Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eaves-dropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve’s CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve’s information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramér–Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, under-scoring the effectiveness of the proposed deep learning–driven friendly jamming framework under practical ISAC impairments.

Agentic AI networking (AgentNet) is a novel AI-native networking paradigm in which a large number of specialized AI agents collaborate to perform autonomous decisions, dynamic environmental adaptation, and complex missions. AgentNet has the potential to facilitate real-time network management and optimization functions, including self-configuration, self-optimization, and self-adaptation across diverse and complex environments, laying the foundation for fully autonomous networking systems. Despite its promise, AgentNet is still in the early stages of development and still lacks an effective networking framework to support automatic goal discovery, multi-agent self-orchestration, and task assignment. This paper proposes SANet, a novel semantic-aware AgentNet architecture for wireless networks. SANet can infer the semantic goal of the user and automatically assign agents associated with different layers of the network stack to fulfill the inferred goal. Motivated by the fact that AgentNet is a decentralized framework in which collaborating agents may generally have different and even conflicting objectives, we formulate the decentralized optimization of SANet as a multi-agent multi-objective problem, and focus on finding the Pareto-optimal solution for agents with distinct and potentially conflicting objectives. We propose three novel metrics for evaluating SANet: (the agents' objective) optimization error, (dynamic environment) generalization error, and (multi-objective) conflicting error. Furthermore, we develop a model partition and sharing (MoPS) framework in which large models, e.g., deep learning models, of different agents can be partitioned into shared and agent-specific parts that are jointly constructed and deployed according to agents' local computational resources. Two decentralized optimization algorithms, static-weighting and dynamic-weighting algorithms, are introduced to optimize the above three metrics. A bandwidth-adaptive compression framework is also proposed to enable different agents to perform in situ compression of their intermediate embeddings, dynamically adjusting to localized resource constraints and task requirements. We derive theoretical bounds for all these performance metrics and prove that there exists a three-way tradeoff among optimization, generalization, and conflicting errors. Finally, to validate our theoretical results, we develop an open-source Radio Access Network (RAN) and core network-based hardware prototype that implements three Transformer-based time-series prediction agents to interact with three different layers of the network. Experimental results show that the proposed MoPS framework achieves performance gains of up to$14.61\%$while requiring only$44.37\%$of the Floating-Point Operations (FLOPs) for inference at each agent compared to state-of-the-art algorithms. Also, compared to the static-weighting algorithm, the dynamic-weighting algorithm achieves up to$83.81\%$reduction in training errors caused by conflicting objectives.

The preamble of a Wi-Fi frame contains multiple Signal (SIG) fields that are crucial for communications. Through measurements and security analysis, we reveal confidentiality, predictability, and integrity vulnerabilities in the SIG fields. We then introduce a SIG tampering attack (SIGTAM), in which the attacker exploits these vulnerabilities to craft and transmit a signal that tampers with legitimate SIG fields while passing parity and cyclic redundancy check (CRC). The tampered SIG fields cause frame discard or decoding errors, and disrupt channel access of neighboring devices. We also develop strategies that make SIGTAM robust to channel impairments and synchronization errors. SIGTAM operates stealthily, affecting$\lt 20\%$of the subcarriers for only 4$\mu$s. Extensive simulations and over-the-air experiments on IEEE 802.11a/ax devices show that SIGTAM causes nearly 100% packet drop and error rates. Compared to jamming on selected SIG subcarriers, SIGTAM consumes$40\times$less energy to cause 100% packet drop, yet keeps overhearing devices deferring channel access for over$10\times$longer. Beyond denial-of-service (DoS), SIGTAM degrades throughput, increases latency, reduces channel utilization, and disrupts spatial reuse. To mitigate the threat of SIGTAM, we propose and evaluate a defense scheme that detects the attack, identifies affected subcarriers, and recovers legitimate SIG fields from their equalized frequency-domain symbols.

Mobile edge computing (MEC) is a key technology to support computationally intensive mobile applications with stringent latency requirements. With MEC servers deployed at network edge (e.g., base stations), the computational tasks generated by various applications can be offloaded to nearby edge nodes (ENs) and timely processed there. Meanwhile, future mobile applications will be more diverse and complex, which will need to be supported by a large number of complicated programs. As the storage space of ENs is limited, it is infeasible for each EN to store the program codes of all applications. Thus, it is necessary to optimize program placement at ENs to fully harvest the potential of MEC. In this paper, we investigate the problem of program placement and user association in storage-limited MEC networks. Such a problem is formulated as a sequential decision-making problem. We first consider the single EN scenario and propose an online learning-based solution. We then propose a solution framework for the multi-EN scenario, where we decompose the original problem into three subproblems and iteratively solve them with low-complexity approaches. Simulation results show that the average latency achieved by our proposed schemes is 30% to 70% lower than two benchmark schemes and is on average less than 10% higher than a lower bound.

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems.

Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications using ubiquitously available radio frequency (RF) signals. Traditional approaches focus on developing a single global model based on a combined dataset collected from different locations. However, wireless signals are known to be location and environment specific. Thus, a global model results in inconsistent and unreliable sensing results. It is also unrealistic to construct individual models for all the possible locations and environmental scenarios. Motivated by the observation that signals recorded at different locations are closely related to a set of physical-layer semantic features, in this paper we propose SANSee, a semantic-aware networking-based framework for distributed wireless sensing. SANSee allows models constructed in one or a limited number of locations to be transferred to new locations without requiring any locally labeled data or model training. SANSee is built on the concept of physical-layer semantic-aware network (pSAN), which characterizes the semantic similarity and the correlations of sensed data across different locations. A pSAN-based zero-shot transfer learning solution is introduced to allow receivers in new locations to obtain location-specific models by directly aggregating the models trained by other receivers. We theoretically prove that models obtained by SANSee can approach the locally optimal models. Experimental results based on real-world datasets are used to verify that the accuracy of the transferred models obtained by SANSee matches that of the models trained by the locally labeled data based on supervised learning approaches.

Radio-frequency coverage maps (RF maps) are essential in wireless communication, but obtaining them through site surveys can be labor-intensive and sometimes impractical. To address this challenge, we propose RecuGAN, a generative adversarial network (GAN)-based approach for generating RF maps. RecuGAN leverages the principles of information maximizing GAN (InfoGAN) to capture latent properties of RF maps, enabling unsupervised categorization and generation of new and diverse RF maps. Unlike traditional methods, RecuGAN does not require labeled data or conditional input, reducing complexity, time, and cost. We enhance the RecuGAN objective function with a customized gradient penalty-based Wasserstein GAN (WGAN) function and a gradient-based loss function for stable training and accurate map generation. We also provide the option to incorporate multiple generators in RecuGAN, enabling high-resolution RF map generation. As demonstrated through extensive training with both experimental and simulation data, RecuGAN can synthesize diverse high-quality RF maps and categorize them based on the RSS distribution. Compared to a UNet-based conditional GAN (cGAN), RecuGAN achieves a mean average percentage error (MAPE) of 1.18%, outperforming the cGAN model, which achieves a MAPE of 2.5%.

Accurate detection of the Physical Cell Identity (PCI) is critical for rapid synchronization and connection establishment in 5G New Radio (5G-NR) systems. This paper introduces a deep learning-based approach for PCI classification, aiming to mitigate the computational complexity associated with traditional methods that rely on decoding the Synchronization Signal Block (SSB). Our approach processes only time-domain baseband samples of the downlink signal, arranged in fixed-length windows. These windows are inputted into a pre-trained Convolutional Neural Network (CNN), which classifies the samples into one of several known PCI values (representing nearby cells) or into an ‘other’ category (representing all non-nearby cells, as well as windows that do not contain SSB samples). Because PCI-related information is contained only in the SSB symbols of a frame, it is possible for an input window to include no or a few SSB samples. Accordingly, in labeling the training set, we use a threshold$\mathrm{T}_{\text{train}}$on the fraction of the samples within a window: if$\mathrm{T}_{\text{train}}$or more of the samples belong to an SSB of cell with a target PCI value, the true label for that window is set to that PCI value; otherwise, it is set to ‘other.’ A separate threshold$\mathrm{T}_{\text{test}}$is used for labeling the test windows. We also study another labeling mechanism whereby only samples of the third OFDM symbol in an SSB (which contains the Secondary Synchronization Signal) is used to determine the label. Our analysis considers two commonly used SSB formats that correspond to 15 and 30 kHz subcarrier spacings, respectively. Extensive simulations are conducted which reveal that the proposed classifier can reliably (above 98%) identify the PCI value of a captured signal even under Signal-to-Noise Ratio (SNR) values as low as -10 dB. This performance comes with a significant reduction in computational complexity as it bypasses the need for traditional SSB decoding procedures used for PCI estimation in 5G networks.

Harmonious coexistence among different unlicensed wireless technologies has became increasingly important due to the spectrum shortage problem. As a representative case, we focus on addressing LTE-LAA and WiFi coexistence in unlicensed bands. Traditionally, collision avoidance-based medium access control (MAC) protocols adopted by both LAA and WiFi have led to low channel utilization and fairness. In this paper, we explore physical-layer interference suppression techniques (e.g., successive interference cancellation, SIC) to enhance the spectrum utilization of coexisting LAA and WiFi networks. We propose a SIC-aware MAC protocol that embraces concurrent transmissions and optimizes the channel access strategy at the MAC layer, so as to mitigate the interference (same-technology or cross-technology) due to excess channel contentions. We theoretically analyze the network throughput by extending Bianchi's Markov model, considering the impact of concurrent transmissions and SIC. We also extend the analysis to consider MIMO and MU-MIMO links with SIC. We validate our theoretical analysis and the effectiveness of the proposed MAC protocol via extensive simulations. We also implement a prototype LAA/WiFi SIC receiver on USRP devices to demonstrate the feasibility of cross-technology SIC and the proposed MAC protocol.

In this paper, we propose MAMBA, a restless multi-armed bandit framework for beam tracking in directional millimeter-wave (mmW) cellular systems. Instead of relying on explicit control messages, MAMBA utilizes the ACK/NACK packets transmitted by user equipments (UEs) to the base station (BS) as a part of the hybrid automatic repeat request (HARQ) procedure. These packets are used to measure the quality of the currently operating downlink beam, and select a new downlink beam along with an appropriate modulation and coding scheme (MCS) for future transmissions. At its core, MAMBA implements an online reinforcement learning technique called adaptive Thompson sampling (ATS), which determines a good beam and associated MCS to be used for the upcoming transmissions. To evaluate MAMBA's performance, we conduct extensive simulations and over-the-air (OTA) experiments over the 28 GHz band using phased-array antennas. We study fixed- as well as adaptive-rate variants of MAMBA, and contrast it with four other beam tracking strategies: a beam selection scheme similar to the one used in 5G NR (called ‘static oracle’), a theoretically optimal but practically infeasible beam tracking scheme (called ‘dynamic oracle’), an$\epsilon$-greedy algorithm [1], and the Unimodal Beam Alignment (UBA) algorithm [2]. Our results show that MAMBA achieves 182% throughput gain over the ‘static oracle’ and is reasonably close to the throughput of the ‘dynamic oracle’. Compared to UBA, MAMBA achieves 25-35% gain in throughput, depending on UE mobility. Finally, when operated at a fixed MCS, MAMBA/ATS achieves 21% gain over the$\epsilon$-greedy algorithm at the lowest applied MCS index, and 255% gain at the highest MCS index.

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over$20\%$improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristics that cannot be removed from model training, we develop an analytical expression to characterize the impact of both staleness effect of asynchronous coordination and straggler effect of FL on the time consumption of TS-FL-ASC. We propose a load forwarding-based solution that allows a slow edge server to offload part of its training samples to trusted edge servers with higher processing capability. We develop a hardware prototype to evaluate the model training time of a heterogeneous FEI system. Experimental results show that our proposed TS-FL-SC and TS-FL-ASC can provide up to 63% and 28% of reduction, in the overall model training time, respectively, compared with traditional FL solutions.

In Wi-Fi networks, the preamble plays a crucial role in frame detection, synchronization, and channel estimation. It also ensures compatibility and interoperability across devices that operate different versions of Wi-Fi (e.g., IEEE 802.11a/g/n/ac/ax/be). Despite its significance, the preamble lacks authenticity and confidentiality guarantees, relying solely on weak integrity protection. In this paper, we introduce novel Preamble Injection and Spoofing (PrInS) attacks that exploit these vulnerabilities. Specifically, we show how an adversary can inject forged preambles without payloads to disrupt legitimate receptions or force legitimate users to defer transmissions. We demonstrate the impact of PrInS attacks both via experiments using software-defined radios (SDRs) and via system-level simulations. Our results show that the adversary can almost silence the channel, degrading the throughput of a legitimate user down to 2% of its normal throughput. Even at$30\,$dB less power than the legitimate signal, the adversary still causes 87% reduction in throughput. Even when the attacker targets only a fraction of legitimate frames, the average packet latency and packet loss rate significantly increase. As a countermeasure, we propose preamble customization and randomization using group keys and timestamps, along with preamble authentication in the receive state machine. Our countermeasure detects forged preambles with nearly 100% accuracy while maintaining low false alarm rates in most scenarios. Most importantly, it remains backward-compatible with existing 802.11 standards and does not impact the synchronization and frame error rates of the Wi-Fi system.

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverage estimation. Conducting site surveys to obtain RF maps is labor-intensive and sometimes not feasible. In this paper, we propose radio-frequency adversarial deep-learning inference for automated network coverage estimation (RADIANCE), a generative adversarial network (GAN) based approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a semantic map, a high-level representation of the indoor environment to encode spatial relationships and attributes of objects within the environment and guide the RF map generation process. We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment. RADIANCE incorporates this loss function along with the antenna pattern to capture signal propagation within a given indoor configuration and generate new patterns under new configuration, antenna (beam) pattern, and center frequency. Extensive simulations are conducted to compare RADIANCE with ray-tracing simulations of RF maps. Our results show that RADIANCE achieves a mean average error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity index (MS-SSIM) of 0.80.

Radio frequency interference (RFI) is a major concern for passive radioastronomical observations. There is a great interest within the wireless and radioastronomy communities to identify in real time man-made RFI in the vicinity of a telescope. This paper proposes the use of weakly supervised machine learning (ML) techniques to detect the presence of RFI in captured astronomical scans. Weakly supervised training is particularly appropriate when only a small subset of captured data is labeled, as is the case with many radioastronomical datasets. Our study is based on scans obtained from the Arizona Radio Observatory (ARO) at Kitt Peak, Arizona. We rely on the experience of astronomical engineers to label ten 20 MHz channels of a small fraction of the captured scans as "clean" or "dirty". The remaining channels of 4 GHz of the observed spectrum are unlabeled. We first use human-labeled data as ground truth and train two ML classifiers in a supervised manner: a Convolutional Neural Networks - Bidirectional Long Short Term Memory (CNN-BiLSTM) classifier and a Deep CNN classifier. For the unlabeled channels, a semi-supervised technique is adopted, whereby the unlabeled data is first fed to the trained supervised classifier and the outputs with high confidence are assigned pseudo labels. These pseudo-labeled data are further used to train a semi-supervised classifier. To test the performance of the semi-supervised technique, the two classifiers are considered again. We observe test accuracies of 94.55% and 93.69% respectively under weakly supervised training.

The licensed spectrum of cellular networks has become increasingly crowded, leading to the standardization of LTE licensed assisted access (LTE-LAA) and 5G NR-U for deployment in unlicensed bands such as 5 GHz. To coexist harmoniously with other unlicensed wireless technologies like WiFi, LAA and 5G NR-U enforce listen-before-talk (LBT) protocol. This paper proposes methods to enhance the overall spectrum efficiency and fairness of each coexisting heterogeneous link. To improve the overall spectrum efficiency, we propose enabling concurrent transmissions of multiple links. Motivated by the need for fair coexistence of heterogeneous networks with concurrent transmissions, we formulate a variant of the multi-armed bandit (MAB) problem that finds a probabilistic transmission strategy to maximize the minimum link throughput. We propose the Fair Probabilistic Explore-Then-Commit (FP-ETC) algorithm, which achieves the expected regret of$O\left(T^{\frac{2}{3}}(K \log T)^{\frac{1}{3}}\right)$. We compare FP-ETC with existing MAB algorithms via extensive simulations, and the results show that FP-ETC significantly outperforms the baseline algorithms.

We investigate signaling and linear precoding designs for secure downlink communications in a multiuser MIMO (MU-MIMO) network. The network is tapped by an external eavesdropper (Eve) who is equipped with a large number of antennas. In addition to the transmission of several (unicast) information signals to downlink users (Bobs), the transmitter (Alice) generates friendly jamming (FJ), aiming to prevent Eve from decoding Alice’s signals. To mitigate both multiuser interference (MUI) and FJ on Bobs, MU-MIMO systems often rely on zero-forcing (ZF) precoders. A condition for the application of such precoders is that the network must beunderloaded, i.e., Alice has more antennas than all Bobs combined. Foroverloadedscenarios, ZF-based precoding cannot guarantee nullification of both MUI and FJ at legitimate receivers. Accordingly, we propose a combined signaling-and-precoding scheme that can also be applied to an overloaded MU-MIMO network. In an underloaded scenario, our technique is shown to impose a more stringent antenna requirement on Eve than a classical ZF-based precoding, i.e., Eve must utilize more antennas if she is to successfully eavesdrop on Alice’s transmissions. Although our scheme is comparable to antenna selection (AS) approaches in terms of Eve’s antenna requirement, it incurs much less delay and complexity by avoiding frequent on/off switching of the RF chains at Bobs. For underloaded scenarios, we provide security analysis that establishes the conditions under which our scheme is superior to the ZF scheme. Using computer simulations, we validate the advantages of our design and contrast it with ZF and AS schemes in terms of the symbol error rate, the EVM, and the achievable rate in both underloaded and overloaded scenarios.

We address the problem of detecting misbehavior in the coexistence etiquette between LTE and Wi-Fi systems operating in the 5GHz U-NII unlicensed bands. We define selfish misbehavior strategies for the LTE that can yield an unfair share of the spectrum resources. Such strategies are based on manipulating the operational parameters of the LTE-LAA standard, namely the backoff mechanism, the traffic class parameters, the clear channel access (CCA) threshold, and others. Prior methods for detecting misbehavior in homogeneous settings are not applicable in a spectrum sharing scenario because the devices of one system cannot decode the transmissions of another. We develop implicit sensing techniques that can accurately estimate the operational parameters of LTE transmissions under various topological scenarios andwithout decoding.These techniques apply correlation-based signal detection to infer the required information. Our techniques are validated through experiments on a USRP testbed. We further apply a statistical inference framework for determining deviations of the LTE behavior from the coexistence etiquette. By characterizing the detection and false alarm probabilities, we show that our framework yields high detection accuracy at a very low false alarm rate. Although our methods focus on detecting misbehavior of the LTE system, they can be generalized to detect Wi-Fi misbehavior and to other coexistence scenarios.

As the Wi-Fi technology transitions into its sixth generation (Wi-Fi 6), there is a growing consensus on the need to support security and coordination functions at the Physical (PHY) layer. In contrast to the costly approach of extending the PHY-layer header to support new functions (e.g., Spatial Reuse field in the Wi-Fi 6 frame), we propose to turn specific parts of the frame preamble into a reliable data field while maintaining its primary functions. Specifically, in this paper, we develop a scheme calledextensible preamble modulation (eP-Mod)for 802.11n/ac/ax protocols that are built on multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM). For each frame,eP-Modcan embed up to 144 user bits into the 802.11ac preamble of an$8\times 8$MIMO$40\;$MHz transmission. The proposed scheme is adaptive to channel conditions and enables several promising PHY-layer services, such as PHY-layer encryption and channel/device authentication, and PHY-layer signaling. At the same time, it allows legacy (eP-Mod-unaware) devices to continue to process the received preamble as normal by guaranteeing that the proposed preamble waveforms satisfy the structural properties of a standardized preamble. Through numerical analysis, extensive simulations, and hardware experiments, we validate the practicality and reliability ofeP-Mod.

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing and workload processing latencies associated with fog computing, ignoring the fact that wireless access latency can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month measurement study of the wireless access latency between connected vehicles and a fog/cloud computing system supported by commercially available LTE networks. We proposeAdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement a fog/cloud infrastructure. AdaptiveFog's main objective is to maximize theservice confidence level, defined as the probability that the latency of a given service type is below some threshold. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two scenarios based on finite- and infinite-horizon optimization of short-term and long-term confidence are investigated. For each scenario, a simple threshold policy based on weighted K-R distance is proposed and proved to maximize the latency confidence for smart vehicles. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30 to 50 percent improvement in the confidence levels of fog and cloud latencies, respectively.

Narrow beams in millimeter-wave (mmWave) communication introduce significant beam misalignment challenges. In this paper, we introduce MAMBA-X, an enhanced version of the MAMBA beam tracking scheme. Basically, MAMBA uses a restless multi-armed bandit framework to capture the dynamics of mmWave links by discounting the relevance of past observations using a “forgetting factor” ($\gamma_{1}$) and increases the weight of recent observations via a “boost factor” ($\gamma_{2}$). Because the original MAMBA uses fixed values for$\gamma_{1}$and$\gamma_{2}$, it cannot quickly adapt to variations in user mobility. Moreover, if the time between consecutive beam selection instances is large compared to channel dynamics, past observations become obsolete. To tackle these issues, we first use the concept of beam coherence time to establish a bound on the beam selection intervals. Secondly, we show that the performance of MAMBA depends primarily on the value of$\gamma_{1}$which, in turn, depends on UE mobility. We develop a Long Short-Term Memory (LSTM) model to dynamically predict and update the optimal value of$\gamma_{1}$. Through extensive simulations at 28 GHz and using publicly available 5G NR experimental dataset, we evaluate MAMBA-X. Our results indicate that the total delivered traffic is improved by up to 46.8% relative to the original MAMBA and 142% compared to the default beam management scheme in 5G NR.

In Wi-Fi networks, every frame begins with a preamble that is used to support frame detection, synchro-nization, and channel estimation. The preamble also establishes compatibility and interoperability among devices that operate different Wi-Fi versions (e.g., IEEE 802.11a/g/n/ac/ax). Despite the crucial functions of the preamble, no guarantees can be made on its authenticity or confidentiality. Only weak integrity protection is currently possible. In this paper, we introduce novel Preamble Injection and Spoofing (PrInS) attacks that exploit the vulnerabilities of the preamble. Specifically, an adversary can inject forged preambles without any payload for the purpose of disrupting legitimate receptions or forcing legitimate users to de-fer their transmissions. The proposed PrInS attacks are effective irrespective of the Wi-Fi versions used by the adversary and its targets, as the attacks take advantage of the physical (PHY) layer receive state machine and/or capture effect. The efficacy of our attacks are validated experimentally using software-defined radios (SDRs). Our results show that the adversary can almost silence the channel, bringing the throughput of a legitimate user to 2% of its normal throughput. Even at 30 dB less power, the adversary still causes an 87% reduction in the legitimate users' throughput. To mitigate the PrInS attacks, we propose a backward-compatible scheme for preamble authentication.

The use of highly directional antennas in millimeter wave (mmWave) cellular networks necessitates precise beam alignment between a base station (BS) and a user equipment (UE), which requires beam sweeping over a large number of directions and causes high initial access (IA) delay. Intuitively, such delay can be lowered by using wider beams, as fewer directions need to be swept. However, this results in a weak received signal and higher misdetection probability, which in turn increases the IA delay as more rounds of beam sweeping would be required to discover a UE. In this paper, we propose a multi-armed bandit approach for beamwidth optimization in 5G New Radio (NR) mmWave cellular networks. We aim to find the optimal beamwidths at the BS and the UE that minimize the beam sweeping delay for a successful IA. We first formulate the beamwidth optimization problem based on analyzing the interplay among beamwidth, beam sweeping overhead, and misdetection probability. Then, we propose a two-stage solution framework based on a multi-armed bandit approach. In the first stage, an initial solution of the BS beamwidth and the optimal solution of UE beamwidth are derived. In the second stage, each BS learns its optimal beamwidth by solving a multi-armed bandit problem with a Thompson sampling-based algorithm. Our extensive simulation results show that, the proposed algorithms can decrease the IA delay by more than 50% compared to the traditional fixed-beamwidth schemes.

