This paper investigates the performance of downlink non-orthogonal multiple access (NOMA) communication in unmanned aerial vehicle (UAV) networks enhanced by partitionable reconfigurable intelligent surfaces (RISs). We analyze three types of links between base station (BS) and UAVs: direct, RIS-only indirect, and composite links, under both Line-of-Sight (LoS) and Non-LoS (NLoS) propagation. The RIS-only indirect link and direct link are modeled using double Nakagami-m and Nakagami-m fading, respectively, while the composite link follows a combined fading channel model. Closed-form expressions for the cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) are derived for all links, enabling tractable outage probability analysis. Then, we formulate a fairness-efficiency bilevel optimization problem to minimize the maximum outage probability among UAVs while minimizing the total number of required RIS reflecting elements. Accordingly, an RIS-assisted UAV Outage Minimization (RUOM) algorithm is proposed, which fairly allocates the NOMA power coefficients while minimizing the total number of RIS reflecting elements required, subject to NOMA-defined constraints, RIS resource limitations, and maximum allowable outage threshold. Simulation results validate the analytical models and demonstrate that the proposed RUOM algorithm significantly improves fairness and efficiency in BS-UAV communication.

In this paper, we propose an Expectation-Maximization-based (EM) Personalized Federated Learning (PFL) framework for multi-objective optimization (MOO) in Integrated Sensing and Communication (ISAC) systems. In contrast to standard federated learning (FL) methods that handle all clients uniformly, the proposed approach enables each base station (BS) to adaptively determine its aggregation weight with the EM algorithm. Specifically, an EM posterior is computed at each BS to quantify the relative suitability between the global and each local model, based on the losses of models on their respective datasets. The proposed method is especially valuable in scenarios with competing communication and sensing objectives, as it enables BSs to dynamically adapt to application-specific trade-offs. To assess the effectiveness of the proposed approach, we conduct simulation studies under both objective-wise homogeneous and heterogeneous conditions. The results demonstrate that our approach outperforms existing PFL baselines, such as FedPer and pFedMe, achieving faster convergence and better multi-objective performance.

Millimeter Wave (mmWave) communication technologies have the potential to establish high data rates for next-generation wireless networks, as well as enable novel applications that were previously untenable due to high throughput requirements. Yet, reliable and efficient mmWave communication remains challenged by intermittent links due to user mobility and frequent line-of-sight blockage. These factors are further exacerbated in multi-user settings where beam alignment overhead, limited RF chains, and heterogeneous user requirements must be balanced. In this paper, we present a hybrid multi-user scheduling solution that jointly accounts for mobility- and blockage-induced unavailability to enhance user experience in mmWave video streaming applications. Our approach integrates two key components: (i) a blockage-aware scheduling strategy modeled via a Restless Multi-Armed Bandit (RMAB) formulation and prioritized using Whittle Indexing, and (ii) a mobility-aware geometric model that estimates beam alignment overhead as a function of receiver motion. We develop a comprehensive and efficient index-based scheduler that fuses these models and leverages contextual information, such as receiver distance, mobility history, and queue state, to schedule multiple users in order to maximize throughput. Simulation results demonstrate that our approach reduces queue backlogs and improves fairness compared to round-robin and traditional index-based baselines.

In this paper, we study the problem of distributed transmission control and video streaming optimization for unmanned aerial vehicles (UAVs) operating in unlicensed spectrum bands. We develop a rigorous cross-layer analysis framework thatjointlyconsiders three inter-dependent factors: (i) in-band interference introduced by ground-aerial nodes at the physical (PHY) layer, (ii) limited-size queues with delay-constrained packet arrival at the medium access control (MAC) layer, and (iii) video encoding rate at the application layer. First, we formulate an optimization problem to maximize the average throughput by optimizing the fading threshold (transmission policy). To this end, we jointly analyze the queue-related packet loss probabilities (i.e., buffer overflow and time threshold event) as well as the outage probability due to the low signal-to-interference-plus-noise ratio (SINR). We introduce the Distributed Transmission Control (DTC) algorithm that maximizes the average throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. Second, we incorporate the video distortion model to develop distributed peak signal-to-noise ratio (PSNR) optimization for video streaming. The formulated optimization incorporates two cross-layer parameters, specifically the fading threshold and video encoding rate. To tackle this problem, we develop the Joint Distributed Video Transmission and Encoder Control (JDVT-EC) algorithm that enhances the average PSNR for all nodes by fine-tuning transmission policies and video encoding rates to balance the trade-offs between packet loss and lossy video compression distortions. Through extensive numerical analysis, we thoroughly examine the proposed algorithms and demonstrate that they are able to find the optimal transmission policies and video encoding rates under various scenarios. Notably, our approach improves the average throughput by 1.7% to 51.65% compared to various baselines, including the selfish and random policies. Additionally, we demonstrate an average PSNR increase of 0.24 dB and 1.7 dB compared to separately optimizing the fading threshold and video encoding rate, respectively.

