This demo introduces WaveBox, a dynamic, software-defined waveform generation system developed to assess the resilience of communication networks against many types of interference scenarios. WaveBox features seamless waveform switching, allowing users to efficiently adjust interference patterns to adapt to diverse operational scenarios. We will showcase the system's effectiveness and versatility, highlighting its ability to adapt to evolving mission requirements. Additionally, the system's intuitive graphical user interface (GUI) supports rapid waveform adjustments, enhancing its responsiveness in dynamic environments. WaveBox can provide a flexible software-defined tool for evaluating the robustness of wireless systems.

Ensuring the safety and reliability of emerging Advanced Aerial Mobility (AAM) systems requires wireless communication to provide real-time monitoring and control information to ground stations. This greatly depends on the quality of the wireless links during aerial transit. In this demonstration, we present AirTwinX, designed to emulate flight control of flying vehicles while generating high-fidelity, context-aware models for air-to-air (AA) and air-to-ground (AG) communication links. Using GPU-accelerated ray tracing, AirTwinX can predict the quality of wireless links with near real-time updates based on the environmental geometry observed during flight. This data is then used to guide autonomous control decision-making. Additionally, the vehicle control toolchain employed in this work is based on software-in-the-loop (SITL) emulation of a commercial flight controller, enabling seamless translation of control policies from simulation to real-world hardware.

In highly dynamic and contested RF environments, communication systems must swiftly adapt to fluctuating spectral conditions while ensuring network quality of service (QoS). Maintaining link synchronization and spectral efficiency during waveform adaptation is particularly challenging due to the high mobility and autonomy of devices, coupled with the possibility of operating in GPS-denied environments. In this demo, we introduce a scalable, high-speed FPGA-based parallel decoding algorithm that leverages the HORNets signature adaptation protocol to address these challenges. Our solution preserves link synchronization within a multi-node network and enables efficient, lightweight waveform adaptation without reliance on GPS. The algorithm's resilience and effectiveness are demonstrated using a three-node cluster configuration, all subjected to non-colored or colored intentional interference.

In this work, we present a new federation framework for Union-Labs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiments. The framework aims to reduce the federation complexity for testbeds developers by automating tedious backend operations, thereby providing scalable federation and remote access to various wireless testbeds. We first describe the key components of the new federation framework, including the Systems Manager Integration Engine (SMIE), the Automated Script Generator (ASG), and the Database Context Manager (DCM). We then prototype and deploy the new Federation Plane on the Amazon Web Services (AWS) public cloud, demonstrating its effectiveness by federating two wireless testbeds: i) UB NeXT, a 5G-and-beyond (5G+) testbed at the University at Buffalo, and ii) UT IoT, an IoT testbed at the University of Utah1.

In this article we present a new RFID antenna design based on conductive thermal transfer printing. We first describe the key antenna design parameters, including antenna shape, dimensions, and number of lobes, among others. The desirable range of these parameters have been determined empirically through an extensive experimentation campaign for impedance matching. Then, we print the antenna using conductive Aluminum ribbon on PET coated paper. We evaluate the performance of the new antenna by comparing it with 13 existing benchmark antennas using a commercial RFID reader at 915 MHz. It is found that the new antenna design can achieve better range-angle trade-off than the benchmarks.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper, we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used to characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively kept below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

In this article we explore new techniques that can enable open remote experimentation for mobile networks. We first propose a cloud-based framework called CloudRAFT, based on which experimenters are allowed to remotely access and control experimental resources via public cloud AWS and share the resulting data and code via the cloud. Then, we discuss the enabling techniques for CloudRAFT, including Amazon serverless service, VNC-based remote command line, and Websocket-based real time communications, among others. Finally, we showcase the application of these techniques in enabling remote access to UB NeXT, a software-defined testbed that has been developed at University at Buffalo for wireless mobile network modeling, optimization and deployment. This work verifies the feasibility of accessing, controlling and sharing wireless testbeds through a remote public cloud.