In this paper, we present Sense-Bandits, an AI-based framework for distributed adaptation of the sensing thresholds (STs) over shared spectrum. This framework specifically targets the coexistence of heterogenous technologies, e.g., Wi-Fi, 4G Licensed-Assisted Access (LAA), and 5G New Radio Unlicensed (NR-U), over unlicensed channels. To access the channel, a device compares the measured power with a predefined ST value and accordingly decides if the channel is idle or not. Improper setting of the ST values creates asymmetric sensing floors, resulting in collisions due to hidden terminals and/or reduction in the spatial reuse due to exposed terminals. Optimal ST setting is challenging because it requires global knowledge of mobility, traffic loads, and channel access behavior of all contending devices. Sense-Bandits tackles this problem by employing a clustering-based multi-armed bandit (MAB) algorithm, which adapts its learning behavior based on network dynamics. Clustering allows the algorithm to track network changes in real-time, ensuring fast learning of the best ST values by classifying the state and dynamics of coexisting networks. We develop a C++-based network simulator that implements Sense-Bandits and we apply it to evaluate the coexistence of Wi-Fi and 5G NR-U systems over the unlicensed 5 GHz U-NII bands. Our simulation results indicate that ST-adaptive devices employing Sense-Bandits do not harm neighboring devices that adopt a fixed ST value.

Accurate identification of the signal type in shared-spectrum networks is critical for efficient resource allocation and fair coexistence. It can be used for scheduling transmission opportunities to avoid collisions and improve system throughput, especially when the environment changes rapidly. In this paper, we develop deep neural networks (DNNs) to detect coexisting signal types based on In-phase/Quadrature (I/Q) samples without decoding them. By using segments of the samples of the received signal as input, a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) are combined and trained using categorical cross-entropy (CE) optimization. Classification results for coexisting Wi-Fi, LTE LAA, and 5G NR-U signals in the 5-6 GHz unlicensed band show high accuracy of the proposed design. We then exploit spectrum analysis of the I/Q sequences to further improve the classification accuracy. By applying Short-time Fourier Transform (STFT), additional information in the frequency domain can be presented as a spectrogram. Accordingly, we enlarge the input size of the DNN. To verify the effectiveness of the proposed detection framework, we conduct over-the-air (OTA) experiments using USRP radios. The proposed approach can achieve accurate classification in both simulations and hardware experiments.

Wi-Fi networks are susceptible to aggressive behavior caused by selfish or malicious devices that reduce their minimum contention window size (CWmin) to below the standard CWmin. In this paper, we propose a scheme called Minimum Contention Window Estimation (CWE) to detect aggressive stations with low CWmin’s, where the AP estimates the CWminvalue of all stations transmitting uplink by monitoring their backoff values over a period of time and keeping track of the idle time each station spends during backoff. To correctly estimate each backoff value, we present a cross-correlation based technique that uses the frequency offset between the AP and each station to identify stations involved in uplink collisions. The AP constructs empirical distributions for the monitored backoff values and compares them with a set of nominal PMF’s, created via Markov analysis of the DCF protocol to estimate CWminof various stations. After detecting the aggressive stations, the AP can choose to stop serving those stations. Simulation results show that the accuracy of our collision detection technique is 96%, 94%, and 88% when there are 3, 6, and 9 stations in the WLAN, respectively. For the former WLAN settings, the estimation accuracy of CWE scheme is 100%, 98.81%, and 96.3%, respectively.

Mobile edge computing (MEC) is a promising technology to support computationally intensive mobile applications with stringent delay requirements. As MEC applications become much more diverse and complex, it becomes more challenging for an edge node (EN) with limited storage to keep the program codes of all tasks. In this paper, we investigate the problem of program placement and user association in storage-limited MEC systems. Formulating the problem as a sequential decision-making problem, we first derive the solution for a single EN by transforming the formulation into a multi-armed bandit (MBA) problem and solving it via a Thompson sampling (TS) algorithm. We then propose a solution framework for the multi-EN scenario, where we decompose the original problem into three subproblems and solve them with low-complexity approaches. The first subproblem is to learn the task popularity, which we also formulate as a MAB problem and solve it via a TS algorithm. The second subproblem is optimizing program placement under a given user association and we propose a greedy algorithm to solve it. The last subproblem relates to user association, which is solved by a dual decomposition-based approach. Simulation results show that the average latency achieved by our proposed schemes is 30% to 100% lower than two benchmark schemes and is on average less than 10% higher than a lower bound.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. NIS is a bargaining mechanism whose terms and conditions must be carefully determined based on mutual benefits in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator with limited resources and uncertain demands, and a more resourceful operator with excessive capacity. The backup reservation agreement requires the local operator (say, operator A) to reserve a certain amount of resources (e.g., spectrum) for future sharing from the resource-owning operator (say, operator B). In return, operator B guarantees availability of its reserved resources to meet the need of operator A. We characterize the bargaining between the operators in terms of the optimal reservation prices and quantities with and without consideration of their competitions in market share, respectively. The conditions under which competing operators have incentive to cooperate are explored. The impact of competition intensity and redundant capacity on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation improves both resource utilization and profits of operators, with the potential to support higher target service levels for end users. We also find that, under certain conditions, operator B may still have the incentive to share its resources even at the risk of impinging on its own users.

Mobile edge computing (MEC) is a key enabler of delay-sensitive vehicle-to-everything (V2X) applications. Determining where to execute a task necessitates accurate estimation of the offloading latency. In this paper, we propose a latency prediction framework that integrates machine learning and statistical approaches. Aided by extensive latency measurements collected during driving, we first preprocess the data and divide it into two components: one that follows a trackable trend over time and the other that behaves like random noise. We then develop a Long Short-Term Memory (LSTM) network to predict the first component. This LSTM network captures the trend in latency over time. We further enhance the prediction accuracy of this technique by employing a k-medoids classification method. For the second component, we propose a statistical approach using a combination of Epanechnikov Kernel and moving average functions. Experimental results show that the proposed prediction approach reduces the prediction error to half of a standard deviation (STD) of the raw data.

Dynamic spectrum access enables opportunistic users (OUs) to access underutilized licensed bands by querying spectrum databases. However, the operational details of the incumbent users may leak to OUs during the query process. Privacy and exclusion zones have been proposed as effective countermeasures to protect the IUs' privacy, while also managing interference. In the case of multiple heterogeneous coexisting IUs, there is an inherent tradeoff between their achieved throughput, which is controlled by the received interference, and the utility provided to OUs, under a fixed privacy constraint. In this paper, we address the problem of maximizing the utility of rational IUs, defined as the weighted sum between the IUs' capacity and compensation from allowing OUs' opportunistic access while meeting the individual IUs' privacy constraints. We formulate the interaction between the heterogeneous IUs as a non-cooperative continuous game and derive the Nash equilibrium that maximizes the utility of each IU. Our simulations show that the NE solution improves the individual utilities of the IUs compared to a joint optimization approach, where the sum of the utilities is maximized while providing more fairness to the IUs.

Spatio-temporal modeling of wireless access latency is of great importance for connected-vehicular systems. The quality of the molded results rely heavily on the number and quality of samples which can vary significantly due to the sensor deployment density as well as traffic volume and density. This paper proposes LaMI (Latency Model Inpainting), a novel framework to generate a comprehensive spatio-temporal of wireless access latency of a connected vehicles across a wide geographical area. LaMI adopts the idea from image inpainting and synthesizing and can reconstruct the missing latency samples by a two-step procedure. In particular, it first discovers the spatial correlation between samples collected in various regions using a patching-based approach and then feeds the original and highly correlated samples into a Variational Autoencoder (VAE), a deep generative model, to create latency samples with similar probability distribution with the original samples. Finally, LaMI establishes the empirical PDF of latency performance and maps the PDFs into the confidence levels of different vehicular service requirements. Extensive performance evaluation has been conducted using the real traces collected in a commercial LTE network in a university campus. Simulation results show that our proposed model can significantly improve the accuracy of latency modeling especially compared to existing popular solutions such as interpolation and nearest neighbor-based methods.

Millimeter-wave (mmW) spectrum is a major candidate to support the high data rates of 5G systems. However, due to directionality of mmW communication systems, misalignments between the transmit and receive beams occur frequently, making link maintenance particularly challenging and motivating the need for fast and efficient beam tracking. In this paper, we propose a multi-armed bandit framework, called MAMBA, for beam tracking in mmW systems. We develop a reinforcement learning algorithm, called adaptive Thompson sampling (ATS), that MAMBA embodies for the selection of appropriate beams and transmission rates along these beams. ATS uses prior beam-quality information collected through the initial access and updates it whenever an ACK/NACK feedback is obtained from the user. The beam and the rate to be used during next downlink transmission are then selected based on the updated posterior distributions. Due to its model-free nature, ATS can accurately estimate the best beam/rate pair, without making assumptions regarding the temporal channel and/or user mobility. We conduct extensive experiments over the 28 GHz band using a 4x8 phased- array antenna to validate the efficiency of ATS, and show that it improves the link throughput by up to 182%, compared to the beam management scheme proposed for 5G.

As the Wi-Fi technology goes through its sixth generation (Wi-Fi 6), there is a growing consensus on the need to support security and coordination functions at the Physical (PHY) layer, beyond traditional functions such as frame detection and rate adaptation. In contrast to the costly approach of extending the PHY-layer header to support new functions (e.g., Target Wake Time field in 802.11ax), we propose to turn a specific part of the frame preamble into a data field while maintaining its primary functions. Specifically, in this paper, we develop a scheme called extensible preamble modulation (ePMod) for the MIMO-based 802.11ac protocol. For each frame, eP-Mod can embed up to 20 bits into the 802.11ac preamble under 1 × 2 or 2 × 1 MIMO transmission modes to support the operations of a given PHY-layer function. The proposed scheme enables several promising PHY-layer services, such as PHY-layer encryption and channel/device authentication, PHYlayer signaling, etc. At the same time, it allows legacy (eP-Modunaware) devices to continue to process the received preamble as normal by guaranteeing that our proposed preamble waveforms satisfy the structural properties of a standardized preamble. Through numerical analysis, extensive simulations, and hardware experiments, we validate the practicality and reliability of ePMod.

5G millimeter-wave (mmW) systems rely on electronically steerable antenna arrays to support directional communications. Directionality complicates the initial access (IA) process, whereby a base station (BS) announces itself to nearby user equipments (UEs), giving them the opportunity to associate with this BS. Existing approaches for IA suffer from long discovery time and/or nonnegligable probability of missing UEs. In this paper, we propose FastLink, an efficient IA protocol for mmW systems, in which discovery beacons are transmitted/received using the narrowest possible beams, allowing for high beamforming gains and low misdetection rate, while maintaining low discovery time. Fastlink executes a unique algorithm, called 3-dimensional peak finding (3DPF), to find the best beam in logarithmic time. We formulate the beam-finding process as a sparse problem and use compressive sensing to determine the minimum number of measurements needed for this process. We first study FastLink for the discovery of a single UE and then extend our analysis to a multi-user scenario. Both simulations and over-the-air experiments based on a custom mmW testbed are used to evaluate FastLink. Our results verify its efficiency, and show that it can reduce the search time by 90% compared to the scanning approach used in 802.11ad systems.

Millimeter wave (mmW) communications have recently attracted considerable attention as a key element of next-generation (5G) wireless systems. Despite significant efforts in this domain, establishing and maintaining directional mmW links in a dynamic environment are still quite challenging, largely due to the search-time overhead of beam scanning, and the vulnerability of directional links to beam misalignment, blockage, and outages. In this paper, we propose SmartLink, a protocol that exploits the multi-cluster scattering phenomenon at mmW frequencies to establish a multi-directional link between a base station and a user. By exploiting multiple clusters, SmartLink enables fast initial access and link maintenance, along with sustained throughput. A search algorithm called multi-lobe beam search (MLBS) is used to discover multiple channel clusters by probing several directions simultaneously using carefully designed multilobe beam patterns. MLBS reduces the search time from linear to logarithmic with respect to the number of directions. We provide detailed analysis of the false alarm and misdetection probabilities for the designed beam patterns. Following cluster discovery, SmartLink divides antennas into sub-arrays to generate the optimal multi-lobe pattern with respect to cluster powers and blockage probabilities. Finally, extensive trace-driven simulations at 29 GHz frequency using phased-array antennas verify the efficiency of SmartLink.

In this paper, we consider the scenario in which mobile network operators (MNOs) share network infrastructure for operating 5G new radio (NR) services in unlicensed bands, whereby they reduce their deployment cost and extend their service coverage. Conserving privacy of MNOs' users, maintaining fairness with coexisting technologies such as Wi-Fi, and reducing communication overhead between MNOs are among top challenges limiting the feasibility and success of this sharing paradigm. To resolve above issues, we present MatchMaker, a novel framework for joint network infrastructure and unlicensed spectrum sharing among MNOs. MatchMaker extends the 3GPP's infrastructure sharing architecture, originally introduced for licensed bands, to have privacy-conserving protocols for managing the shared infrastructure. We also propose a novel privacy-conserving algorithm for channel assignment among MNOs. Although achieving an optimal channel assignment for MNOs over unlicensed bands dictates having global knowledge about MNOs' network conditions and their interference zones, our channel assignment algorithm does not require such global knowledge and maximizes the cross-technology fairness for the coexisting systems. We let the manager, controlling the shared infrastructure, estimate potential interference among MNOs and Wi-Fi systems by asking MNOs to propose their preferred channel assignment and monitoring their average contention delay overtime. The manager only accepts/rejects MNOs' proposals and builds contention graph between all colocated devices. Our results show that MatchMaker achieves fairness up to 90% of the optimal alpha-fairness-based channel assignment while still preserving MNOs' privacy.

In transitioning to 5G, the high infrastructure cost, the need for fast rollout of new services, and the frequent technology/system upgrades triggered wireless operators to consider adopting the cost-effective network infrastructure sharing (NIS), even among competitors, to gain technology and market access. To collaborate with competitors, NIS is a bargain whose terms and conditions need to be carefully determined to guarantee profitability in a market with uncertainties. In this work, we propose a strategic NIS framework for contractual backup reservation between a small/local network operator of limited resources and uncertain demands, and one resourceful operator with potentially redundant capacity. The backup reservation agreement requires the local operator (say, operator A) to pay a fixed reservation fee to the resource-owning operator (say, operator B) at fixed time intervals. In return, the operator B guarantees availability of its resource (e.g., spectrum) up to a predetermined level. In such a way, a certain amount of backup resource capacity is reserved for future use under high traffic demand. We characterize the bargaining between the operators in terms of the optimal reservation prices and resource reservation quantities w/o considerations of the competitions between operators in market share. The conditions under which the competitive operators will cooperate are explored. The impacts of competition intensity, redundant capacity, and demand uncertainty on performance under backup reservation are also investigated. Our study shows that NIS through backup reservation leads to both higher resource utilization and profits for operators, as well as higher service levels for end users. We also find that, under certain conditions, operator B will share its resources with operator A even at the risk of impinging on its own users, and the impact of competition intensity on the sharing decisions is highly dependent on the amount of potential redundant capacity.

Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing the queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the end-to-end service latency. This motivates the work in this paper, where we report a five-month urban measurement study of the wireless access latency between a connected vehicle and a fog computing system supported by commercially available multi-operator LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE operators that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that the tolerable latency threshold for each supported type of service can be guaranteed. AdaptiveFog has been implemented on a smart phone app, running on a moving vehicle. The app periodically measures the round-trip time between the vehicle and fog/cloud servers. An empirical spatial statistic model is established to characterize the spatial variation of the latency across the main driving routes of the city. To quantify the performance difference between different LTE networks, we introduce the weighted Kantorovich-Rubinstein (K-R) distance. An optimal policy is derived for the vehicle to dynamically switch between LTE operators' networks while driving. Extensive analysis and simulation are performed based on our latency measurement dataset. Our results show that AdaptiveFog achieves around 30% and 50% improvement in the confidence level of fog and cloud latency, respectively.

Directionality in millimeter-wave (mmW) systems make link establishment and maintenance challenging, due to the search-time overhead of beam scanning and the vulnerability of directional links to blockages. In this paper, we propose a communication protocol called SmartLink, which exploits the clustering phenomenon at mmW frequencies to establish a multi-beam link between a base station and a user. By utilizing multiple clusters, SmartLink enables efficient link maintenance and sustained throughput. We develop a logarithmic-time search algorithm called multi-lobe beam search (MLBS), which is used in SmartLink to discover the clusters. MLBS probes several directions simultaneously, using multi-lobe beam patterns. The number of simultaneous lobes is selected to minimize the search time of the clusters. We provide detailed analysis of the false alarm and misdetection probabilities for the designed beam patterns. Following cluster discovery, SmartLink divides antennas into sub-arrays to generate the optimal multi-lobe beam pattern that maximizes the average data rate under blockage. Extensive simulations using actual channel traces obtained by utilizing phased-array antennas at 29 GHz are used to verify the efficiency of SmartLink. MLBS decreases the discovery time by up to 88 compared to common existing search schemes, and exploiting multiple clusters improves the average data rate by 10.

Carrier frequency offset (CFO) arises from the intrinsic mismatch between the oscillators of a wireless transmitter and the corresponding receiver, as well as their relative motion (i.e., Doppler effect). Despite advances in CFO estimation and tracking techniques, estimation errors are still present. Residual CFO creates a time-varying phase error, which degrades the decoder's performance by increasing the symbol error rate. The impact is particularly visible in dense constellation maps (e.g., high-order QAM modulation), often used in modern wireless systems such as 5G NR, 802.11ax, and mmWave, as well as in physical security techniques, such as modulation obfuscation (MO). In this paper, we first derive the probability distribution function for the residual CFO under Gaussian noise. Using this distribution, we compute the maximum-likelihood demodulation boundaries for OFDM signals in a non-closed form. For modulation schemes with unequal-amplitude reference constellation points (e.g., 16-QAM and higher, APSK, etc.), the “optimal” boundaries have irregular shapes, and more importantly, they depend on the time since the last CFO correction instance, e.g., reception of frame preamble. To approximate the optimal boundaries and provide a practical (real-time) demodulation scheme, we explore machine learning techniques, specifically, support vector machine (SVM). Our SVM approach exhibits better accuracy and lower complexity in the test phase than other state-of-the-art machine-learning approaches. As a case study, we apply our CFO-aware demodulation to enhance the performance of a MO technique. Our analytical results show a gain of up to 3dB over conventional demodulation schemes, which exceeds 3dB in complete system simulations. Finally, we implement our scheme on USRPs and experimentally corroborate our analytic and simulation-based findings.

We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users. The BS acquires the channel state information (CSI) for various receivers using uplink pilot transmissions. We demonstrate the vulnerability of the CSI estimation process to pilot-contamination (PC) attacks. In our attack model, the attacker aims at minimizing the sum rate of downlink transmissions by contaminating the uplink pilots. We first study these attacks for two downlink power allocation strategies under the assumption that the attacker knows the locations of the BS and its users. Later on, we relax this assumption and consider the case when such knowledge is probabilistic. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under PC attacks and provide an upper bound on these rates. We also study this scenario without a priori knowledge of user locations at the attacker by introducing chance constraints. Our results indicate that such attacks can degrade the throughput of a massive MIMO system by more than 50%.

We consider the application of asymmetric fullduplex (AFD) communications in wireless local area networks (WLANs), exemplified by a Wi-Fi system. A full-duplex (FD)enabled Wi-Fi access point communicates simultaneously uplink (UL) and downlink (DL) with a pair of half-duplex (HD) Wi-Fi stations (STAs). AFD enhances spectrum efficiency and reduces latency for STAs; however, it faces challenges related to node selection, switching between AFD and HD modes, and rate control. In this paper, we propose the asymmetric FD-mode and rate adaptation (AFRA) scheme. AFRA relies on partially observable Markov decision process (POMDP) to adapt the UL and DL transmission rates as well as the transmission mode (i.e., AFD or HD; UL-only or DL-only), considering the future expected channel and interference conditions. Our simulation results indicate that AFRA outperforms classical rate-adaptation schemes and achieves up to 95% of the optimal performance of AFD communications.

Carrier frequency offset (CFO) arises from the intrinsic mismatch between the operating frequencies of the transmitter and the receiver, as well as their relative speeds (i.e., Doppler effect). Despite advances in CFO estimation techniques, estimation errors are still present. Residual CFO creates time-varying phase error. Modern wireless systems, including WLANs, 5G cellular systems, and satellite communications, use high-order modulation schemes, which are characterized by dense constellation maps. Accounting for the phase error is critical for the demodulation performance of such schemes. In this paper, we analyze the post-estimation probability distribution of residual CFO and use it to develop a CFO-aware demodulation approach for a set of modulation schemes (e.g., QAM and APSK). For a given distribution of the residual CFO, symbols with larger amplitudes are less densely distributed on the constellation map. We explore one important application of our adaptive demodulation approach in the context of PHY-layer security, and more specifically modulation obfuscation (MO) mechanisms. In such mechanisms, the transmitter attempts to hide the modulation order of a frame's payload from eavesdroppers, which could otherwise exploit such information to breach user privacy or launch selective attacks. We go further and complement our CFO-aware demodulation scheme by optimizing the design of a low-complexity MO technique with respect to phase errors. Our results show that when combined, our CFO-aware demodulation and optimized MO techniques achieve up to 5 dB gain over conventional demodulation schemes that are not obfuscated and are oblivious to residual CFO.

To overcome the high propagation loss and satisfy a given link budget, millimeter wave (mmW) communication systems rely on highly directional antennas, both at the base station (BS) and the user equipment (UE). Due to this directionality, initial access (IA) and association can be particularly challenging. Existing approaches for IA in directional networks suffer from long discovery time and/or high misdetection probability of the UE. In this paper, we propose FastLink, an efficient IA protocol for mmW systems with electronically steerable antennas. FastLink always transmits/receives using the narrowest possible beam, allowing high beamforming gains and low misdetection rate. It uses a unique binary-search-based algorithm, called 3DPF, to scan only a small subset of the angular space and find in logarithmic time the best transmit-receive beam pair. We formulate the beam-finding process as a sparse problem, exploiting the poor scattering nature of mmW channels. Compressive sensing is then used to determine the minimum number of measurements needed to reconstruct the sparse channel. 3DPF is incorporated into FastLink to establish the directional link, and the required messaging between the BS and the UE is explained in detail. For performance evaluation purposes, we first conduct simulations based on NYU mmW channel model and then experiment with a custom mmW testbed utilizing uniform planar arrays and operating at $29$ GHz frequency. Our extensive simulations and hardware experiments verify the efficiency of FastLink, and show that 3DPF can reduce the search time by $65-99%$ compared to 802.11ad-like beam finding scheme.

Network slicing has been considered as a key enabling technology for 5G due to its ability to customize and "slice" a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can cooperate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For the licensed band slicing, we propose the inter-operator spectrum aggregation method which allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Since all MNOs enjoy equal rights to access unlicensed bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelop method for the MNOs to evaluate their value of rights when coexisting with other wireless technologies. We develop a network slicing game based on the overlapping coalition formation game to investigate the possible cooperation between MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To evaluate the practical performance of our proposed framework, we develop a C++-based discrete-event simulator and simulate a possible implementation of our proposed framework over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed framework can almost double the capacity for all supported services for each operator under certain conditions.

In this paper, we consider the fair coexistence between LTE and Wi-Fi systems in unlicensed bands. We focus on the misbehavior opportunities that stem from the heterogeneity of the coexisting systems and the lack of explicit coordination mechanisms. We show that a selfishly behaving LTE can gain an unfair share of the spectrum resources through the manipulation of the parameters defined in the LAA-LTE standard, including the manipulation of the backoff mechanism of LAA, the traffic class, the clear channel assignment threshold and others. We develop a detection mechanism for the Wi-Fi system that can identify a misbehaving LTE system. Our mechanism advances the state of the art by providing an accurate monitoring method of the LTE behavior under various topological scenarios, without explicit cross-system coordination. Deviations from the expected behavior are determined by computing the statistical distance between the protocol-specified and estimated distributions of the LAA-LTE protocol parameters. We analytically characterize the detection and false alarm probabilities and show that our detector yields high detection accuracy at very low false alarm rate, for a wise choice of statistical parameters.

Network slicing is one of the key enabling technologies for 5G due to its ability to customize and “slice” a common resource to support diverse services and verticals. This paper introduces a novel inter-operator network slicing framework in which multiple mobile network operators (MNOs) can coordinate and jointly slice their accessible spectrum resources in both licensed and unlicensed bands. For licensed band slicing, we propose an inter-operator spectrum aggregation method that allows two or more MNOs to cooperate and share their licensed bands to support a common set of service types. We then consider the sharing of unlicensed bands. Because all MNOs enjoy equal rights to access these bands, we introduce the concept of right sharing for MNOs to share and trade their spectrum access rights. We develop a modified back-of-the-envelope method for MNOs to evaluate their Value-of-Rights when coexisting with other wireless technologies. A network slicing game based on the overlapping coalition formation game is formulated to investigate the possible cooperation among MNOs. We prove that our proposed game always has at least one stable slicing structure that maximizes the social welfare. To implement our proposed framework without requiring MNOs to reveal private information to other MNOs, we develop a distributed algorithm called distributed alternating direction method of multipliers with partially variable splitting. Performance evaluation of our proposed framework is provided using a discrete-event simulator that is driven by real MNO deployment scenarios based on over 400 base station locations deployed by two primary cellular operators in the city of Dublin. Numerical results show that our proposed frameworks can almost double the capacity for all supported services for each MNO in an urban setting.

Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on an energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed alternating direction method of multipliers via variable splitting. We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes. Finally, to evaluate the performance of our proposed concept, we simulate a possible implementation of a city-wide self-driving bus system supported by fog computing in the city of Dublin. The fog computing network topology is set based on a real cellular network infrastructure involving 200 base stations deployed by a major cellular operator in Ireland. Numerical results show that our proposed framework can balance the power usage efficiency among fog nodes and reduce the service latency for users by around 50% in urban scenarios.

This paper studies fog computing systems, in which cloud data centers can be supplemented by a large number of fog nodes deployed in a wide geographical area. Each node relies on harvested energy from the surrounding environment to provide computational services to local users. We propose the concept of dynamic network slicing, in which a regional orchestrator coordinates workload distribution among local fog nodes, providing partitions/slices of energy and computational resources to support a specific type of service with certain quality-of-service guarantees. The resources allocated to each slice can be dynamically adjusted according to service demands and energy availability. A stochastic overlapping coalition-formation game is developed to investigate the distributed cooperation and joint network slicing between fog nodes under randomly fluctuating energy harvesting and workload arrival processes. We observe that the overall processing capacity of the fog computing network can be improved by allowing fog nodes to maintain a belief function about the unknown state and the private information of other nodes. An algorithm based on a belief-state partially observable Markov decision process is proposed to achieve the optimal resource slicing structure among all fog nodes. We describe how to implement our proposed dynamic network slicing within the 3GPP network sharing architecture and evaluate the performance of our proposed framework using the real base station (BS) location data of a real cellular system with over 200 BSs deployed in the city of Dublin. Our numerical results show that our framework can significantly improve the workload processing capability of fog computing networks. In particular, even when each fog node can coordinate only with its closest neighbor, the total amount of workload processed by fog nodes can be almost doubled under certain scenarios.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD). Although operating in FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the coexistence problem of concurrent transmissions between multiple FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex mode. First, we consider two links and formulate the interactions between them as a Bayesian game. In this game, each link tries to maximize its throughput while minimizing the transmission power cost. We derive a closed-form expression for the Bayesian Nash equilibrium and determine the conditions under which no outage occurs at either link. Then, we study the coexistence problem between more than two links, assuming that each link is only affected by its dominant interfering link. We show that under this assumption, no more than two links will be involved in a single game. Finally, we corroborate our analytical findings via extensive simulations and numerical results.

We propose a distributed interference management method for a single-stream MIMO interference network that is tapped by an external eavesdropper. Along with its information signal, each legitimate transmitter creates a bogus signal, known as transmit-based friendly jamming (TxFJ), to confuse the eavesdropper. Although generating TxFJ protects the link from eavesdropping, it creates interference at other unintended but legitimate links. Using non-cooperative game theory, we design a distributed method for maximizing the sum of secrecy rates. Each link is a player in the game. It seeks to maximize its secrecy rate subject to a given information-rate constraint and power budget. The strategy profile of each player is to control the amount of TxFJ it generates. Because a pure non-cooperative game may not have Nash equilibria that result in (Pareto-)optimal secrecy sum-rate, we propose a modified price-based game, in which each link is penalized for generating interference on other legitimate links. Under the exact knowledge of eavesdropping channels, we show that the price-based game has a comparable secrecy sum-rate to a centralized approach. We then relax the assumption of knowledge of eavesdropping channels and leverage mixed-strategy games to provide robust solutions to the distributed secrecy sum-rate maximization problem.

This paper studies the workload offloading problem for fog computing networks in which a set of fog nodes can offload part or all the workload originally targeted to the cloud data centers to further improve the quality-of-experience (QoE) of users. We investigate two performance metrics for fog computing networks: users' QoE and fog nodes' power efficiency. We observe a fundamental tradeoff between these two metrics for fog computing networks. We then consider cooperative fog computing networks in which multiple fog nodes can help each other to jointly offload workload from cloud data centers. We propose a novel cooperation strategy referred to as offload forwarding, in which each fog node, instead of always relying on cloud data centers to process its unprocessed workload, can also forward part or all of its unprocessed workload to its neighboring fog nodes to further improve the QoE of its users. A distributed optimization algorithm based on distributed alternating direction method of multipliers (ADMM) via variable splitting is proposed to achieve the optimal workload allocation solution that maximizes users' QoE under the given power efficiency. We consider a fog computing platform that is supported by a wireless infrastructure as a case study to verify the performance of our proposed framework. Numerical results show that our proposed approach significantly improves the performance of fog computing networks.

The rapid increase in wireless demand prompted the FCC to open up parts of the 5-GHz band for unlicensed access. This caught the interest of 4G/LTE providers, who wish to extend their LTE-A services to the unlicensed spectrum (LTE-U). In LTE-U, small-cell base stations aggregate unlicensed and licensed bands to increase the throughput. Wi-Fi/LTE-U coexistence is a challenging issue due to the different access mechanisms of these two systems, which may cause high collision rates and delays. By leveraging self-interference-suppression techniques, we propose joint mode/rate adaptation strategies for Wi-Fi/LTE-U coexistence. Specifically, a full-duplex enabled Wi-Fi station can transmit and receive data simultaneously to increase the throughput, or transmit and sense (TS mode) simultaneously to monitor the LTE-U activity. We model the LTE-U interference as a hidden Markov process, and solve the problem of jointly adapting Wi-Fi rates/modes using a framework of partially observable Markov decision process. A detection approach based on the sliding window correlator is analyzed for the TS mode, which can differentiate between Wi-Fi and LTE-U signals. Our results indicate that our scheme provides 1.5x (1.9x) average throughput gain for Wi-Fi system in the low (high) signal-to-interference-and-noise regime relative to a half-duplex-based scheme.

We consider joint optimization of artificial noise (AN) and information signals in a MIMO wiretap interference network, wherein the transmission of each link may be overheard by several MIMO-capable eavesdroppers. Each information signal is accompanied with AN, generated by the same user to confuse nearby eavesdroppers. Using a noncooperative game, a distributed optimization mechanism is proposed to maximize the secrecy rate of each link. The decision variables here are the covariance matrices for the information signals and ANs. However, the nonconvexity of each link's optimization problem (i.e., best response) makes conventional convex games inapplicable, even to find whether a Nash equilibrium (NE) exists. To tackle this issue, we analyze the proposed game using a relaxed equilibrium concept, called quasi-NE (QNE). Under a constraint qualification condition for each player's problem, the set of QNEs includes the NE of the proposed game. We also derive the conditions for the existence and uniqueness of the resulting QNE. It turns out that the uniqueness conditions are too restrictive, and do not always hold in typical network scenarios. Thus, the proposed game often has multiple QNEs, and convergence to a QNE is not always guaranteed. To overcome these issues, we modify the utility functions of the players by adding several specific terms to each utility function. The modified game converges to a QNE even when multiple QNEs exist. Furthermore, players have the ability to select a desired QNE that optimizes a given social objective (e.g., sum rate or secrecy sum rate). Depending on the chosen objective, the amount of signaling overhead as well as the performance of resulting QNE can be controlled. Simulations show that not only can we guarantee the convergence to a QNE, but also due to the QNE selection mechanism, we can achieve a significant improvement in terms of secrecy sum rate and power efficiency, especially in dense networks.

We consider a broadcast channel in which a multi-antenna transmitter (Alice) sends K confidential information signals to K legitimate users (Bobs) in the presence of L eavesdroppers (Eves). Alice uses multiple-input multiple-output (MIMO) precoding to generate the information signals along with her own (Tx-based) friendly jamming (FJ). Interference at each Bob is removed by MIMO zero-forcing. This, however, leaves a “vulnerability region” around each Bob, which can be exploited by a nearby Eve. We address this problem by augmenting Tx-based FJ (TxFJ) with Rx-based FJ (RxFJ), generated by each Bob. Specifically, each Bob uses self-interference suppression to transmit a friendly jamming signal, while simultaneously receiving an information signal over the same channel. We minimize the powers allocated to the information, TxFJ, and RxFJ signals under given guarantees on the individual secrecy rate for each Bob. The problem is solved for the cases when the eavesdropper's channel state information is known/unknown. Simulations show the effectiveness of the proposed solution. Furthermore, we discuss how to schedule transmissions when the rate requirements need to be satisfied on average rather than instantaneously. Under special cases, a scheduling algorithm that serves only the strongest receivers is shown to outperform the one that schedules all receivers.

Full-duplex (FD) communications and self-interference suppression (SIS) techniques can be exploited in opportunistic spectrum access (OSA) systems for simultaneous transmission-sensing (TS) or simultaneous transmission-reception (TR). Motivated by the competing goals of primary user (PU) protection (in the TS mode) and secondary user (SU) performance (in the TR mode), we present an optimal adaptive switching strategy and an associated communication protocol for FD OSA systems. Specifically, we optimize the spectrum-awareness/efficiency tradeoff by allowing the SU link to adaptively switch between various modes, depending on the forecasted PU dynamics. The proposed three-stage adaptive mode-selection strategy maximizes an SU utility function subject to a constraint on the PU collision probability. We also propose a protocol that executes the switching mechanism in a distributed fashion. In practice, SIS is imperfect, resulting in residual self-interference that degrades the sensing performance in the TS mode. Accordingly, we study different spectrum sensing techniques in the TS mode, while illustrating their accuracy-complexity tradeoff. We evaluate the performance of the proposed switching scheme against the listen-before-talk (LBT) scheme using numerical results, simulations, and hardware USRP experiments.

Presents the introductory editorial for this issue of the publication.

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50 percent, and the end-to-end delay by more than 30 percent. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

The frame preamble in current WiFi systems is designed to facilitate various PHY-layer functions, including frequency offset estimation and frame detection. However, this preamble is typically fixed and is never used to convey any user-specific bits. Embedding information into the preamble opens the door for several new PHY-layer applications. For example, the PHY header no longer needs to be transmitted at a known (lowest) rate if this rate can be announced earlier in the preamble. A full-duplex transmitter can use the embedded information to inform other devices of its current operation mode (e.g., transmit/receive versus transmit/sense), obviating the need for additional control packets. In security applications, a PHY-layer sender identifier can be embedded in the preamble to facilitate PHY-level encryption. However, modifying the standard preamble to embed user information may disrupt the operation of 802.11a/n/ac devices. In this paper, we propose P-modulation, a method that enables an OFDM-based 802.11 transmitter to embed up to 19 user-specific bits in the frame preamble while maintaining the highest reliability required by the system. The proposed P-modulation is also backward-compatible with legacy receivers. Our analysis and USRP-based experimental results confirm the practicality of the scheme. Our scheme further provides insights into designing time-varying preambles for future wireless systems.

Satellite communications (SATCOM) are prone to both intentional and unintentional interference, which can significantly degrade the reliability of packet transmissions. Here, we investigate different approaches for interference mitigation in SATCOM based on dynamic frequency hopping (DFH). We consider a star topology, where multiple LEO satellites transmit packets to a common GEO satellite. The FH pattern of each LEO-GEO link is adjusted according to an outcome of out-of-band proactive sensing scheme, carried out by a cognitive radio (CR) module that resides in the GEO satellite. Based on sensing results, channels with high predicted interference are replaced with better channels, without disrupting the communications of other satellites in the network. In searching for replacement channels, we aim to ensure that all satellite links are assigned channels such that their SINR requirements are met. At the same time, the total transmission power and the communication overhead resulting from altering the FH patterns are minimized. We formulate the problem as a multi-objective minimization problem. Continuous-time Markov chain analysis is used to predict future channel conditions. The proposed scheme is compared with two other schemes, namely best-channel-to-closest- user and best-channel-to-farthest-user. Finally, we use simulations to study the effects of different system parameters on the performance of the proposed DFH design.

In this paper, we investigate the coexistence problem between Wi-Fi and a pre-standard form of LTE over unlicensed bands, namely, LTE-Unlicensed (LTE-U). We address two coexistence problems. First, the different access mechanisms for Wi-Fi and LTE-U can lead to an increase in the collision rate and higher latency for both systems. We propose a modified Wi-Fi operation mode, whereby Wi-Fi stations (STAs) carry out simultaneous spectrum sensing and transmission to reduce the time required for collision detection. Specifically, we propose and analyze a full-duplex (FD) based detection framework that can differentiate between Wi-Fi and LTE-U signals while taking into account residual self- interference. Second, the ability to differentiate between Wi-Fi and LTE-U signals motivates the idea of adapting the clear channel assessment (CCA) threshold according to the type of the detected signal. Inspired by upcoming Wi-Fi standards (e.g., IEEE 802.11ax), we propose a CCA threshold adaptation scheme and study via simulations its optimal setting so as to maximize the spatial reuse while maintaining fairness between LTE-U and Wi-Fi systems.

In cognitive radio networks, secondary users (SUs) face two conflicting objectives. Each SU seeks to minimize the sensing duration while maximizing the detection probability of primary users (PU) to avoid interfering with their transmissions. Both objectives have a substantial effect on energy efficiency. This paper investigates a noncooperative setting for selecting the sensing duration when multiple SUs operate in the same network. Here, each SU has a certain throughput requirement. The interaction among SUs is captured via a satisfaction strategic game with explicitly stated throughput demands. We prove that depending on the throughput requirements, either zero, one or two Satisfaction Equilibria (SE) exist. We then provide a fully distributed learning algorithm (SELA) to discover them. Extensive simulation results show the validity of the proposed SELA and illustrate the relationship between the throughput demand and the sensing duration.

The article considers the joint optimization of artificial noise (AN) and information signal precoders in a MIMO wiretap interference network where the transmission of each user may be overheard by several MIMO-capable eavesdroppers. We use the theory of non-cooperative games to propose a distributed framework to optimize the covariance matrices of the information signal and AN at each link. To tackle the non-convexity of each link/player's optimization problem, we recruit a relaxed equilibrium concept in game theory, called quasi-Nash equilibrium (QNE). Under the assumption of no coordination between links, we derive sufficient conditions for the existence and uniqueness of the resulting QNE. It turns out that the uniqueness of QNE is not always guaranteed, especially in the case of high interference. Hence, multiple QNEs might exist, and an ordinary updating process (e.g., Gauss-Seidel, Jacobi, or asynchronous update) does not guarantee the convergence to a QNE. Instead, by using the Tikhonov regularization method for variational inequality problems, we modify our algorithm to guarantee the game's convergence to a QNE even in the case of having multiple QNEs. The modified algorithm also allows the links to select between multiple QNEs so as to reduce the received interference at the legitimate receivers. Simulations are then used to confirm the above theoretical findings and the efficacy (in terms of secrecy sum-rate, convergence guarantee, and energy efficiency) of the latter algorithm.

In order to meet the exponential increase in wireless demand, new technologies are being considered for next-generation Wi-Fi systems (e.g., IEEE 802.11ax). Among these technologies is the adaptation of clear channel assessment (CCA) thresholds for high-efficiency (HE) stations (STAs) according to the beacon's received signal strength indicator (RSSI). The motivation behind this approach is to enhance the network throughput by improving the spatial reuse (i.e., allowing simultaneous transmissions from nearby STAs). There exists an inherent tradeoff between increasing the network throughput, via adapting the CCA thresholds for HE STAs, and maintaining fairness between legacy and HE STAs. In this paper, we provide a theoretical framework to evaluate the aforementioned tradeoff. We also propose a centralized fairness mechanism (CFM), in which STAs switch between an adaptive phase (CCA adaptation is allowed) and a fixed phase (legacy and HE STAs use the same CCA threshold). We formulate an optimization problem with the objective of determining the optimal switching strategy that maximizes the network throughput while maintaining a lower bound on per-STA throughput. Finally, we validate the proposed mechanism using simulations.

Recent advances in the design of in-band full-duplex (IBFD) radios promise to double the throughput of a wireless link. However, IBFD-capable nodes are more vulnerable to jamming attacks than their out-of-band full-duplex (OBFD) counterparts, and any advantages offered by them over the OBFD nodes can be jeopardized by such attacks. A jammer needs to attack both the uplink and the downlink channels to completely break the communication link between two OBFD nodes. In contrast, he only needs to jam one channel (used for both uplink and downlink) in the case of two IBFD nodes. Even worse, a jammer with the IBFD capability can learn the transmitters' activity while injecting interference, allowing it to react instantly with the transmitter's strategies. In this paper, we investigate frequency hopping (FH) technique for countering jamming attacks in the context of IBFD wireless radios. Specifically, we develop an optimal strategy for IBFD radios to combat an “IBFD reactive sweep jammer”. First, we introduce two operational modes for IBFD radios: transmission reception and transmission-detection. These modes are intended to boost the anti-jamming capability of IBFD radios. We then jointly optimize the decision of when to switch between the modes and when to hop to a new channel using Markov decision processes. Numerical investigations show that our policy significantly improves the throughput of IBFD nodes under jamming attacks.

In this paper, we expand the scope of PHY-layer security by investigating TX-based friendly jamming (FJ) for the wiretap channel in multi-link settings. For the single-link scenario, creating a TX-based FJ is an effective and practical method in improving the secrecy rate. In a multi-link setting, several information signals must be transmitted simultaneously. Thus, the design must guarantee that the FJ signal of a given transmitter does not interfere with unintended but legitimate receivers. Under the assumption of exact knowledge of the eavesdropping channel, we first propose a distributed price-based approach to improve the secrecy sum-rate of a two-link network with one eavesdropper while satisfying an information-rate constraint for both link. Simulations show that price-based FJ control outperforms greedy FJ, and is close to the performance of a centralized approach. Next, we propose a method based on mixed strategic games that can offer robust solutions to the distributed secrecy sum-rate maximization problem under the assumption of an unknown eavesdropping channel. Lastly, we use simulations to show that in addition to outperforming the greedy approach, our robust optimization also satisfies practical network considerations. In particular, the transmission time for the robust optimization can be determined flexibly to match the channel's coherence time.

Two promising solutions have been recently proposed to address the massive growth in mobile traffic and wireless devices: LTE-U and in-band full-duplex (FD) wireless. LTE-U extends the benefits of LTE-A to the unlicensed 5 GHz band, used mainly by Wi-Fi users. However, the uncertainty in Wi-Fi user activities makes provisioning QoS guarantees to LTE-U users challenging. On the other hand, FD wireless can double spectrum efficiency by enabling simultaneous transmission and reception over the same frequency band. Our objective in this paper is to exploit excess capacity of deployed Wi-Fi networks (operating in the 5 GHz band) to orchestrate a ‘robust’ virtual LTE-U network from a hybrid set of half-duplex (HD) and FD Wi-Fi access points (APs). Although the orchestrated LTE-U network does not support deterministic QoS guarantees, it is designed to provide prespecified probabilistic QoS guarantees (hence, it is robust). Towards achieving our goal, we develop novel stochastic resource allocation formulations that optimally orchestrate a virtual LTE-U network from a hybrid set of HD/FD APs with the minimum cost. We first consider the single small-cell problem and propose a stochastic formulation, which we refer to as CCLTEUsingle. Then, we study the multi-cell stochastic allocation problem and develop another formulation, which we refer to as CCLTEUmulti. Our formulations adopt a ‘chance-constrained stochastic programming’ approach. We derive the deterministic equivalent programs of CCLTEUsingleand CCLTEUmultiand evaluate them numerically under various system parameters.

In an attempt to face the anticipated spectrum crunch and inspired by the possibility of extending LTE-A to the unlicensed spectrum (LTE-U), in this paper we devise an adaptive channel assignment scheme for video streaming over LTE-U small cells. The proposed scheme adaptively assigns video frames to a subset of monitored channels with the objective of reducing possible starvation instants at user equipments (UEs), and hence maintaining continuous video playback. Specifically, considering coexisting LTE-U small cells and Wi-Fi networks, and leveraging the carrier aggregation (CA) feature in LTE-A, our scheme takes into account the quality of monitored channels, the occupancy of the playback buffers at the UEs, the deadlines and priorities of transmitted video frames, as well as the activity of Wi-Fi users when optimizing the assignment of video frames to channels in the licensed and unlicensed spectrum. According to the numerical results, the proposed scheme returns higher utility, and hence higher probability of correctly receiving video frames, compared to traditional assignment schemes.

Recently, tremendous progress has been made in self-interference cancellation (SIC) techniques that enable a wireless device to transmit and receive data simultaneously on the same frequency channel, a.k.a. in-band full-duplex (FD) communications. Although operating in a FD mode significantly improves the throughput of a single wireless link, it doubles the number of concurrent transmissions, which limits the potential for coexistence between multiple FD-enabled links. In this paper, we consider the problem of concurrent transmissions between two FD-enabled links with different SIC capabilities; each link can operate in either FD or half-duplex (HD) mode. Following a game-theoretic framework, we aim to determine the stable behavior (FD or HD) for the two coexisting links. To achieve this objective, we first analyze a simple normal form game between the two links, which provides some insight into the coexistence problem. It turns out that the outcome of this game depends on two factors: The amount of residual self-interference (due to imperfect SIC) and the external interference from one link on the other. To capture the impact of residual self-interference, we formulate a Bayesian game between two links with heterogeneous SIC capabilities. In this game, each link (player) tries to maximize its throughput while minimizing the transmission power cost. We derive the Bayesian Nash equilibrium for this game. Furthermore, we determine the conditions on the external interference under which no outage occurs at both links. Finally, we conduct simulations and USRP hardware experiments to corroborate our analytical findings.

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

The broadcast nature of wireless communications exposes various transmission attributes, such as the packet size, inter-packet times, and the modulation scheme. These attributes can be exploited by an adversary to launch passive (e.g., traffic analysis) or selective jamming attacks. This security problem is present even when frame headers and payloads can be encrypted. For example, by determining the modulation scheme, the attacker can estimate the data rate, and hence the payload size. In this paper, we propose Friendly CryptoJam (FCJ), a scheme that decorrelates the payload's modulation scheme from other transmission attributes by embedding information symbols into the constellation map of the highest-order modulation scheme supported by the system (a concept we refer to as indistinguishable modulation unification). Such unification is done using the least-complex trellis-coded modulation schemes, which are combined with a secret pseudo-random sequence in FCJ to conceal the rate-dependent pattern imposed by the code. It also preserves the bit error rate performance of the payload's original modulation scheme. At the same time, modulated symbols are encrypted to hide PHY-/MAC layer fields. To identify the Tx and synchronously generate the secret sequence at the Tx and Rx, an efficient identifier embedding technique based on Barker sequences is proposed, which exploits the structure of the preamble and overlays a frame-specific identifier on it. We study the implications of the scheme on PHY-layer functions through simulations and testbed experiments. Our results confirm the efficiency of FCJ in hiding the targeted attributes.

Wireless transmissions are inherently vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been separately used to mitigate jamming. When RA is used alone, it has been shown that a jammer who randomizes its power levels can force the transmitter toalwaysoperate at the lowest rate, by maintaining the average jamming power above a certain threshold. On the other hand, when only FH is used, a high throughput overhead is incurred due to frequent channel switching. In this paper, we propose to mitigate jamming by jointly optimizing the FH and RA techniques. This way, the transmitter can escape the jammer by changing its channel, adjusting its rate, or both. We consider a power-constrained “reactive-sweep” jammer who aims at degrading the throughput of the wireless link. The jammer sweeps through the set of channels, jamming a subset of them at a time, using the optimal jamming power. We model the interactions between the legitimate transmitter and jammer as a constrained zero-sum Markov game. The transmitter’s optimal defense strategy is derived by obtaining the equilibria of the constrained Markov game. This policy informs the transmitter when to hop to another channel and when to stay on the current channel. Furthermore, it gives the best transmission rate to use in both cases (hop or stay). The structure of the transmitter’s optimal policy is shown to be threshold type, whereby the transmitter stays on the same channel up to a certain number of time slots after which it hops. We analyze the “constrained Nash equilibrium” of the Markov game and show that the equilibrium defense strategy of the transmitter is deterministic. Numerical investigations show that the new scheme improves the average throughput and provides better jamming resiliency.

Frequency offset (FO) refers to the difference in the operating frequencies of two radio oscillators. Failure to compensate for the FO may lead to decoding errors, particularly in OFDM systems. To correct the FO, wireless standards append a publicly known preamble to every frame before transmission. In this paper, we demonstrate how an adversary can exploit the known preamble structure of OFDM-based wireless systems, particularly IEEE802.11a/g/n/ac, to launch a very stealth (low energy/duty cycle) reactive jamming attack against the FO estimation mechanism. In this attack, the adversary quickly detects a transmitted OFDM frame and subsequently jams a tiny part of the preamble that is used for FO estimation at the legitimate receiver. By optimizing the energy and structure of the jamming signal and accounting for frame detection timing errors and unknown channel parameters, we empirically show that the adversary can induce a bit error rate close to$0.5$, making the transmission practically irrecoverable. Such vulnerability to FO jamming exists even when the frame is shielded by efficient channel coding. We evaluate the FO estimation attack through simulations and USRP experimentation. We also propose three approaches to mitigate such an attack.

