveness of the proposed flow aggregation scheme.

In this correspondence, we develop a formal method for solving a class of level-crossing problems in discrete-time with application to the analysis of soft handoff performance in cellular networks such as code-division multiple-access (CDMA) systems. In such networks, proper dimensioning of soft handoff parameters is critical to overcoming propagation impairments and providing a transparent radio access service for multiple user profiles. We obtain exact expressions for the cell assignment and active set update probabilities of a mobile station traveling along an arbitrary straight-line trajectory. We develop recursive algorithms to compute performance measures such as outage probability, macrodiversity gain, and signaling load. The discrete-time level-crossing analysis yields an accurate and efficient computational tool for designing and dimensioning high performance soft handoff algorithms while avoiding the need for approximations or time-consuming computer simulations.

Quality-of-service (QoS) signaling protocols for mobile ad hoc networks (MANETs) are highly vulnerable to attacks. In particular, a class of denial-of-service (DoS) attacks can severely cripple network performance with relatively little effort expended by the attacker. We propose a distributed QoS signaling protocol that is resistant to a large class of DoS attacks. The key elements of the scheme are: sensing of available bandwidth, traffic policing, and rate monitoring. The proposed signaling scheme provides QoS for real-time traffic and achieves a compromise between signaling protocols that require the maintenance of per-flow state and those that are completely stateless. The signaling scheme scales gracefully in terms of the number of nodes and/or traffic flows in the MANET. We analyze the key security properties of the protocol and present simulation results to demonstrate its resistance to DoS attacks

We propose two algorithms for real-time tracking of the location and dynamic motion of a mobile station in a cellular network using the pilot signal strengths from neighboring base stations. The underlying mobility model is based on a dynamic linear system driven by a discrete command process that determines the mobile station's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. The first algorithm consists of an averaging filter for processing pilot signal, strength measurements and two Kalman filters, one to estimate the discrete command process and the other to estimate the mobility state. The second algorithm employs a single Kalman filter without prefiltering and is able to track a mobile station even when a limited set of pilot signal measurements is available. Both of the proposed tracking algorithms can be used to predict future mobility behavior, which can be, useful in resource allocation applications. Our numerical results show that the proposed tracking algorithms perform accurately over a wide range of mobility parameter values.

We propose an integrated scheme for estimating the mobility state and model parameters of a user based on a first-order autoregressive model of mobility that accurately captures the characteristics of realistic user movements in wireless networks. Estimation of the mobility parameters is performed by applying the Yule-Walker equations to the training data. Estimation of the mobility state, which consists of the position, velocity, and acceleration of the mobile station is accomplished via an extended Kalman filter using measurements from the wireless network. The integration of mobility state and model parameter estimation results in an efficient and accurate real-time mobility tracking scheme that can be applied in a variety of wireless networking applications. The mobility estimation scheme can also be used to generate realistic mobility patterns to drive computer simulations of mobile networks. We validate the proposed mobility estimation scheme using mobile trajectories collected from drive-test data obtained from a live cellular network.

We present a two-tier composite model of node mobility that captures group behavior in a mobile ad hoc network. The first tier represents individual node movement and is based on an autoregressive model of mobility. The second tier captures group mobility behavior by considering correlation among node mobility states. Based on the two-tier model, we propose a scheme to detect the presence of groups among the nodes of a network by performing a correlation index test on the node mobility states. We also propose a group-based mobility estimation scheme, which uses the mobility state of a representative node in a group to estimate the mobility states of the rest of the group members. The group estimation scheme can significantly reduce the amount of data collection required to track nodes exhibiting group mobility in mobile ad hoc networks. The two-tier mobility model and the group detection and estimation schemes are validated through simulations and with GPS data.

We introduce an important system performance measure related to handoff called handoff interference, which characterizes the additional interference noise induced by the handoff process. The handoff interference experienced by a mobile unit is determined by the parameters of the handoff algorithm and is critical for accurate evaluation of the wireless system capacity in the context of multiple cell and user scenarios. We develop an efficient numerical procedure to compute the handoff interference along arbitrary trajectories in a cellular network. We present numerical results showing the tradeoff between the handoff interference and the number of handoffs along a given trajectory, which should be taken into account in dimensioning the handoff parameters to optimize system performance.