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

We introduce RF-SITL, a radio frequency (RF) software-in-the-loop (SITL) channel emulator developed with GNU Radio and the University at Buffalo’s Airborne Networking and Communications (UB-ANC) emulator to enable integrated simulation of systems comprising multiple unmanned aerial vehicles (UAVs) interacting over a wireless communication channel. RF-SITL could be paired with any multi-robot simulator to enable I/Q sample-level fidelity simulation of communication interactions between the robots by accurately simulating channel effects, including interference, noise, distance-dependent path loss, and packet losses. RF-SITL works as follows: 1) it instantiates a virtual software-defined transceiver in GNU Radio for each UAV simulated in the UB-ANC Emulator; 2) it builds an interference channel model in which each network node receives the superposition of signals transmitted from other nodes; and 3) it synchronizes the location of each simulated UAV in the UB-ANC Emulator with the virtualized RF transceivers in RF-SITL, such that the communication channel between nodes can accurately model distance-dependent channel effects, such as path loss. With these capabilities, we can use both off-the-shelf and custom-built signal processing flowgraphs that simulate Gaussian Minimum Shift Keying (GMSK), 802.11-like Orthogonal Frequency Division Multiplexing (OFDM), and direct sequence spread-spectrum (DSSS) links in GNU Radio to simulate swarm UAV networks prior to their deployment in software-defined radios in a swarm UAV network.

Wireless Multimedia Sensor Network (WMSN) is increasingly being deployed for surveillance, monitoring and Internet-of-Things (IoT) sensing applications where a set of cameras capture and compress local images and then transmit the data to a remote controller. Such captured local images may also be compressed in a multi-view fashion to reduce the redundancy among overlapping views. In this paper, we present a novel paradigm for compressed-sensing-enabled multi-view coding and streaming in WMSN. We first propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. Then, we present a modeling framework that exploits the aforementioned coding architecture. The proposed mathematical problem minimizes the power consumption by jointly determining the encoding rate and multi-path rate allocation subject to distortion and energy constraints. Extensive performance evaluation results show that the proposed framework is able to transmit multi-view streams with guaranteed video quality at lower power consumption.

This article focuses on designing high-data-rate wireless communications for drone networks in the mmWave and terahertz (THz) frequency bands. MmWave/THz-band communications have been envisioned as key technologies to achieve ultra broadband wireless links through beamforming in 5G and beyond networks. However, a main challenge with these frequency bands is that the narrow-beam directional wireless links can be easily disconnected because of the beam misalignment in mobile environments. To address this challenge, in this article we design a new beam control scheme calledLeBeam, with the objective of maximizing the expected capacity of the mmWave/THz-band links by determining the optimal beamwidth dynamically under the mobility uncertainties of flying drones. InLeBeam, an Echo State Network (ESN) is adopted to capture the mobility uncertainties of the drones dynamically and predict the optimal beamwidth based on the first- and second-order moments of the drone mobility. The ESN has been trained based on real drone flight traces. To this end, we measure and analyze the mobility uncertainties of flying drones by carrying out a series of field experiments in different weather. It is found that flying drones experience micro-, small- and large-scale mobility uncertainties, and the resulting mobility behavior cannot be characterized with any existing statistical models. The performance ofLeBeamis evaluated over UBSim, a newly developed trace-driven Universal Broadband Simulator for integrated aerial and ground wireless networking. Results indicate that the micro-scale mobility has only negligible effects on the link capacity (less than 1 percent), while the wireless links may experience significant capacity degradation (over 50 percent on average) in the presence of small- and large-scale mobility uncertainties.

Terahertz (THz)-band communications have been envisioned as a key technology to support ultra-high-data-rate applications in 5G-beyond (or 6G) wireless networks. Compared to the microwave and mmWave bands, the main challenges with the THz band are in its i) large path loss hence limited network coverage and ii) visible-light-like propagation characteristics hence poor support of mobility in blockage-rich environments. This paper studies quantitatively the applicability of THz-band communications in blockage-rich mobile environments, focusing on a new network scenario calledFlyTera. InFlyTera, a set of hotspots mounted on flying drones collaboratively provide data streaming services to ground users, in the microwave, mmWave and THz bands. We first provide a mathematical formulation of theFlyTeracontrol problem, where the objective is to maximize the network spectral efficiency by jointly controlling the flight of the drone hotspots, their association to the ground users, and the spectrum bands used by the users. To solve the resulting problem, which is shown to be a mixed integer nonlinear nonconvex programming (MINLP) problem, we design distributed solution algorithms based on a combination of echo state learning and reinforcement learning. An extensive simulation campaign is then conducted with SimBAG, a newly developedSimulator ofBroadbandAerial-Ground wireless networks. It is shown that no single spectrum band can meet the requirements of high data rate and wide coverage simultaneously. Moreover, from the network-level point of view, THz-band communications can significantly benefit from the mobility of the flying drones, and on average$4 - 6$timeshigher (rather than lower)throughput can be achieved in mobile than in static environments.