This paper considers a broadcast channel with a multi-antenna transmitter (Alice) sending two independent confidential data streams to two legitimate users (Bob and Charlie) in the presence of a passive eavesdropper (Eve). To enhance their secrecy rates, Bob and Charlie are assumed to be capable of self-interference suppression (SIS). Alice, on the other hand, uses MIMO precoding to generate the two confidential information signals along with its own (Tx-based) friendly jamming. The interfering signals at Bob and Charlie are removed by employing the zero-forcing technique. This, however, leaves ``vulnerability regions'' around Bob and Charlie, which can be exploited by a nearby eavesdropper. We address this problem by augmenting Tx-based friendly jamming with Rx-based friendly jamming, generated by Bob and Charlie. For the resulting broadcast channel, a secrecy encoding scheme is developed to construct the signals intended to Bob and Charlie. The corresponding achievable secrecy sum-rate is characterized, and an optimization problem is formulated. A special case of this problem is investigated. Simulation results show the effectiveness of utilizing (Tx- and/or Rx-based) jamming, and the impact of the degree of SIS on physical-layer security.

Friendly jamming is a PHY-layer technique used to secure wireless communications. Unlike previous efforts that fix the placement of the friendly jamming devices, in this paper we consider small- scale multi-link wireless networks, e.g., peer-to- peer or multihop, and jointly optimize the powers and locations of the friendly jamming devices so as to minimize the total jamming power while simultaneously achieving a given secrecy constraint. We use distributed MIMO techniques and incorporate the necessary conditions to ensure nullification of the friendly jamming signals at legitimate receivers. Two optimization strategies are explored: per-link and network-wide. Our optimization framework is based on formulating a signomial programming problem using condensation techniques to approximate the problem as a geometric program, which can then be transformed into a convex problem. We also consider the secrecy-aware routing problem for multihop networks and propose a routing metric based on the total jamming power along the path. Simulations show that our proposed schemes outperform previous schemes in terms of energy efficiency (55%-99% power saving). Moreover, our formulation ensures protecting legitimate transmissions by nullifying friendly jamming signals at legitimate receivers.

Full-duplex (FD) radios have the potential to double a link's capacity. However, it has been recently reported that the network throughput gain of FD radios over half-duplex (HD) ones is unexpectedly marginal or even negative. This is because both ends of each link transmit at the same time, a set of concurrent FD links will experience more network interference (hence, reduction in the spatial reuse). This article identifies the unique advantages of FD radios and leverage multi-input multioutput (MIMO) communications to translate the FD spectral efficiency gain at the PHY level to throughput and power efficiency gain at the network layer. To that end, we first study the power minimization problem subject to rate demands in a FD-MIMO network. Sufficient conditions under which the FD network throughput can asymptotically double that of an HD network are then established. These conditions also guarantee the existence of a unique Nash Equilibrium that the game quickly converges to. By capturing “spatial signatures” of other radios, a FD-MIMO radio can instantly adjust its ongoing radiation pattern to avoid interfering with the reception directions at other radios. We exploit that to develop a novel MAC protocol that allows multiple FD links to concurrently communicate while adapting their radiation patterns to minimize network interference. The protocol does not require any feedback or coordination among nodes, but relies on the network interference perceived by these FD radios. Extensive simulations show that the proposed MAC design dramatically outperforms traditional FD-based CSMA protocols and HD radios w.r.t. both throughput and energy efficiency. A centralized algorithm for the FD network-wide transmit power minimization problem is also developed. Simulations show that, the proposed MAC protocol on average achieves almost the same power efficiency as the centralized algorithm. Interestingly, we even observe cases when the proposed distributed algorithm outperforms the centralized approach.

In this paper, we investigate the spectrum management problem in TV White Space (TVWS) Cognitive Radio Networks using a game theoretical approach, accounting for adjacent-channel interference. TV Bands Devices (TVBDs) compete to access available TV channels and choose idle blocks that optimize some objective function. Specifically, the goal of each TVBD is to minimize the price paid to the Database operator and a cost function that depends on the interference between unlicensed devices. We show that the proposed TVWS management game admits a potential function under general conditions. Accordingly, we use a Best Response algorithm to converge in few iterations to the Nash Equilibrium (NE) points. We evaluate the performance of the proposed game, considering both static and dynamic TVWS scenarios and taking into account users' mobility. Our results show that at the NE, the game provides an interesting tradeoff between efficient TV spectrum use and reduction of interference between TVBDs.

Rendezvous is a vital process for connection establishment and recovery in dynamic spectrum access (DSA) networks. Frequency hopping (FH) is an effective rendezvous method that does not rely on a predetermined control channel. Recently, quorum-based FH approaches have been proposed for enabling asynchronous rendezvous between two or more secondary users (SUs). In this paper, we consider two collocated secondary networks, each represented by a pair of SUs. Both networks try to rendezvous concurrently, each aiming at maximizing its rendezvous performance, as measured by the average time-to-rendezvous and the number of rendezvous opportunities. To study this form of coexistence rendezvous, we follow a non-cooperative combinatorial game-theoretic framework, which we refer to as CORE. In this framework, SUs have different preferences towards various available licensed channels. Assuming first that SUs are time-synchronized, we formulate the interactions between the two networks as a two-player symmetric combinatorial game. We show the existence and uniqueness of a finite-population evolutionary stable strategy for this game. Furthermore, we conjecture that the game attains a pure-strategy Nash equilibrium (NE) for a wide range of design parameters. We also show that when SU pairs have the same preference towards all available channels, our game is an exact potential game, and hence the sequential best-response update is guaranteed to converge to a pure-strategy NE. We then study the time-asynchronous rendezvous game when SU pairs have the same preference towards all available channels. In this case, the game is also shown to be an exact potential game.

Recent research has demonstrated the feasibility of full-duplex (FD) communication over the same frequency channel. This capability, facilitated by new self-interference suppression techniques, has great potential to increase the network capacity. However, exploiting FD in the context of a multi-user, multi-channel network is still being debated. This paper focuses on the channel access issue and presents a novel multi-channel MAC (MMAC) protocol for wireless ad hoc networks with FD and orthogonal frequency-division multiple access (OFDMA) capabilities. Through these capabilities, a node can simultaneously carry out multiple transmissions and/or receptions over the same or different channels. In our MMAC protocol, a pair of nodes negotiate data channels, transmission rates, transmission powers, and transmission modes (e.g., FD or half-duplex) in a distributed manner so that their spectral usages are minimized while their rate demands are still met. Extensive ns3 simulations show that our MMAC protocol increases the end-to-end network goodput by up to 150% and decreases the end-to-end delay by up to 300% compared with an OFDMA-based protocol without FD.

We address the problem of jamming-resistant broadcast communications under an internal threat model. We propose a time-delayed broadcast scheme (TDBS), which implements the broadcast operation as a series of unicast transmissions distributed in frequency and time. TDBS does not rely on commonly shared secrets, or the existence of jamming-immune control channels for coordinating broadcasts. Instead, each node follows a unique pseudo-noise (PN) frequency hopping sequence. Contrary to conventional PN sequences designed for multi-access systems, the PN sequences in TDBS exhibit correlation to enable broadcast. Moreover, they are designed to limit the information leakage due to the exposure of a subset of sequences by compromised nodes. We map the problem of constructing such PN sequences to the 1-factorization problem for complete graphs. We further accommodate dynamic broadcast groups by mapping the problem of updating the assigned PN sequences to the problem of constructing rainbow paths in proper edge-colored graphs.

Establishing communications between devices in a dynamic spectrum access (DSA) system requires the communicating parties to “rendezvous” before transmitting data packets. Frequency hopping (FH) is an effective rendezvous method that does not rely on a predetermined control channel. Previous FH-based rendezvous designs mainly target unicast rendezvous, and do not intrinsically support multicast rendezvous, where a group of nodes need to rendezvous simultaneously. Furthermore, these designs do not account for fast-primary user (PU) dynamics, leading to long time-to-rendezvous (TTR). In this paper, we exploit the uniform k-arbiter and Chinese Remainder Theorem quorum systems to develop three FH-based multicast rendezvous algorithms, which provide different tradeoffs between rendezvous efficiency (e.g., low TTR) and security (e.g., robustness to node compromise). Our rendezvous algorithms are tailored for asynchronous and spectrum-heterogeneous DSA systems. To account for fast PU dynamics, we develop an algorithm for adapting the proposed FH designs on the fly. This adaptation is done through efficient mechanisms for channel ordering and quorum selection. Our simulations validate the effectiveness of the proposed rendezvous algorithms, their PU detection accuracy, and their robustness to insider attacks.

Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We are especially interested in the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads. To reduce the computation overhead of the baseline scheme, a packet-block-based mechanism is also proposed, which allows one to trade detection accuracy for lower computation complexity. Through extensive simulations, we verify that the proposed mechanisms achieve significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

Fading and shadowing along with the primary user dynamics make channel quality in dynamic spectrum access networks uncertain. Furthermore, the imperfect design of filters and amplifiers in wireless devices motivates the need for guard-bands (GBs) to prevent adjacent-channel interference. In this paper, we develop novel stochastic GB-aware sequential and batch channel assignment schemes that aim at maximizing the spectrum efficiency. In line with recent IEEE 802.11 and LTE standards, our schemes support bonding and aggregation. We propose two assignment models for each of the sequential and batch schemes: a static single-stage and an adaptive two-stage. In the static model, channel assignment is performed once such that the rate demands are probabilistically met. The adaptive model is a two-stage model, where the initial assignment may be corrected once uncertainties are partially revealed. We refer to our formulations of the sequential and batch static assignments as chance-constrained stochastic subset-s um problem (CSSP) and chance-constrained stochastic multiple s ubset-sum problem (CMSSP), respectively. Moreover, we develop stochastic formulations for the sequential and batch adaptive assignments, which we refer to as two-stage CSSP with recourse (CSSPR) and two-stage CMSSP with recourse (CMSSPR), respectively. Finally, we present computationally efficient simplified versions of CSSP and CSSPR with near-optimal performance.

Inspired by recent developments in full-duplex (FD) communications, we consider an opportunistic spectrum access (OSA) network in which secondary users (SUs) are capable of partial/complete self-interference suppression (SIS). This enables them to operate in either simultaneous transmit-and-sense (TS) or simultaneous transmit-and-receive (TR) modes, with the goal of achieving improved primary user (PU) detection and/or higher SU throughput. We first consider an overlay OSA setup, and we study the TS and TR modes. We also explore the spectrum awareness/efficiency tradeoff and determine an efficient adaptive strategy for the SU link. We then consider a spectrum underlay model, with the objective of optimizing SUs' transmission powers so as to maximize the sum-throughput of $K$ FD secondary links subject to a PU outage constraint. Operating in an FD fashion is not always efficient for SUs. Hence, we propose an optimal policy for switching between FD and half-duplex. The criteria for this policy depend mainly on the SIS capabilities of SUs. Finally, we propose a mode selection algorithm for the switching process. Numerical results indicate that operating in the TS mode can reduce the PU outage probability by up to 100% compared with the classical listen-before-talk scheme.

Frequency offset (FO) is an inherent feature of wireless communications. It results from differences in the operating frequency of different radio oscillators. Failure to compensate for the FO may lead to a decoding failure, particularly in OFDM systems. IEEE 802.11a/g systems use a globally known preamble to deal with this issue. In this paper, we demonstrate how an adversary can exploit the structure and publicity of 802.11a's frame preamble to launch a low-power reactive jamming attack against the FO estimation mechanism. In this attack, the adversary will need to quickly detect a PHY frame and subsequently distort the FO estimation mechanism, irrespective of the channel conditions. By employing a fast frame detection technique, and optimizing the energy and structure of the jamming signal, we show the feasibility of such an attack. Furthermore, we propose some mitigation techniques and evaluate one of them through simulations and USRP testbed experimentation.

Wireless transmissions are inherently broadcast and are vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been used to mitigate jamming. However, recent works have shown that using either FH or RA (but not both) is inefficient against smart jamming. In this paper, we propose mitigating jamming by jointly optimizing the FH and RA techniques. We consider a power constrained “reactive-sweep” jammer who aims at degrading the goodput of a wireless link. We model the interaction between the legitimate transmitter and jammer as a zero-sum Markov game, and derive the optimal defense strategy. Numerical investigations show that the new scheme improves the average goodput and provides better jamming resiliency.

We are concerned with the throughput of a full-duplex (FD) MIMO network. Unlike conventional half-duplex (HD) MIMO, two wireless devices of a bidirectional FD-MIMO link have freedom of selecting which antennas/RF-chains to transmit or receive before tuning their radiation patterns to maximize the link's throughput. The freedom in configuring the function of available RF-chains, resulting in various FD-MIMO transmission modes, is referred to asFD-MIMO freedomthat is shown to significantly improve the spectral efficiency of a given link. For a given RF-chain/antenna selection of a set of FD-MIMO links, we end up with a non-convex throughput maximization problem of a heterogeneous MIMO network. We design both centralized (using the augmented Lagrange function) and distributed algorithm (using a hierarchical game and pricing) to solve the problem for its locally optimal solutions. Comparing the achieved throughput of the FD-MIMO network, averaged over all obtained locally optimal solutions, with that when FD-MIMO nodes choose to operate in an HD mode, we find the HD mode surprisingly outperforms the FD mode. This trend is also observed when exploring all possible communication modes of a small size FD-MIMO network.

The broadcast nature of wireless communications exposes various "transmission attributes," such as the packet size, the inter-packet times, and the modulation scheme. These attributes can be exploited by an adversary to launch passive or active attacks. A passive attacker threatens user's privacy and confidentiality by performing traffic analysis and classification, whereas an active attacker exploits captured attributes to launch selective jamming/dropping attacks. This so-called PHY-layer security problem is present even when the payload is encrypted. For example, by determining the modulation scheme, the attacker can estimate the data rate, and hence the payload size, and later use it to launch traffic classification or selective rate-adaptation attacks.

Spectrum access/sharing algorithms for dynamic spectrum access (DSA) networks are often designed without accounting for adjacent-channel interference. In practice, guard bands are needed to prevent such interference. Introducing guard bands naturally constrains the effective use of the spectrum. In this work, we investigate the problem of assigning channels/powers to opportunistic transmissions, while accounting for such a constraint. Specifically, we propose a novel guard-band-aware channel assignment scheme for DSA systems. Our scheme reduces the number of required guard channels for a given transmission by exploiting the benefit of utilizing adjacent channels and considering already reserved guard channels. We analytically formulate the channel access problem as a joint power control and channel assignment optimization problem, with the objective of minimizing the required spectrum resource for a given CR transmission. We show that the optimization problem is a binary linear program (BLP), which is, in general, NP-hard. Accordingly, we present a near-optimal solution based on sequential fixing, where the binary variables are determined iteratively by solving a sequence of linear programs. Based on the proposed channel assignment algorithm, we develop an operational MAC protocol that enables DSA users to dynamically utilize the spectrum. The proposed protocol realizes our channel assignment algorithm in a distributed manner while relying only on information provided by the two communicating users. Simulation results are provided, which verify the effectiveness of our protocol and demonstrate the significant gain achieved through guard-band-aware channel assignment.

Energy harvesting is a promising solution for reducing network maintenance and the overhead of replacing chemical batteries in sensor networks. In this article, problems related to controlling an active wireless sensor network comprised of nodes powered by both rechargeable batteries and solar energy are investigated. The objective of this control is to maximize the network's Quality of Coverage (QoC), defined as the minimum number of targets that can be covered by the network over a 24-hour period. Assuming a time-varying solar profile, the underlying problem is to optimally control the sensing range of each sensor so as to maximize the QoC. The problem is further constrained by requiring all active sensors to report any sensed data to a centralized base station, making connectivity a key factor in sensor management. Implicit in the solution is the allocation of solar energy during the day to sensing tasks and recharging of the battery so that a minimum coverage is guaranteed at all times. The problem turns out to be a nonlinear optimal control problem of high complexity. By exploiting the particular structure of the problem, we present a novel method for determining near-optimal sensing radii and routing paths as a series of quasiconvex (unimodal) optimization problems. The runtime of the proposed solution is 60X less than the standard optimal control method based on dynamic programming, while the worst-case error is less than 8%. The proposed method is scalable to large networks consisting of hundreds of sensors and targets. Several insights in the design of energy-harvesting networks are provided.

We explore the use of cooperative multi-input multi-output (MIMO) communications to prolong the lifetime of a wireless sensor network (WSN). Single-antenna sensor nodes are clustered into virtual antenna arrays that can act as virtual MIMO (VMIMO) nodes. We design a distributed cooperative clustering protocol (CCP), which exploits VMIMO's diversity gain by optimally selecting the cooperating nodes (CNs) within each cluster and balancing their energy consumption. The problem of optimal CN selection at the transmit and receive clusters is formulated as a nonlinear binary program. Aiming at minimizing the imbalance in the residual energy at various nodes, we decompose this problem into two subproblems: finding the optimal number of CNs (ONC) in a cluster and the CN assignment problem. For the ONC problem, we first analyze the energy efficiency of two widely used VMIMO methods: distributed Space Time Block Code (DSTBC) and distributed Vertical-Bell Laboratories-Layered-Space-Time (DVBLAST). Our analysis provides an upper bound on the optimal number of CN nodes, which greatly reduces the computational complexity of the ONC problem. The second subproblem is addressed by assigning CNs based on the residual battery energy. To make CCP scalable to large WSNs, we propose a multihop energy-balanced routing mechanism for clustered WSNs (C-EBR) with a novel cost metric. Finally, we derive sufficient conditions on the intra- and intercluster ranges, under which CCP guarantees connectivity of the intercluster topology. Extensive simulations show that the proposed approach dramatically improves the network lifetime.

The uncertainty in channel quality due to fading and shadowing along with the unpredictability of primary user (PU) activity make channel assignment in opportunistic spectrum access (OSA) networks quite challenging. In this paper, we propose two per-link channel assignment models under channel uncertainty: a static single-stage and an adaptive two-stage. In the static model, channel assignment is performed once, such that the rate demands are met with a probability greater than a certain threshold. This model is appropriate for a distributed network with no centralized spectrum manager. The adaptive model is a two-stage assignment model, where the initial assignment may be corrected once the uncertainties are partially revealed, such that the excess spectrum is returned back to the spectrum manager. This adaptive model is more appropriate when a centralized spectrum manager is available. Our channel assignment algorithms account for adjacent channel interference (ACI) by introducing guard-bands between adjacent channels that are assigned to different links. These algorithms aim at maximizing the spectral efficiency, considering the impact of guard-bands. The static ACI-aware channel assignment problem is formulated as a chance-constrained stochastic subset-sum problem (CSSP), and the adaptive assignment problem is formulated as a two-stage chance-constrained stochastic subset-sum problem with recourse (CSSPR). We develop heuristic algorithms for both models and test their performance. Preliminary results demonstrate that the proposed heuristic algorithms are highly efficient.

We are interested in addressing a fundamental question: what are conditions under which an ad hoc cognitive radio MIMO (CMIMO) network can support a given rate-demand profile, defined as the set of rates requested by individual links? From an information theoretic view, a rate profile can be supported if it is within the network capacity region. However, the network capacity region of interfering MIMO networks is essentially unknown. In dynamic spectrum access, the problem is even more challenging due to the dynamics of primary/legacy users (PUs), resource constraints, and the heterogeneity of opportunistic spectrum (i.e., the set of available channels varies from one to another). Considering a non-centralized setup, we address the above question in a noncooperative game framework where each CMIMO link independently optimizes its spectrum, power allocation, and MIMO precoders to meet its rate demand. We derive sufficient conditions for the existence of a NE are derived. These conditions establish an explicit relationship between the rate-demand profile and interference from PUs, CMIMO network's interference, and CMIMO nodes' power budget. We also show that a NE, if exists, is unique. Our results help to characterize the network capacity region of CMIMO networks.

The operation of a wireless network relies extensively on exchanging messages over a universally known channel, referred to as the control channel. The network performance can be severely degraded if a jammer launches a denial-of-service (DoS) attack on such a channel. In this paper, we design quorum-based frequency hopping (FH) algorithms that mitigate DoS attacks on the control channel of an asynchronous ad hoc network. Our algorithms can establish unicast as well as multicast communications under DoS attacks. They are fully distributed, do not incur any additional message exchange overhead, and can work in the absence of node synchronization. Furthermore, the multicast algorithms maintain the multicast group consistency. The efficiency of our algorithms is shown by analysis and simulations.

Inspired by recent developments in full-duplex communications, we propose and study new modes of operation for cognitive radios with the goal of achieving improved primary user (PU) detection and/or secondary user (SU) throughput. Specifically, we consider an opportunistic PU/SU setting in which the SU is equipped with partial/complete self-interference suppression (SIS), enabling it to transmit and receive/sense at the same time. Following a brief sensing period, the SU can operate in either simultaneous transmit-and-sense (TS) mode or simultaneous transmit-and-receive (TR) mode. We analytically study the performance metrics for the two modes, namely the detection and false-alarm probabilities, the PU outage probability, and the SU throughput. From this analysis, we evaluate the sensing-throughput tradeoff for both modes. Our objective is to find the optimal sensing and transmission durations for the SU that maximize its throughput subject to a given outage probability. We also explore the spectrum awareness/efficiency tradeoff that arises from the two modes by determining an efficient adaptive strategy for the SU link. This strategy has a threshold structure, which depends on the PU traffic load. Our study considers both perfect and imperfect sensing as well as perfect/imperfect SIS.

Multi-channel medium access control (MMAC) has the potential to significantly improve the network throughput by enabling parallel transmissions over different frequency channels. In many MMAC protocols, nodes exchange control packets over a dedicated control channel (CC). The CC transmission rate (CCR) is usually set to the lowest possible value, so as to maximize the reachability of control packets. However, in a multi-hop ad hoc network, this choice of the CCR may lead to a CC bottleneck, especially under high traffic load. While increasing the CCR can alleviate this bottleneck and improve the single-hop MMAC performance, it may also increase the number of hops along the path and hence degrade the end-to-end network performance. In this paper, we investigate the impact of the CCR on the performance of a multi-channel, multi-hop wireless network under a general MMAC protocol. To derive the queuing and channel access delays, we model the network as a G/G/1 queuing system. In our analysis, we consider detailed packet-level operations and non-saturated traffic. The average number of hops is also analytically obtained when nodes are randomly distributed. Our analysis is evaluated via network simulations, using 802.11a parameters. The simulation and numerical results reveal that the lowest transmission rate is not the optimal CCR. Our simulations show that the received power threshold has a significant impact on the optimal CCR.

We consider a multi-channel multi-user cognitive radio MIMO network in which each node controls its antenna radiation directions and allocates power for each data stream by adjusting its precoding matrices. Under a noncooperative game, we optimize the set of precoding matrices (one per channel) at each node so as to minimize the total transmit power in the network. Using recession analysis and the theory of variational inequalities, we obtain sufficient conditions that guarantee the existence and uniqueness of the game's Nash Equilibrium (NE). Low-complexity distributed algorithms are also developed by exploiting the strong duality of the convex per-user optimization problem. To improve the efficiency of the NE, we introduce pricing policies that employ a novel network interference function. Existence and uniqueness of the new NE under pricing are studied. Simulations confirm the effectiveness of our joint optimization approach.

This paper makes preliminary steps at exploring a novel application of cognitive radios (CRs) for reliable satellite communications. We propose the use of dynamically adjusted frequency hopping (FH) sequences for satellite transmissions. Such sequences are more robust against smart eavesdropping and targeted interference than fixed FH sequences. In our approach, FH sequence is adjusted according to the outcome of out-of-band sensing, carried out by a CR module that resides in the satellite itself or at the receiving ground station. Our protocol, called OSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed OSDFH using a finite state Markov chain (FSMC) framework. Level crossing rate (LCR) analysis is used to determine the transition probabilities of the Markov chain. These probabilities are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. We use simulations to study the effects of different system parameters on the performance of our proposed protocol.

Wireless communications are prone to both unintentional and intentional RF interference. Such interference has significant impact on the reliability of packet transmissions. In this paper, we consider interference mitigation in frequency hopping (FH) systems. We employ cognitive radios (CRs) for proactive interference sensing in such systems. Through this proactive approach, we propose a scheme for dynamic adjustment of the FH sequence. Our protocol, called SSDFH, relies on exploiting the spectrum sensing capabilities of CRs for proactive detection of channel quality. We analyze the characteristics of the proposed SSDFH using a continuous-time Markov chain framework. Level crossing rate (LCR) analysis is used to determine the transition rates for the Markov chain, which are then used to measure the “channel stability,” a metric that reflects the freshness of sensed channel interference. The selection of different protocol parameters is studied by means of analysis. In particular, we provide a numerical procedure for determining the “optimal” total sensing time that minimizes the probability of “black holes.” We run simulations to study the performance of our proposed protocol.

