Reactive jammers pose a severe security threat to robotic-swarm networks by selectively disrupting inter-agent communications and undermining formation integrity and mission success. Conventional countermeasures such as fixed power control or static channel hopping are largely ineffective against such adaptive adversaries. This paper presents a multi-agent reinforcement learning (MARL) framework based on the QMIX algorithm to improve the resilience of swarm communications under reactive jamming. We consider a network of multiple transmitter–receiver pairs sharing channels while a reactive jammer with Markovian threshold dynamics senses aggregate power and reacts accordingly. Each agent jointly selects transmit frequency (channel) and power, and QMIX learns a centralized but factorizable action-value function that enables coordinated yet decentralized execution. We benchmark QMIX against a genie-aided optimal policy in a no–channel-reuse setting, and against local Upper Confidence Bound (UCB) and a stateless reactive policy in a more general fading regime with channel reuse enabled. Simulation results show that QMIX rapidly converges to cooperative policies that nearly match the genie-aided bound, while achieving higher throughput and lower jamming incidence than the baselines, thereby demonstrating MARL’s effectiveness for securing autonomous swarms in contested environments.

We present a retrieval-augmented question answering framework for 5G/6G networks, where the Open Radio Access Network (O-RAN) has become central to disaggregated, virtualized, and AI-driven wireless systems. While O-RAN enables multi-vendor interoperability and cloud-native deployments, its fast-changing specifications and interfaces pose major challenges for researchers and practitioners. Manual navigation of these complex documents is labor-intensive and error-prone, slowing system design, integration, and deployment. To address this challenge, we adopt Contextual Retrieval-Augmented Generation (Contextual RAG), a strategy in which candidate answer choices guide document retrieval and chunk-specific context to improve large language model (LLM) performance. This improvement over traditional RAG achieves more targeted and context-aware retrieval, which improves the relevance of documents passed to the LLM, particularly when the query alone lacks sufficient context for accurate grounding. Our framework is designed for dynamic domains where data evolves rapidly and models must be continuously updated or redeployed, all without requiring LLM fine-tuning. We evaluate this framework using the ORAN-Benchmark-13K dataset, and compare three LLMs, namely, Llama3.2, Qwen2.5-7B, and Qwen3.0-4B, across both Direct Question Answering (Direct Q&A) and Chain-of-Thought (CoT) prompting strategies. We show that Contextual RAG consistently improves accuracy over standard RAG and base prompting, while maintaining competitive runtime and CO2emissions. These results highlight the potential of Contextual RAG to serve as a scalable and effective solution for domain-specific Q&A in O-RAN and broader 5G/6G environments, enabling more accurate interpretation of evolving standards while preserving efficiency and sustainability.

Current wind energy data platforms face significant challenges in securing and managing extensive data from both offshore and onshore wind farms. These challenges include vulnerabilities to cyber-attacks, data tampering, breaches, complex data-sharing issues due to privacy concerns and regulatory compliance, and a lack of scalability and flexibility in analytical tools for real-time data processing. This paper proposes a novel multilayered data security architecture, termed "VindSec-Llama," to address these challenges. It integrates Generative AI, blockchain, federated learning, and Pipeline Bill of Materials (PBOM) to enhance data analytics, model development, and security across several layers, including Infrastructure, Data Lake, Federated Learning, MLOps, Data Provenance, and LLM. Each layer is designed to meet specific functional requirements, such as handling large datasets, facilitating secure federated learning, automating risk management, and ensuring data provenance and traceability. The platform, deployable in server environments (cloud or on-premises), complies with the Risk Management Framework (RMF) guidelines and security standards. It features a blockchain-enabled, coordinator-less federated learning system to enhance data privacy and security by enabling the development of privacy-preserving machine learning models with data from different wind farms. Automation plays a pivotal role throughout VindSec-Llama, with Meta’s custom-trained Llama-3 LLM used for generating remediation scripts in the Infrastructure Layer and for producing PPBOM in the MLOps Layer. The Llama-3 LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The MLOps pipeline setup, a critical functionality of VindSec-Llama, ensures seamless integration and deployment of machine learning models, embodying best practices in continuous integration and delivery. This setup is geared towards maximizing security, compliance, and operational efficiency. A prototype of the platform has been implemented within a wind-energy testbed with the collaboration of Department of Energy US, illustrating its practical applications and benefits.

Large Language Models (LLMs) are widely recognized for their applications in natural language processing tasks, but their potential extends far beyond traditional use cases. This paper introduces "Bassa-Llama," a novel platform that harnesses LLMs for predictive tasks in the realm of network security. Specifically, we propose a platform for real-time network attack detection in Wind Power Plants, leveraging a fine-tuned version of Meta’s Llama-3 LLM alongside blockchain and NFT-based data storage. Using a network PCAP dataset containing both malicious and benign packets, we fine-tune the Llama-3 LLM, with Quantized Low-Rank Adapter (QLoRA), to detect anomalies in network traffic. This approach ensures optimal performance on consumer-grade hardware while significantly enhancing the model’s ability to accurately analyze PCAP data and identify attack patterns. The end-to-end orchestration of the real-time network attack detection flow for Wind Power Plants is fully automated through blockchain smart contracts, and NFTs for storing identified attack data from the PCAP. To the best of our knowledge, this research represents the first effort to utilize a fine-tuned LLM for real-time network attack detection tasks. The results highlight the transformative potential of combining fine-tuned LLMs with blockchain and NFTs to build robust and secure network defense systems for Wind Power Plants. A prototype of the proposed platform was developed in collaboration with the U.S. Department of Energy, utilizing a simulated Wind Power Plant as a testbed.

We study an Age-of-Information (AoI) scheduling problem where users can tolerate occasional violations of AoI for each source at the base station. Each user’s AoI is associated with a violation tolerance constraint. We are interested in determining whether a set of users, each with a given AoI deadline, a violation tolerance constraint, and a packet loss rate (due to channel condition) is schedulable, and if so, find a feasible scheduler. For this problem, we study two cases: 1) the stable tolerant case where the tolerance rate is higher than the packet loss rate for each source and 2) the unstable tolerant case where the tolerance rate is lower than the packet loss rate for at least one source. For the stable tolerant case, we design an algorithm called stable tolerant scheduler (STS), which can find a feasible scheduler for any network when the system load is no greater than$\ln 2$(roughly 70%). When the system load is between$\ln 2$and 1, we offer a necessary and sufficient condition for STS to find a feasible scheduler by solving an optimization problem. Likewise, for the unstable tolerance case, we develop a scheduler called unstable tolerant scheduler (UTS) and its corresponding schedulability conditions. Through extensive simulations, we show that STS and UTS match our theoretical results.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to AoI-related problems. These problems vary in specific AoI-based objectives and network settings. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we envision and pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization and decision problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve three important problems: to minimize weighted sum of AoIs, to minimize bandwidth requirement under AoI constraints, and to determine the existence of feasible schedulers to satisfy AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new (or general) results that are not available in the literature.

In this work, we study the tradeoff between total throughput of a network and the cumulative emitted energy experienced by one or more external nodes that are not members of the network. We formulate the linear program to determine the routing and scheduling that maximizes the fair throughput for uplink and downlink traffic subject to the energy at each external node being below a threshold. Due to the spatial reuse with simultaneous transmitters and the multi-path routing approach, the number of variables in the linear program is exponential in the number of nodes in the network, so we solve this large linear program using an iterative approach known as column generation. We present numerical results in the form of energy heatmaps generated by the optimal schedule for individual network realizations, as well as the average energy heatmap over many Monte Carlo realizations, demonstrating the impact of the external node locations. We also present throughput results as a function of the node distance and the energy threshold for the single external node case. These results characterize the tradeoff between throughput and the distance and sensed energy sensitivity level, which provides a bound with which to compare the performance of practical routing and scheduling algorithms.

We examine a simplified distributed collaborative beamforming (DCB) system with two transmitters sending data over independent channels with significant time-varying phase distortion. The transmitters sample the state of their respective channels using a periodic sounding waveform sent by the receiver and use the last sensed state along with the sample age to correct for the channel phase distortion. As the age of the last sampled channel state increases, the transmitters are correctly estimating the current state with decreasing likelihood, leading to reduced beamforming gain. However, because sensing and transmitting must occur on the same channel, the transmitters cannot both send data and sample the channel at the same time. As a result, the cost associated with channel sensing is the transmitters not sending data for some duration; this compromise is embodied by the frame-averaged expected beamforming gain. Expressions for instantaneous and averaged expected gain are developed, and an example is presented to demonstrate finding the optimal sensing period and how the optimal sensing period can change depending on the last sensed states of the channels.

Age of Information (AoI) is a promising metric to measure information freshness and optimizing AoI through scheduling is one of the most intensely studied areas in AoI research. To date, the vast majority of research on AoI scheduling has been based on simplified communication models that often fail to capture the complexities found in real-world network systems such as 5G. While there are some limited efforts on AoI scheduling that have ventured into exploring OFDMA-based data transmission models similar to those in 5G, they tend to neglect essential elements, such as channel-dependent resource block (RB) allocation and modulation and coding scheme (MCS) assignment, rendering limited utility to real-world 5G systems. In this article, we focus on developing 5G-compliant AoI schedulers. We study a specific problem with the objective of minimizing outdated information across all source nodes. This problem arises from practice where there is a specific information freshness requirement, known as AoI deadline for each source node. We present Aequitas, an innovative 5G scheduler designed to optimize this objective through joint RB allocation and MCS assignment, both of which are dependent on frequency and time-selective channel fading. We exploit a property called “uniform fairness,” derived from the analysis of an optimal offline scheduler, to develop Aequitas. To meet stringent timing requirement in 5G, Aequitas leverages the parallel computing capability of a commercial off-the-shelf GPU. Extensive evaluations demonstrate that Aequitas closely approaches the theoretical lower bound in terms of objective performance, while maintaining operational times below the 5G timing requirement.