This paper aims at designing high-data-rate swarm UAV networks with distributed beamforming capabilities. The primary challenge is that the beamforming gain in swarm UAV networks is highly affected by the UAVs’ flight altitude, their movements and the resulting intermittent link blockages, as well as the availability of channel state information (CSI) at individual UAVs. To address this challenge, we propose FlyBeam, a learning- based framework for joint flight and beamforming control in swarm UAV networks. We first present a mathematical formulation of the control problem with the objective of maximizing the throughput of swarm UAV networks by jointly controlling the flight and distributed beamforming of UAVs. Then, a distributed solution algorithm is designed based on a combination of Echo State Network learning and online reinforcement learning. The former is adopted to approximate the utility function for individual UAVs based on online measurements, by jointly considering the unknown blockage dynamics and other factors that affect the beamforming gain. The latter is used to guide the exploitation and exploration in FlyBeam. The effectiveness of FlyBeam is evaluated through an extensive simulation campaign. Results indicate that significant (up to 450%) beamforming gain can be achieved by FlyBeam. We also investigate the effects of blockages and UAV flight altitude on the beamforming gain. It is found that, which is somewhat surprising, higher (rather than lower) beamforming gain can be achieved by FlyBeam with denser blockages in swarm UAV networks.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

Designing efficient medium access control protocols for underwater acoustic sensor networks (UW-ASNs) is a major challenge because of the spatial and temporal interference uncertainty caused by asynchronous transmissions and by the low propagation speed of sound. To address these challenges, in this article we propose a new approach for distributed underwater medium access based on lightweight and asynchronous distributed algorithms that optimize the access probability profile over a series of time slots based on a new statistical physical interference model. The latter is based on measuring the level of interference at multiple instants of time in each time slot in order to capture the effects of temporal uncertainty and of unaligned interference. At each measurement instant, the statistical properties of time-varying interference are represented by a Gamma probability distribution. The model is validated through extensive channel measurement experiments conducted with an underwater acoustic testbed in Lake LaSalle. Based on this model, we formulate the problem of queue-aware stochastic channel access. The objective is to maximize the sum throughput of a set of concurrent and mutually interfering source-destination pairs by letting the transmitters adjust their own transmission probability profiles, without collaborating with each other, over a series of time slots based on a statistical characterization of interference obtained through past observations. We propose an iterative distributed solution algorithm for this problem based on a best-response strategy. At each iteration, each node individually solves a non-convex optimization problem of logarithmic complexity. The performance of the proposed distributed algorithm is evaluated by comparing it with two alternative distributed schemes and with the global optimum obtained through a newly-developed centralized globally optimal solution algorithm. Results indicate that by jointly taking the queueing and multi-slot optimization into consideration considerable improvement in terms of sum-throughput can be achieved by the proposed distributed algorithm.

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware. We first discuss the main architectural principles of WNOS. Then, we discuss a new approach to automatically generate solution algorithms for each of the resulting subproblems in an automated fashion. Finally, we illustrate a prototype implementation of WNOS on software-defined radio devices and test its effectiveness by considering specific cross-layer control problems. Experimental results indicate that, based on the automatically generated distributed control programs, WNOS achieves 18%, 56% and 80.4% utility gain in networks with low, medium and high levels of interference; maybe more importantly, we illustrate how the global network behavior can be controlled by modifying a few lines of code on a centralized abstraction.

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

This article studies an essential yet challenging problem in 5G wireless networks: Is it possible to enable spectrally-efficient spectrum sharing for heterogeneous wireless networks with different, possibly incompatible, spectrum access technologies on the same spectrum bands; without modifying the protocol stacks of existing wireless networks? To answer this question, this article explores the system challenges that need to be addressed to enable a new spectrum sharing paradigm based on beamforming, which we refer to as CoBeam. In CoBeam, a secondary wireless network is allowed to access a spectrum band based on cognitive beam-forming without mutual temporal exclusion, i.e., without interrupting the ongoing transmissions of coexisting wireless networks on the same bands; and without cross-technology communication. We first describe the main components of CoBeam, including programmable physical layer driver, cognitive sensing engine, and beamforming engine, and then we showcase the potential of the CoBeam framework by designing a practical coexistence scheme between Wi-Fi and LTE on unlicensed bands. We present a prototype of the resulting coexisting Wi-Fi/U-LTE network built on off-the-shelf software radios based on which we evaluate the performance of CoBeam through an extensive experimental campaign. Performance evaluation results indicate that CoBeam can achieve on average 169% throughput gain while requiring no signaling exchange between the coexisting wireless networks.