Opportunistic routing has recently been proposed for ad hoc networks. Several works showed that opportunistic routing improves network performance. None of these works, however, studied the integration of opportunistic routing into multi-rate ad hoc networks. In this paper, we study this integration. Specifically, we consider the problem of determining the required number of retransmissions in opportunistic routing and the transmission rate in each retransmission such that the required (re)transmission time for a sent packet is minimized while at the same time a target end-to-end PER is satisfied. We show that this problem is NP-hard. Accordingly, we propose an approximate solution called Opportunistic routing Over Multi-rate Multi-hop ad hoc networks (OOMM). Extensive simulations over a multi-rate OFDM-based UWB network show that OOMM achieves high performance relative to different routing techniques (21%-48% throughput improvement).

We consider the problem of minimizing network's transmit power for given transmission rate demands of all links in a multi-input multi-output (MIMO) ad hoc network. The problem is nonconvex, hence, challenging to be solved, even in a centralized manner. To derive a distributed solution, we reformulate the problem as a noncooperative game. We then propose a network interference function (NIF) that captures the total interference incurred at unintended receivers by all transmitters. The proposed NIF sets the light for designing transmitter-dependent pricing policies for the above game. A price-based iterative water-filling algorithm (PIWF) is proposed to find MIMO precoding matrices, which determines both beam directions and transmission power allocation among antennas (or data streams) at each transmitter. Simulations show that PIWF is more power-efficient than all existing MIMO precoding methods. Additionally, NIF under PIWF is also the least. Simulations also show the fast convergence of PIWF.

Energy harvesting in a sensor network is essential in situations where it is either difficult or not cost effective to access the network's nodes to replace the batteries. In this paper, we investigate the problems involved in controlling an active wireless sensor network that is powered both by rechargeable batteries and solar energy. The objective of this control is to maximize the network's quality of coverage (QoC), defined as the minimum number of targets that must be covered over a 24-hour period. Assuming a time varying solar profile, the problem is to optimally control the sensing range of each sensor so as to maximize the QoC. Implicit in the solution is the dynamic allocation of solar energy during the day to sensing tasks and to recharging the battery so that minimum coverage is guaranteed even during the night, when only the batteries can supply energy to the sensors. The problem turns out to be a nonlinear optimal control problem of high complexity. Exploiting the specific structure of the problem, we present a method to solve it as a series of quasiconvex (unimodal) optimization problems. The runtime of the proposed solution is 60X less than a naive method that is based on dynamic programming, while its worst-case error is less than 8%. Unlike the dynamic programming method, the proposed method is scalable to large networks consisting of hundreds of sensors and targets. This paper also offers several insights in the design of energy-harvesting networks, which result in minimum network setup cost through the determination of the optimal configuration of the number of sensors and the sampling time.

We consider the problem of maximizing the throughput of a multi-input multi-output (MIMO) cognitive radio (CR) network. CR users are assumed to share the available spectrum without disturbing primary radio (PR) transmissions. With spatial multiplexing performed over each frequency band, a multi-antenna CR node controls its antenna radiation patterns and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one for each band) at each CR node so that power and spectrum are optimally allocated for that node (in terms of throughput) and its interference is steered away from other CR and PR transmissions. In other words, the problems of power, spectrum and interference management are jointly investigated. We formulate a multi-carrier MIMO network throughput optimization problem subject to frequency-dependent power constraints. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. Such a problem is difficult to solve, even in a centralized manner. To tackle it, we translate it into a noncooperative game and derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE is at least equal to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (the best response), a low-complexity distributed algorithm is developed by exploiting the strong duality of the per-user convex optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed, serving as a performance benchmark. Simulations show that the network throughput under the distributed algorithm converges rapidly to that of the centralized one. The fast convergence of the game facilitates MAC design, which we briefly discuss in the paper. The application of our results is not limited to CR systems, but extends to multi-carrier (e.g., OFDM) MIMO systems.

In a multi-hop wireless ad hoc network, packet losses are attributed to harsh channel conditions and intentional packet discard by malicious nodes. In this paper, while observing a sequence of packet losses, we are interested in determining whether losses are due to link errors only, or due to the combined effect of link errors and malicious drop. We are especially interested in insider's attacks, whereby a malicious node that is part of the route exploits its knowledge of the communication context to selectively drop a small number of packets that are critical to network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This architecture is privacy preserving, collusion proof, and incurs low communication and storage overheads. Through extensive simulations, we verify that the proposed mechanism achieves significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

We consider the problem of maximizing the throughput of a multi-antenna cognitive radio (CR) network. With spatial multiplexing over each frequency band, a multi-antenna CR node controls its antenna radiation directions and allocates power for each data stream by appropriately adjusting its precoding matrix. Our objective is to design a set of precoding matrices (one per band) at each CR node so that power and spectrum are optimally allocated for the node and its interference is steered away from unintended receivers. The problem is non-convex, with the number of variables growing quadratically with the number of antenna elements. To tackle it, we translate it into a noncooperative game. We derive an optimal pricing policy for each node, which adapts to the node's neighboring conditions and drives the game to a Nash-Equilibrium (NE). The network throughput under this NE equals to that of a locally optimal solution of the non-convex centralized problem. To find the set of precoding matrices at each node (best response), we develop a low-complexity distributed algorithm by exploiting the strong duality of the convex per-user optimization problem. The number of variables in the distributed algorithm is independent of the number of antenna elements. A centralized (cooperative) algorithm is also developed. Simulations show that the network throughput under the distributed algorithm rapidly converges to that of the centralized one. Finally, we develop a MAC protocol that implements our resource allocation and beamforming scheme. Extensive simulations show that the proposed protocol dramatically improves the network throughput and reduces power consumption.

Coordination of network functions in wireless networks requires frequent exchange of control messages among participating nodes. Typically, such messages are transmitted over a universally known communication channel referred to as the control channel. Due to its critical role, this channel can become a prime target of Denial-of-Service (DoS) attacks. In this paper, we address the problem of preventing control-channel DoS attacks manifested in the form of jamming. We consider a sophisticated adversary who has knowledge of the protocol specifics and of the cryptographic quantities used to secure network operations. This type of adversary cannot be prevented by antijamming techniques that rely on shared secrets, such as spread spectrum. We propose new security metrics to quantify the ability of the adversary to deny access to the control channel, and introduce a randomized distributed scheme that allows nodes to establish and maintain the control channel in the presence of the jammer. Our method is applicable to networks with static or dynamically allocated spectrum. Furthermore, we propose two algorithms for unique identification of the set of compromised nodes, one for independently acting nodes and one for colluding nodes. Detailed theoretical evaluation of the security metrics and extensive simulation results are provided to demonstrate the efficiency of our methods in mitigating jamming and identifying compromised nodes.

Cognitive radio networks (CRNs) involve extensive exchange of control messages, which are used to coordinate critical network functions such as distributed spectrum sensing, medium access, and routing, to name a few. Typically, control messages are broadcasted on a preassigned common control channel, which can be realized as a separate frequency band in multichannel systems, a given time slot in TDMA systems, or a frequency hopping sequence (or CDMA code) in spread spectrum systems. However, a static control channel allocation is contrary to the opportunistic access paradigm. In this paper, we address the problem of dynamically assigning the control channel in CRNs based on time- and space-varying spectrum opportunities. We propose a cluster-based architecture that allocates different channels for control at various clusters in the network. The clustering problem is formulated as a bipartite graph problem, for which we develop a class of algorithms that provide different tradeoffs between two conflicting factors: number of common channels in a cluster and the cluster size. Clusters are guaranteed to have a desirable number of common channels for control, which facilitates for graceful channel migration when primary radio (PR) activity is detected, without the need for frequent reclustering. We perform extensive simulations that verify the agility of our algorithms in adapting to spatial-temporal variations in spectrum availability.

In this paper, we explore the economic aspects of routing/relaying in a profit-driven opportunistic spectrum access (OSA) network. In this network, primary users lease their licensed spectrum to secondary radio (SR) providers, who in turn provide opportunistic routing/relaying service to end-users if this service is profitable, i.e., if the payment offered by the end-user (a.k.a. the price) exceeds the SR's relaying spectrum cost. This cost is considered private information known only to SRs. Therefore, the end-user has to rely on costs reported by SRs to determine his routing and payment strategy. The challenge comes from the selfish nature of SRs; an SR may exaggerate his cost to achieve greater profit. To give incentive to an SR to report the true cost, the payment must typically be higher than the actual cost. However, from the end-user's perspective, “overpayment” should be avoided as much as possible. Therefore, we are interested in the “optimal” route selection and payment determination mechanism that minimizes the price of the selected route while simultaneously guaranteeing truthful cost reporting by SRs. We formulate this problem as finding the least-priced path (LPP), and we investigate it without and with link capacity constraints. In the former case, polynomial-time algorithm is developed to find LPP and calculate its truthful price. In the latter case, we show that calculating the truthful price of the LPP is in general computationally infeasible. Consequently, we consider a suboptimal but computationally feasible approximate solution, which we refer to as truthful low-priced path (LOPP) routing. A polynomial-time algorithm is proposed to find the LOPP and efficiently calculate its truthful price. A payment materialization algorithm is also developed to guarantee truthful capacity reporting by SRs. The effectiveness of our algorithms in terms of price saving is verified through extensive simulations.

A key challenge in cognitive radio networks (CRNs) is how to adaptively and efficiently select a route and assign resources along that route according to the surrounding environment. In this work, we propose a distributed routing protocol for mobile CRNs, in which path selection and resource allocation (e.g., spectrum, transmission power, and transmission rate) are determined by receivers. Because this process is done on perpacket and per- hop basis, the proposed protocol can efficiently adapt to spectrum dynamics and node mobility. In addition, spectrum efficiency is increased through dynamic spectrum allocation and transmission power control. Simulation results show that delivery ratio and throughput are dramatically improved with our routing protocol.

Spectrum sharing algorithms for cognitive radio networks (CRNs) are often designed ignoring adjacent-channel interference (i.e., interference from other transmissions operating on adjacent channels). In practice, such an assumption is unrealistic as guard bands are needed to prevent such interference. Introducing guard bands naturally constrains the effective use of the spectrum. In this work, we investigate the problem of assigning channels/powers to CR transmissions, while accounting for such a constraint. Specifically, we propose a novel guard-band-aware channel assignment scheme for CRNs. Our scheme reduces the number of required guard channels for a given transmission by exploiting the benefits of utilizing adjacent channels while considering already reserved guard channels. We analytically formulate the channel access problem as a joint power control and channel assignment optimization problem, with the objective of minimizing the required spectrum resource for a CR transmission. We show that the optimization problem is a binary linear program (BLP), which is, in general, NP-hard. Accordingly, we present a near-optimal solution based on a sequential fixing procedure. Simulation results are provided, which verify the accuracy of our algorithm and demonstrate the significant gain achieved through guard-band-aware channel assignment.

Multiple-input multiple-output (MIMO) technology is known to improve energy efficiency in energy-constrained wireless networks, such as wireless sensor networks (WSN). Although in WSNs, a node is often equipped with a single antenna, nodes can be clustered into virtual antenna arrays that can act as virtual MIMO (VMIMO) nodes. In this paper, we propose a distributed cooperative clustering protocol (CCP) that aims at conserving energy and prolonging network lifetime by taking advantage of VMIMO communications. In contrast to previously proposed protocols, CCP fully exploits the diversity gain of the VMIMO technique by optimally selecting the cooperating nodes (CNs) within a cluster and balancing their energy consumption. We first formulate the problem of optimal CN selection at the transmit and receive clusters as a nonlinear binary program, and show the problem is NP-hard. Aiming at minimizing the imbalance in the residual energy at various nodes, we reduce the problem into two sub-problems: finding the optimal number of CNs (ONC) in a cluster and the CN assignment problem. To analytically address the ONC problem, we analyze the energy efficiency of two existing VMIMO methods: distributed Space Time Block Code (DSTBC) and distributed Vertical-Bell Laboratories-Layered-Space-Time (DVBLAST). The second sub-problem is addressed by assigning CNs to nodes with stronger residual energy. To make CCP scalable to large WSNs, we propose a multi-hop energy-balanced routing mechanism for clustered WSNs with a novel cost metric. Our routing method is also applicable to other clustering protocols (e.g., CMIMO, MIMO-LEACH). Extensive simulations are used to validate our analysis.

We address the problem of jamming-resistant broadcast com-munications under an internal threat model. We propose a time-delayed broadcast scheme (TDBS), which implements the broadcast operation as a series of unicast transmissions, distributed in frequency and time. TDBS does not rely on commonly shared secrets, or the existence of jamming-immune control channels for coordinating broadcasts. In-stead, each node follows a unique pseudo-noise (PN) fre-quency hopping sequence. Contrary to conventional PN se-quences designed for multi-access systems, our sequences ex-hibit high correlation to enable broadcast. Moreover, their design limits the information leakage due to the exposure of a subset of sequences by compromised nodes. We map the problem of constructing such PN sequences to the 1-factorization problem for complete graphs. Our evaluation results show that TDBS can maintain broadcast communi-cations in the presence of inside jammers.

We introduce the concepts of monitoring paths (MPs) and monitoring cycles (MCs) for unique localization of shared risk linked group (SRLG) failures in all-optical networks. An SRLG failure causes multiple links to break simultaneously due to the failure of a common resource. MCs (MPs) start and end at the same (distinct) monitoring location(s). They are constructed such that any SRLG failure results in the failure of a unique combination of paths and cycles. We derive necessary and sufficient conditions on the set of MCs and MPs needed for localizing any single SRLG failure in an arbitrary graph. When a single monitoring location is employed, we show that a network must be (k+2)-edge connected for localizing all SRLG failures, each involving up toklinks. For networks that are less than (k+2)-edge connected, we derive necessary and sufficient conditions on the placement of monitoring locations for unique localization of any single SRLG failure of up toklinks. We use these conditions to develop an algorithm for determining monitoring locations. We show a graph transformation technique that converts the problem of identifying MCs and MPs with multiple monitoring locations to a problem of identifying MCs with a single monitoring location. We provide an integer linear program and a heuristic to identify MCs for networks with one monitoring location. We then consider the monitoring problem for networks with no dedicated bandwidth for monitoring purposes. For such networks, we use passive probing of lightpaths by employing optical splitters at various intermediate nodes. Through an integer linear programming formulation, we identify the minimum number of optical splitters that are required to monitor all SRLG failures in the network. Extensive simulations are used to demonstrate the effectiveness of the proposed monitoring technique.

Ultra-wideband (UWB) communications has emerged as a burgeoning technology for high data rate wireless personal area networks (WPANs). In this paper, we propose a novel resource utilization mechanism (RUM) for improving the throughput in multi-rate UWB-based WPANs. RUM is intended to remedy a critical issue in both unicast and multicast transmissions. In unicast (single- and multi-hop), the connectivity of a source-destination pair is defined by the ability to overhead control messages (e.g., route requests, request-to-send/clear-to-send, etc.). These messages are usually sent at a low transmission rate to extend their reachability, hence a node can directly communicate with faraway destinations. Such destinations cannot be reliably reached by high transmission rates. This leads to a long channel reservation time and hence a high blocking probability for prospective reservations and low network throughput. In the case of multicast, the maximum transmission rate is bottlenecked by the farthest destination. RUM exploits opportunistic-relaying and time-spreading techniques to improve link reliability and increase the transmission rate, and hence network throughput. Simulations are used to demonstrate the performance gain of RUM.

Ultra-wideband (UWB) communications has emerged as a promising technology for high data rate wireless personal area networks (WPANs). In this paper, we address a key issue that impacts the performance of multi-hop, multi-rate UWB-based WPANs, namely joint routing and rate selection. Arbitrary selection of routes (including direct links) and transmission rates along these routes results in unnecessarily long channel reservation time and high blocking rate for prospective reservations, and leads to low network throughput. To remedy this situation, we propose a novel overhearing-aware joint routing and rate selection (ORRS) scheme, which improves the network throughput by exploiting the dependence between the channel reservation time and the multi-rate capability of an UWB system. At the same time, ORRS takes advantage of packet overhearing, a typical characteristic of broadcast communications. For a given source-destination pair, ORRS aims at selecting a path and its transmission rates that achieve the minimum reservation time, leading to low blocking rate for prospective reservations and high network throughput. We show that achieving this goal while simultaneously exploiting packet overhearing and satisfying a target packet delivery probability over the selected route leads to an NP-hard problem. Accordingly, ORRS resorts to approximate solutions (proactive and reactive) to find a near-optimal result with reasonable computational/communication overhead. We further propose other variants that exploit packet overhearing in different ways to improve ORRS performance.

We study the problem of finding the least-priced path (LPP) between a source and a destination in opportunistic spectrum access (OSA) networks. This problem is motivated by economic considerations, whereby spectrum opportunities are sold/leased to secondary radios (SRs). This incurs a communication cost, e.g., for traffic relaying. As the beneficiary of these services, the end user must compensate the service-providing SRs for their spectrum cost. To give an incentive (i.e., profit) for SRs to report their true cost, typically the payment to a SR should be higher than the actual cost. However, from an end user's perspective, unnecessary overpayment should be avoided. So we are interested in the optimal route selection and payment determination mechanism that minimizes the price tag of the selected route and at the same time guarantees truthful cost reports from SRs. This setup is in contrast to the conventional truthful least-cost path (LCP) problem, where the interest is to find the minimum-cost route. The LPP problem is investigated with and without capacity constraints at individual SRs. For both cases, our algorithmic solutions can be executed in polynomial time. The effectiveness of our algorithms in terms of price saving is verified through extensive simulations.

In this paper, we are interested in cognitive radio networks (CRNs) whose operation does not rely on channel sensing. A spectrum server is responsible for collecting spectrum availability and location information from primary radio networks (PRNs), and broadcasting this information to cognitive radios. By subscribing to this broadcast, a CR knows about the spectrum opportunities without sensing channels. Spectrum opportunity under this paradigm presents a multi-level structure that generalizes the well-known channel-sensing-based binary structure. This multi-level structure reflects a microscopic spectrum opportunity for CRs, and can be exploited to increase the CRN throughput. Under this structure, we study efficient spectrum access in a multi-CR environment, with the objective of maximizing the network-wide utilization of spectrum opportunity. The difficulty of our problem comes from the fact that different CRs may decide the same channel to be available, but at different levels. Therefore, channel access needs to be carefully coordinated. Both centralized and distributed solutions are provided, supporting different modes of operation. Numerical results verify the accuracy of our algorithms and the significant gain achieved by the multi-level framework.

Ultrawideband (UWB) communications has emerged as a promising technology for high data rate wireless personal area networks (WPANs). Several proposals for UWB-based WPANs have been made. One widely popular proposal was standardized by ECMA-368, and is based on OFDM. In this paper, we address one of the important aspects that impact the performance of this standard, namely interference management between different uncooperative beacon groups that operate simultaneously over the same area. We first propose an interference management distributed reservation protocol (IM-DRP) for OFDM-based UWB communications. IM-DRP aims at improving the throughput of an UWB WPAN by reducing interference between uncooperative beacon groups. We then integrate IM-DRP into the design of a rate adaptation strategy that exploits the multirate capability of OFDM-based UWB systems. Besides maintaining a target packet error rate, our proposed strategy attempts to reduce the required reservation time over a link, hence allowing more links to be simultaneously activated. This improves the overall network throughput. Simulations are used to demonstrate the performance gain of our proposed schemes.

Under the current opportunistic spectrum access (OSA) paradigm, a common belief is that a cognitive radio (CR) can use a channel only when this channel is not being used by any neighboring primary radio (PR). Therefore, the existence of a spectrum opportunity hinges on the absence of active cochannel PRs in a macroscopic region. In this paper, we propose the concept of microscopic spectrum opportunity and show that CRs can still utilize this type of opportunities without interfering with active cochannel PRs, even when these PRs are close to them. As a result, a channel may at the same time present different levels of availability to different CRs. Channel access needs to be carefully coordinated between these CRs to avoid collisions, and more importantly, ensure efficient utilization of the spectrum opportunity from a network's standpoint. In this paper, we formulate the coordinated channel access as a joint power/rate control and channel assignment optimization problem, with the objective of maximizing the sum-rate achieved by the cognitive radio network (CRN). We develop both centralized and distributed algorithms to solve this problem. Our simulation results show that even when accounting for the implementation overhead, significant throughput gain is achieved under our designs.

Compromised node and denial of service are two key attacks in wireless sensor networks (WSNs). In this paper, we study data delivery mechanisms that can with high probability circumvent black holes formed by these attacks. We argue that classic multipath routing approaches are vulnerable to such attacks, mainly due to their deterministic nature. So once the adversary acquires the routing algorithm, it can compute the same routes known to the source, hence, making all information sent over these routes vulnerable to its attacks. In this paper, we develop mechanisms that generate randomized multipath routes. Under our designs, the routes taken by the ¿ shares¿ of different packets change over time. So even if the routing algorithm becomes known to the adversary, the adversary still cannot pinpoint the routes traversed by each packet. Besides randomness, the generated routes are also highly dispersive and energy efficient, making them quite capable of circumventing black holes. We analytically investigate the security and energy performance of the proposed schemes. We also formulate an optimization problem to minimize the end-to-end energy consumption under given security constraints. Extensive simulations are conducted to verify the validity of our mechanisms.

The cognitive radio (CR) paradigm calls for open spectrum access according to a predetermined etiquette. Under this paradigm, CR nodes access the spectrum opportunistically by continuously monitoring the operating channels. A key challenge in this domain is how the nodes in a CR network (CRN) cooperate to access the medium in order to maximize the CRN throughput. Typical multichannel MAC protocols assume that frequency channels are adjacent and that there are no constraints on the transmission power. However, a CRN may operate over a wide range of frequencies, and a power mask is often enforced on the transmission of a CR user to avoid corrupting the transmissions of spectrum-licensed primary-radio (PR) users. To avoid unnecessary blocking of CR transmissions, we propose a noveldistance-dependentMAC protocol for CRNs. Our protocol, called DDMAC, attempts to maximize the CRN throughput. It uses a novel probabilistic channel assignment mechanism that exploits the dependence between the signal's attenuation model and the transmission distance while considering the traffic profile. DDMAC allows a pair of CR users to communicate on a channel that may not be optimal from one user's perspective, but that allows more concurrent transmissions to take place, especially under moderate and high traffic loads. Simulation results indicate that, compared to typical multichannel CSMA-based protocols, DDMAC reduces the blocking rate of CR requests by up to 30%, which consequently improves the network throughput.

IEEE 802.22 wireless regional area network (WRAN) is an emerging cognitive radio-based system. One of the major challenges for WRANs is how to efficiently schedule both channel sensing and data transmission for multiple adjacent WRAN cells. This challenge is known as coexistence problem. In this paper, we propose four schemes that aim at reducing the coexistence-problem effect. These schemes are based on a well-known operation mode of IEEE 802.22, namely dynamic frequency hopping (DFH). The first and second schemes are based on using omni-directional antennas at the base stations (BSs), whereas the BSs in the other two schemes use directional antennas. The first scheme, coined fixed-scheduling DFH (FDFH), bases upon a fixed scheduling of working channels for adjacent WRAN cells. The second scheme, called cooperative DFH (CDFH), cooperatively selects working channels. The third scheme, namely sectoral DFH (SDFH), is proposed to reduce the coordination overhead of CDFH via dividing a WRAN cell into sectors to decrease the chances of collisions between adjacent cells. Finally, we integrate FDFH and SDFH into a new scheme, called fixed-scheduling sectoral DFH (FSDFH), which exploits the advantages of both schemes with no additional overhead. Computer simulations are used to demonstrate the performance gain of the proposed schemes.

In this work, we investigate a statistical approach for dynamic spectrum access and radio resource management (RRM) in opportunistic cognitive radio (CR) networks. We propose a distributed MAC protocol for such networks that enables unlicensed users to dynamically utilize the available spectrum while limiting the imposed interference on primary (PR) users. Our proposed protocol is novel in three aspects. First, it does not require CR users to coordinate with PR users. Second, it does not assume any predefined CR-to-PR power mask, and thus can exploit the available spectrum more efficiently. Third, it provides the PR users with a statistical guarantee on the fraction of time that their reception may be corrupted by CR users. To avoid corrupting PR user receptions, the protocol computes the maximum power that a CR transmission can use based on current network conditions. We show how to compute this maximum power by deriving models for the PR-to-CR and PR-to-PR interference. Simulation experiments illustrate that our MAC protocol can satisfy the statistical guarantee for PR users under various user deployment models and traffic loads.