Open radio access network (O-RAN) enables cooperative signal processing among multiple cells at a centralized O-RAN distributed unit (O-DU). It is a key technology for cellular networks to increase spectrum efficiency. To achieve cooperative signal processing across multiple cells, a new scheduler is needed. Specifically, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users from all the cells that are involved in multi-cell processing. In addition, the scheduler must obtain its scheduling solution within each TTI (i.e., at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present O-$\mathbf M^{3}$—a real-time scheduler formulti-cellMIMO networks under the O-RAN architecture. O-$\mathbf M^{3}$can meet the stringent timing requirement with joint optimization of beamforming matrices, RB allocation, and MCS assignment among multiple cells. O-$\mathbf M^{3}$is developed through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is performed for cell-center users to explore MU-MIMO transmission. We implement O-$\mathbf M^{3}$on a commercial off-the-shelf (COTS) GPU. Experimental results show that O-$\mathbf M^{3}$is capable of offering a scheduling solution within 500$\mu \text{s}$for an O-RAN system with 7 O-RAN radio units (O-RUs), 100 users, 100 RBs, and$2\times 8$MIMO. O-$\mathbf M^{3}$can also meet the 1 ms requirement for$2\times 12$MIMO systems. Meanwhile, O-$\mathbf M^{3}$can provide ~40% throughput gain on average through joint transmission across multiple cells.

This paper investigates a bandwidth minimization problem with Age of Information (AoI) constraints—a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment while, at the same time, there is an expectation of AoI requirement on the application side. We present a novel polynomial-time algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure calledFractional Consecutively Divisible(FCD). In the general case when the given AoI constraint vector is not FCD, we show that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. We validate the performance of Aion through a large number of simulations and all results confirm our theoretical findings. The results from this paper lay a foundation for future research on bandwidth minimization with AoI guarantee.

This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling Markovian fading wireless channels and emerging network performance metrics such as age-of-information (AoI) and timely-throughput. Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it to an unsolved network optimization problem where some clients wish to minimize AoI while others wish to maximize timely-throughput. We show that this framework accurately characterizes AoI and timely-throughput. Moreover, it leads to a tractable scheduling policy that outperforms other existing work.

In environments crowded with electromagnetic activity, cognitive radios (CR) must have the ability to differentiate between different signals, so as to understand their origins if needed, and decide whether they are friendly or unfriendly signals. This is extremely important in the case of military and intelligence applications, but is beginning to become important in the commercial world as well, given the recent discussion around 5G and ORAN. In this paper, we develop and compare three open and three closed-set signal classification models based on various Machine Learning (ML) architectures. Results show that open-set networks are able to perform very close to their closed-set counterparts while also being able to classify unknown signals that they have not been trained on.

Age of Information (AoI) is a metric that can be used to measure the freshness of information. Since its inception, there have been active research efforts on designing scheduling algorithms to various AoI-related optimization problems. For each problem, typically a custom-designed scheduler was developed. Instead of following the (custom-design) path, we pursue a general framework that can be applied to design a wide range of schedulers to solve AoI-related optimization problems. As a first step toward this vision, we present a general framework—Eywa, that can be applied to construct high-performance schedulers for a family of AoI-related optimization problems, all sharing a common setting of an IoT data collection network. We show how to apply Eywa to solve two important AoI-related problems: to minimize the weighted sum of AoIs and to minimize the bandwidth requirement under AoI constraints. We show that for each problem, Eywa can either offer a stronger performance guarantee than the state-of-the-art algorithms or provide new results that are not available in the literature.

Recently, has been recognized that there is a practical limitation with the original notion of Age of Information (AoI) metric in terms of quantifying the freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. In this article, we introduce the notion of AoII+ metric by modifying AoII with practical considerations. Then, we investigate a scheduling problem to minimize AoII+ in an IoT data collection network. We derive a theoretical lower bound for the minimum AoII+. Then, we present Heh—a low-complexity online scheduler to minimize AoII+. The design of Heh is based on the estimation of a novel offline scheduling priority metric without any future knowledge. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes AoII+. Through extensive simulations, we show that the lower bound is very tight and that the AoII+ obtained by Heh is close to optimal.

The proliferation of IoT devices, with various capabilities in sensing, monitoring, and controlling, has prompted diverse emerging applications, highly relying on effective delivery of sensitive information gathered at edge devices to remote controllers for timely responses. To effectively deliver such information/status updates, this paper undertakes a holistic study of AoI in multi-hop networks by considering the relevant and realistic factors, aiming for optimizing information freshness by rapidly shipping sensitive updates captured at a source to its destination. In particular, we consider the multi-channel with OFDM (orthogonal frequency-division multiplexing) spectrum access in multi-hop networks and develop a rigorous mathematical model to optimize AoI at destination nodes. Real-world factors, including orthogonal channel access, wireless interference, and queuing model, are taken into account for the very first time to explore their impacts on the AoI. To this end, we propose two effective algorithms where the first one approximates the optimal solution as closely as we desire while the second one has polynomial time complexity, with a guaranteed performance gap to the optimal solution. The developed model and algorithms enable in-depth studies on AoI optimization problems in OFDM-based multi-hop wireless networks. Numerical results demonstrate that our solutions enjoy better AoI performance and that AoI is affected markedly by those realistic factors taken into our consideration.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users’ viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users’ experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

Cloud Radio Access Network (C-RAN) is a novel centralized architecture for cellular networks. C-RAN can significantly improve spectrum efficiency by performing cooperative signal processing for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed before we can take advantage of C-RAN's multi-cell processing capability. Under C-RAN architecture, the scheduler must jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under all covering cells. In addition, it is necessary to obtain a scheduling solution within each TTI (at most 1 ms) to be useful for the frame structure defined by 5G NR. In this paper, we present M3—a sub-ms scheduler for multi-cell MIMO networks under C-RAN architecture. M3addresses the stringent timing requirement through a novel multi-pipeline design that exploits parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. Experimental results show that M3is capable of offering a scheduling solution within 1 ms for 7 remote radio heads (RRHs), 100 users, 100 RBs, and 2×12 MIMO. Meanwhile, M3provides ~40%. throughput gain on average by employing joint transmission.

This paper introduces a new theoretical framework for optimizing second-order behaviors of wireless networks. Unlike existing techniques for network utility maximization, which only consider first-order statistics, this framework models every random process by its mean and temporal variance. The inclusion of temporal variance makes this framework well-suited for modeling stateful fading wireless channels and emerging network performance metrics such as age-of-information (AoI). Using this framework, we sharply characterize the second-order capacity region of wireless access networks. We also propose a simple scheduling policy and prove that it can achieve every interior point in the second-order capacity region. To demonstrate the utility of this framework, we apply it for an important open problem: the optimization of AoI over Gilbert-Elliott channels. We show that this framework provides a very accurate characterization of AoI. Moreover, it leads to a tractable scheduling policy that outperforms other existing work.

Recently, it has been recognized that there is a serious limitation with the original Age of Information (AoI) metric in terms of quantifying true freshness of information content. A new metric, called Age of Incorrect Information (AoII), has been proposed. By further refining this new metric with practical considerations, we introduce Age of Outdated Information (Ao2I) metric. In this paper, we investigate a scheduling problem for minimizing Ao2I in an IoT data collection network. We derive a theoretical lower bound for the minimum Ao2I that any scheduler can achieve. Then we present Heh—a low-complexity online scheduler. The design of Heh is based on the estimation of a novel offline scheduling priority metric in the absence of knowledge of the future. We prove that at each time, transmitting one source with the largest offline scheduling priority metric minimizes Ao2I. Through extensive simulations, we show that the lower bound is very tight and that the Ao2I obtained by Heh is close-to-optimal.

This work studies the role of Age of Information (AoI) in the network state updating process for wireless software defined networks (SDN). The SDN routers must routinely update their knowledge of the network state, which is used as a basis for making routing and scheduling decisions. However, the network updates require communication resources, so there is a tradeoff between the frequency of updates and maximum network throughput. We assume the network state is Markovian and no new observations are received in between updates, so the AoI of the network state information impacts the ability of the network to optimize its performance. We formulate the problem as a finite-horizon Partially Observable Markov Decision Process (POMDP) for each period. For a symmetric fading model of the network, we derive the limiting performance and an upper bound. To generate policies for a range of fixed time horizons, we use Monte Carlo planning-based POMDP solvers. Simulation of these policies show that there is a finite optimal update period that maximizes network throughput. In addition, we study non-uniform update intervals, which can yield even higher throughput if the interval is chosen based on the state observed. We conclude that AoI itself is not sufficient to characterize performance, but what matters is the AoI for the specific network state information.

Age of Information (AoI) is an application layer performance metric that quantifies the freshness of information. This paper investigates scheduling problems at network edge when there is an AoI requirement for each source node, which we call Maximum AoI Threshold (MAT). Specifically, we want to determine whether or not a vector of MATs corresponding to the source nodes is schedulable, and if so, find a feasible scheduler for it. For a small network, we present an optimal procedure calledCyclic Scheduler Detection(CSD) that can determine the schedulability with absolute certainty. For a large network where CSD is not applicable, we present a novel low-complexity procedure, calledFictitious Polynomial Mapping(FPM), and prove that FPM can find a feasible scheduler for any MAT vector when the load is under$\ln 2$. We use extensive numerical results to validate our theoretical results and show that the performance of FPM is significantly better than a state-of-the-art scheduling algorithm.

While recent years have witnessed a steady trend of applying Deep Learning (DL) to networking systems, most of the underlying Deep Neural Networks (DNNs) suffer two major limitations. First, they fail to generalize to topologies unseen during training. This lack of generalizability hampers the ability of the DNNs to make good decisions every time the topology of the networking system changes. Second, existing DNNs commonly operate as "blackboxes" that are difficult to interpret by network operators, and hinder their deployment in practice. In this paper, we propose to rely on a recently developed family of graph-based DNNs to address the aforementioned limitations. More specifically, we focus on a network congestion prediction application and apply Graph Attention (GAT) models to make congestion predictions per link using the graph topology and time series of link loads as inputs. Evaluations on three real backbone networks demonstrate the benefits of our proposed approach in terms of prediction accuracy, generalizability, and interpretability.