Traditional Federated Learning (FL) approaches often struggle with data heterogeneity across clients, leading to suboptimal model performance for individual clients. To address this issue, Personalized Federated Learning (PFL) emerges as a solution to the challenges posed by non-independent and identically distributed (non-IID) andunbalanced dataacross clients. Furthermore, in most existing decentralized machine learning works, a perfect communication channel is considered for model parameter transmission between clients and servers. However, decentralized PFL over wireless links introduces new challenges, such as resource allocation and interference management. To overcome these challenges, we formulate a joint optimization problem that incorporates the underlying device-to-device (D2D) wireless channel conditions into a server-free PFL approach. The proposed method, dubbed pFedWN, optimizes the learning performance for each client while accounting for the variability in D2D wireless channels. To tackle the formulated problem, we divide it into two sub-problems: PFL neighbor selection and PFL weight assignment. The PFL neighbor selection is addressed through channel-aware neighbor selection within unlicensed spectrum bands such as Industrial, Scientific, and Medical (ISM) bands. Next, to assign PFL weights, we utilize the Expectation-Maximization (EM) method to evaluate the similarity between clients’ data and obtain optimal weight distribution among the chosen PFL neighbors. Empirical results show that pFedWN provides efficient and personalized learning performance with non-IID and unbalanced datasets. Furthermore, it outperforms the existing FL and PFL methods in terms of learning efficacy and robustness, particularly under dynamic and unpredictable wireless channel conditions.

Mobile edge devices (e.g., AR/VR headsets) typically need to complete timely inference tasks while operating with limited on-board computing and energy resources. In this paper, we investigate the problem of collaborative inference in wireless edge networks, where energy-constrained edge devices aim to complete inference tasks within given deadlines. These tasks are carried out using neural networks, and the edge device seeks to optimize inference performance under energy and delay constraints. The inference process can be split between the edge device and an edge server, thereby achieving collaborative inference over wireless networks. We formulate an inference utility optimization problem subject to energy and delay constraints, and propose a novel solution called Bayes-Split-Edge, which leverages Bayesian optimization for collaborative split inference over wireless edge networks. Our solution jointly optimizes the transmission power and the neural network split point. The Bayes-Split-Edge framework incorporates a novel hybrid acquisition function that balances inference task utility, sample efficiency, and constraint violation penalties. We evaluate our approach using the VGG19 model on the ImageNet-Mini dataset, and Resnet101 on Tiny-ImageNet, and real-world mMobile wireless channel datasets. Numerical results demonstrate that Bayes-Split-Edge achieves up to 2.4× reduction in evaluation cost compared to standard Bayesian optimization and achieves near-linear convergence. It also outperforms several baselines, including CMA-ES, DIRECT, exhaustive search, and Proximal Policy Optimization (PPO), while matching exhaustive search performance under tight constraints. These results confirm that the proposed framework provides a sample-efficient solution requiring maximum 20 function evaluations and constraint-aware optimization for wireless split inference in edge computing systems.

We investigate multitask edge-user communication-computation resource allocation for$360^\circ$video streaming in an edge-computing enabled millimeter wave (mmWave) multi-user virtual reality system. To balance the communication-computation trade-offs that arise herein, we formulate a video quality maximization problem that integrates interdependent multitask/multi-user action spaces and rebuffering time/quality variation constraints. We formulate a deep reinforcement learning framework formulti-taskrate adaptation andcomputation distribution (MTRC) to solve the problem of interest. Our solution does not rely on a priori knowledge about the environment and uses only prior video streaming statistics (e.g., throughput, decoding time, and transmission delay), and content information, to adjust the assigned video bitrates and computation distribution, as it observes the induced streaming performance online. Moreover, to capture the task interdependence in the environment, we leverage neural network cascades to extend our MTRC method to two novel variants denoted as R1C2 and C1R2. We train all three methods with real-world mmWave network traces and$360^\circ$video datasets to evaluate their performance in terms of expected quality of experience (QoE), viewport peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. We outperform state-of-the-art rate adaptation algorithms, with C1R2 showing best results and achieving$5.21-6.06$dB PSNR gains,$2.18-2.70$x rebuffering time reduction, and$4.14-4.50$dB quality variation reduction.