We develop analytical models to evaluate the performance of optical-burst switch (OBS) architectures employing fiber delay lines (FDLs) as optical buffers to reduce burst-loss probability. The performance of such architectures cannot be captured accurately using traditional queueing models, since FDLs behave fundamentally differently from conventional electronic buffers. We formulate a Markovian model to evaluate the system performance when the burst-arrival process is Poisson and the burst lengths are exponentially distributed under an idealized model of FDL behavior. The model accurately captures both the balking and deterministic delay properties of FDLs, but the complexity of the model makes it infeasible for solving problems of practical interest. By considering approximations of the model in the regimes of short and long FDLs, we develop relatively simple closed-form expressions that can be used for dimensioning OBS architectures. We also extend the approximate model to include the impact of FDL delay granularity. We present numerical results that validate our modeling approach and demonstrate that significant performance gains in optical-burst switching are achievable when FDLs are employed as optical buffers.

The handoff algorithm employed in a cellular network has a significant impact on overall network performance, but evaluation of handoff performance has for the most part been done using only crude approximations or brute-force computer simulation. We introduce a new discrete-time approach to analyze the performance of handoff algorithms based on pilot signal strength measurements. We derive exact analytical expressions and develop a recursive numerical procedure to evaluate handoff performance metrics for a mobile station moving along a straight-line trajectory in a cellular network employing hard handoff. The numerical procedure provides a computational solution to a level-crossing problem in discrete time. Our discrete-time approach provides valuable analytical insight into the performance impact of handoff algorithms. Moreover, our numerical procedure for discrete-time handoff analysis provides an accurate and efficient tool for the design and dimensioning of high-performance handoff algorithms. The accuracy of the numerical procedure is validated by simulation.

Multiplexing gain has been studied extensively in the context of statistical characterizations of traffic streams with quality-of-service criteria such as packet loss probability, mean delay, and delay variance. In this paper, we demonstrate that multiplexing gain can also arise in the context of deterministic traffic constraint functions, service curve scheduling, and quality-of-service requirements based on deterministic delay constraints. We show how to evaluate this multiplexing gain via the use of deterministic network calculus for both worst-case and time-averaged delay constraints. We show that significant multiplexing gain can be achieved in a deterministic setting using numerical examples drawn from a number of well-known MPEG video traces. Our results have application to provisioning services with tight, real-time constraints on end-to-end delay performance.

We present a new performance model for a prioritized optical burst switch architecture employing fiber delay lines (FDLs) as optical buffers to reduce the burst loss probability. The performance of such architecture cannot be captured accurately using traditional queueing models since FDLs behave fundamentally differently from conventional electronic buffers. We formulate a Markovian model to evaluate the system performance when the burst arrival process is Poisson and the burst lengths are exponentially distributed. Both the balking and bounded delay characteristics of FDLs are captured in the model. A conservation law is used to extend the analysis to a system implementing differentiated services with two prioritized traffic classes. The extended model captures the system dynamics for high priority traffic and yields a good approximation for low priority traffic. We also find that the previously developed models are approximations of our general model in the regimes of short and long FDLs. Our numerical results validate the accuracy of our modeling approach and demonstrate significant performance gains when FDLs are employed as optical buffers.

We introduce a new hard handoff algorithm that employs local averaging, a drop timer, and hysteresis. The proposed algorithm achieves better performance than conventional hysteresis-based handoff algorithms that employ exponential averaging, particularly in microcellular environments. We develop an analytical model and an efficient numerical procedure to evaluate accurately the performance and evaluation technique greatly simplify the practical task of dimensioning the cellular network for optimum performance.

We propose a novel scheme for tracking the mobility of users in a wireless ad hoc network. Mobile nodes track their positions using pilot signal strengths from neighboring nodes within a local coordinate system based on relative distances between nodes. Node mobility is modeled as a linear system driven by a discrete command semi-Markov process. Mobility tracking is performed using an extended Kalman filter preceded by an averaging filter. Our numerical results show that the mobility tracking scheme performs effectively and can be used to enhance routing performance in ad hoc networks.

Proper design of soft handoff mechanisms for CDMA cellular networks is critical to overcoming propagation impairments and providing a transparent radio access service for multiple data user profiles. The connectivity of the mobile user to the core network via an active set of access points is determined dynamically by the handoff algorithm via measurements of received pilot signal strengths. Using a discrete-time framework, we derive analytical expressions and develop associated numerical procedures for evaluating the soft handoff performance of a mobile unit travelling along an arbitrary straight-line trajectory in a CDMA cellular network. In particular, we obtain expressions for the cell assignment probabilities, which can be used to compute performance measures such as outage probability, macrodiversity gain, and signaling load. Our methodology provides an accurate and efficient means of designing and dimensioning high performance soft handoff algorithms for CDMA networks, while avoiding the need for approximations or time-consuming computer simulation.