This paper investigates bandwidth minimization under AoI constraints - a fundamental problem that has not been studied in AoI research. The problem is of critical importance in bandwidth-limited IoT environment when AoI is used as a constraint. We present a novel fast algorithm called Aion that can construct a scheduler to satisfy AoI constraints with strong theoretical guarantee in terms of minimizing required bandwidth. Specifically, we prove that the bandwidth required by Aion is minimum if the AoI constraint vector meets a special mathematical structure called Fractional Consecutively Divisible (FCD). In the general case when the given AoI constraint vector is not FCD, we prove that the bandwidth required by Aion is tightly upper bounded by a factor of the minimum. The results from this paper lay the foundation for future research on bandwidth minimization with AoI guarantee.

In this paper, generalized expressions for system gain in a coherent communication system are developed. In an ad hoc or distributed network, the transmit and receive operations can be coordinated at the RF level to produce an array gain, similar to a fixed antenna array; such formations are known as coherent distributed arrays. The system examined in this paper consists of two open-loop coherent distributed arrays: one transmitter array and one receiver array. The gain of the entire system containing both arrays is described by the coherent communication gain. The concept of group array factor, which is the multi-receiver extension of array factor, is also introduced. Group array factor allows for a characterization of gain independent from propagation model and properties of the individual transmitters and receivers. The univariate optimization problem of maximizing group array factor as a function of transmitter array beam angle in the azimuth plane is described, and a practical bounding on the search space is introduced. The physical dimensions of the communication system are analyzed in terms of their influence on the objective function, and a condition for guaranteeing convexity of the bounded objective function is given. An example transmitter array-receiver array system is introduced and used to showcase the concepts herein.

Age of information is often studied as a primary objective to be optimized, but for problems where age is not the primary objective, it can still have a major role that can be utilized. This work studies a two-user, single-channel cognitive radio network, where the primary user’s transmit/idle dynamics are modeled as a binary Markov chain, and the secondary user decides to either sense or transmit. Under this setup, the age of the information sensed by the secondary user has a direct impact on its performance. The secondary user aims to maximize its throughput subject to a constraint on the probability of collision experienced by the primary. Using the Markov chain model of the primary user, the secondary user decides on its transmission and sensing strategy based on the estimated evolution of the primary user transmission state. For a stationary randomized transmission policy that depends on the sensed state, we derive the secondary throughput and the collision probability. Due to the complexity of the resulting expressions, we develop an alternative formulation of the problem by recognizing that the throughput and collision probability are functions of the age of each type of sensed information. Therefore, we transform the problem by converting the randomized policy to its induced age distribution function. As a result, the age distribution-based formulation results in a linear program, which can be solved efficiently. We include numerical results and simulations, and discuss the role of the age distribution and other related qualities of the information.

The Age of Information (AoI) is a key metric to measure the freshness of information for IoT applications. Most of the existing analytical models for AoI are overly idealistic and do not capture state-of-the-art transmission technologies such as 5G as well as channel dynamics in both frequency and time domains. In this article, we present Kronos, a real-time 5G-compliant scheduler that minimizes AoI for IoT data collection. Kronos is designed to cope with highly dynamic channel conditions. Its main function is to perform RB allocation and to select the modulation and coding scheme for each source node based on channel conditions, with the objective of minimizing long-term AoI. To meet the stringent real-time requirement for 5G, we develop a GPU-based implementation of Kronos on commercial off-the-shelf Nvidia GPUs. Through extensive experimentation, we show that Kronos can find near-optimal solutions under submillisecond time scale. To the best of our knowledge, this is the first real-time AoI scheduler that is 5G compliant.

We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data freshness is measured by a non-decreasing penalty function of age of information, where all sources have the same age-penalty function. Sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new packet in its turn. We aim to find the optimal scheduler-sampler pairs that minimize the total-average age-penalty at delivery times (Ta-APD) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler are jointly optimal for minimizing the Ta-APD. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman's equation. This threshold-type sampler reduces to a simple water-filling sampler for a linear age-penalty function.

While considerable work has addressed the optimal AoI under different circumstances in single-hop networks, the exploration of AoI in multi-hop wireless networks is rarely attempted. More importantly, the inherent relationships between AoI and throughput are yet to be explored, especially in multi-hop networks. This paper studies AoI in multi-hop wireless networks and explores its potential relationships with throughput for the very first time, particularly focusing on the impacts of flexible routes on the two metrics, i.e., AoI and throughput. By developing a rigorous mathematical model with interference, channel allocation, link scheduling, and routing path selection taken into consideration, we build the interrelation between AoI and throughput in multi-hop networks. A multi-criteria optimization problem is formulated with the goal of simultaneously minimizing AoI and maximizing network throughput. By qualitatively analyzing their relationships, we exhibit that the two metrics may conflict with each other, implying the optimal solutions for the multi-criteria problem will include a set of Pareto-optimal points rather than a single point existing in the traditional optimization problem. We resort to a novel approach by transforming the multi-criteria problem into a single objective one so as to find the weakly Pareto-optimal points iteratively, thereby allowing us to screen all Pareto-optimal points for the solution. Through formal proof, our solution is demonstrated to be able to identify all Pareto-optimal points and terminate in a finite number of iterations. We conduct the simulation evaluation to identify the optimal tradeoff points of AoI and throughput, demonstrating that one performance metric may improve at the expense of degrading the other, with the routing path found as one of the key factors in determining such a tradeoff.

The Age-of-Information (AoI) is a newly introduced metric for capturing information updating timeliness, as opposed to the network throughput, which is a conventional performance metric to measure the network transmission speed and robustness as a whole. While considerable work has addressed either optimal AoI or throughput individually, the inherent relationships between the two performance metrics are yet to be explored, especially in multi-hop networks. In this paper, we explore their relationships in multi-hop networks for the very first time, particularly focusing on the impacts of flexible routes on the two metrics. By developing a rigorous mathematical model with interference, channel allocation, link scheduling, and routing path selection taken into consideration, we build the interrelation between AoI and throughput in multi-hop networks. A multi-criteria optimization problem is formulated with the goal of simultaneously minimizing AoI and maximizing network throughput. To solve this problem, we resort to a novel approach by transforming the multi-criteria problem into a single objective one so as to find the weakly Pareto-optimal points iteratively, thereby allowing us to screen all Pareto-optimal points for the solution. A new algorithm based on the piece-wise linearization technique is then developed to closely linearize the non-linear terms in the single objective problem via their linear approximation segments to make it solvable. We formally prove that our algorithms can find all Pareto-optimal points in a finite number of iterations. From simulation results, we identify the tradeoff points of the optimal AoI and throughput, demonstrating that one performance metric improves at the expense of degrading the other, with the routing path found as one of the key factors in determining such a tradeoff.

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

Since the early development of Software-Defined Network (SDN) technology, researchers have been concerned with the idea of physical distribution of the control plane to address scalability and reliability challenges of centralized designs. However, having multiple controllers managing the network while maintaining a “logically-centralized” network view brings additional challenges. One such challenge is how to coordinate the management decisions made by the controllers which is usually achieved by disseminating synchronization messages in a peer-to-peer manner. While there exist many architectures and protocols to ensure synchronized network views and drive coordination among controllers, there is no systematic methodology for deciding the optimal frequency (or rate) of message dissemination. In this paper, we fill this gap by introducing the SDN synchronization problem: how often to synchronize the network views for each controller pair. We consider two different objectives; first, the maximization of the number of controller pairs that are synchronized, and second, the maximization of the performance of applications of interest which may be affected by the synchronization rate. Using techniques from knapsack optimization and learning theory, we derive algorithms with provable performance guarantees for each objective. Evaluation results demonstrate significant benefits over baseline schemes that synchronize all controller pairs at equal rate.

In this paper, we consider the problem of minimizing the age of information in a multi-source system, where samples are taken from multiple sources and sent to a destination via a channel with random delay. Due to interference, only one source can be scheduled at a time. We consider the problem of finding a decision policy that determines the sampling times and transmission order of the sources for minimizing the total average peak age (TaPA) and the total average age (TaA) of the sources. Our investigation of this problem results in an important separation principle: The optimal scheduling strategy and the optimal sampling strategy are independent of each other. In particular, we prove that, for any given sampling strategy, the Maximum Age First (MAF) scheduling strategy provides the best age performance among all scheduling strategies. This transforms our overall optimization problem into an optimal sampling problem, given that the decision policy follows the MAF scheduling strategy. While the zero-wait sampling strategy (in which a sample is generated once the channel becomes idle) is shown to be optimal for minimizing the TaPA, it does not always minimize the TaA. We use Dynamic Programming (DP) to investigate the optimal sampling problem for minimizing the TaA. Finally, we provide an approximate analysis of Bellman's equation to approximate the TaA-optimal sampling strategy by a water-filling solution which is shown to be very close to optimal through numerical evaluations.

Existing software-defined SDx architectures highly depend on a centralized control plane and hence can face substantial reliability challenges in software-defined coalition (SDC) settings, in which the centralized control plane can be weakly connected to the data plane, or even disconnected from the data plane due to high dynamicity. On the contrary, distributed control planes (e.g., OLSRv2) provide autonomy but lose flexibility and global policy guarantees. In this paper, we present Magnalium, a novel system to achieve high reliability in SDC networks by composing multiple control planes in real-time. Magnalium introduces a novel, unified composition framework that uses a distributed verification to systematically generate forwarding rules in accordance with desired policy requirements. Magnalium also introduces several supporting components to address challenges in wireless environment and resource management. We conduct data-driven simulations, showing that Magnalium benefits from both centralized and distributed control planes and even reduces downtime by 65% over the most reliable individual control plane.