We consider a multi-user joint rate adaptation and computation distribution problem in a millimeter wave (mmWave) virtual reality (VR) system. The VR system that we consider comprises an edge computing unit (ECU) that serves 360° videos to VR users. We formulate a multi-user quality of experience (QoE) maximization problem, in which VR users are assisted with the ECU to decode/render 360° videos. The ECU provides additional computational resources that can be used for processing video frames, at the expense of increased data volume and required bandwidth. To balance this trade-off, we leverage deep reinforcement learning (DRL) for joint rate adaptation and computational resource allocation optimization. Our proposed method, dubbed Deep VR, does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train Deep VR with real-world mmWave network traces and 360° video datasets to obtain evaluation results in terms of the average QoE, peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the Deep VR improves the users’ QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 3.08 dB to 4.49 dB improvement in video quality in terms of PSNR, a 12.5x to 14x reduction in rebuffering time, and a 3.07 dB to 3.96 dB improvement in quality variation.

Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy concerns. The Federated Vehicular Network (FVN) is a promising technology that tackles these concerns by transmitting model parameters instead of raw data, thereby protecting the privacy of drivers. Nevertheless, the performance of Federated Learning (FL) in a vehicular network depends on the joining ratio, which is restricted by the limited available wireless resources. To address these challenges, this paper proposes to apply Non-Orthogonal Multiple Access (NOMA) to improve the joining ratio in a FVN. Specifically, a vehicle selection and transmission power control algorithm is developed to exploit the power domain differences in the received signal to ensure the maximum number of vehicles capable of joining the FVN. Our simulation results demonstrate that the proposed NOMA-based strategy increases the joining ratio and significantly enhances the performance of the FVN. Index Terms—Federated Vehicular Network, NOMA

In this paper, we investigate the problem of distributed transmission control for unmanned aerial vehicles (UAVs) operating in unlicensed spectrum bands. We develop a rigorous interference-aware queuing analysis framework that jointly considers two inter-dependent factors: (i) limited-size queues with delay-constrained packet arrival, and (ii) in-band interference introduced by other ground/aerial users. We aim to optimize the expected throughput by jointly analyzing these factors. In the queuing analysis, we explore two packet loss probabilities including, buffer overflow model and time threshold model. For interference analysis, we investigate the outage probability and packet losses due to low signal-to-interference-plus-noise ratio (SINR). We introduce two algorithms namely, Interference-Aware Transmission Control (IA-TC), and Interference-Aware Distributed Transmission Control (IA-DTC). These algorithms maximize the expected throughput by adjusting transmission policies to balance the trade-offs between packet drop from queues vs. transmission errors due to low SINRs. We implement the proposed algorithms and demonstrate that the optimal transmission policy under various scenarios is found.

Renewable energy sources, such as wind and solar power, are increasingly being integrated into smart grid systems. However, when compared to traditional energy resources, the unpredictability of renewable energy generation poses significant challenges for both electricity providers and utility companies. Furthermore, the large-scale integration of distributed energy resources (such as photovoltaics (PV) systems) creates new challenges for energy management in microgrids. To tackle these issues, we consider a framework with two objectives: (i) combating uncertainty of renewable energy in smart grid by leveraging time-series forecasting with Long-Short Term Memory (LSTM) solutions, and (ii) establishing distributed and dynamic decision-making framework with multi-agent reinforcement learning (RL) with uncertain and imperfect state information. The proposed framework addresses these objectives while considering both wholesale and retail markets, thereby enabling efficient energy management in the presence of uncertain and distributed renewable energy sources. Through extensive numerical simulations based on the Deep Deterministic Policy Gradient (DDPG) RL algorithm, we demonstrate that the proposed solution significantly improves the profit of load serving entities (LSE) by providing a more accurate wind generation forecast. Furthermore, our results demonstrate that the households with PV and battery installations can increase their profits by using intelligent battery charge/discharge actions determined by the DDPG agents.