In a cognitive radio network (CRN), spectrum opportunities should be efficiently utilized through careful coordination between cognitive radio (CR) users. In this paper, we formulate the coordinated channel access as a joint power/rate control and channel assignment optimization problem, with the objective of maximizing the sum-rate achieved by all CRs over all channels. The problem is formulated under a generalized multi-level spectrum opportunity framework, which reflects the microscopic spatial opportunity available to CRs. A centralized polynomial-time approximate algorithm to the problem is developed. We prove the algorithm's correctness and show its accuracy through numerical examples.

Compromised-node and denial-of-service are two key attacks in wireless sensor networks (WSNs). In this paper, we study routing mechanisms that circumvent (bypass) black holes formed by these attacks. We argue that existing multi-path routing approaches are vulnerable to such attacks, mainly due to their deterministic nature. So once an adversary acquires the routing algorithm, it can compute the same routes known to the source, and hence endanger all information sent over these routes. In this paper, we develop mechanisms that generate randomized multi-path routes. Under our design, the routes taken by the "shares" of different packets change over time. So even if the routing algorithm becomes known to the adversary, the adversary still cannot pinpoint the routes traversed by each packet. Besides randomness, the routes generated by our mechanisms are also highly dispersive and energy-efficient, making them quite capable of bypassing black holes at low energy cost. Extensive simulations are conducted to verify the validity of our mechanisms.

Employing multi-input multi-output (MIMO) links can improve energy efficiency in wireless sensor networks (WSNs). Although a sensor node is likely to be equipped with only one antenna, it is possible to group several sensors to form a virtual MIMO link. Such grouping can be formed by means of clustering. In this paper, we propose a distributed MIMO-adaptive energy-efficient clustering/routing scheme, coined cooperative MIMO (CMIMO), which aims at reducing energy consumption in multi- hop WSNs. In CMIMO, each cluster has up to two cluster heads (CHs), which are responsible for routing traffic between clusters (i.e., inter-cluster communications). CMIMO has the ability to adapt the transmission mode and transmission power on a per-packet basis. The transmission mode can be one of four transmit/receive configurations: 1 times 1 (SISO), 2 times 1 (MISO), 1 times 2 (SIMO), and 2 times 2 (MIMO). We study the performance of CMIMO via simulations. Results indicate that our proposed scheme achieves a significant reduction in energy consumption, compared to non-adaptive clustered WSNs.

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic transmission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all channels and then pick the best among them, due to the potentially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid collisions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR's average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that addresses all the above considerations is derived by formulating the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to conduct a "second-round" optimization over the operational parameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone.

We address the problem of dynamic assignment of coordination (control) channels in cognitive radio networks (CRNs) by exploiting time- and space-varying spectrum opportunities. Motivated by the inherent grouping of Cognitive Radio (CR) users according to channel availability, we propose a cluster-based architecture for control-channel assignment in a CRN. CRs are grouped in the same cluster if they roughly sense similar idle channels and are within communication range, either directly or via a cluster-head. We formulate the clustering design as a maximum edge biclique problem. A distributed cluster agreement algorithm called Spectrum-Opportunity Clustering (SOC) is proposed to solve this problem. SOC provides a desirable balance between two competing factors: the set of common idle channels within each cluster and the cluster size. A large set of common idle channels within each cluster allows graceful migration from the current control channel should primary radio (PR) activity appear on that channel. Hence, SOC provides a stable network partition with respect to local coordination, with no need for frequent re-clustering. Moreover, when re-clustering has to be performed (due to CR mobility or PR activity), CRs agree on new clusters after the broadcast of only three messages, thus incurring low communication overhead.

We address the problem of control-channel jamming attacks in multi-channel ad hoc networks. Deviating from the traditional view that sees jamming attacks as a physical-layer vulnerability, we consider a sophisticated adversary who exploits knowledge of the protocol mechanics along with cryptographic quantities extracted from compromised nodes to maximize the impact of his attack on higher-layer functions. We propose new security metrics that quantify the ability of the adversary to deny access to the control channel, and the overall delay incurred in re-establishing the control channel. We also propose a randomized distributed scheme that allows nodes to establish a new control channel using frequency hopping. Our method differs from classic frequency hopping in that no two nodes share the same hopping sequence, thus mitigating the impact of node compromise. Furthermore, a compromised node is uniquely identified through its hop sequence, leading to its isolation from any future information regarding the frequency location of the control channel.

Spectrum-agile radios, also known as cognitive radios, have a great potential to improve spectrum utilization by enabling dynamic access to the spectrum. A key challenge in operating these radios is how to implement an efficient medium access control (MAC) mechanism that adaptively and efficiently allocates transmission powers and spectrum according to the surrounding environment. In this work, we propose a distributed MAC protocol for operating spectrum-agile radios in a multihop ad hoc network. Our protocol differs from previous designs in that it exploits the ldquodual-receiverdquo capability of radios, thus overcoming various channel access problems that are common to multi-channel designs. We conduct theoretical analysis of the protocol, and study its performance via simulations. We show significant improvement in the system throughput under the proposed MAC design. To maximize this throughput, we propose a cross-layer framework for joint adaptive load and medium access controls. In this framework, the loads of individual nodes are adapted based on the values of local MAC parameters. Simulation results show that the proposed scheme achieves more than 90% of the maximum (global) system throughput that is achieved at saturation, while guaranteeing low collision rates.

Cognitive radio (CR) is the key enabling technology for an efficient dynamic spectrum access. It aims at exploiting an underutilized licensed spectrum by enabling opportunistic communications for unlicensed users. In this work, we first develop a distributed cognitive radio MAC (COMAC) protocol that enables unlicensed users to dynamically utilize the spectrum while limiting the interference on primary (PR) users. The main novelty in COMAC lies in not assuming a predefined CR-to-PR power mask and not requiring active coordination with PR users. COMAC provides a statistical performance guarantee for PR users by limiting the fraction of the time during which the PR users' reception is negatively affected by CR transmissions. To provide such a guarantee, we develop probabilistic models for the PR-to-PR and the PR-to-CR interference under a Rayleigh fading channel model. From these models, we derive closed-form expressions for the mean and variance of interference. Empirical results show that the distribution of the interference is approximately lognormal. Based on the developed interference models, we derive a closed-form expression for the maximum allowable power for a CR transmission. We extend the min-hop routing to exploit the available channel information for improving the perceived throughput. Our simulation results indicate that COMAC satisfies its target soft guarantees under different traffic loads and arbitrary user deployment scenarios. Results also show that exploiting the available channel information for the routing decisions can improve the end-to-end throughput of the CR network (CRN).

Multiple description coding (MDC) is a powerful source coding technique that involves encoding a media stream into r independently decodeable substreams. With every successful reception of a substream, decoded signal quality improves. We consider the problem of placing a set of servers in the network such that a desired quality of service can be provided to a community of clients. We formulate the server placement (SP) problem, whose goal is to identify the minimum number of server locations that can provide r descriptions to a set of clients such that the delay associated with each path from a chosen server location to a given client is bounded by a given delay constraint and the total "unreliability" associated with the group of paths to a given client is also upper bounded. We show that the SP problem is NP-complete. We propose a mixed-integer linear programming (MILP) formulation and heuristic solution for the SP problem. Simulations are conducted to evaluate the performance of the proposed algorithm and to compare it with the MILP solution.

Ultra-wideband (UWB) communications has emerged as a promising technology for high data rate wireless personal area networks (WPANs). Several proposals for UWB- based WPANs have been made. One widely popular proposal was standardized by ECMA-368, and is based on OFDM. In this paper, we address one of the important aspects that impact the performance of this standard, namely interference management between different uncooperative beacon groups operating simultaneously over the same area. We propose an interference management distributed reservation protocol (IM-DRP) for OFDM-based UWB communications. IM-DRP aims at improving the throughput achieved by WPANs by reducing interference between uncooperative beacon groups. Computer simulations are used to demonstrate the performance gain of the proposed protocol.

We present a novel routing approach for multichannel cognitive radio networks (CRNs). Our approach is based on probabilistically estimating the available capacity of every channel over every CR-to-CR link, while taking into account primary radio (PR). Our routing design consists of two main phases. In the first phase, the source node attempts to compute the most probable path (MPP) to the destination (including the channel assignment along that path) whose bandwidth has the highest probability of satisfying a required demand D. In the second phase, we verify whether the capacity of the MPP is indeed sufficient to meet the demand at confidence level delta. If that is not the case, we judiciously add channels to the links of the MPP such that the augmented MPP satisfies the demand D at the confidence level delta. We show through simulations that our protocol always finds the best path to the destination, achieving in some cases up to 200% improvement in connection acceptance rate compared to the traditional Dijkstra.

We introduce the concepts of monitoring paths (MPs) and monitoring cycles (MCs) for unique localization of shared risk linked group (SRLG) failures in all-optical networks. An SRLG failure is a failure of multiple links due to a failure of a common resource. MCs (MPs) start and end at same (distinct) monitoring location(s). They are constructed such that any SRLG failure results in the failure of a unique combination of paths and cycles. We derive necessary and sufficient conditions on the set of MCs and MPs needed for localizing an SRLG failure in an arbitrary graph. When a single monitoring location is employed, we show that a network must be (k + 2)-edge connected for localizing all SRLG failures with up to k links. For networks that are less than (k + 2)-edge connected, we derive necessary and sufficient condition on the placement of monitoring locations for unique localization of any SRLG failure of up to k links. We use these conditions to develop an algorithm for the placement of monitoring locations. We show a graph transformation technique that converts the problem of identifying MCs and MPs with multiple monitoring locations to a problem of identifying MCs with single monitoring location. We provide an integer linear program and a heuristic to identify MCs for networks with one monitoring location. Through extensive simulations, we demonstrate the effectiveness of the proposed monitoring technique.

In this paper, we present a novel joint power/channel allocation scheme that uses a distributed pricing strategy to improve the network's performance. According to this scheme, the spectrum allocation problem is modeled as a non-cooperative game. A price-based iterative water-filling (PIWF) algorithm is proposed, which allows users to converge to the Nash Equilibrium (NE). This PIWF algorithm can be implemented distributively, with CRs repeatedly negotiating their best transmission powers and spectrum. We propose a protocol that implements our price- based resource allocation algorithm. The proposed MAC protocol allows multiple CR pairs to first contend through an admission phase, and then to iteratively negotiate their transmission powers and spectrum via control-packet exchanges. Subsequently, CRs proceed concurrently with their data transmissions. Simulations are used to study the performance of our protocol and demonstrate its effectiveness in improving the overall network throughput and reducing the average transmission power.

The scarcity of unlicensed spectrum has triggered great interest in cognitive radio (CR) technology as a means to improve spectrum utilization. An important challenge in this domain is how to enable nodes in a CR network (CRN) to access the medium opportunistically. Multi-channel MAC protocols for typical ad hoc networks assume that frequency channels are adjacent and that there are no strict constraints on the transmission power. However, a CRN may occupy a wide range of frequencies. In addition, a power mask is often enforced on the transmission power of a CR user to avoid corrupting the transmissions of spectrum-licensed primary-radio (PR) users. Obviously, CR users operating in different licensed bands will be subject to different PR-to-CR interference conditions. To avoid unnecessary blocking of CR transmissions under these constraints, we propose a novel distance-dependent MAC protocol for CRNs (DDMAC) that attempts to maximize the CRN throughput. DDMAC introduces a novel suboptimal probabilistic channel assignment algorithm that exploits the dependence between the signal's attenuation model and the transmission distance while considering the traffic profile. The protocol allows a pair of CR users to communicate on a channel that may not be optimal from one user's perspective, but that allows more transmissions to take place simultaneously, especially under moderate to high traffic loads. Simulation results indicate that compared to typical multi-channel CSMA-based protocols, DDMAC decreases the connection blocking rate of CR transmission requests by up to 30%, which improves the network throughput at no additional cost in energy consumption. On the whole, our protocol is simple yet effective. It can be incorporated into existing multi-channel systems with little extra processing overhead.

Multiple description coding (MDC) has emerged as a powerful technique for reliable real-time communications over lossy packet networks. In its basic form, it involves encoding a media stream intorsubstreams that are sent independently from a source to a destination. Each substream (or description) can be decoded independent of the otherr-1 substreams. With every successful reception of a substream, the quality of the decoded signal improves. In this paper, we consider the problem of placing a set of servers in the network such that a desired quality of service can be provided to a community of clients that request MDC-coded traffic. We formulate the server placement (SP) problem, with the goal of identifying the minimum number of server locations that can providerdescriptions to a set of clients such that the delay associated with each path from a chosen server location to a given client is bounded by a given delay constraint and the total ldquounreliabilityrdquo associated with the group of paths to a given client is also upper bounded. We show that the SP problem belongs to the class of NP-complete problems. We propose a mixed-integer linear programming (MILP) formulation and an efficient heuristic solution for the SP problem. Simulations are conducted to evaluate the performance of the proposed algorithm and compare it with the optimal solution provided by the MILP solution.

Transmission power control (TPC) is used in wireless networks to improve channel reuse and/or reduce energy consumption. It has been applied mainly to single-input single-output (SISO) systems, where each node is equipped with a single antenna. Multi-input multi-output (MIMO) systems can improve the throughput or the signal-to-noise ratio (SNR) by providing multiplexing or diversity gains, respectively. In this paper, we propose a power-controlled MAC protocol that combines different types of MIMO gains in a wireless network with two antennas per node. Our protocol, coined CMAC, allows for dynamic switching between diversity and multiplexing modes so as to maximize a utility function that depends on both the energy consumption and throughput. CMAC adapts the “antenna mode,” the transmission power, and the modulation order on a per-packet basis. By “antenna mode“ we mean one of the five possible transmit/receive antenna configurations: 1×1 (SISO), 2×1 (MISO-D), 1×2 (SIMO-D), 2×2 (MIMO-D), and 2×2 (MIMO-M), where the second, third, and fourth configurations offer a diversity (D) gain, whereas the last configuration offers a multiplexing (M) gain. By using control packets to bound the transmission power of potentially interfering terminals, CMAC allows for multiple interference-limited transmissions to take place in the vicinity of a receiving terminal. We study via simulations the performance of CMAC in ad hoc topologies. Our results indicate that relative to non-adaptive protocols, CMAC achieves a significant improvement in both the overall energy consumption and the throughput.

Transmission power control (TPC) has been used in wireless ad hoc networks to improve channel reuse and/or reduce energy consumption. It has been mainly applied to single-input single-output (SISO) systems, in which each node is equipped with a single antenna. In this paper, we study MAC protocols for mobile ad hoc networks (MANETs) with MIMO (multi-input multi-output) capability. In particular, we consider the integration of MIMO into two MAC protocols. The first protocol is the IEEE 802.11 standard, which is a conservative protocol that does not use TPC. The second protocol is POWMAC, which exploits TPC to maximize the perceived throughput. We refer to these protocols when applied to MIMO systems as MIMO-802.11 and MIMO-POWMAC. We compare the performance of MIMO-802.11 and MIMO- POWMAC with SISO-802.11 and SISO-POWMAC, in terms of the perceived throughput and energy consumption. Our simulations reveal that although the MIMO system doubles the bit rate per link over the SISO system, the network throughput is not necessarily doubled due a reduction in the number of concurrent transmissions. In addition, the throughput gains in the MIMO system come at a non-negligible energy cost.

When sensors are redundantly deployed, a subset of senors should be selected to actively monitor the field (referred to as a "cover"), while the rest of the sensors should be put to sleep to conserve their batteries. We consider networks in which all the nodes are not aware of their locations or the relative directions of neighbors. We develop several geometric and density-based tests for deciding whether a sensor should turn itself off without degrading the quality of field coverage. These tests rely on estimated neighbor distances and locally advertised two-hop neighborhood information. We design an algorithm (LUC) that exploits these tests for computing covers. Based on LUC, we propose two distributed protocols (LUC-I and LUC-P) that periodically select covers and switch between them to extend "coverage time" and tolerate unexpected failures. Our protocols are highly efficient in terms of message overhead and processing complexity. We implement LUC-I in TinyOS and evaluate it using the TOSSIM simulator. Experimental results indicate that our approach significantly prolongs coverage time.

In this paper, we determine the maximum achievable "performance" of a wireless CDMA network that employs a conventional matched filter receiver and that operates under optimal link-layer adaptation where each user individually achieves the Shannon capacity. The derived bounds serve as benchmarks against which adaptive CDMA systems can be compared. We focus on two optimization criteria: minimizing the maximum service time and maximizing the sum of users rates (i.e., network throughput). We show that the problem of joint optimization of the transmission powers and rates so as to minimize the maximum service time can be formulated as a generalized geometric program (GGP), which can be transformed into a nonlinear convex problem and solved optimally and efficiently. When the goal is to maximize the sum of the rates, we show that the problem can be approximated as a GGP. Our derivation methodologies are applicable to both ad hoc and cellular networks. Numerical results are provided to show how well variable-rate, variable-power adaptation schemes perform relative to the performance bounds derived in this paper.

Wireless sensor networks (WSN) are typically employed for monitoring applications that require data collection at specific nodes, called drains. In order to improve the robustness of data collection, multiple drains may be employed. Data from every sensor is required to be logged into two distinct sensors for data collection to be resilient to any single drain failures. In this paper, we develop a routing mechanism based on colored trees. Every node forwards the packets based on the drain address and one additional bit. The number of routing table entries at each node is 2|D|, where |D| denotes the number of drains in the network. The construction of the colored trees guarantees that every node has two node-disjoint paths to two distinct drains. The running time complexity of the algorithm is O(|D||L|), where |L| denotes the number of links in the network. Through extensive simulations, we demonstrate that employing multiple drains and disjoint routing to two distinct drains reduces the average path length compared to disjoint routing to one of the multiple drains.

The perceived video quality in a wireless streaming application strongly depends on the channel's dynamics and the fluctuations of the source bit rate. In this paper, we introduce two channel-adaptive rate control schemes for slowly and fast varying channels, respectively. Both schemes account for the playback buffer occupancy in the joint optimization of the source rate and channel-code forward error correction parameters. For the first scheme, we assume that the channel state does not change during the transmission of a video frame. We optimize the channel-code parameters and maximize the per-frame source rate subject to satisfying a constraint on the probability of delivering the next video frame within a buffer-occupancy-dependent critical time (Tc). For the second scheme, we allow the channel state to change within the frame delivery period, and we compute the optimal system parameters and maximize the source rate while satisfying a constraint on the mean frame delivery time. Our schemes aim at maintaining the occupancy of the playback buffer around a predefined threshold value, hence ensuring continuous video playback. Simulation and numerical investigations are carried out to study the interactions among various key parameters and verify the adequacy of the analysis.

We propose a joint source-rate/channel-code control scheme for streaming video over a wireless channel. The scheme is designed to maximize the achievable source rate while guaranteeing an upper bound on the probability of starvation at the playback buffer. It can be applied to both one-way and interactive video communications. Rate control is performed adaptively on a per-cycle basis, where a cycle consists of a "good" channel period and the ensuing "bad" period. This cycle-based approach has two advantages. First, it reduces the fluctuations in the source bit rate, ensuring smooth variations in video quality. Second, it makes it possible to derive simple expressions for the starvation probability at the playback buffer, which we use to determine the optimal source rate and channel code for the good and bad periods of the subsequent cycle

Efficient routing and wavelength assignment (RWA) in wavelength-routed all-optical networks is critical for achieving high efficiency over the backbone links. Extensive research has been conducted to find strategies to solve the RWA problem with exact network state available at the time of path selection. We consider the problem of minimizing the blocking probability in an all-optical network with partial wavelength conversion and with imprecise network state information. We model imprecision in link-state information (wavelength availability) probabilistically and use Markovian analysis to predict the availability of each link based on its previous advertisement and the estimated average traffic over the link. The estimated probabilities are then used to find the most probable path between a source destination pair. We also consider the problem of lightpath establishment with 1+1 protection. We use the same Markovian model to predict the link availability and then use the probabilistic estimate and a modified version of flow algorithm to find two link-disjoint paths between the source and destination. Simulations are conducted to compare the performance of random-fit and the proposed wavelength assignment schemes. We have also performed extensive simulations to study the performance of the proposed flow algorithm. It is observed that the proposed wavelength assignment scheme performs significantly better in terms of blocking probability than conventional wavelength assignment schemes.

This work proposes new retransmission and backoff strategies for network-wide broadcasting in multi-hop wireless networks. A comparative analysis is presented between existing algorithms as well as the ones proposed herein. Simulation experiments and analysis are used throughout this work to study or demonstrate the properties and performance of specific strategies as well as other properties or results of a more general nature. Several topics not considered in previous work are also studied. The broadcasting strategies are evaluated with respect to their impact on routing protocols that rely on flooding to perform path discovery, and research is conducted in designing schemes that maximize the route lifetime. Different backoff strategies are proposed and their performance is examined.

Summary form only given. This paper introduces an integrated source/channel rate-control scheme for video broadcast over wireless channels. The scheme aims at sustaining continuity of playback process at each receiver under varying channel conditions while gracefully degrading the rendered quality. An adaptive playback was used to bound probability of starvation at playback buffers and guarantee a bound on the average delay introduced

Multiple description coding (MDC) has emerged as a powerful technique for reliable real-time communications over lossy packet networks. In its basic form, it involves encoding media into m substreams that are routed independently towards a given destination. Each substream can be decoded independently and with every successful reception of a substream, the overall quality of the decoded signal is improved. In this paper, we consider the problem of placing a set of servers in the network such that a desired QoS can be provided to a community of clients that request MDC coded traffic. Specifically, we consider the server placement (SP) problem where the goal is to identify the "optimal" server positions and associated set of client-server paths such that if MDC content is placed at these servers a cost function that is a linear combination of average delay and path disjointness is minimized. We propose an MILP formulation and a highly efficient heuristic to solve the SP problem. Simulations are conducted to evaluate the performance of the proposed algorithm and compare it with the optimal solution obtained by using the MILP solution.

This paper presents an adaptive encapsulation scheme that significantly improves the effective throughput in 802.11 networks by maximizing the number of IP packets that are encapsulated in a single MAC frame. Encapsulation efficiency is improved by deferring transmissions based on the estimation of packet inter-arrival times and by performing early discarding of packets for which delay violations are anticipated. The scheme supports both real-time and non-real-time traffic types. Simulations indicate that the proposed adaptive encapsulation approach achieves significant throughput improvement over the classic encapsulation approach.

A multi-channel parallel transmission protocol is proposed for the medium access control in cognitive radio networks (CRNs). This protocol contains two key elements: multi-channel assignment and multi-channel contention. For an incoming flow-based connection request, the minimum number of parallel channels are assigned to satisfy the rate and interference mask constraints. For the contention of the assigned channels, our protocol provides an extension of the single-channel RTS- CTS-DATA-ACK handshaking of the IEEE 802.11 scheme. The proposed MAC coherently integrates optimization results into a practical implementation. Through numerical examples, we verify that our protocol provides lower connection blocking probability and higher system throughput for CRNs than its single-channel counterpart.

For CDMA-based WSNs, we quantitatively investigate and compare the optimal energy efficiencies and control complexities for three different power/time control (PTC) schemes: PTC with independent transmission power and time (PTC-IPT), PTC with unified transmission time (PTC-UT), and PTC with unified spreading gain (PTC-USG). These schemes provide different degrees of control and require different amounts of overhead. Under each scheme, the minimization of system's energy consumption is formulated as a non-convex optimization problem. The optimal transmission power and time are derived analytically through a variable-decoupling approach. The analytical nature of our results makes it feasible to compare the performance in closed form. Numerical examples and simulations are provided to validate our analysis.

We investigate the energy efficiency in a wireless sensor networks that implements a non-persistent CSMA MAC protocol with adaptive MQAM modulation at the physical layer. The system throughput is estimated based on the number of received ACK packets. The backoff probability at the MAC layer and the modulation order at the physical layer are jointly adapted according to the traffic dynamics, leading to improved system energy efficiency while satisfying a given constraint on the packet retransmission delay. Through numerical examples and simulations, we verify the significant energy-efficiency improvements achieved by this joint optimization compared to the backoff- probability-only and the modulation-order-only adaptations.

Wireless sensor networks (WSNs) employed in monitoring applications require data collected by the sensors to be deposited at specific nodes, referred to as drains. To improve robustness in data collection, we consider a dual homing network in which two drains are employed and every node is required to send data to the two drains over link- or node-disjoint paths. One approach to reduce the number of routing table entries at a node is to construct two trees, namely red and blue, each rooted at a particular drain such that the paths from any node to the two drains on the trees are link- or node-disjoint. In this paper, we develop the first distributed algorithm for constructing colored trees in a dual-homing network whose running time is linear in the number of links. In addition, we show that the average path length may be optimized by employing the generalized low-point concept rather than the traditional low-point concept.