Distributed software-defined networks (SDN), consisting of multiple inter-connected network domains, each managed by one SDN controller, is an emerging networking architecture that offers balanced centralized control and distributed operations. Under such a networking paradigm, most existing works focus on designing sophisticated controller-synchronization strategies to improve joint controller-decision-making for inter-domain routing. However, there is still a lack of fundamental understanding of how the performance of distributed SDN is related to network attributes, thus it is impossible to justify the necessity of complicated strategies. In this regard, we analyze and quantify the performance enhancement of distributed SDN architectures, which is influenced by intra-/inter-domain synchronization levels and network structural properties. Based on a generic network model, we establish analytical methods for performance estimation under four canonical inter-domain synchronization scenarios. Specifically, we first derive an asymptotic expression to quantify how dominating structural and synchronization-related parameters affect the performance metric. We then provide performance analytics for an important family of networks, where all links are of equal preference for path constructions. Finally, we establish fine-grained performance metric expressions for networks with dynamically adjusted link preferences. Our theoretical results reveal how network performance is related to synchronization levels and intra-/inter-domain connections, the accuracy of which is confirmed by simulations based on both real and synthetic networks. To the best of our knowledge, this is the first work quantifying the performance of distributed SDN in terms of network structural properties and synchronization levels.

In recent years, degree-of-freedom (DoF) based models were proven to be very successful in studying MIMO-based wireless networks. However, most of these studies assume channel matrix is of full-rank. Such assumption, although attractive, quickly becomes problematic as the number of antennas increases and propagation environment is not close to ideal. In this paper, we address this problem by developing a general theory for DoF-based model under rank-deficient conditions. We start with a fundamental understanding on how MIMO's DoFs are consumed for spatial multiplexing (SM) and interference cancellation (IC) in the presence of rank deficiency. Based on this understanding, we develop a general DoF model that can be used for identifying DoF region of a multi-link MIMO network and for studying DoF scheduling in MIMO networks. Specifically, we found that shared DoF consumption at transmit and receive nodes is critical for optimal allocation of DoF for IC. The results of this paper serve as an important tool for future research of many-antenna based MIMO networks.

Communication over an interference channel, which is fundamental and pervasive in the wireless and wireline environment, is often intended to carry information among different transmitter-receiver pairs. For applications that require time critical updates, it is desirable to maintain the freshness of the received information, which is quantified by the age metric (unlike the familiar delay metric). In this paper, we consider the case of two transmitter-receiver pairs, and address the impact of interference on information freshness by formulating a two-player “interference” game, in which each player is a transmitter desiring to maintain the freshness of the information updates it sends to its receiver. The strategy of a player is the choice of power level at which it will transmit. We then derive both Nash and Stackelberg strategies for the game. Our analysis shows that the Stackelberg strategy uses less power than the Nash strategy, and that it dominates the Nash strategy (i.e., the Stackelberg total cost function is lower than the Nash total cost function). Our obtained Nash and Stackelberg strategies are desirable user operating points in competitive situations.

We study the age of information, which is a measure of the freshness of a continually updated piece of information as observed at a remote monitor. The age of information metric has been studied for a variety of different queueing systems, and in this paper, we introduce a packet deadline as a control mechanism to study its impact on the average age of information for an M/M/1/2 queueing system. We analyze the system for the cases of a fixed deadline and a random exponential deadline and derive closed-form expressions for the average age. We also derive a closed-form expression for the optimal average deadline for the random exponential case. Our numerical results show the relationship of the age performance to that of the M/M/1/1 and M/M/1/2 systems, and we demonstrate that using a deadline can outperform both the M/M/1/1 and M/M/1/2 without deadline.

Fog architectures at the network edge are becoming a popular research trend to provide elastic resources and services to end-users, where the processing capacity resides at the network periphery as opposed to traditional data-centers. Despite their momentum, the control plane of these architectures remains complex and challenging to implement. To enhance control capability, in this paper, we propose to use software defined networking (SDN). SDN moves the control logic off data plane devices and onto external network entities, the controllers. We provide a proof-of-concept implementation of a multi-controller edge system and measure traffic delay and overheads. The results reveal the sensitivity of delay to the location of controllers and the magnitude of inter-controller and controller-node overheads. Guided by the above, we model the problem of determining the placement of controllers in the edge network. Using linearization and supermodular function techniques, we present approximation solutions which perform close to optimal and substantially better than state-of-the-art methods. Finally, we analyze the interplay between various performance and reliability objectives.

Increasing data traffic demands over wireless spectrum have necessitated spectrum sharing and coexistence between heterogeneous systems such as radar and cellular communications systems. In this context, we specifically investigate the co- channel coexistence between an air traffic control (ATC) radar and a wide area cellular communication (comms) system. We present a comprehensive characterization and analysis of interference caused by the comms system on the ATC radar with respect to multiple parameters such as radar range, protection radius around the radar, and radar antenna elevation angle. The analysis suggests that maintaining a protection radius of $50$ km around the radar will ensure the required INR protection criterion of $-10$ dB at the radar receiver with $\sim 0.9$ probability, even when the radar beam is in the same horizon as the comms BS. Detailed evaluations of the radar target detection performance provide a framework to choose appropriate protection radii around the radar to meet specific performance requirements.

We propose a model for mobile caching in which the rate of requests for content is dependent on the popularity and the freshness of the information. We model popularity based on the history of requests and freshness based on the age of the content. We consider a discrete time (slotted) system in which new packets arrive at a limited capacity cache at discrete times. We prove that the optimal policy for choosing the set of packets to reside in a full cache when a packet arrives is to reject the one with the lowest request rate in that particular slot. Thus, there is no advantage to separately knowing the history of requests or the age of the content. Since the optimal policy depends on the profile of the request process, we also study the expected behavior of the request model. We provide a sufficient condition under which the change in the request rate goes to zero and provide some numerical examples that illustrate this behavior. We also consider a slight alteration to the model, in which only the recent history of requests is used for determining the request rate. In this case, we provide a sufficient condition for when the rate is equal to zero, which approximates the duration of requests for content.

For time critical updates, it is desirable to maintain the freshness of the received information. We address the impact of hostile interference on information freshness by formulating a non-zero-sum two-player game, in which one player is the transmitter aiming to maintain the freshness of the information updates it sends to its receiver, and the other player is the interferer aiming to prevent this. The strategy of a player is the power level transmitted by that player. We then derive the equilibria for both Nash and Stackelberg strategies. We show that both players have the same power cost at Nash equilibrium. In addition, the Stackelberg strategy dominates the Nash strategy, i.e., the Stackelberg utility function exceeds the Nash utility function.

Underwater acoustic (UWA) networks are a key form of communications for human exploration and activities in the oceanographic space of the earth. A fundamental issue of UWA communications is large propagation delays due to water medium, which has posed a grand challenge in UWA network protocol design. Conventional wisdom of addressing this issue is to live with this disadvantage by inserting a guard interval to introduce immunity to propagation delays. Recent advances in interference alignment (IA) open up a new direction to address this issue and promise a great potential to improve network throughput by exploiting large propagation delays. In this paper, we investigate propagation delay-based IA (PD-IA) in multi-hop UWA networks. We first develop a set of simple constraints to characterize PD-IA feasible region at the physical layer. Based on the set of PD-IA constraints, we develop a distributed PD-IA scheduling algorithm to greedily maximize interference overlapping possibilities in a multi-hop UWA network. Simulation results show that the proposed PD-IA algorithm yields higher throughput than an idealized benchmark algorithm without propagation delays, indicating that large propagation delays are not adversarial but beneficial for network throughput performance.

Existing spectrum sharing paradigms have set clear boundaries between the primary and secondary networks. There is either no or very limited node-level cooperation between the primary and secondary networks. In this paper, we develop a new and bold spectrum-sharing paradigm beyond the state of the art for future wireless networks. We explore network cooperation as a new dimension for spectrum sharing between the primary and secondary users. Such network cooperation can be defined as a set of policies under which different degrees of cooperation are to be achieved. The benefits of this paradigm are numerous, as they allow integrating resources from two networks. There are many possible node-level cooperation policies that one can employ under this paradigm. For the purpose of performance study, we consider a specific policy called United cooperation of Primary and Secondary (UPS) networks. UPS allows a complete cooperation between the primary and secondary networks at the node level to relay each other's traffic. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. We also develop an approximation solution based on a piece-wise linearization technique. Simulation results show that UPS offers significantly better throughput performance than that under the interweave paradigm.

We study the age of information, which is a recently introduced metric for measuring the freshness of a continually updated piece of information as observed at a remote monitor. The age of information metric has been studied for a variety of different queuing systems. In this work, we introduce a packet deadline as a control mechanism and study its impact on the average age of information for an M/M/1/2 queuing system. We analyze the system for a fixed deadline and derive a mathematical expression for the average age. We numerically evaluate the expression and show the relationship of the age performance to that of the M/M/1/1 and M/M/1/2 systems. We show that the system with a deadline constraint can outperform both the M/M/1/1 and M/M/1/2 without such a deadline.

We formulate the interaction between communication and hostile interference in wireless systems as a non-zero-sum two-player game. One player is the transmitter aiming to establish or maintain the communication to its receivers, and the other player is the interferer aiming to prevent or disrupt the communication. The strategy of the transmitter is a transmission power level, while the strategy of the interferer is an interfering power level. We provide closed-form equilibria for both Nash and Stackelberg models. We show that, while a Stackelberg equilibrium always exists, a Nash equilibrium exists only when the wireless channel is affected by fading. In addition, for the case of Rayleigh channel fading, we show that both players have the same power cost at Nash equilibrium.

Network-coded cooperative communications (NC-CC) refers to the use of network coding (NC) in cooperative communications (CC). Prior studies have shown that NC has the potential to improve the performance of CC when there are multiple sessions in the wireless network. These studies were done for the case when multiple sessions are sharing a single relay node. However, how NC-CC behaves when multiple relay nodes are employed remains an open problem. In this paper, we explore this problem by analyzing the achievable rate of each session in this setting. We develop closed form formulas for the mutual information and the achievable data rate for each session. We show that prior results for a single relay is a special case of our result. Based on these findings, we then study a network optimization problem that requires joint optimization of session grouping, relay node grouping, and matching of session/relay groups. We show that this problem is NP-hard, and present a polynomial time heuristic algorithm to solve this problem. Using simulation results, we show this algorithm is highly competitive and can produce results that are near to optimality.