We propose a robust estimation algorithm for tracking the location and dynamic motion of a mobile unit in a cellular network. The underlying mobility model is a dynamic linear system driven by a discrete command process that determines the mobile unit's acceleration. The command process is modeled as a semi-Markov process over a finite set of acceleration levels. Our proposed tracking algorithm is based on a modified Kalman filter in combination with an efficient hidden semi-Markov model (HSMM) estimation algorithm to estimate the parameters of the command process. Numerical results show that the proposed tracking algorithm performs accurately over a wide range of mobility parameter values.

This paper presents a robust and flexible real-time scheme for determining appropriate parameter values for usage parameter control (UPC) of an arbitrary source in an asynchronous transfer mode network. In our approach, the UPC parameters are chosen as a function of the statistical characteristics of the observed cell stream, the user's tolerance for traffic shaping, and a measure of the network cost. For this purpose, we develop an approximate statistical characterization for an arbitrary cell stream. The statistical characterization is mapped to a UPC descriptor that can be negotiated, with the network. The selected UPC descriptor is optimal in the sense of minimizing a network cost function, subject to meeting user-specified constraints on shaping delay. The UPC estimation scheme is extended to adapt to slow time-scale changes in traffic characteristics via dynamic renegotiation of the UPC parameters. We illustrate the effectiveness of our methodologies with examples taken from MPEG video sequences.

This paper proposes a growable switch architecture with multiple modules comprising a CORE module to support highspeed switching and EDGE modules with large buffers, providing a large number of traffic classes. For open-loop traffic such as CBR and VBR, the proposed switch uses a weighted fair queueing scheduler at each EDGE buffer module based on the equivalent bandwidth allocated by the call admission control (CAC). On the other hand, for best effort class services (e.g., ABR and UBR), closed-loop control using adaptive rate control at the actual source and at the virtual source queue of the input EDGE module is proposed to avoid internal congestion and to achieve fair throughput performance among the flows competing on the same link. Simulation results show that the proposed architecture achieves ATM switches having hundreds of Gbps capacity, while providing a large number of QoS classes with high throughput and fair sharing of bandwidth by all flows.

In high-speed networks based on asynchronous transfer mode (ATM), variable bit rate (VBR) sources generate bursty cell streams which share the link bandwidth wherever multiplexing occurs. In theory, the bandwidth requirement per stream to support a given quality-of-service at a multiplexer should generally decrease as the number of streams increases. In practice, high statistical multiplexing gain is difficult to achieve because good traffic characterizations are usually not available. This paper proposes the use of peakedness-based measures to capture the statistical information from traffic streams needed to determine bandwidth allocations. The standard definition of peakedness applies only to point process models of traffic. Yet fluid models of traffic have certain advantages in terms of tractability. Hence, we introduce a new measure called modified peakedness, which encompasses point process and fluid models under a common framework. We develop some of its properties and specialize it to several common traffic models. Finally, we study the effectiveness of the peakedness/modified peakedness as burstiness measures for real-time traffic.

In networks based on the asynchronous transfer mode (ATM), traffic sources are subjected to a usage parameter control (UPC) function which controls the emitted cell stream by either discarding or delaying cells according to the parameters of a deterministic algorithm (e.g. the dual leaky bucket). The UPC parameters constitute a traffic descriptor which are used to allocate network resources to the traffic source. This paper presents a scheme for determining dual leaky bucket UPC parameter values for an arbitrary traffic source from observations of its emitted cell stream. The selection of UPC parameters is based on a novel statistical characterization of the observed cell stream, the user's tolerance for traffic shaping and a cost function based on the network resource allocation policy. Our scheme makes weak modeling assumptions and is robust for a wide class of traffic streams.

A fast eigenvector technique for obtaining good initial node partitions of netlists for use in interchange heuristics is described. The method is based on approximating the netlist or hypergraph by a weighted graph G, such that the sum of the cut edges in G tightly underestimates the number of cut nets in any netlist partition. An eigenvector technique is used to partition the graph G into k blocks of fixed module size. Another feature of this graph underestimation model of the netlist is that it allows one to obtain lower bounds on the actual number of cut nets. A multiblock node interchange heuristic is tested on the one resulting netlist partition obtained by this eigenvector approach on a variety of small to large sized benchmark netlist partitioning problems (between 300 to 12000 modules and nets). Test results on the larger netlists show that in most cases this eigenvector-node interchange approach yields netlist partitions with comparable or fewer cut nets than the best netlist partitions obtained by using node interchange heuristics alone on many random initial netlist partitions.>