In this paper, we consider a task offloading problem in a multi-access edge computing (MEC) network, in which edge users can either use their local processing unit to compute their tasks or offload their tasks to a nearby edge server through multiple communication channels each with different characteristics. The main objective is to maximize the energy efficiency of the edge users while meeting computing tasks deadlines. In the multi-user multi-channel offloading scenario, users are distributed with partial observations of the system states. We formulate this problem as a stochastic optimization problem and leverage contextual neural multi-armed bandit models to develop an energy-efficient deadline-aware solution, dubbed E2DA. The proposed E2DA framework only relies on partial state information (i.e., computation task features) to make offloading decisions. Through extensive numerical analysis, we demonstrate that the E2DA algorithm can efficiently learn an offloading policy and achieve close-to-optimal performance in comparison with several baseline policies that optimize energy consumption and/or response time. Furthermore, we provide a comprehensive set of results on the MEC system performance for various applications such as augmented reality (AR) and virtual reality (VR).

In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users to opportunistically utilize detected spectrum holes. To this end, we propose a multi-class classification problem for wideband spectrum sensing to detect vacant spectrum spots based on collected I/Q samples. To enhance the accuracy of the spectrum sensing module, the outputs from the multi-class classification by each individual UAV are fused at a server in the unmanned aircraft system traffic management (UTM) ecosystem. In the spectrum scheduling phase, we leverage reinforcement learning (RL) solutions to dynamically allocate the detected spectrum holes to the secondary users (i.e., UAVs). To evaluate the proposed methods, we establish a comprehensive simulation framework that generates a near-realistic synthetic dataset using MATLAB LTE toolbox by incorporating base-station (BS) locations in a chosen area of interest, performing ray-tracing, and emulating the primary users channel usage in terms of I/Q samples. This evaluation methodology provides a flexible framework to generate large spectrum datasets that could be used for developing ML/AI-based spectrum management solutions for aerial devices.

Millimeter wave (mmWave) communication has the potential to achieve very high data rates but is highly vulnerable to blockage. In this paper, we provision an integrated mmWavesub-6 GHz architecture to combat blockage and intermittent connectivity of the mmWave communications. To this end, we model the mmWave channel as a two-state Markov channel and investigate the problem of scheduling packets across the mmWave and sub-6 GHz interfaces such that the long-term average delay of system is minimized. We prove that the optimal policy is of a threshold-type with state-dependent thresholds, i.e., packets should always be routed to the mmWave interface as long as the number of packets in the system is smaller than the state-dependent threshold. Numerical results demonstrate that under heavy traffic, integrating sub-6 GHz with mmWave can reduce the average delay by over 70%. Moreover, considering the difficulty of tracking the mmWave channel state in practice, we develop heuristics of substituting a single fixed threshold (state-independent) for two state-dependent thresholds. Our simulation results indicate that the replacement only incurs a slight increase in average delay. Moreover, when system parameters are not known, we propose a certainty-equivalence threshold-based learning algorithm, and provide an upper bound on its regret.

In this paper, we consider the multi-user scheduling problem in millimeter wave (mmWave) video streaming networks, which comprises a streaming server and several users, each requesting a video stream with a different resolution. The main objective is to optimize the long-term average quality of experience (QoE) for all users. We tackle this problem by considering the physical layer characteristics of the mmWave network, including the beam alignment overhead due to pencil-beams. To develop an efficient scheduling policy, we leverage the contextual multi-armed bandit (MAB) models to propose a beam alignment overhead and buffer predictive streaming solution, dubbed B2P-Stream. The proposed B2P-Stream algorithm optimally balances the trade-off between the overhead and users’ buffer levels, and improves the QoE by reducing the beam alignment overhead for users of higher resolutions. We also provide a theoretical guarantee for our proposed method and prove that it guarantees a sub-linear regret bound. Finally, we examine our proposed framework through extensive simulations. We provide a detailed comparison of the B2P-Stream against a uniformly random and Round-robin (RR) policies and show that it outperforms both of them in providing a better QoE and fairness. We also analyze the scalability and robustness of the B2P-Stream algorithm with different network configurations.

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale. A key challenge in providing this capability is the requirement for judicious management of the heterogeneous communication and computation resources that exist at the edge to meet processing needs. In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing. Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices. We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a “best-effort” at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient. As a component of these two methods, we develop a novel coordinated beamforming algorithm which we show obtains the optimal beamformer for a common receiver characteristic. Through numerical experiments, we find that our methodology yields substantial improvements in network overhead compared with local computation and partially optimized methods, which validates our joint optimization approach. Further, we find that the efficient alternate optimization scales well with the number of nodes, and thus can be a practical solution for D2D computing in large networks.