Terahertz (THz)-band communications has been envisioned as a key technology to support ultra-high-data-rate applications in 5G-beyond (or 6G) wireless networks. Compared to the microwave and mmWave bands, the main challenges with the THz band are in its i) large path loss hence limited network coverage and ii) visible-light-like propagation characteristics hence poor support of mobility in blockage-rich environments. This paper studies quantitatively the applicability of THz-band communications in mobile blockage-rich environments, focusing on a new network scenario called FlyTera. In FlyTera, a set of hotspots mounted on flying drones collaboratively provide data streaming services to ground users, in the microwave, mmWave and THz bands. We first provide a mathematical formulation of FlyTera, where the objective is to maximize the network spectral efficiency by jointly controlling the flight of the drone hotspots, their association to the ground users, and the spectrum bands used by the users. To solve the resulting problem, which is shown to be a mixed integer nonlinear nonconvex programming (MINLP) problem, we design distributed solution algorithms based on a combination of echo state learning and reinforcement learning techniques. An extensive simulation campaign is then conducted with SimBAG, a newly developed Simulator of Broadband Aerial-Ground wireless networks. It is shown that no single spectrum band can meet the requirements of high data rate and wide coverage simultaneously. Moreover, from the network-level point of view, THz-band communications can significantly benefit from the mobility of the flying drones, and on average 4 - 6 times higher (rather than lower) throughput can be achieved in mobile than in static environments.

Current cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors. Their configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from performing fine grained performance optimization, especially on a per-user basis. To address these key issues, this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention (“zero-touch”). CellOS leverages softwarization and automatic optimization principles to bridge Software-Defined Networking (SDN) and cross-layer optimization. Unlike state-of-the-art SDN-inspired solutions for cellular networking, CellOS: (i) Hides low-level network details through a general virtual network abstraction; (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices. CellOS has been implemented and evaluated on an indoor testbed with two different LTE-compliant implementations: OpenAirInterface and srsLTE. We further demonstrated CellOS capabilities on the long-range outdoor POWDER-RENEW PAWR 5G platform. Results from scenarios with multiple base stations and users show that CellOS is platform-independent and self-adapts to diverse network deployments. Our investigation shows that CellOS outperforms existing solutions on key metrics, including throughput (up to 86% improvement), energy efficiency (up to 84%) and fairness (up to 29%).

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Indoor Visible Light Communications (VLC) are a promising technology to alleviate the looming spectrum crunch crisis in traditional RF spectrum bands. This article studies how to provide throughput-optimal WiFi-like downlink access to users in indoor visible light networks through a set of centrally-controlled and partially interfering light emitting diodes (LEDs). To reduce the effect of interference among users created by the partial overlap of each LED's field of view, we propose LiBeam, a cooperative beamforming scheme, based on forming multiple LED clusters. Each cluster then serves a subset of users by jointly determining the user-LED association strategies and the beamforming vectors of the LEDs. The paper first proposes a mathematical model of the cooperative beamforming problem, presented as maximizing the sum throughput of all VLC users. Then, we solve the resulting mixed integer nonlinear nonconvex programming (MINCoP) problem by designing a globally optimal solution algorithm based on a combination of branch and bound framework as well as convex relaxation techniques. We then design for the first time a large programmable visible light networking testbed based on USRP X310 software-defined radios, and experimentally demonstrate the effectiveness of the proposed joint beamforming and association algorithm through extensive experiments. Performance evaluation results indicate that over 95% utility gain can be achieved compared to suboptimal network control strategies.

This article investigates the basic design principles for a new Wireless Network Operating System (WNOS), a radically different approach to software-defined networking (SDN) for infrastructure-less wireless networks. Departing from well-understood approaches inspired by OpenFlow, WNOS provides the network designer with an abstraction hiding (i) the lower-level details of the wireless protocol stack and (ii) the distributed nature of the network operations. Based on this abstract representation, the WNOS takes network control programs written on a centralized, high-level view of the network and automatically generates distributed cross-layer control programs based on distributed optimization theory that are executed by each individual node on an abstract representation of the radio hardware.