Cooperation has become an essential element in spectrum sharing between the primary and secondary networks. A new trend in cooperation is to allow the primary and secondary networks to cooperate on the node level for data forwarding. This new paradigm allows to pool network resources from both the primary and secondary networks and allows users in each network to access a much richer network infrastructure in a combined network. This paper offers an in-depth study of such node-level cooperation by explaining its optimal throughput curve—the maximum achievable throughput for both the primary and secondary users. We formulate the problem as a multicriteria optimization problem with the goal of maximizing the throughput of both the primary and secondary users. Through a novel approach based on weighted Chebyshev norm, we transform the multicriteria optimization problem into a single criteria optimization problem and find a sequence of Pareto-optimal points iteratively. Based on the Pareto-optimal points, we construct the throughput curve and show that it provides an $\varepsilon $ -approximation to the optimal curve. We prove some important properties of the optimal throughput curve. Through a case study, we show that the throughput region (the area under the throughput curve) under node-level cooperation is substantially larger than that when there is no node-level cooperation.

This paper focuses on status age, which is a metric for measuring the freshness of a continually updated piece of information (i.e., status) as observed at a remote monitor. In paper, we study a system in which a sensor sends random status updates over a dynamic network to a monitor. For this system, we consider the impact of having messages take different routes through the network on the status age. First, we consider a network with plentiful resources (i.e., many nodes that can provide numerous alternate paths), so that packets need not wait in queues at each node in a multihop path. This system is modeled as a single queue with an infinite number of servers, specifically as an M/M/∞ queue. Packets routed over a dynamic network may arrive at the monitor out of order, which we account for in our analysis for the M/M/∞ model. We then consider a network with somewhat limited resources, so that packets can arrive out of order but also must wait in a queue. This is modeled as a single queue with two servers, specifically an M/M/2 queue. We present the exact approach to computing the analytical status age, and we provide an approximation that is shown to be close to the simulated age. We also compare both models with M/M/1, which corresponds to severely limited network resources, and we demonstrate the tradeoff between the status age and the unnecessary network resource consumption.

Interference alignment (IA) is a powerful technique to handle interference in wireless networks. Since its inception, IA has become a central research theme in the wireless communications community. Due to its intrinsic nature of being a physical layer technique, IA has been mainly studied for point-to-point or single-hop scenario. There is a lack of research of IA from a networking perspective in the context of multi-hop wireless networks. The goal of this paper is to make such an advance by bringing IA technique to multi-hop MIMO networks. We develop an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine IA for a subset of interfering streams. We further prove the feasibility of this IA model by showing that a DoF vector can be supported free of interference at the physical layer as long as it satisfies the constraints in our IA model. Based on the proposed IA model, we develop an IA design space for a multi-hop MIMO network. To study how IA performs in a multi-hop MIMO network, we compare the performance of a network throughput optimization problem based on our developed IA design space against the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

We investigate the problem of streaming multi-user videos over the downlink of a cognitive radio network (CRN), where each cognitive user (CU) can access one channel at a time. We first consider the case where each CU can sense one channel at a time slot at most. To make the problem tractable, we tackle the optimal spectrum sensing and access problems separately and develop matching-based optimal algorithms to the subproblems, which yield an overall suboptimal solution. We then consider the case where each CU can sense multiple channels. We show that under the assumption that all the spectrum sensors work on the same operating point, a two-step approach can derive the optimal spectrum sensing and access policies that maximize the quality of experience (QoE) of the streaming videos. The superior performance of the proposed approaches is validated with simulations and comparisons with benchmark schemes, where a performance gain from 25% to 30% is demonstrated.

We tackle the challenging problem of streaming multiuser videos over the downlink of a cellular cognitive radio network (CRN), where each cognitive user (CU) can sense and access multiple channels at a time. Spectrum sensing, channel assignment, and power allocation strategies are jointly optimized to maximize the quality of service (QoS) for the CUs. We show that the formulated mixed integer nonlinear programming (MINLP) problem can be decomposed into two subproblems: 1) SP1 for the optimal spectrum sensing strategy and 2) SP2 for the optimal channel assignment and power allocation, without sacrificing optimality. We show that SP1 can be optimally solved if there is no restriction on the sensing capability for each CU, and develop a column generation (CG)-based algorithm to solve SP2 iteratively in a distributed manner. We also develop a heuristic algorithm for spectrum sensing with greatly reduced requirement on CU hardware, while still achieving a highly competitive sensing performance. We analyze the proposed algorithms with respect to complexity and time efficiency, and derive a performance upper bound. The proposed algorithms are validated with simulations.

The classical approach to interference management in wireless medium access is based on avoidance. Recently, there is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. This was made possible by a number of advances at the physical layer. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we aim to close this gap by offering a systematic study of SIC in a multi-hop wireless network. After gaining a fundamental understanding of SIC's capability and limitation, we propose a cross-layer optimization framework for SIC that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC behaves in a multi-hop wireless network.

The transparent coexistence (TC) paradigm allows simultaneous activation of the secondary users with the primary users as long as their interference to the primary users can be properly canceled. This paradigm has the potential to offer much more efficient spectrum sharing than the traditional interweave paradigm. In this paper, we design a distributed algorithm to achieve this paradigm for a secondary multi-hop network. For interference cancelation (IC), we employ MIMO at secondary nodes. We present a distributed iterative algorithm to maximize each secondary session's throughput while meeting all IC requirements under TC. By maintaining two local sets for each node, we can keep track of the node's IC responsibility. Although no explicit node ordering is maintained in our distributed algorithm, we prove that our distributed data structure at each node (with the use of two local sets) can be mapped to an explicit global node ordering for IC among all nodes in the network. This guarantees that each active node's degree-of-freedoms allocated for IC is feasible at the physical layer. Our algorithm is iterative in nature and all steps can be accomplished based on local information exchange among the neighboring nodes. We present the simulation results to show that the performance of our distributed algorithm is highly competitive when compared with an upper bound solution from the corresponding centralized problem.

We study the problem of minimizing the scheduling time length to serve users' traffic demand by link scheduling in multi-hop mmWave wireless networks. We formulate a constrained Binary Integer Programming (BIP) problem incorporating a flexible interference model for directional transmissions and a Markov chain based blockage model. Since the problem is NP hard, we propose a heuristic algorithm with greatly reduced complexity, which first finds the optimal streaming path for each data flow and then maximizes the instant network throughput by optimizing the link scheduling at each time slot. The performance of the heuristic algorithm is validated with simulations.

We study the minimum latency broadcast scheduling problem, in which a single source has a quantity of data that must be transmitted to all other nodes in a multi-hop network in minimum time. Aside from the obvious application to classical communications, this problem also relates to some more general problems in the field of network science. Previous approaches to scheduling have assumed a simplistic collision model of interference, while others have studied the more realistic physical model of total received interference power. Existing suboptimal approaches for transmitting the data typically assume a collision-free, fixed-rate, single packet transmission. In this work, we devise an optimal approach for broadcast under a physical interference model with fixed-rate, single packet transmission by converting it to a shortest path problem for an unweighted, undirected graph. Since this optimal approach does not scale well, we also consider a suboptimal layered approach which separates the routing and scheduling functions, but relaxes the fixed-rate, single packet assumption. This goes beyond the signal-to-interference-plus-noise (SINR) threshold model to allow for rate adaptation as a function of SINR. We include improvements on previous routing approaches, and we formulate a linear programming approach to the variable-rate scheduling for broadcast. Simulations show that in some special cases, this variable-rate layered approach can even outperform the optimal fixed-rate, single packet approach.

We consider a wireless network consisting of source-destination pairs, in which each source is required to transmit a given bit volume to its destination. The goal is for all the sources to transmit the given bit volumes, under a time constraint, so that the total transmission energy is minimized. Our approach is the joint optimization of link scheduling and power control for minimum energy. We show that TDMA scheduling is appropriate for this goal, in the sense that TDMA is asymptotically optimal when the time constraint approaches infinity. When the time constraint is strictly bounded, we show that TDMA is also optimal for the case of equal channel gains.

The dominate spectrum sharing paradigm of today is interference avoidance, where a secondary network can use the spectrum only when such a use is not interfering with the primary network. However, with the advances of physical-layer technologies, the mindset of this paradigm is being challenged. This paper explores a new paradigm called “transparent coexistence” for spectrum sharing between primary and secondary nodes in a multihop network environment. Under this paradigm, the secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency is accomplished through a systematic interference cancelation (IC) by the secondary nodes without any impact on the primary network. Although such a paradigm has been studied in the information theory (IT) and communications (COMM) communities, it is not well understood in the wireless networking community, particularly for multihop networks. This paper offers an in-depth study of this paradigm in a multihop network environment and addresses issues such as scheduling (both in frequency channels and time slots) and IC (to/from primary network and within the secondary network). Through a rigorous modeling and formulation, problem formulation, solution development, and simulation results, we show that transparent coexistence paradigm offers significant improvement in terms of spectrum access and throughput performance as compared to the current prevailing interference avoidance paradigm.

We consider the problem of streaming multi-user videos over the downlink of a Cognitive Radio Network (CRN), where each Cognitive User (CU) can access one channel at a time. Motivated by the prior work that establishes a separation principle for the joint design of spectrum sensor, sensing, and access polices, we first model cooperative spectrum sensing as an Integer Programming problem (IP) and develop a Greedy Poly-matching scheme to solve it for the optimal sensing strategies. We then formulate the problem of CU Quality of Experience (QoE) maximization as a maximum weight matching problem and solve it with the Hungarian Method for optimal channel assignments. The proposed spectrum sensing and channel assignment algorithms are compared with benchmark schemes in simulations, and are found to outperform the benchmark schemes in terms of available channels discovered and CU QoE achieved.