This paper presents a broadcasting algorithm that considerably reduces the number of retransmissions in applications such as onroad vehicular broadcasting where nodes are assumed to be arranged on a strip. Analysis and simulation results are presented to describe the overhead, coverage and latency characteristics of the algorithm.

Adaptive rate/power control schemes have great po- tential to increase the throughput of wireless CDMA networks. In this paper, we investigate the additional gains achieved through adaptation of the orthogonal modulation (OM) order .W e show that adaptive orthogonal modulation (AOM) can significantly increase network throughput while simultaneously reducing the per-bit energy consumption (compared to variable-rate, variable-power, fixed-order OM schemes). We study the problem of joint rate/power control in AOM under two different objective functions: minimizing the maximum service time and maximizing the sum of users rates. For the first objective function, we show that the optimization problem can be formulated as a generalized geometric program (GGP), which can be transformed into a nonlinear convex problem and solved optimally and efficiently. In the case of the second objective function, we obtain a lower bound on the performance gain of AOM over fixed-order OM schemes. Unlike previous works on adaptive transmission, which have focused mainly on cellular networks, ours is applicable to both ad hoc and cellular networks. Numerical results indicate that relative to a variable-rate, variable- power fixed-order OM scheme, the proposed AOM scheme achieves significant throughput and energy gains.

Transmission power control has often been used in wireless packet networks to improve the channel reuse and reduce energy consumption. It has been mainly applied to single-input single-output (SISO) systems, where each node is equipped with a single antenna. In this paper, we propose a power-controlled channel access protocol for MIMO-capable wireless LANs with two antennas per node. Our protocol, called E-BASIC, is based on a modification of the classic CSMA/CA access scheme, where we dynamically adjust the "transmission mode" and the transmission power on a per-packet basis so as to minimize the total energy consumption. By "transmission mode" we mean one of the four possible transmit/receive antenna configurations: 1 x 1 (SISO), 2 x 1 (MISO), 1 x 2 (SIMO), and 2 x 2 (MIMO). Our energy model accounts for both the transmission and the circuit powers. While the MIMO mode requires less RF transmission energy than the other three modes, it also incurs the highest circuit energy consumption. Depending on the transmitter-receiver distance, any of the four modes can be the "optimal" one in terms of minimizing the total energy consumption. We study the performance of E-BASIC in ad hoc and infrastructure-based scenarios and compare it with two channel access protocols (802.11 and BASIC) that use a fixed transmission mode (SISO or MIMO) all the time. Our simulations show that E-BASIC often consumes much less total energy than the reference protocols. We further incorporate E-BASIC into the design of a power-aware routing (PAR) scheme that uses a variant of Dijkstra's algorithm to select the most energy-efficient end-to-end path between nodes.

Improving the network throughput is a primary objective in mobile ad hoc networks (MANETs), which is motivated by the over-conservative nature of the 802.11 standard. Transmission power control (TPC) was proposed for improving spatial reuse and reducing energy consumption in MANETs. Previous TPC protocols either incur extra hardware cost (e.g., require multiple transceivers) or do not fully exploit the potential of power control. In this work, we propose distributed, single-channel MAC protocols for MANETs that exploit TPC and account for different packet sizes in the system to further maximize spatial reuse. We model channel contention in the network as a non-cooperative game. Multiple potential transmitters are first involved in an admission phase which enables terminals to compute the transmission powers that achieve a Nash equilibrium (NE) for a given utility function. Subsequently, successful contenders in the same neighborhood can simultaneously proceed with their transmissions. Simulation results show that our protocols significantly improve the network throughput (in terms of transmitted bits/second or the number of admitted contenders) over previously proposed schemes. Our results also indicate that these gains do not require additional energy cost

The conservative nature of the 802.11 ad hoc scheme has instigated extensive research whose goal is to improve the spatial reuse and/or energy consumption of this scheme. Transmission power control (TPC) was shown to be effective in achieving this goal. Despite their demonstrated performance gains, previously proposed power-controlled channel access protocols often incur extra hardware cost (e.g., multiple transceivers). Furthermore, they do not fully exploit the potential of power control due to the heuristic nature of power allocation and “interference margin” computations. In this paper, we propose a distributed, single-channel MAC protocol (GMAC) that is inspired by game theory. In GMAC, multiple potential transmitters are first involved in an admission phase, which enables terminals to compute the transmission powers that achieve a Nash equilibrium (NE) for the given utility function. Subsequently, successful contenders can simultaneously proceed with their transmissions. Simulation results indicate that GMAC improves the network throughput over the 802.11 scheme by about 80%, and over another single-channel power-controlled MAC protocol (POWMAC) by about 40%. These gains are achieved at no extra energy cost.

Recent research in wireless code-division multiple-access systems has shown that adaptive rate/power control can considerably increase network throughput relative to systems that use only power or rate control. In this paper, we consider joint power/rate optimization in the context of orthogonal modulation (OM) and investigate the additional performance gains achieved through adaptation of the OM order. We show that such adaptation can significantly increase network throughput, while simultaneously reducing the per-bit energy consumption relative to fixed-order modulation systems. The optimization is carried out under two different objective functions: minimizing the maximum service time and maximizing the sum of user rates. For the first objective function, we prove that the optimization problem can be formulated as a generalized geometric program (GGP). We then show how this GGP can be transformed into a nonlinear convex program, which can be solved optimally and efficiently. For the second objective function, we obtain a lower bound on the performance gain of adaptive OM (AOM) over fixed-modulation systems. Numerical results indicate that relative to an optimal joint rate/power control fixed-order modulation scheme, the proposed AOM scheme achieves significant throughput and energy gains.

In this paper, we address the problem of minimizing energy consumption in a CDMA-based wireless sensor network (WSN). A comprehensive energy consumption model is proposed, which accounts for both the transmit and circuit energies. Energy consumption is minimized by jointly optimizing the transmit power and transmission time for each active node in the network. The problem is formulated as a non-convex optimization. Numerical as well as closed-form approximate solutions are provided. For the numerical solution, we show that the formulation can be transformed into a convex geometric programming (GP), for which fast algorithms, such as interior point method, can be applied. For the closed-form solution, we prove that the joint power/time optimization can be decoupled into two sequential sub-problems: optimization of transmit power with transmission time serving as a parameter, and then optimization of the transmission time. We show that the first sub-problem is a linear program while the second one can be well approximated as a convex programming problem. Taking advantage of these analytical results, we further derive the per-bit energy efficiency. Our results are verified through numerical examples and simulations

Ethernet over SONET (EoS) is a popular approach for interconnecting geographically distant Ethernet segments using a SONET transport infrastructure. It typically uses virtual concatenation (VC) for dynamic bandwidth management. The aggregate SONET bandwidth that supports a given EoS system is obtained by "concatenating" a number of virtual channels (VCs), which together form a virtually concatenated group (VCG). This aggregate bandwidth can be increased on demand by adding one or more VCs to the existing VCG. The new VC must be selected such that its end-to-end delay is within a certain range that reflects the delays of all existing VCs in the VCG and the available memory buffer of the EoS system. Algorithmically, the problem of selecting such a VC becomes that of finding a path in a graph network that is bounded by an upper and lower bounds. In this paper, we first prove that the TSCP problem is NP-complete. We then propose a new solution for it based on the "backward-forward" search approach. We show that this solution is much more efficient than the previously proposed MLW-KSP algorithm. We then consider the TSCP problem under inaccurate link information, in which the delay and available bandwidth for each link are taken as random variables. The problem is now formulated as that of finding the most probable path that satisfies the upper and lower delay constraints. We consider two cases. In the first case, we assume that the link delays are random but the link bandwidths are exact. We then consider the more general case where both the link delays and bandwidths are random. Heuristic solutions are presented for both cases. Simulations are conducted to evaluate the performance of the proposed algorithms and to demonstrate the advantages of the probabilistic path selection approach over the classic trigger-based approach.

Video streaming over wireless networks is challenged with the time-varying nature of the underlying channels and the stringent requirements of video applications. In this paper, we propose a new scheme for streaming video over wireless channels. The scheme is designed to prevent potential playback discontinuities that may result due to quality variations of the wireless link while gracefully degrading the rendered video quality. We approximate the frame delay statistics and playback process by a simple queueing model. In this queueing system, the rates at which frames are received and played back are occupancy-dependent. This allows us to compute the steady-state probability of the playback buffer occupancy. This in turn, allows us to obtain an expression for the probability of starvation which we use to determine a threshold occupancy and hence the optimal source and channel rates for the upcoming frames. Simulation and numerical investigations are carried out to study the interactions among various key parameters and verify the adequacy of the analysis

We consider a wireless sensor network in which sensors are grouped into clusters, each with its own cluster head (CH). Each CH collects data from sensors in its cluster and relays them to a sink node directly or through other CHs. The coverage time of the network is defined as the time until one of the CHs runs out of battery, resulting in an incomplete coverage of the sensing region. We study the maximization of coverage time by balancing the power consumption of different CHs. Using a Rayleigh fading channel model for inter-cluster communications, we provide optimal power allocation strategies that guarantee (in a probabilistic sense) an upper bound on the end-to-end (inter-CH) path reliability. Our allocation strategies account for the interaction between routing and clustering by considering the impacts of intra- and inter-cluster traffic at each CH. Two mechanisms are proposed for achieving balanced power consumption: the routing-aware optimal cluster planning and the clustering-aware optimal random relay. For both mechanisms, the problem is formulated as a signomial optimization, which can be efficiently solved using generalized geometric programming. Numerical examples and simulations are used to validate our analysis and study the performance of the proposed schemes.

Directional antennas can significantly improve the spatial reuse of a mobile ad hoc network (MANET), leading to higher network throughput. This gain comes with a substantial energy saving that results from beamforming the transmitter and/or receiver antennas in the appropriate directions. However, several medium access problems resurface when directional antennas are integrated into existing MAC protocols. We propose a power-controlled MAC protocol for directional antennas that ameliorates these problems. Our protocol allows for dynamic adjustment of the transmission power for both data and clear-to-send (CTS) packets to optimize energy consumption. It provides a mechanism for permitting interference-limited concurrent transmissions and choosing the appropriate tradeoff between throughput and energy consumption. The protocol enables nodes to implement load control in a distributed manner, whereby the total interference in the neighborhood of a receiver is upper-bounded. Simulation results demonstrate that the combined gain from concurrent transmissions using directional antennas and power control results in up to 89% saving in energy compared to a previously proposed protocol and to the CSMA/CA scheme used in the IEEE 802.11 standard. At the same time, network throughput is improved by 79% and 185%, respectively, over these protocols.

Transmission power control (TPC) has great potential to increase the throughput of a mobile ad hoc network (MANET). Existing TPC schemes achieve this goal by using additional hardware (e.g., multiple transceivers), by compromising the collision avoidance property of the channel access scheme, by making impractical assumptions on the operation of the medium access control (MAC) protocol, or by overlooking the protection of link-layer acknowledgment packets. In this paper, we present a novel power controlled MAC protocol called POWMAC, which enjoys the same single-channel, single-transceiver design of the IEEE 802.11 ad hoc MAC protocol but which achieves a significant throughput improvement over the 802.11 protocol. Instead of alternating between the transmission of control (RTS/CTS) and data packets, as done in the 802.11 scheme, POWMAC uses an access window (AW) to allow for a series of request-to-send/clear-to-send (RTS/CTS) exchanges to take place before several concurrent data packet transmissions can commence. The length of the AW is dynamically adjusted based on localized information to allow for multiple interference-limited concurrent transmissions to take place in the same vicinity of a receiving terminal. Collision avoidance information is inserted into the CTS packet and is used to bound/ the transmission power of potentially interfering terminals in the vicinity of the receiver, rather than silencing such terminals. Simulation results are used to demonstrate the significant throughput and energy gains that can be obtained under the POWMAC protocol.

Providing continuous video playback with graceful quality degradation over wireless channels is fraught with challenges. Video applications require stringent delay guarantees and a relatively high throughput. Wireless channels are error prone, time varying, and bandwidth limited. To improve the reliability of the wireless link, forward error correction (FEC) and automatic repeat request (ARQ) are often used. If designed for the worst channel conditions, FEC can provide constant throughput and bounded delay. However, this causes unnecessary overhead and reduces the maximum achievable throughput when the channel is in good conditions. On the other hand, it is difficult to achieve strict delay guarantees using ARQ schemes alone, especially when the channel is in deep fading. Playback buffer occupancy plays a major role in the target video quality. The retransmission of erroneous packets and the reduction in throughput due to FEC overhead can lead to playback buffer starvation as well as transmitter buffer fullness. Therefore, it is desirable to reduce the bit rate of the transmitted video signal and increase error protection when the channel is anticipated to be bad or the receiver playback buffer starvation is predicted. In this study, we introduce a scalable and adaptive source-channel rate control scheme for video transmission over wireless packet networks. In this scheme, the level of adaptiveness is optimized to reduce the bandwidth requirement while guaranteeing delay and loss bounds. Simulation and numerical investigations are carried out to study the interactions among various key parameters and verify the adequacy of the analysis.

Protocols for mobile ad hoc networks (MANETs) are often designed with the assumption that nodes are equipped with omnidirectional antennas. Spatial reuse in such networks can be significantly improved by using directional antennas, leading to higher system capacity. This gain is associated with a substantial energy saving that results for beamforming the transmitter and/or receiver antennas in the appropriate directions. However, several medium access problems (e.g., hidden terminal, deafness) resurface when directional antennas are integrated into existing MAC protocols. In this paper, we propose a power-controlled MAC protocol for directional antennas that ameliorates many these problems. Our protocol uses separate control and data channels to reduce collisions. It allows for dynamic adjustment of the data-packet transmission power, such that this power is just enough to overcome interference at the receiver. Simulation results demonstrate that the combined gain from using directional antennas and power control results in significant energy saving and improved throughput performance.

The performance of a WWW caching system can be dramatically increased by integrating document prefetching (a.k.a., "proactive caching") into its design. While prefetching reduces the perceived user response time, it also increases network load, which in turn may increase the response time. In this study, we investigate this tradeoff through a mathematical model of a WWW caching/prefetching system. In our model, the client cache is divided into a "regular" cache for on-demand requests and a "prefetching cache" for prefetched requests. A set of Web clients connect to a proxy server through bandwidth-limited dedicated lines (e.g., dialup phone lines). The proxy server implements its own caching system. Forecasting of future documents is performed at the client, based on the client access profile and hints from the servers. Our analysis sheds light on the interesting tradeoff between aggressive and conservative prefetching, and can be used to optimize the parameters of a combined caching/prefetching system.

Forward error correction (FEC) and automatic repeat request (ARQ) are typically used to improve the reliability of the wireless link. During fading periods, it is difficult to achieve strict delay guarantees using ARQ schemes alone. On the other hand, FEC can provide constant throughput and bounded delay. However, if designed for the worst channel conditions, this causes unnecessary overhead and reduces the maximum achievable throughput when the channel is in good conditions. The target video quality is greatly affected by the instantaneous playback buffer occupancy. Retransmissions of erroneous packets and the reduction in throughput due to FEC overhead can lead to playback buffer starvation. In this study, we introduce a scalable source-rate control scheme for video transmission over wireless packet networks. The scheme aims at maximizing the source bit rate at the encoder while preventing occurrences of starvation at the playback buffer. We carry out the analysis while taking into account the channel variations during video-frame transmission. We characterize the evolution of the transmission process by a finite-state Markov chain (FSMC) that facilitates the analysis. Then, we provide a time average expression that contains the key parameters of the proposed scalable scheme. In our scheme, the amount of frame scaling is optimized while guaranteeing certain delay and loss bounds. Simulation and numerical investigations are carried out to study the interactions among various key parameters and verify the adequacy of the analysis.

World Wide Web (WWW) traffic models are used to generate the representative workloads that can be used for cache design and the study of "what if" scenarios for WWW-servers planning. Such models should incorporate the essential WWW traffic properties, including temporal locality, spatial locality, and popularity. In this paper, we propose a new WWW server traffic model that accurately captures these properties. Our model uses the inter-request distance string, which we found to be more accurate than the previously proposed stack distance approach in capturing traffic properties. Trace-driven simulation is used to demonstrate the goodness of the proposed model.

In this paper, we present a novel scheme for power control in mobile ad hoc networks called the CONSET (connectivity set) protocol. CONSET is a cross-layer solution in which the MAC layer indirectly influences the selection of the next hop along the end-to-end path by manipulating the transmission power of the route request (RREQ) messages. These messages are commonly used in on-demand (reactive) routing protocols, such as DSR and AODV. In CONSET, network topology is dynamically constructed from channel-gain and directional information obtained from overheard RTS and CTS packets. RREQ messages are broadcast at the minimum power required to maintain network connectivity, leading to longer end-to-end paths at the network layer but with less distance per hop. This results in a significant reduction in the overall end-to-end energy consumption per delivered packet. Extensive simulations are used to compare the CONSET protocol with a DSR-based power-aware routing protocol that uses the transmission energy as its routing metric. The results indicate that CONSET achieves significant improvements in network throughput, energy consumption, and packet delay.

We consider the problem of minimizing the differential delay in a virtually concatenated Ethernet over SONET (EoS) system by suitable path selection. The link capacity adjustment scheme (LCAS) enables network service providers to dynamically add STS-n channels to or drop them from a virtually concatenated group (VCG). A new STS-n channel can be added to the VCG provided that the differential delay between the new STS-n channel and the existing STS-n channels in the VCG is within a certain bound that reflects the available memory buffer supported by the EoS system. We model the problem of finding such a STS-n channel as a constrained path selection problem where the cost of the required (feasible) path is constrained not only by an upper bound but also by a lower bound. We propose two algorithms to find such a path. Algorithm I uses the well-known k-shortest-path algorithm. Algorithm II is based on a modified link metric that linearly combines the original link weight (the link delay) and the inverse of that weight. The theoretical properties of such a metric are studied and used to develop a highly efficient heuristic for path selection. Simulations are conducted to evaluate the performance of both algorithms in terms of the miss rate and the execution time (average computational complexity).

Quality of service support for multimedia applications has to date focused on "core-network" schemes, with little attention to the last-mile issue. We extend the QoS support to Ethernet users through a novel adaptive encapsulation scheme. The scheme supports both real-time (RT) and non-real-time (NRT) traffic types. It is adaptive in that it dynamically varies the number of IP packets in an Ethernet frame, depending on the delay requirements of the transported packets, the framing overhead, and the state of the shared medium. For RT traffic, the proposed scheme is designed to maximize the encapsulation efficiency while probabilistically guaranteeing the delay requirements of the traffic. For NRT traffic, the scheme aims at maximizing the efficiency. Extensive simulations are conducted to evaluate the performance of the proposed scheme. The results indicate a substantial improvement in the effective throughput, ranging from 20% to more than 200%, depending on the type of traffic (RT or NRT) and on the underlying packet sizes.

We propose a novel source-rate control scheme for streaming video over wireless channels. This scheme is designed to maximize the bit rate at the encoder while guaranteeing an upper bound on the probability of starvation at the playback buffer. Channel dynamics are captured using the Gilbert-Elliot model, with alternating good and bad periods. In contrast to previous approaches, rate control in our scheme is performed adaptively on a per-cycle basis, where a cycle consists of one good period and the ensuing bad period. The cycle-based approach has two advantages. First, it reduces the fluctuations in the source bit rate, ensuring smooth variations in video quality and avoiding the "saw" effect that is typically observed in frame-by-frame rate control. Second, it makes it possible to derive a closed-form expression for the starvation probability, which we use to determine the optimal source bit rates for the good and bad periods of the following cycle. Because of its low computational complexity, the proposed scheme is attractive for real-time video streaming. Simulations are carried out to assess the performance of the scheme and study the interactions among various system parameters.

Transmission power control (TPC) has a great potential to increase the throughput of a mobile ad hoc network (MANET). Existing TPC schemes achieve this goal by using additional hardware (e.g., multiple transceivers), by compromising the collision avoidance property of the channel access scheme, or by imposing impractical requirements on the operation of the MAC protocol. In this paper, we present a novel power control MAC protocol, known as POWMAC, for MANETs that enjoys the same simple single-channel, single-transceiver design of the IEEE 802.11 ad hoc MAC protocol, but that achieves a significant throughput improvement over the 802.11 scheme. Collision avoidance is integrated into the design of POWMAC. Instead of alternating between the transmission of control (RTS/CTS) and data packets, as done in the 802.11 scheme, POWMAC uses an access window (AW) to allow for a series of RTS/CTS exchanges to take place before multiple, concurrent data packet transmissions can commence. The length of the AW is dynamically adjusted (based on local traffic load information) to allow for concurrent interference-limited transmissions to take place in the same vicinity of a receiving node. Collision avoidance information is inserted into the CTS packet and is used to bound the transmission powers of potential interferers, rather than to silence such nodes. Simulation results for “random-grid” and “clustered ” topologies are used to demonstrate the significant throughput and energy gains that can be obtained under the POWMAC protocol.

We present a media-oriented mechanism for selecting the appropriate transmission mode in 802.11-based wireless LANs (WLANs). The main goal of this mechanism is to improve the effective throughput for transporting loss-tolerant multimedia traffic over a WLAN by taking into account both the application characteristics and the physical channel conditions. In particular, the proposed cross-layer mechanism exploits the robustness of multimedia coding by allowing packets with corrupted payloads reach the receiving application. The sending application specifies its quality of service requirements (data rate, BER tolerance, etc.), and the receiver selects the best transmission mode (transmission rate, modulation scheme, FEC scheme) while taking into account the time-varying channel conditions. We discuss the modifications needed for the control and data-packet headers to implement our approach in the framework of the IEEE 802.11 standards. We use ns2 simulations to contrast our scheme with an existing 802.11 rate selection algorithm. The results indicate that the proposed cross-layer approach achieves up to 5 Mbps increase in throughput and 20-meter increase in the coverage range. Furthermore, by disabling FEC from some of the standard transmission modes, we show that the goodput of loss-tolerant applications can be improved significantly.

In this paper, we propose a comprehensive solution for power control in mobile ad hoc networks (MANETs). Our solution emphasizes the interplay between the MAC and network layers, whereby the MAC layer indirectly influences the selection of the next-hop by properly adjusting the power of route request packets. This is done while maintaining network connectivity. Channel-gain information obtained mainly from overheard RTS and CTS packets is used to dynamically construct the network topology. Unlike the IEEE 802.11 approach and previously proposed schemes, ours does not use the RTS/CTS packets to silence the neighboring nodes. Instead, collision avoidance information is inserted in the CTS packets and sent over an out-of-band control channel. This information is used to dynamically bound the transmission power of potentially interfering nodes in the vicinity of a receiver. By properly estimating the required transmission power for data packets, our protocol allows for interference-limited simultaneous transmissions to take place in the neighborhood of a receiving node. Simulation results indicate that, compared to the IEEE 802.11 approach, the proposed protocol achieves a significant increase in the channel utilization and end-to-end network throughput and a significant decrease in the total energy consumption.

Finite-state Markov chain (FSMC) models have often been used to characterize the wireless channel. The fitting is typically performed by partitioning the range of the received signal-to-noise ratio (SNR) into a set of intervals (states). Different partitioning criteria have been proposed in the literature, but none of them was targeted to facilitating the analysis of the packet delay and loss performance over the wireless link. In this paper, we propose a new partitioning approach that results in an FSMC model with tractable queueing performance. Our approach utilizes Jake's level-crossing analysis, the distribution of the received SNR, and the elegant analytical structure of Mitra's producer-consumer fluid queueing model. An algorithm is provided for computing the various parameters of the model, which are then used in deriving closed-form expressions for the effective bandwidth (EB) subject to packet loss and delay constraints. Resource allocation based on the EB is key to improving the perceived capacity of the wireless medium. Numerical investigations are carried out to study the interactions among various key parameters, verify the adequacy of the analysis, and study the impact of error control parameters on the allocated bandwidth for guaranteed packet loss and delay performance.

As wireless networks continue to grow, users will be demanding more service diversity and differential quality-of-service (QoS). At the packet level, differential QoS is met by means of scheduling for channel access. Several algorithms have been proposed for packet scheduling over the wireless link. The main consideration in the design of these algorithms is the characteristics of the wireless channel. Previous works used a two-state Markov model to characterize the channel and design the scheduling algorithm. This coarse approximation can lead to a considerable amount of inaccuracy in the expected user performance, i.e., when implemented under realistic channel conditions the algorithm may not perform as expected. In this paper, we propose a scheduling algorithm and an associated MAC scheme that enable operation under realistic channel conditions. We use an JV-state Markov model to characterize the channel, where N > 2. The channel characteristics are incorporated into the scheduler through the use of future channel estimates, making the scheduler more immune to channel variations. Our scheme allows for adaptive FEC, whereby the code rate varies according to the forecasted channel state. Comparisons between the proposed algorithm and wireless fair service (WFS) S. Lu et al., (1998) show that the proposed algorithm gives better results in terms of both throughput and delay, while preserving the fairness characteristics. It is also shown that the proposed algorithm is more stable under variations in mobility and network load. Furthermore, by being able to significantly reduce the number of retransmissions, the proposed algorithm makes better utilization of the channel bandwidth and reduces the energy consumed in delivering a packet.