We study the problem of user cost minimization in mobile cloud computing (MCC) networks. We consider a MCC model where multiple brokers assign cloud resources to mobile users. The model is characterized by an heterogeneous cloud architecture (which includes a public cloud and a cloudlet) and by the heterogeneous pricing strategies of cloud service providers. In this setting, we investigate two classes of cloud reservation strategies, i.e., a competitive strategy, and a compete-then-cooperate strategy as a performance bound. We first study a purely competitive scenario where brokers compete to reserve computing resources from remote public clouds (which are affected by long delays) and from local cloudlets (which have limited computational resources but short delays). We provide theoretical results demonstrating the existence of disagreement points (i.e., the equilibrium reservation strategy that no broker has incentive to deviate unilaterally from) and convergence of the best-response strategies of the brokers to disagreement points. We then consider the scenario in which brokers agree to cooperate in exchange for a lower average cost of resources. We formulate a cooperative problem where the objective is to minimize the total average price of all brokers, under the constraint that no broker should pay a price higher than the disagreement price (i.e., the competitive price). We design new globally optimal solution algorithm to solve the resulting non-convex cooperative problem, based on a combination of the branch and bound framework and of advanced convex relaxation techniques. The resulting optimal solution provides a lower bound on the achievable user cost without complete collusion among brokers. Compared with pure competition, we found that (i) noticeable cooperative gains can be achieved over pure competition in markets with a few brokers only, and (ii) the cooperative gain is only marginal in crowded markets, i.e., with a high number of brokers, hence there is no clear incentive for brokers to cooperate.

In this paper, we design a novel multiview video encoding/decoding architecture for wirelessly multiview video streaming applications, e.g., 360 degrees video, Internet of Things (IoT) multimedia sensing, among others, based on distributed video coding and compressed sensing principles. Specifically, we focus on joint decoding of independently encoded compressively sampled multiview video streams. We first propose a novel side-information (SI) generation method based on a new interview motion compensation algorithm for multiview video joint reconstruction at the decoder end. Then, we propose a technique to fuse the received measurements with resampled measurements from the generated SI to perform the final recovery. Based on the proposed joint reconstruction method, we also derive a blind video quality estimation technique that can be used to adapt online the video encoding rate at the sensors to guarantee desired quality levels in multiview video streaming. Extensive simulation results of real multiview video traces show the effectiveness of the proposed fusion reconstruction method with the assistance of SI generated by an interview motion compensation method. Moreover, they also illustrate that the blind quality estimation algorithm can accurately estimate the reconstruction quality.

To cope with the increasing scarcity of spectrum resources, researchers have been working to extend LTE/LTE-A cellular systems to unlicensed bands, leading to so-called unlicensed LTE (U-LTE). However, this extension is by no means straightforward, primarily because the radio resource management schemes used by LTE and by systems already deployed in unlicensed bands are incompatible. Specifically, it is well known that coexistence with scheduled systems like LTE degrades considerably the throughput of Wi-Fi networks that are based on carrier-sense medium access schemes. To address this challenge, we propose for the first time a cognitive coexistence scheme to enable spectrum sharing between U-LTE and Wi-Fi networks, referred to as CU-LTE. The proposed scheme is designed to jointly determine dynamic channel selection, carrier aggregation and fractional spectrum access for U-LTE networks, while guaranteeing fair spectrum access for Wi-Fi based on a newly designed cross-technology fairness criterion. We first derive a mathematical model of the spectrum sharing problem for the coexisting networks; we then design a solution algorithm to solve the resulting fairness constrained mixed integer nonlinear optimization problem. The algorithm, based on a combination of branch and bound and convex relaxation techniques, maximizes the network utility with guaranteed optimality precision that can be set arbitrarily to 1 at the expense of computational complexity. Performance evaluation indicates that near-optimal spectrum access can be achieved with guaranteed fairness between U-LTE and Wi-Fi. Issues regarding implementation of CU-LTE are also discussed.