Fundamental requirements of mmWave systems are peak data rates of multiple Gbps and latencies of the order of at most a few milliseconds. However, highly directional mmWave links are susceptible to frequent link failures under stress conditions such as mobility and human blockage. Under these conditions, multi-hop routing can achieve reliable and robust performance. In this paper, we consider multi-hop millimeter wave (mmWave) wireless systems and propose proactive route refinement schemes that are particularly important under dynamic scenarios. First, we consider the AODV-type protocols and propose a cross-layer approach that integrates sectorized communication at the MAC layer with on-demand multi-hop routing at the network layer. Next, we consider Backpressure routing protocol, and enhance this protocol with periodic HELLO status messages. System-level simulation results based on the IEEE 802.11ad standard are provided that confirm the benefits of proactive route refinement for the ADOV-type and Backpressure routing protocols.

Fundamental requirements of millimeter wave (mmWave) systems are peak data rates of multiple Gbps and latencies of the order of at most a few milliseconds. However, highly directional mmWave links are susceptible to frequent link failures under stress conditions such as mobility and human blockage. Under these conditions, multi-hop routing can achieve reliable and robust performance. In this paper, we consider multi-hop mmWave systems and propose proactive route refinement schemes under dynamic scenarios. First, we consider the AODV-type protocols and propose a cross-layer approach that integrates sectorized communication at the MAC layer with on-demand multi-hop routing at the network layer. Next, we consider Backpressure routing protocol, and enhance this protocol with periodic HELLO status messages. System-level simulation results based on the IEEE 802.11ad standard confirm the benefits of proactive route refinement for the ADOV-type and Backpressure routing protocols.

We investigate the problem of delay-efficient and reliable data delivery in ultra-dense networks (UDNs) that constitute macro base stations (MBSs), small base stations (SBSs), and mobile users. Considering a two-hop data delivery system, we propose a partial decode-and-forward (PDF) relaying strategy together with a simple and intuitive amicable encoding scheme for rateless codes to significantly improve user experience in terms of end-to-end delay. Simulation results verify that our amicable encoding scheme is efficient in improving the intermediate performance of rateless codes. It also verifies that our proposed PDF significantly improves the performance of the decode-and-forward (DF) strategy, and that PDF is much more robust against channel degradation. Overall, the proposed strategy and encoding scheme are efficient towards delay-sensitive data delivery in the UDN scenarios.

In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. For distributed computing, resource management is challenging due to limited network and communication resources, leading to inter-channel interference. To overcome this, recent research has addressed the problems of wireless scheduling, subchannel allocation, power allocation, and multiple-input multiple-output (MIMO) signal design, but has not considered them jointly. In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency. Given that the resulting problem is a non-convex mixed integer program (MIP) which is prohibitive to solve at scale, we decompose its solution into two parts: (i) a resource allocation subproblem, which optimizes the link selection and subchannel allocations, and (ii) MIMO signal design subproblem, which optimizes the transmit beamformer, transmit power, and receive combiner. Simulation results using wireless edge topologies show that our method yields substantial improvements in energy efficiency compared with cases of no offloading and partially optimized methods and that the efficiency scales well with the size of the network.

Millimeter wave (mmWave) technologies have the potential to achieve very high data rates, but suffer from intermittent connectivity. In this paper, we provision an architecture to integrate sub-6 GHz and mmWave technologies, where we incorporate the sub-6 GHz interface as a fallback data transfer mechanism to combat blockage and intermittent connectivity of the mmWave communications. To this end, we investigate the problem of scheduling data packets across the mmWave and sub-6 GHz interfaces such that the average delay of system is minimized. This problem can be formulated as Markov Decision Process. We first investigate the problem of discounted delay minimization, and prove that the optimal policy is of the threshold-type, i.e., data packets should always be routed to the mmWave interface as long as the number of packets in the system is smaller than a threshold. Then, we show that the results of the discounted delay problem hold for the average delay problem as well. Through numerical results, we demonstrate that under heavy traffic, integrating sub-6 GHz with mmWave can reduce the average delay by up to 70%. Further, our scheduling policy substantially reduces the delay over the celebrated MaxWeight policy.

In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.