Transparent coexistence, also known as underlay, offers much more efficient spectrum sharing than traditional interweave coexistence paradigm. In a previous work, the transparent coexistence for a multi-hop secondary networks is studied. In this paper, we design a distributed solution to achieve this paradigm. In our design, we show how to increase the number of data streams iteratively while meeting constraints in the MIMO interference cancelation (IC) model and achieving transparent coexistence. All steps in our distributed algorithm can be accomplished based on local information exchange among the neighboring nodes. Our simulation results show that the performance of our distributed algorithm is highly competitive when compared to an upper bound solution for the centralized problem.

We investigate the performance of a status monitoring system, in which a sensor sends random status updates over a network to a remote monitor. Specifically, we analyze the status age metric, which characterizes how old the information at the monitor is from the last received status update. The system on which we focus is a single queue with 2 servers (specifically, an M/M/2). In a dynamic network, different status packets may take different routes to the monitor, which allows for the possibility of packets arriving out of order. In the case of the status monitoring system, only the latest status is useful. Studying a system with 2 servers allows for the possibility of packets to arrive out-of-order while still having to queue. We present the exact approach to computing the analytical status age, and we provide an approximation that matches very closely with the simulated age. We also compare with the M/M/∞ and M/M/1, and we demonstrate the tradeoff between status age and network resource consumption.

We study the energy-efficient transmission problem for a time-varying interference channel. Assume that each source transmits in each time slot according to a transmission probability, which is a continuous value between 0 and 1. Our goal is to determine the values of the transmission probabilities and the transmission power levels so that the network energy efficiency is maximized. We show that the energy efficiency is maximized when the transmission probabilities are either 0 or 1. We also show that simultaneous transmissions reduce energy efficiency. We then address the impact of the accuracy and timeliness of channel state information (CSI) on energy efficiency. The following cases are considered: perfect CSI, erroneous CSI, delayed CSI, and unknown CSI.

Network-coded cooperative communications (NC-CC) is a new paradigm for communications in wireless networks that employs network coding (NC) to improve the performance of CC. A key problem to harness the potential of NC-CC is how to put sessions into different groups, and assign a relay node for each group. In this paper, we study this joint grouping and relay node selection problem for NC-CC. We provide a formal proof of NP-hardness for this problem. Due to NP-hardness, we propose a distributed and online algorithm and show that it offers near-optimal solution to this problem. The key idea in this algorithm is to have each neighboring relay node of a new session calculate the best local group that it can offer and advertise this information; and then to have the source node of the new session select the best local group to join among all offers. We show that our distributed algorithm has polynomial time complexity. Using extensive numerical results, we show that our distributed algorithm adapts well to online network dynamics.

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

We evaluate the throughput that can be achieved under BER constraints by combined use of transmission scheduling and multiuser detection. A schedule is a rule that specifies which subset of users is allowed to simultaneously transmit in a time slot. These subsets of overlapping transmissions, which can vary in different time slots of the transmission frame, are decoded by receivers equipped with multiuser detection. The goal is to find the largest such subsets under the requirement that each transmission satisfies a BER constraint. The joint problem of scheduling and multiuser-detection is highly complex in general. However, for certain class of multiuser detectors, the problem has efficient and optimal solution. By constructing transmission schedules that directly reflect the performance and exploit the capability of the multiuser detection receivers, as expected, multiuser detection-based scheduling significantly outperforms other methods such as TDMA and the conventional single-user detection-based scheduling.

In this work, we investigate the queue stability of a two-user cognitive radio system with multicast traffic. We study the impact of network-level cooperation, in which one of the nodes can relay the packets of the other user that are not received at the destinations. Under this approach, if a packet transmitted by the primary user is not successfully received by the destination set but is captured by the secondary source, then the secondary user assumes responsibility for completing the transmission of the packet; therefore, the primary releases it from its queue, enabling it to process the next packet. We demonstrate that the stability region of this cooperative approach is larger than that of the noncooperative approach, which translates into a benefit for both users of this multicast system. Our system model allows for the possibility of multipacket reception, and the optimal transmission strategies for different levels of multipacket reception capability are observed in our numerical results.

Interference alignment (IA) is a major advance in information theory. Despite its rapid advance in the information theory community, most results on IA remain point-to-point or single-hop and there is a lack of advance of IA in the context of multi-hop wireless networks. The goal of this paper is to make a concrete step toward advancing IA technique in multi-hop MIMO networks. We present an IA model consisting of a set of constraints at a transmitter and a receiver that can be used to determine a subset of interfering streams for IA. Based on this IA model, we develop an IA optimization framework for a multihop MIMO network. For performance evaluation, we compare the performance of a network throughput optimization problem under our proposed IA framework and the same problem when IA is not employed. Simulation results show that the use of IA can significantly decrease the DoF consumption for IC, thereby improving network throughput.

We consider the system where a source randomly generates status update messages and transmits them via a network cloud to the intended destination. These update message can take different times to traverse the network, which we model as exponential service times, and may result in packets reaching the destination out of order, rendering some of the earlier transmissions obsolete. We analyze the status update age for such a system, and show that it tracks well with simulation results.

The dominant spectrum sharing paradigm of today is the interweave paradigm. This paper advocates a new and alternative paradigm called United network of Primary and Secondary networks (UPS). UPS allows a complete cooperation between primary and secondary networks at the node level to relay each other's traffic, in addition to existing dynamic spectrum access (DSA) in time, space, and frequency domains. Such cooperation allows the primary and secondary networks to access a much richer network resources from the combined network. As a case study, we consider a problem with the goal of supporting the rate requirement of the primary network traffic while maximizing the minimum throughput of the secondary sessions. For this problem, we develop an optimization model and formulate a combinatorial optimization problem. Although this problem is in the form of mixed integer linear program (MILP), we can use CPLEX to solve it efficiently. Simulation results show that the UPS paradigm offers much better throughput performance than the interweave DSA paradigm.

This paper explores the so-called “transparent coexistence” paradigm for spectrum sharing between primary and secondary nodes in a multi-hop network environment. Although such paradigm has been studied in the information theory and communications communities, it is not well understood in the wireless networking community, particularly for multihop networks. Under this paradigm, a secondary network is allowed to use the same spectrum simultaneously with the primary network as long as their activities are “transparent” (or “invisible”) to the primary network. Such transparency can be accomplished through a systematic interference cancellation (IC) by the secondary nodes without any impact on the primary network. This paper offers an in-depth study of this paradigm in a multi-hop network environment and addresses issues such as channel selection, IC to/from primary network, and IC within the secondary network. Through a rigorous modeling and formulation, we develop an optimization problem under this paradigm with the objective of maximizing secondary user's throughput. Through simulation results, we show that such paradigm offers significant improvement to a multi-hop network in terms of spectrum efficiency and throughput performance as compared to the prevailing interference-avoidance paradigm.

Network throughput and energy consumption are two important performance metrics for a multihop wireless network. Current state-of-the-art research is limited to either maximizing throughput under some energy constraint or minimizing energy consumption while satisfying some throughput requirement. Although many of these prior efforts were able to offer some optimal solutions, there is still a critical need to have a systematic study on how to optimize both objectives simultaneously. In this paper, we take a multicriteria optimization approach to offer a systematic study on the relationship between the two performance objectives. To focus on throughput and energy performance, we simplify link layer scheduling by employing orthogonal channels among the links. We show that the solution to the multicriteria optimization problem characterizes the envelope of the entire throughput-energy region, i.e., the so-called optimal throughput-energy curve. We prove some important properties of the optimal throughput-energy curve. For case study, we consider both linear and nonlinear throughput functions. For the linear case, we characterize the optimal throughput-energy curve precisely through parametric analysis, while for the nonlinear case, we use a piecewise linear approximation to approximate the optimal throughput-energy curve with arbitrary accuracy. Our results offer important insights on exploiting the tradeoff between the two performance metrics.

This paper tries to reconcile the tension between the physical model and the protocol model that have been used to characterize interference relationship in a multihop wireless network. The physical model (a.k.a. signal-to-interference-and-noise ratio model) is widely considered as a reference model for physical layer behavior but its application in multihop wireless networks is limited by its complexity. On the other hand, the protocol model (a.k.a. disk graph model) is simple but there have been doubts on its validity. This paper explores the following fundamental question: How to correctly use the protocol interference model? We show that, in general, solutions obtained under the protocol model may be infeasible and, thus, results based on blind use of protocol model can be misleading. We propose a new concept called "reality check” and present a method of using a protocol model with reality check for wireless networks. Subsequently, we show that by appropriate setting of the interference range in the protocol model, it is possible to narrow the solution gap between the two models. Our simulation results confirm that this gap is indeed small (or even negligible). Thus, our methodology of joint reality check and interference range setting retains the protocol model as a viable approach to analyze multihop wireless networks.

The underwater acoustic channel is characterized by a path loss that is dependent on both the distance and the frequency of communication. Given this dependence, it has been previously demonstrated that for a given communication distance, there is an optimal operating frequency, where conditions for signal propagation and noise are most favorable. In this work, we consider extending this optimal frequency concept to scenarios in which the frequencies that can be employed by the system are constrained. Such constraints are important considerations for practical system design. The first problem we study is to find a single frequency that minimizes the energy over a number of links of varying lengths. An approximate model for this frequency is proposed that is very close to the true optimal. We then generalize this problem to finding the best frequency band, within which the frequency can be tuned for different link lengths. We demonstrate how our model is applied to a 2-D network scenario. We simulate random node placement for such a network, and we observe that the optimal frequencies are very close to the proposed model.