We study distributed queueing games in interference-limited wireless networks. We formulate the throughput maximization problem via distributed selection of users' transmission thresholds as a Nash Equilibrium Problem (NEP). We first focus on the solution analysis of the NEP and derive sufficient conditions for the existence and uniqueness of a Nash Equilibrium (NE). Then, we develop a general best-response-based algorithmic framework wherein the users can explicitly choose the degree of desired cooperation and signaling, converging to different types of solutions, namely: 1) a NE of the NEP when there is no cooperation among users and 2) a stationary point of the Network Utility Maximization (NUM) problem associated with the NEP, when some cooperation among the users in the form of (pricing) message passing is allowed. Finally, as a benchmark, we design a globally optimal but centralized solution method for the nonconvex NUM problem. Our experiments show that in many scenarios the sum-throughput at the NE of the NEP is very close to the global optimum of the NUM problem, which validates our noncooperative and distributed approach. When the gap of the NE from the global optimality is non negligible (e.g., in the presence of “high” coupling among users), exploiting cooperation among the users in the form of pricing enhances the system performance.

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

Most research in body area networks to date has focused on traditional RF wireless communications, typically along the body surface. However, the core challenge of enabling networked intra-body communications through body tissues is substantially unaddressed. RF waves are in fact known to suffer from high absorption and to potentially lead to overheating of human tissues. In this paper, we consider the problem of designing optimal network control algorithms for distributed networked systems of implantable medical devices wirelessly interconnected by means of ultrasonic waves, which are known to propagate better than radio-frequency electromagnetic waves in aqueous media such as human tissues. Specifically, we propose lightweight, asynchronous, and distributed algorithms for joint rate control and stochastic channel access designed to maximize the throughput of ultrasonic intra-body area networks under energy constraints. We first develop (and validate through testbed experiments) a statistical model of the ultrasonic channel and of the spatial and temporal variability of ultrasonic interference. Compared to in-air radio frequency (RF), human tissues are characterized by a much lower propagation speed, which further causes unaligned interference at the receiver. It is therefore inefficient to perform adaptation based on instantaneous channel state information (CSI). Based on this model, we formulate the problem of maximizing the network throughput by jointly controlling the transmission rate and the channel access probability over a finite time horizon based only on a statistical characterization of interference. We then propose a fully distributed solution algorithm, and through both simulation and testbed results, we show that the algorithm achieves considerable throughput gains compared with traditional algorithms.

Denial-of-service attacks launched by malicious jammers can pose significant threats to infrastructure-less wireless networks without a centralized controller. While significant recent research efforts have dealt with such attacks and several possible countermeasures have been proposed, little attention has been paid to the idea of cooperative anti-jamming. Inspired by this observation, this paper proposes and studies a cooperative anti-jamming scheme designed to enhance the quality of links degraded by jammers. To achieve this objective, users are allowed to cooperate at two levels. First, they cooperate to optimally regulate their channel access probabilities, so that jammed users gain a higher share of channel utilization. Second, users leverage multiple-input single-output cooperative communication techniques to enhance the throughput of jammed links. The problem of optimal cooperative anti-jamming is formulated as a distributed pricing-based optimization problem, and a best-response algorithm is proposed to solve it in a distributed way. Simulations demonstrate that the proposed algorithm achieves considerable gains (compared with traditional non-cooperative anti-jamming) especially under heavy traffic or high jamming power. Furthermore, the proposed distributed algorithm is shown to achieve close-to-global optimality with moderate traffic load.

We consider the problem of designing optimal network control algorithms for distributed networked systems of implantable medical devices wirelessly interconnected by means of ultrasonic waves, which are known to propagate better than radio-frequency electromagnetic waves in aqueous media such as human tissues. Specifically, we propose lightweight, asynchronous, and distributed algorithms for joint rate control and stochastic channel access designed to maximize the throughput of ultrasonic intra-body area networks under energy constraints. We first develop (and validate through testbed experiments) a statistical model of the ultrasonic channel and of the spatial and temporal variability of ultrasonic interference. Compared to in-air radio frequency (RF), human tissues show a much lower propagation speed, which further causes unaligned interference at the receiver. It is therefore inefficient to perform adaptation based on instantaneous channel state information (CSI). Based on this model, we formulate the problem of maximizing the network throughput by jointly controlling the transmission rate and the channel access probability over a finite time horizon based only on a statistical characterization of interference. We then propose a fully distributed solution algorithm, and through both simulation and testbed results, we show that the algorithm achieves considerable throughput gains compared with traditional algorithms.