In this paper, we propose a comprehensive solution for power control in mobile ad hoc networks (MANETs). Our solution emphasizes the interplay between the MAC and network layers, whereby the MAC layer indirectly influences the selection of the next-hop by properly adjusting the power of route request packets. This is done while maintaining network connectivity. Directional and channel-gain information obtained mainly from overheard RTS and CTS packets is used to dynamically construct the network topology. By properly estimating the required transmission power for data packets, our protocol allows for interference-limited simultaneous transmissions to take place in the neighborhood of a receiving node. Simulation results indicate that compared to the IEEE 802.11 approach, the proposed protocol achieves a significant increase in the channel utilization and end-to-end network throughput, and a significant decrease in the total energy consumption.

We propose a CDMA-based power controlled medium access protocol for mobile ad hoc networks (MANETs). Unlike previously proposed protocols, ours accounts for the multiple access interference (MAI), thereby addressing the notorious near-far problem that undermines the throughput performance in MANETs. Channel-gain information obtained from overheard RTS and CTS packets over an out-of-band control channel is used to dynamically bound the transmission power of mobile terminals in the vicinity of a receiver. By properly estimating the required transmission power for data packets, the proposed protocol allows for interference-limited simultaneous transmissions to take place in the neighborhood of a receiving terminal. Simulation results indicate that compared to the IEEE 802.11 approach, the proposed protocol achieves a significant increase in network throughput at no additional cost in energy consumption.

One of the challenging issues in exchanging multimedia information over a network is how to determine a feasible path that satisfies all the quality-of-service (QoS) requirements of multimedia applications while maintaining high utilization of network resources. The latter objective implies the need to impose an additional optimality requirement on the feasibility problem. This can be done through a primary cost function (e.g., administrative weight, hop-count) according to which the selected feasible path is optimal. In general, multiconstrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial time. Heuristics and approximation algorithms with polynomial- and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions suffer either from excessive computational complexities that cannot be used for online network operation or from low performance. Moreover, they only deal with special cases of the problem (e.g., two constraints without optimization, one constraint with optimization, etc.). For the feasibility problem under multiple constraints, some researchers have recently proposed a nonlinear cost function whose minimization provides a continuous spectrum of solutions ranging from a generalized linear approximation (GLA) to an asymptotically exact solution. In this paper, we propose an efficient heuristic algorithm for the most general form of the problem. We first formalize the theoretical properties of the above nonlinear cost function. We then introduce our heuristic algorithm (H/spl I.bar/MCOP), which attempts to minimize both the nonlinear cost function (for the feasibility part) and the primary cost function (for the optimality part). We prove that H/spl I.bar/MCOP guarantees at least the performance of GLA and often improves upon it. H/spl I.bar/MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs and realistic network topologies with correlated and uncorrelated link weights from several distributions including uniform, normal, and exponential, we show the efficiency of H/spl I.bar/MCOP over its (less general) contenders in terms of finding feasible paths and minimizing their costs under the same level of computational complexity.

A key issue in any QoS routing framework is how to compute a path that satisfies given QoS constraints. We focus on the path computation problem subject to bandwidth and delay constraints. This problem can be solved easily if the exact state information is available to the node computing the path. In practice, nodes have only imprecise knowledge of the network state. Reliance on outdated information and treating it as exact can significantly degrade the effectiveness of the path selection. We adopt a probabilistic approach in which the state parameters (available bandwidth and delay) are characterized by random variables. The goal is then to find the most-probable bandwidth-delay-constrained path (MP-BDCP). We provide efficient solutions for the MP-BDCP problem by decomposing it into the most-probable delay-constrained path (MP-DCP) problem and the most-probable bandwidth-constrained path (MP-BCP) problem. MP-DCP by itself is known to be NP-hard, necessitating the use of approximate solutions. We use the central limit theorem and Lagrange relaxation techniques to provide two complementary solutions for MP-DCP. These solutions are highly efficient, requiring on average a few iterations of Dijkstra's shortest path algorithm. As for MP-BCP, it can be easily transformed into a variant of the shortest path problem. Our MP-DCP and MP-BCP solutions are then combined to obtain a set of near-nondominated paths for the MP-BDCP problem. Decision makers can then select one or more of these paths based on a specific utility function. Extensive simulations demonstrate the efficiency of the proposed algorithmic solutions and, more generally, to contrast the probabilistic path selection approach with the standard threshold-based triggered approach.

Current link-state routing protocols (e.g., OSPF) use flooding to disseminate link-state information throughout the network. Despite its simplicity and reliability, flooding incurs unnecessary communications overhead since nodes may receive multiple copies of the same advertisement. This extra overhead becomes an Issue in the context of quality-of-service (QoS) routing, where link state is dynamic and needs to be advertised frequently. The advertisement overhead can be significantly reduced by using tree-based broadcasting approaches. Although several of these approaches have been proposed in the literature, they are not used in practice because of their complexity and/or unreliability. We propose a new link-state dissemination approach that combines the best features of flooding and tree-based broadcasting. Our hybrid approach is particularly suited for "dynamic" link metrics (e.g., available bandwidth). It uses periodic flooding to advertise topology changes and first-time LSAs (link-state advertisements), and uses tree-based broadcasting to disseminate subsequent refresh LSAs. The broadcast trees in our approach are constructed dynamically during the flooding of the first LSA, without the need for the complex algorithms of previous tree-based approaches. Two versions of our dissemination approach are presented, with one being more suitable for networks with frequent topological changes. We prove the correctness of our approach and contrast its communications overhead with flooding and pure tree-based broadcasting. The results indicate that our hybrid approach has a significantly lower overhead than flooding; yet It enjoys the simplicity, reliability, and fast convergence of flooding. Finally, we outline how OSPF can be extended to support the proposed dissemination approach.

We explore the viability of multifractal analysis in modeling the traffic generation process at a WWW server. In principle, a WWW traffic model can be used for generating representative WWW traces and in designing prefetching and cache replacement policies. Multifractal processes constitute a superset of monofractal (selfsimilar) processes. They are characterized by a time-dependent scaling law, which provides flexibility in describing irregularities that are localized in time. R. Riedi et al. (see IEEE Trans. on Inf. Theory, vol.45, no.3, p.992-1018, 1999) presented a multifractal process that can be fitted to empirical time series with an arbitrary autocorrelation function (ACF) and with an approximately lognormal marginal distribution. We use this model to capture simultaneously the temporal and spatial localities of WWW traffic. Furthermore, the popularity profile is captured by a construction using the LRU (least recently used) stack and the popularity profiles of each file in the real trace. We classify files into several classes according to their popularity profile and model the stack distance of each class separately. Trace-driven simulations are used to study the performance of our model and contrast it with a previously proposed model.

QoS routing has generally been addressed in the context of reservation-based network services (e.g. ATM, IntServ), which require explicit (out of band) signaling of reservation requests and maintenance of per-flow state information. It has been recognized that the processing of per-flow state information poses scalability problems, especially at core routers. To remedy this situation, in this paper we introduce an approach for stateless QoS routing in IP networks that assumes no support for signaling or reservation from the network. Simple heuristics are proposed to identify a low-cost delay-constrained path. These heuristics essentially divide the end-to-end path into at most two "superedges" that are connected by a "relay node". Routers that lie on the same superedge use either the cost metric or the delay metric (but not both) to forward the packet. Simulations are presented to evaluate the cost performance of the proposed approach.

In this paper, we propose a fast multicast routing heuristic, called DCMA, for delay-sensitive applications. The running complexity of DCMA is O(m log m+|V|), where m is the size of the multicast group and |V| is the number of nodes in the network. We first present a unicast version of DCMA, called SDCR, which is used to setup an end-to-end, cost effective delay-constrained path between two nodes. SDCR can always find a delay-constrained path if one exists. Its running complexity is O(|V|). SDCR is used in constructing DCMA, which is guaranteed to be loop free. Simulation results are used to demonstrate the high performance and cost effectiveness of the proposed heuristic.

Providing quality-of-service (QoS) guarantees in packet networks gives rise to several challenging issues. One of them is how to determine a feasible path that satisfies a set of constraints while maintaining high utilization of network resources. The latter objective implies the need to impose an additional optimality requirement on the feasibility problem. This can be done through a primary cost function (e.g., administrative weight, hop count) according to which the selected feasible path is optimal. In general, multi-constrained path selection, with or without optimization, is an NP-complete problem that cannot be exactly solved in polynomial-time. Heuristics and approximation algorithms with polynomial and pseudo-polynomial-time complexities are often used to deal with this problem. However, existing solutions suffer either from excessive computational complexities that cannot be used for online network operation or from low performance. Moreover, they only deal with special cases of the problem (e.g., two constraints without optimization, one constraint with optimization, etc.). For the feasibility problem under multiple constraints, some researchers have proposed a nonlinear cost function whose minimization provides a continuous spectrum of solutions ranging from a generalized linear approximation (GLA) to an asymptotically exact solution. We propose an efficient heuristic algorithm for the most general form of the problem. We first formalize the theoretical properties of the above nonlinear cost function. We then introduce our heuristic algorithm (H MCOP), which attempts to minimize both the nonlinear cost function (for the feasibility part) and the primary cost function (for the optimality part). We prove that H MCOP guarantees at least the performance of GLA and often improves upon it. H MCOP has the same order of complexity as Dijkstra's algorithm. Using extensive simulations on random graphs with correlated and uncorrelated link weights, we show that under the same level of computational complexity, H MCOP outperforms its (less general) contenders in its success rate in finding feasible paths and in the cost of such paths.

The objective of this paper is to demonstrate the limitations of the variance-time (VT) test as a statistical tool for inferring long-range dependence (LRD) in network traffic. Since the early Bellcore studies, LRD has been in the center of a continuous debate within the teletraffic community. The controversy is typically focused on the utility of LRD models to predict the performance at network buffers. Our work here is not meant to advocate one modeling approach over another, but to point out (experimentally and theoretically) to the caveats in using the VT test as a tool for detecting LRD. We do that by deriving simple analytical expressions for the slope of the aggregated variance in three autocorrelated traffic models: M/G//spl infin/ process (short-range dependent (SRD) but non-Markovian), the discrete autoregressive of order one model (SRD Markovian), and the fractional ARIMA process (LRD). Our main result is that the VT test often indicates, falsely, the existence of an LRD structure (i.e., H>0.5) in synthetically generated traces from the two SRD models. The bias in the VT test, however, diminishes monotonically with the length of the trace. We provide some guidelines on selecting the minimum trace length so that the bias is negligible.

Providing quality-of-service (QoS) guarantees over wireless links requires thorough understanding and quantification of the interactions among the traffic source, the wireless channel, and the underlying link-layer error control mechanisms. We account for such interactions in an analytical model that we use to investigate the delay distribution and the packet discard rate (PDR) over a wireless link. Our analysis accommodates the inherent autocorrelations in both the traffic source as well as the channel error characteristics. An on-off fluid process is used to model the arrival of packets at the transmitter. These packets are temporarily stored in a first-in-first-out (FIFO) buffer before being transmitted over a channel with a time-varying and autocorrelated service rate. Using fluid analysis, we first derive the distribution for the queueing delay at the transmitter. As part of this analysis, we solve a fundamental fluid problem, namely, the probability distribution for the workload generated by a two-state fluid source over a fixed time interval. We then use the delay analysis to derive the PDR at the receiver. A closed-form expression for the effective bandwidth subject to a delay constraint is provided as a function of the source, channel, and error scheme parameters. This expression enables fast assessment of the bandwidth requirement of real-time traffic over QoS-based wireless networks. Numerical results and simulations are used to verify the adequacy of the analysis and to study the interactions among various system parameters.

We apply nonlinear chaos theory in modeling and forecasting variable-bit-rate (VBR) video sequences. Nonlinear chaos modeling offers an alternative approach to stochastic (typically, linear) approaches, with the advantages of lower dimensionality and more determinism. However, the goodness of its predictions strongly depends on the accuracy with which the dimensionality of a chaotic model is estimated from empirical data. The contributions of this paper are twofold. First, we present a novel approach for estimating the embedding dimension of any chaotic time series that satisfies the functional relationship of Farmer and Sidorowich (1987). The proposed approach is applied to VBR video data and is used to show the existence of chaos in packetized video traffic. Second, we develop a chaos-theory-based model for VBR intracoded video, which can be used to generate a rich set of synthetic traces that exhibit similar statistical structure to the original data. These traces are useful in performance evaluation and resource allocation in integrated computer networks.

One of the key issues in providing end-to-end quality-of-service guarantees in packet networks is how to determine a feasible route that satisfies a set of constraints while simultaneously maintaining high utilization of network resources. In general, finding a path subject to multiple additive constraints (e.g., delay, delay-jitter) is an NP-complete problem that cannot be exactly solved in polynomial time. Accordingly, heuristics and approximation algorithms are often used to address to this problem. Previously proposed algorithms suffer from either excessive computational cost or low performance. In this paper, we provide an efficient approximation algorithm for finding a path subject to two additive constraints. The worst-case computational complexity of this algorithm is within a logarithmic number of calls to Dijkstra's shortest path algorithm. Its average complexity is much lower than that, as demonstrated by simulation results. The performance of the proposed algorithm is justified via theoretical performance bounds. To achieve further performance improvement, several extensions to the basic algorithm are also provided at low extra computational cost. Extensive simulations are used to demonstrate the high performance of the proposed algorithm and to contrast it with other path selection algorithms.

We analyze the autocorrelation structure for a class of scene-based MPEG video models at the groups-of-pictures (GOP) (course grain) and frame (fine grain) levels assuming an arbitrary scene-length distribution. At the GOP level, we establish the relationship between the scene-length statistics and the short-range/long-range dependence (SRD/LRD) of the underlying model. We formally show that when the intrascene dynamics exhibit SRD, the overall model exhibits LRD if and only if the second moment of the scene length is infinite. Our results provide the theoretical foundation for several empirically derived scene-based models. We then study the impact of traffic correlations on the packet loss performance at a video buffer. Two popular families of scene-length distributions are investigated: Pareto and Weibull. In the case of Pareto distributed scene lengths, it is observed that the performance is rather insensitive to changes in the buffer size even as the video model enters the SRD regime. For Weibull distributed scene lengths, we observe that for small buffers the loss performance under a frame-level model can be larger than its GOP-level counterpart by orders of magnitude. In this case, the reliance on GOP-level models will result in very optimistic results.

Providing quality-of-service (QoS) guarantees over wireless packet networks poses a host of technical challenges that are not present in wireline networks. One of the key issues is how to account for the characteristics of the time-varying wireless channel and for the impact of link-layer error control in the provisioning of packet-level QoS. We accommodate both aspects in analyzing the packet-loss performance over a wireless link. We consider the cases of a single and multiplexed traffic streams. The link capacity fluctuates according to a fluid version of Gilbert-Elliott channel model. Traffic sources are modeled as on-off fluid processes. For the single-stream case, we derive the exact packet-loss rate (PLR) due to buffer overflow at the sender side of the wireless link. We also obtain a closed-form approximation for the corresponding wireless effective bandwidth. In the case of multiplexed streams, we obtain a good approximation for the PLR using the Chernoff-dominant eigenvalue (CDE) approach. Our analysis is then used to study the optimal forward error correction code rate that guarantees a given PLR while minimizing the allocated bandwidth. Numerical results and simulations are used to verify the adequacy of our analysis and to study the impact of error control on the allocation of bandwidth for guaranteed packet-loss performance.

Providing quality of service (QoS) guarantees over wireless links requires thorough understanding and quantification of the interactions among the traffic source, the wireless channel, and the underlying error control mechanisms. In this paper, we account for such interactions in a network-layer model that we use to investigate the delay performance for an on/off traffic stream transported over a wireless link. The capacity of this link fluctuates according to a fluid version of Gilbert-Elliot's model. We derive the packet delay distribution via two different approaches: uniformization and Laplace transform. Computational aspects of both approaches are discussed. The delay distribution is then used to quantify the wireless effective bandwidth under a given delay guarantee. Numerical results and simulations are used to verify the adequacy of our analysis and to study the impact of error control and bandwidth allocation on the packet delay performance.

Several technical issues must be resolved before ATM services can be efficiently extended to the wireless environment. Key issues include incorporating the characteristics of the time-varying wireless channel in the provisioning of the cell-level QoS, and improving the transport performance using error control mechanisms. We analyze the cell loss and delay performance over a wireless ATM link. We consider both cases of a single and multiplexed ATM connections. The link capacity fluctuates according to a fluid version of Gilbert-Elliot channel model. Traffic sources are modeled as on-off fluid processes. The analytical framework incorporates the effects of error control schemes (i.e., ARQ and/or FEC), which are used to improve the transport performance over the wireless link. For the single-stream case, we derive the mean delay and the cell loss rate (CLR) due to buffer overflow at the sender side of the wireless link. We also obtain a closed-form approximation for the corresponding wireless effective bandwidth. In the case of multiplexed streams, we obtain a good approximation for the CLR using the Chernoff-dominant eigenvalue (CDE) approach. The expressions for the CLR and effective bandwidth are then used to study the optimal FEC code rate that guarantees the requested QoS while maximizing the utilization of the wireless bandwidth. Numerical results and simulations are used to verify the adequacy of our analysis and to study the impact of error control on the allocation of bandwidth for guaranteed cell loss and delay performance.

In this paper we present an access scheduling scheme for real-time streams (RTS) over the peripheral component interconnect (PCI) bus. We derive a bus model based on the rate monotonic scheduling (RMS) algorithm that guarantees the timing quality of service (QoS) for real-time streams over the PCI bus. The proposed model is valid for constant-bit-rate (CBR) as well as for variable-bit-rate (VBR) streams. We define the effective bus utilization (EBU) as the worst case bus utilization and we determine the value of the internal latency timer (ILT) that minimizes EBU. Finally, we present some simulation results to demonstrate the practicality of the proposed scheme.

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by /spl rho/(k)=e/sup -/spl beta//spl radic/(k)/ than by /spl rho/(k)=/sup -/spl beta//=e/sup -/spl beta/ log k/ (long-range dependence) or /spl rho/(k)=e/sup -/spl beta/k/. A video model with such a correlation structure is introduced based on the so-called M/G//spl infin/ input processes. Though not Markovian, the model exhibits short-range dependence. Using the queueing performance under "real" video traffic as a reference, we study via simulations the queueing performance under two video models: the M/G//spl infin/ model and the fractional ARIMA (F-ARIMA) model (which exhibits LRD). Our results indicate that the M/G//spl infin/ model is much more accurate in predicting the actual queueing performance than the F-ARIMA model. Furthermore, only /spl Oscr/(n) computations are required to generate an M/G//spl infin/ trace of length n, compared to /spl Oscr/(n/sup 2/) for a F-ARIMA trace.

Statistical evidence suggests that the autocorrelation function p(k) (k=0,1,...) of a compressed-video sequence is better captured by p(k)=e/sup -/spl beta//spl radic/k/ than by p(k)=k/sup -/spl beta//=e/sup -/spl beta/logk/ (long-range dependence) or p(k)=e/sup -/spl beta/k/ (Markovian). A video model with such a correlation structure is introduced based on the so-called M/G//spl infin/ input processes. In essence, the M/G//spl infin/ process is a stationary version of the busy-server process of a discrete-time M/G//spl infin/ queue. By varying G, many forms of time dependence can be displayed, which makes the class of M/G//spl infin/ input models a good candidate for modeling many types of correlated traffic in computer networks. For video traffic, we derive the appropriate G that gives the desired correlation function p(k)=e/sup -/spl beta//spl radic/k/. Though not Markovian, this model is shown to exhibit short-range dependence. Poisson variates of the M/G//spl infin/ model are appropriately transformed to capture the marginal distribution of a video sequence. Using the performance of a real video stream as a reference, we study via simulations the queueing performance under three video models: our M/G//spl infin/ model, the fractional ARIMA model (which exhibits LRD), and the DAR(1) model (which exhibits a Markovian structure). Our results indicate that only the M/G//spl infin/ model is capable of consistently providing acceptable predictions of the actual queueing performance. Furthermore, only O(n) computations are required to generate an M/G//spl infin/ trace of length n, compared to O(n/sup 2/) for an F-ARIMA trace.

We examine various schemes for dynamically routing virtual circuits (VCs) in a multi-class network. A VC setup request may be rejected by admission control because resources are either unavailable or being reserved for future incoming VCs. We examine least-loaded based schemes, which attempt to balance the load among available routes. In addition, we examine packing based schemes, which attempt to reduce bandwidth fragmentation possibly at the expense of load balancing. Our simulation results show that under skewed workload, our packing based scheme outperforms a traditional least-loaded based scheme in terms of revenue (or equivalently, network utilization). Under uniform workload, both schemes provide similar revenue.

We present efficient scheduling schemes for transporting archived MPEG-coded video over a constant-bit-rate (CBR) channel. A video source is characterized by a time-varying traffic envelope, which constitutes an upper bound on the actual bit rate. To provide a relatively tight bound, window-based envelopes are used in which the bound is based on fixed-length segments of the movie. Using such envelopes, we show that video streams can be scheduled for transmission over the network such that the per-stream allocated bandwidth is significantly less than the source peak rate. In certain cases, a reduction of up to 85% of the source peak rate was achieved. Bandwidth gain is obtained via statistical multiplexing, while providing stringent, deterministic quality of service. Online procedures for bandwidth computation and admission control under the examined scheduling schemes are presented.

We present a novel bandwidth allocation scheme for transporting variable-bit-rate MPEG traffic from a video server. Using time-varying envelopes to characterize the traffic, this scheme achieves significant bandwidth gain, via statistical multiplexing, while supporting stringent, deterministic QoS guarantees. The gain can be maximized by allowing the server to appropriately schedule the starting times of video sources, at the expense of some negligible startup delay. For homogeneous streams, we give the optimal schedule that results in the minimum allocated bandwidth. A suboptimal schedule is given in the heterogeneous case, which is shown to be asymptotically optimal. Efficient online procedures for bandwidth computation are provided. Numerical examples based on traces of MPEG-coded movies are used to demonstrate the benefits of our allocation strategy.

We present a comprehensive model for variable-bit-rate MPEG video streams. This model captures the bit-rate variations at multiple time scales. Long-term variations are captured by incorporating scene changes, which are most noticeable in the fluctuations of I frames. The size of an I frame is modeled by the sum of two random components: a scene-related component and an AR(2) component that accounts for the fluctuations within a scene. Two random processes of i.i.d. rvs are used to model the sizes of P and B frames, respectively. The complete model is then obtained by intermixing the three sub-models according to a given GOP pattern. It is shown that the composite model exhibits long-range dependence (LRD) in the sense that its autocorrelation function is non-summable. The LRD behavior is caused by the repetitive GOP pattern which induces periodic cross-correlations between different types of frames. Using standard statistical methods, we successfully fit our model to several empirical video traces. We then study the queueing performance for video traffic at a statistical multiplexer. The results show that the model is sufficiently accurate in predicting the queueing performance for real video streams.

Compression of digital video is the only viable means to transport real-time full-motion video over BISDN/ATM networks. Traffic streams generated by video compressors exhibit complicated patterns which vary from one compression scheme to another. In this paper we investigate the traffic characteristics of video streams which are compressed based on the MPEG standard. Our study is based on 23 minutes of video obtained from an entertainment movie. A particular significance of our data is that it contains all types of coded frames, namely: Intra-coded (I), Prediction (P), and Bidirectional (B) MPEG frames. We describe the statistical behavior of the VBR stream using histograms and autocorrelation functions. A procedure is developed to determine the instants of a scene change based on the changes in the size of successive I frames. It is found that the length of a scene can be modeled by a geometric distribution.A model for an MPEG traffic source is developed in which frames are generated according to the compression pattern of the captured video stream. For each frame type, the number of cells per frame is fitted by a lognormal distribution whose parameters are determined by the frame type. The appropriateness and limitations of the model are examined by studying the multiplexing performance of MPEG streams. Simulations of an ATM multiplexer are conducted, in which traffic sources are derived from the measured VBR trace as well as the proposed model. The queueing performance in both cases is found to be relatively close.

The convergence and bias properties of a general class of optimal bounding ellipsoid (OBE) algorithms are discussed. OBE algorithms are set-membership (SM) based identification algorithms which are applied to models which are linear-in-parameters, and are closely related to weighted recursive least square error (WRLS) methods.>