There is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we try to answer the following fundamental questions. What are the limitations of SIC? How to overcome such limitations? How to optimize the interaction between SIC and interference avoidance? How to incorporate multiple layers (physical, link, and network) in an optimization framework? We find that SIC alone is not adequate to handle interference in a multi-hop wireless network, and advocate the use of joint SIC and interference avoidance. To optimize a joint scheme, we propose a cross-layer optimization framework that incorporates variables at physical, link, and network layers. This is the first work that combines successive interference cancellation and interference avoidance in multi-hop wireless network. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC and interference avoidance can complement each other in an optimal manner.

Capacity scaling laws offer fundamental understanding on the trend of user throughput behavior when the network size increases. Since the seminal work of Gupta and Kumar, there have been active research efforts in developing capacity scaling laws for ad hoc networks under various advanced physical layer technologies. These efforts led to many custom-designed solutions, most of which were intellectually challenging and lacked universal properties that can be extended to address scaling laws of ad hoc networks with other physical layer technologies. In this paper, we present a set of simple yet powerful tool that can be applied to quickly determine the capacity scaling laws for various physical layer technologies under the protocol model. We prove the correctness of our proposed criteria and demonstrate their usage through a number of case studies, such as ad hoc networks with directional antenna, MIMO, multi-channel multi-radio, cognitive radio, and multiple packet reception. These simple criteria will serve as powerful tools to networking researchers to obtain throughput scaling laws of ad hoc networks under different physical layer technologies, particularly those to be developed in the future.

In this paper, we consider the problem of calculating the stability region of a two-user cognitive shared channel where the secondary (lower priority) user, whose channel is modeled as a two-state Gilbert-Elliott channel, utilizes the channel state information to adapt its transmission probabilities accordingly. The analysis also takes into account the compound effects of multipacket reception at the receiver as well as the cooperative relaying capability of the secondary node, on the stability region of the cognitive network. Results clearly illustrate that the knowledge of the secondary channel state benefits not only the secondary user, but also the primary user as well.

It has been shown that cooperative communications (CC) has the potential to significantly increase the capacity of wireless networks. However, most of the existing results are limited to single-hop wireless networks. To explore the behavior of CC in multi-hop wireless networks, we study a joint optimization problem of relay node assignment and flow routing for a group of sessions. We develop a mathematical model and propose a solution procedure based on the branch-and-bound framework augmented with cutting planes (BB-CP). We design several novel components to speed-up the computational time of BB-CP. Via numerical results, we show the potential rate gain that can be achieved by incorporating CC in multi-hop networks.

A major benefit of employing network coding (NC) in cooperative communications (CCs) is its ability to reduce time-slot overhead. Such approach is called network-coded CC (or NC-CC). Most of the existing works have mainly focused on exploiting this benefit without considering its potential adverse effect. In this paper, we show that NC may not always benefit CC. We substantiate this important finding with two important scenarios: employing analog network coding (ANC) in amplify-and-forward (AF) CC, and digital network coding (DNC) in decode-and-forward (DF) CC. For both scenarios, we introduce the important concept of network coding noise (NC noise). We analyze the origin of this noise via a careful study of signal aggregation at a relay node and signal extraction at a destination node. We derive a closed-form expression for NC noise at each destination node and show that the existence of NC noise could diminish the advantage of NC in CC. Our results shed new light on how to use NC in CC most effectively.

Network-coded cooperative communications (NC-CC) refers to the use of network coding (NC) in cooperative communications (CC). Prior studies have shown that NC has the potential to improve the performance of CC when there are multiple sessions in the wireless network. These studies were done for the case when multiple sessions are sharing a single relay node. However, how NC-CC behaves when multiple relay nodes are employed remains an open problem. In this paper, we explore this problem by analyzing the achievable rate of each session in this setting. We develop closed form formulas for the mutual information and the achievable data rate for each session and show that prior results for a single relay is a special case of our result. Our findings in this paper offer an important building block on the theory of NC-CC. To demonstrate the application of our theoretical result, we apply it in a numerical study to understand the impact on a session's achievable rate when different sets of relay nodes are employed in NC-CC.

Cognitive radio is envisioned to be an enabling radio technology for future wireless networks. In this paper, we study the capacity scaling laws for cognitive radio ad hoc networks (CRNs), i.e., how each individual node's capacity scales as the number of nodes in the network increases. This effort is critical to the fundamental understanding of the scalability of such network. However, due to the heterogeneity in available frequency bands at each node, the asymptotic capacity is much more difficult to develop than prior efforts for other types of wireless networks. To overcome this difficulty, we introduce two auxiliary networks ζ and α to analyze the capacity upper bound and lower bound. We derive the capacity results under both the protocol model and the physical model. Further, we show that the results developed by Gupta and Kumar for the simple single-channel single-radio (SC-SR) networks are special cases under the results for CRNs.

Abstract-Network throughput and energy consumption are two important performance metrics for a multi-hop wireless network. Current state-of-the-art is limited to either maximizing throughput under some energy constraint or minimizing energy consumption while satisfying some throughput requirement. In this paper, we take a multicriteria optimization approach to offer a systematic study on the relationship between the two performance objectives. We show that the solution to the multi criteria optimization problem is equivalent to finding an optimal throughput-energy curve, which characterizes the envelope of the entire throughput-energy region. We prove some important prop erties of the optimal throughput-energy curve. For case study, we consider both linear and nonlinear throughput functions. In the linear case, we characterize the optimal throughput-energy curve precisely through parametric analysis, while in the nonlinear case, we use a piece-wise linear approximation to approximate the optimal throughput-energy curve with arbitrary accuracy. Our results offer important insights on exploiting the trade-off between the two performance metrics. I.

This paper addresses fundamental issues in a shared channel where the users have different priority levels. In particular, we characterize the stable-throughput region in a two user cognitive shared channel where the primary (higher priority) user transmits whenever it has packets to transmit while the secondary (cognitive) node transmits its packets with probability p. Therefore, in this system, the secondary link is allowed to share the channel along with the primary link, in contrast to the traditional notion of cognitive radio, in which the secondary user is required to relinquish the channel as soon as the primary is detected. The analysis also takes into account the compound effects of multi-packet reception as well as of the relaying capability on the stability region of the network. We start by analyzing the non-cooperation case where nodes transmit their own packets to their respective destinations. We then extend the analysis to a system where the secondary node cooperatively relays some of the primary's packets. Specifically, in the cooperation case, the secondary node relays those packets that it receives successfully from the primary, but are not decoded properly by the primary destination. In such cognitive shared channels, a tradeoff arises in terms of activating the secondary along with the primary so that both transmissions may be successful, but with a lower probability, compared to the case of the secondary node staying idle when the primary user transmits. Results show the benefits of relaying for both the primary as well as the secondary nodes in terms of the stable-throughput region.

Network-coded cooperative communications (NC-CC) is a new paradigm in wireless networks that employs network coding (NC) to improve the performance of CC. The core mechanism to harness the benefits of NC-CC is to appropriately combine sessions into separate groups, and then have each group select the most beneficial relay node for NC-CC. In this paper, we study this joint grouping and relay node selection problem for NC-CC. Due to NP-hardness of problem, we propose a distributed and online algorithm that offers near-optimal solution to this problem. The key idea in our algorithm is to have each neighboring relay node of a new session determine and offer its best local group; and then to have the source node of the new session select the best group among all offers. We show that our distributed algorithm has polynomial complexity. Using extensive numerical results, we show that our distributed algorithm adapts well to online network dynamics.

Cognitive radio (CR) and multiple-input multiple-output (MIMO) are two independent physical layer technologies that have made significant impact on wireless networking. CR operates on the channel/band level to exploit white space across spectrum dimension while MIMO operates within the same channel to improve spectral efficiency within the same band. In this paper, we explore MIMO-empowered CR network, which we call {\rm CRN}^{{\rm MIMO}}, to achieve the ultimate flexibility and efficiency in dynamic spectrum access and spectrum utilization. Given that CR and MIMO handle interference at different levels (across channels vs. within a channel), we are interested in how to jointly optimize both so as to maximize user throughput in a multihop network. To answer this question, we develop a tractable mathematical model for {\rm CRN}^{{\rm MIMO}}, which captures the essence of channel assignment (for CR) and degree-of-freedom (DoF) allocation (for MIMO) within a channel. Based on this mathematical model, we use numerical results to show how channel assignment in CRN and DoF allocation in MIMO can be jointly optimized to maximize throughput. More important, for a {\rm CRN}^{{\rm MIMO}} with A_{{\rm MIMO}} antennas at each node, we show that joint optimization of CR and MIMO offers more than A_{MIMO}-fold throughput increase than a CRN (without MIMO).

Cognitive radio networks (CRNs) have the potential to utilize spectrum efficiently and are positioned to be the core technology for the next-generation multihop wireless networks. An important problem for such networks is its capacity. We study this problem for CRNs in the SINR (signal-to-interference-and-noise-ratio) model, which is considered to be a better characterization of interference (but also more difficult to analyze) than disk graph model. The main difficulties of this problem are two-fold. First, SINR is a nonconvex function of transmission powers; an optimization problem in the SINR model is usually a nonconvex program and NP-hard in general. Second, in the SINR model, scheduling feasibility and the maximum allowed flow rate on each link are determined by SINR at the physical layer. To maximize capacity, it is essential to follow a cross-layer approach, but joint optimization at physical (power control), link (scheduling), and network (flow routing) layers with the SINR function is inherently difficult. In this paper, we give a mathematical characterization of the joint relationship among these layers. We devise a solution procedure that provides a (1- \varepsilon ) optimal solution to this complex problem, where \varepsilon is the required accuracy. Our theoretical result offers a performance benchmark for any other algorithms developed for practical implementation. Using numerical results, we demonstrate the efficacy of the solution procedure and offer quantitative understanding on the interaction of power control, scheduling, and flow routing in a CRN.