Multi-view wireless video streaming has the potential to enable a new generation of efficient and low-power pervasive surveillance systems that can capture scenes of interest from multiple perspectives, at higher resolution, and with lower energy consumption. However, state-of-the-art multi-view coding architectures require relatively complex predictive encoders, thus resulting in high processing complexity and power requirements. To address these challenges, we consider a wireless video surveillance scenario and propose a new encoding and decoding architecture for multi-view video systems based on Compressed Sensing (CS) principles, composed of cooperative sparsity-aware block-level rate-adaptive encoders, feedback channels and independent decoders. The proposed architecture leverages the properties of CS to overcome many limitations of traditional encoding techniques, specifically massive storage requirements and high computational complexity. It also uses estimates of image sparsity to perform efficient rate adaptation and effectively exploit inter-view correlation at the encoder side.

The hundreds of millions of deployed smartphones provide an unprecedented opportunity to collect data to monitor, debug, and continuously adapt wireless networks to improve performance. In contrast with previous mobile devices, such as laptops, smartphones are always on but mostly idle, making them available to perform measurements that help other nearby active devices make better use of available network resources. We present the design of PocketSniffer, a system delivering wireless measurements from smartphones both to network administrators for monitoring and debugging purposes and to algorithms performing realtime network adaptation. By collecting data from smartphones, PocketSniffer supports novel adaptation algorithms designed around common deployment scenarios involving both cooperative and self-interested clients and networks. We present preliminary results from a prototype and discuss challenges to realizing this vision.

Denial-of-service (DoS) attacks launched by malicious jammers can pose significant threats to infrastructure-less wireless networks where a centralized controller may not be available. While significant recent research efforts have dealt with such attacks and several possible countermeasures have been proposed, little attention has been paid to the idea of cooperative anti-jamming. Inspired by this observation, we propose and study a cooperative anti-jamming scheme designed to enhance the quality of links degraded by jammers. To achieve this objective, users are allowed to cooperate at two levels. First, they cooperate to optimally regulate their channel access probabilities so that jammed users gain a higher share of channel utilization. Second, users leverage multiple-input single-output cooperative communication techniques to enhance the throughput of jammed links. We formulate the problem of optimal cooperative anti-jamming as a distributed pricing-based optimization problem and propose a best response algorithm to solve it in a distributed way. Simulations demonstrate that the proposed algorithm achieves considerable gains (compared to traditional noncooperative antijamming) especially under heavy traffic or high jamming power. Furthermore, by comparing the proposed algorithm with a provably-optimal centralized algorithm, we show that it achieves close-to-global optimality under moderate traffic load.

We study distributed queueing games in interference-limited ad-hoc wireless networks. We formulate system design as a Nash Equilibrium (NE) problem, where the users aim at maximizing their own throughput by choosing the optimal transmission threshold. We first derive conditions for the existence and uniqueness of the NE; then we propose a distributed best-response algorithm solving the game along with its convergence properties. A second contribution of the paper is to develop a Branch and Bound-based (centralized) algorithm solving the associated (nonconvex) social problem, which one can use as benchmark to evaluate the performance of the proposed game theoretical formulation. Interestingly, our numerical results show that the sum-throughput achievable at the NE of the proposed game are very close to that of the social problem, which validates our game theoretical formulation. The performance loss is not negligible only in high interference scenarios. For such cases, we proposed a pricing-based algorithm yielding sum-throughput solutions very close to the globally optimal ones, at the cost of very limited signaling among the users.

We design a video coding and decoding framework for multi-view video systems based on compressed sensing imaging principles. Specifically, we focus on joint decoding of independently encoded compressively-sampled multi-view video streams. We first propose a novel distributed coding/decoding architecture designed to leverage inter-view correlation through joint decoding of the received compressively-sampled frames. At the encoder side, we select one view (referred to as K-view) as a reference for the other views (referred to as CS-views). The video frames of the CS-view are encoded and transmitted at a lower measurement rate than those of the selected K-view. At the decoder side, we generate side information to decode the CS-views as follows. First, each K-view frame is down-sampled and reconstructed, and then compared with the initially reconstructed CS-view frame to obtain an estimate of the inter-view motion vector. The original CS-view measurements are then fused with the generated side image to reconstruct the CS-view frame through a newly designed algorithm that operates in the measurement domain. We also propose a blind video quality estimation method that can be used within the proposed framework to design channel-adaptive rate control algorithms for quality-assured multi-view video streaming. We extensively evaluate the proposed scheme using real multi-view video traces. Results indicate that up to 1.6 dB improvement in terms of PSNR can be achieved by the proposed scheme compared with traditional independent decoding of CS frames.

Physical-layer cooperation allows leveraging the spatial diversity of wireless channels without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical-layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. A global optimization algorithm based on the branch and bound framework and on convex relaxation of nonconvex constraints is then proposed to solve the problem. The proposed algorithm can provide a theoretical upper bound on the achievable video quality and is shown to provably converge to the optimal solution. In addition, it is shown that cooperative relaying allows nodes to save energy without leading to a perceivable decrease in video quality. Based on this observation, an uncoordinated, distributed, and localized low-complexity algorithm is designed, for which we derive conditions for convergence to a Nash equlibrium (NE) of relay selection. The distributed algorithm is also shown to achieve performance comparable in practice to the optimal solution.

The problem of optimal resource allocation to share high-quality multimedia content in cognitive ad hoc networks with cooperative relays is addressed in this paper. Cooperative transmission is a promising technique to increase the capacity of wireless links by exploiting spatial diversity without multiple antennas at each node. However, mainstream research in this field focuses on optimizing physical layer performance measures, with little consideration for application-specific and network-wide performance measures. In this paper, the problem of joint video encoding rate control, power control, relay selection and channel assignment is formulated as a mixed-integer nonlinear problem(MINLP), and a solution algorithm based on a combination of the branch and bound framework and convex relaxation techniques is then proposed. The proposed solution jointly allocates channel, power, video encoding rate, and relay nodes for secondary users to maximize the video quality under the constraints posed by delay-sensitive video applications. Performance evaluation results show that cognitive networks with cooperative relaying can provide considerably higher video quality (in terms of the average peak signal-to-noise ratio (PSNR)) than solutions that do not rely on cooperation or without dynamic spectrum allocation.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign (i) portions of the spectrum and (ii) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate.

Physical-layer cooperation allows leveraging the spatial diversity of the wireless channel without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint video rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. An asynchronous, distributed and localized low-complexity algorithm is designed, based on the iterative solution of convex optimization problems at each individual node. In addition, a global-optimization centralized algorithm based on convex relaxations of non-convex constraints is also proposed as performance benchmark. The distributed algorithm is shown to achieve performance within a few percentage points of the optimal solution. It is also shown that cooperative relaying allows nodes to reduce the overall power consumption without leading to a perceivable decrease in video quality.

This paper focuses on using the mindset of co-opetition for collaborative multimedia resource allocation. The co-opetition suggests a judicious mixture of competition and cooperation. We present a novel co-opetition strategy based on the Kalai-Smorodinsky bargaining solution (KSBS), and apply it to video rate allocation. The proposed strategy makes satisfied users stop competing for resources such that QoS of unsatisfied users can be improved. Our strategy is evaluated through comparing to existing competition-based strategies. Numerical results indicate that, the co-opetition strategy can result in an improved number of satisfied users. Algorithm with low complexity is also presented.

Power control is critical for wireless communications that allow spectrum sharing among secondary users and primary users. In this paper, we derive an optimal distributed power control strategy aiming at the total capacity maximization of secondary network with interference constraints to primary users. Due to the nonconvexity of system utility, geometric programming is introduced to transform nonconvex optimization problems into convex optimization problems. Furthermore, system utility is usually coupled which means each utility depends not only on its local variables but also on the variables of other utilities. We introduce auxiliary variables and extra equality constraints to transfer the coupling in utility to coupling in constraints. The solution of the proposed power control strategy is shown to be globally optimal and leads to excellent performance.

This paper presents a novel coopetition paradigm based on bargaining theory for collaborative multimedia resource management. The paradigm consists of a judicious mixture of competition and cooperation. For competition, the well-known Kalai-Smorodinsky Bargaining Solution (KSBS) is adopted as the fairness criteria, and for cooperation each user stops competing for resources as long as it achieves a predefined threshold of Quality o Service (QoS). We apply the propose paradigm to rate allocation amongst multiple video users and compare its performance to other two schemes, traditional KSBS, and generalized KSBS for similar video quality. Results indicate that our paradigm adapts the best to the variation of resources as well as the user number. Also, importantly, our paradigm can result in an improved number of satisfied users while simultaneously avoid penalizing same users in the case of scarce resources. Complexity of the proposed paradigm is also analyzed.