Recently, cooperative communications, in the form of having each node equipped with a single antenna and exploit spatial diversity via some relay node's antenna, is shown to be a promising approach to increase data rates in wireless networks. Under this communication paradigm, the choice of a relay node (among a set of available relay nodes) is critical in the overall network performance. In this paper, we study the relay node assignment problem in a cooperative ad hoc network environment, where multiple source-destination pairs compete for the same pool of relay nodes in the network. Our objective is to assign the available relay nodes to different source-destination pairs so as to maximize the minimum data rate among all pairs. The main contribution of this paper is the development of an optimal polynomial time algorithm, called ORA, that achieves this objective. A novel idea in this algorithm is a “linear marking” mechanism, which maintains linear complexity of each iteration. We give a formal proof of optimality for ORA and use numerical results to demonstrate its capability.

Multi-input multi-output (MIMO) is a key technology to increase the capacity of wireless networks. Although there has been extensive work on MIMO at the physical and link layers, there is limited work on MIMO at the network layer (i.e., multi-hop MIMO network), particularly results on capacity scaling laws. In this paper, we investigate capacity scaling laws for MIMO ad hoc networks. Our goal is to find the achievable throughput of each node as the number of nodes in the network increases. We employ a MIMO network model that captures spatial multiplexing and interference cancellation. We show that for a MIMO network with n randomly located nodes, each equipped with α antennas and a rate of W on each data stream, the achievable throughput of each node is Θ(αW/√(n ln n)).

We study transmission strategies in a multiple-source, multiple-destination wireless network. Each source transmits packets that are intended for a particular destination. However, a transmitted packet can cause interference at other destinations. Our primary performance measure is throughput, which we define to be the average number of packets that are successfully received per intended destination per time slot. The sources are first divided into groups, based on the intended destination of their packets. In our parallel method, each group operates according to its own local protocol (e.g., TDMA), concurrently with and independently of the other groups. Our results show the impact of transmission schedules, channel fading, receiver noise, and other-user interference on network performance. We then show that, for given channel statistics and topology configurations, the network performance can be significantly improved when the groups in the network coordinate their transmissions according to an optimal schedule. Further, in many cases, even the use of randomly generated parallel schedules can provide considerably higher performance than traditional TDMA.

It has been shown that cooperative communications (CC) have the potential to significantly increase the capacity of wireless networks. However, most of the existing results are limited to single-hop wireless networks. To illustrate the benefits of CC in multi-hop wireless networks, we solve a joint optimization problem of relay node assignment and flow routing for concurrent sessions. We study this problem via mathematical modeling and solve it using a solution procedure based on the branch-and-cut framework. We design several novel components to speed-up the computation time of branch-and-cut. Via numerical results, we show the significant rate gains that can be achieved by incorporating CC in multi-hop networks.

Network coding (NC) is a promising approach to reduce time-slot overhead for cooperative communications (CC) in a multi-session environment. Most of the existing works take advantage of the benefits of NC in CC but do not fully recognize its potential adverse effect. In this paper, we show that employing NC may not always benefit CC. We substantiate this important finding in the context of analog network coding (ANC) and amplify-and-forward (AF) CC. This paper, for the first time, introduces an important concept of network coding noise (NC noise). Specifically, we analyze the signal aggregation at a relay node and signal extraction at a destination node. We then use the analysis to derive a closed-form expression for NC noise at each destination node in a multi-session environment. We show that NC noise can diminish the advantage of NC in CC. Our results formalizes an important concept on using NC in CC.

In this paper, we revisit the problem of determining the minimum-length schedule that satisfies certain traffic demands in a wireless network. Traditional approaches for the determination of minimum-length schedules are based on a collision channel model, in which neighboring transmissions cause destructive interference if and only if they are within the “interference region” of the receiving nodes. By contrast, we adopt here a more realistic model for the physical layer by requiring that a threshold be exceeded by the signal-to-interference-plus-noise ratio (SINR) for a transmission to be successful. We present a novel formulation of the problem that incorporates various power and rate adaptation schemes while seamlessly integrating the generation of “matchings” (i.e., sets of links that can be activated simultaneously) by taking into consideration the SINR constraints at the receivers. For the formulated problem, we propose a column-generation-based solution method and show that it theoretically converges to a globally optimal solution, with a potential advantage of not having to enumerate all the feasible matchings a priori. We also discuss the influence of power control, spatial reuse, and variable transmission rates on network performance. Furthermore, we include aspects of the routing problem and provide computational results for our proposed column-generation-based solution procedure.

We consider the problem of minimum-length scheduling of point-to-point links in a spatial TDMA (STDMA) based wireless network with Rayleigh fading of both desired and interference signals. The problem formulation integrates the activation of multiple sets of links in the network, while taking into account their explicit statistical variations. We assume uniform (fixed) transmission power at all nodes and propose an algorithm based on a column generation approach, which takes into consideration the signal-to-interference and noise ratio (SINR) constraints at the receivers in order to generate a link schedule that minimizes the schedule length. For the formulated problem, we show that this column generation based approach can converge to a globally optimal solution.

This paper tries to reconcile the tension between physical model and protocol model that have been used to characterize interference relationship in a multi-hop wireless network. The physical model (a.k.a. SINR model) is widely considered as a reference model for physical layer behavior but its application in multi-hop wireless networks is limited by its complexity. On the other hand, the protocol model (a.k.a. unified disk graph model) is simple but there have been doubts on its validity. This paper explores the following fundamental question: How to correctly use the protocol interference model? We show that in general, solutions obtained under the protocol model may be infeasible in practice and thus, results based on blind use of protocol model can be misleading. We propose a novel concept called "reality check" and present a method of using protocol model with reality check for wireless networks. Subsequently, we show that by appropriate setting of the interference range in the protocol model, it is possible to narrow the solution gap between the two models. Our simulation results confirm that this gap is indeed small (or even negligible). Thus, our methodology of joint reality check and interference range setting retains the protocol model as a viable approach to analyze multi-hop wireless networks.

Recently, cooperative communications, in the form of keeping each node with a single antenna and having a node exploit a relay node's antenna, is shown to be a promising approach to achieve spatial diversity. Under this communication paradigm, the choice of relay node plays a significant role in the overall system performance. In this paper, we study the relay node assignment problem in a network environment, where multiple source-destination pairs compete for the same pool of relay nodes in the network. The main contribution of this paper is the development of a polynomial time algorithm to solve this problem. A key idea in this algorithm is a "linear marking" mechanism, which is able to offer a linear complexity for each iteration. We give a formal proof of optimality for this algorithm. We also show several attractive properties associated with this algorithm.

Maximizing the total mutual information of multiuser multiple-input multiple-output (MIMO) systems with interference is a challenging problem. In this paper, we consider the power control problem of finding the maximum sum of mutual information for a multiuser network with mutually interfered MIMO links. We propose a new and powerful global optimization method using a branch-and-bound (BB) framework, coupled with a novel reformulation-linearization technique (RLT). The proposed BB/RLT guarantees finding a global optimum for multiuser MIMO networks with interference. To reduce the complexity of BB/RLT, we propose a modified BB variable selection strategy to accelerate the convergence process. Numerical examples are also given to demonstrate the efficacy of the proposed solution.

Researchers have recently shown that the dirty-paper coding (DPC) is the optimal transmission strategy for multiple-input multiple-output Gaussian broadcast channels (MIMO BC). Moreover, by the channel duality, the nonconvex MIMO BC sum rate problem can be transformed to the convex dual MIMO multiple-access channel (MIMO MAC) problem with a sum power constraint. In this paper, we design an efficient algorithm based on conjugate gradient projection (CGP) to solve the MIMO BC maximum sum rate problem. Our proposed CGP algorithm solves the dual sum power MAC problem by utilizing the powerful concept of Hessian conjugate. We also develop a rigorous algorithm to solve the projection problem. We show that CGP enjoys provable convergence, scalability, and efficiency for large MIMO BC systems.

In this paper, we consider the problem of optimal multipath routing for providing application performance guarantees in multi-hop wireless networks, using multiple description video streaming as our target application. We address this problem, which is shown to be NP-hard, with a novel reformulation-linearization technique (RLT) and branch-and-bound-based approach, and develop an algorithm that produces a pair of paths within the (1 - epsiv) range of the global optimum. The proposed algorithm is computationally efficient and this (1 - epsiv) optimal algorithm provides an elegant tradeoff between optimality and computational complexity.

Traditionally, routing is considered solely as a network layer problem and has been decoupled from application layer objectives. Although such an approach offers simplicity in the design of the protocol stack, it does not offer good performance for certain applications such as video. In this paper, we explore the problem of how to perform routing with the objective of optimizing application layer performance. Specifically, we consider how to perform multipath routing for multiple description (MD) video in a multi-hop wireless network. We formulate this problem into an optimization problem with application performance metric as the objective function and routing and link layer considerations as constraints. We develop a formal branch-and-bound framework and exploit the so-called reformulation-linearization technique (RLT) in the solution procedure. We show that this solution procedure is able to produce a set of routes whose objective value is within (1 - e) of the optimum. We use simulation results to substantiate the efficacy of the solution procedure and compare the performance with that under non-cross-layer approach.

Video communication is an important application area for a multihop wireless network. This paper studies the problem of finding the optimal encoding rates for a number of video sessions in such network. The objective is to maximize the video quality at the receivers and the optimization space takes into consideration, the interaction among all the active video sessions. A branch-and-bound solution procedure is proposed to solve this nonconvex, non-polynomial programming problem. Using analytical and simulation results, we show that this solution procedure is an effective approach for addressing such complex cross-layer optimization problem

Real-time multimedia communication is an important service that should be supported in wireless ad hoc networks, In this paper we consider the problem of how to optimally support multiple concurrent video communication sessions in an ad hoc network. Our problem formulation follows an application-centric cross-layer approach with the objective of minimizing the average distortion for all video sessions via finding optimal paths for each session. Since this network-wide optimization problem is shown to be NP-complete, we pursue to develop competitive heuristic algorithms to address this problem. We find that genetic algorithms (GA) are eminently efficient in solving such cross-layer problems with complex objective functions and constraints. We describe a detailed solution procedure based on the GA approach and use numerical results to demonstrate its superior performance over other conventional approaches. Our efforts in this work provide an important methodology for addressing cross-layer network-wide optimal routing problems for video applications.