Communications in the millimeter wave (mmWave) band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. Moreover, speeds for electronic processing of data is of the same order as typical rates for mmWave interfaces, making the use of complex algorithms for tracking channel variations and adjusting resources impractical. In order to mitigate some of these challenges, we propose an architecture that integrates the sub-6 GHz and mmWave technologies. Our system exploits the spatial correlations between the sub-6 GHz and mmWave interfaces for beamforming and data transfer. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that analog beamforming can be used in mmWave without incurring large overhead, thanks to the spatial correlations with sub-6 GHz. In addition, we incorporate the sub-6 GHz interface as a fallback (secondary) data transfer mechanism such that: 1) the negative effects of highly intermittent mmWave connectivity are mitigated and 2) the abundant mmWave capacity is fully exploited. To achieve these goals, we consider the problem of scheduling the arrival traffic over the mmWave or sub-6 GHz in order to maximize the mmWave throughput while delay (due to mmWave outages) is guaranteed to be bounded. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. Numerical results demonstrate that our scheduler provides a bounded mmWave delay performance, while it achieves a similar throughput performance as the throughput-optimal policies (e.g., MaxWeight).

We propose a hybrid architecture to integrate RF (i.e., sub-6 GHz) and millimeter wave (mmWave) interfaces for 5G cellular systems. To alleviate the challenges associated with mmWave communications, our proposed architecture integrates the RF and mmWave interfaces for beamforming and data transfer, and exploits the spatio-temporal correlations between the interfaces. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that an integrated RF/mmWave signaling and channel estimation scheme can remedy the problem of high training overhead associated with mmWave beamforming. In addition, cooperation between two interfaces at the higher layers effectively addresses the high delays caused by highly intermittent connectivity in mmWave channels. Subsequently, we formulate an optimal scheduling problem over the RF and mmWave interfaces where the goal is to maximize the delay-constrained throughput of the mmWave interface. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. We design an optimal scheduler that opportunistically schedules the packets over the mmWave interface, while the RF link acts as a fallback mechanism to prevent high delay.

One key requirement for fountain (rateless) coding schemes is to achieve a high intermediate symbol recovery rate. Recent coding schemes have incorporated the use of a feedback channel to improve the intermediate performance of traditional rateless codes; however, these codes with feedback are designed based on uniformly at random selection of input symbols. In this paper, on the other hand, we develop feedback-based fountain codes with dynamically adjusted nonuniform symbol selection distributions, and show that this characteristic can enhance the intermediate decoding rate. We provide an analysis of our codes, including bounds on computational complexity and failure probability for a maximum likelihood decoder; the latter is tighter than bounds known for classical rateless codes. Through numerical simulations, we also show that the feedback information paired with a nonuniform selection distribution can highly improve the symbol recovery rate, and that the amount of feedback sent can be tuned to the specific transmission properties of a given feedback channel.

We propose a novel transport protocol that incorporates a light-weight acknowledgment (ACK) into a rateless coding framework, resulting in a protocol that provides more reliability than the User Datagram Protocol (UDP) and higher throughput than the Transmission Control Protocol (TCP) under lossy and dynamic channel conditions. Unlike traditional ACKs, which acknowledge the reception of individual (possibly encoded) symbols, our ACKs acknowledge the complete decoding of the symbols. This subtle modification permits us to dynamically adjust rateless encoding in order to naturally track decoder progress, regardless of channel conditions. We provide simulation and an analysis of our protocol, including an upper bound on failure probability for a maximum likelihood decoder, which is tighter than bounds known for classical rateless codes. Our protocol can be implemented directly on top of UDP, without requiring changes to the underlying network stack implementations.

We present TeaCP, a prototype toolkit for the evaluation and analysis of collection protocols in both simulation and experimental environments running on TinyOS. Our toolkit consists of a testing system, which runs a collection protocol of choice, and an optional SD card-based logging system, which stores the logs generated by the testing system. The SD card datalogger allows a wireless sensor network (WSN) to be deployed flexibly in various environments, especially where wired transfer of data is difficult. Using the saved logs, TeaCP evaluates a wide range of performance metrics, such as reliability, throughput, and delay. TeaCP further allows visualization of packet routes and the topology evolution of the network, under both static and dynamic conditions, even in the face of transient disconnections. Through simulation of an intra-car WSN and real lab experiments, we demonstrate the functionality of TeaCP for comparing the performance of two prominent collection protocols, the Collection Tree Protocol (CTP) and the Backpressure Collection Protocol (BCP). We also present the usage of TeaCP as a high level diagnosis tool, through which an inconsistency of the BCP implementation for the CC2420 radio chips is identified and resolved.

We experimentally investigate the benefits of multi- hop networking for intra-car data aggregation under the current state-of-the-art Collection Tree Protocol (CTP). We show how this protocol actively adjusts collection routes according to channel dynamics in various practical car environments, resulting in performance gains over single-hop aggregation. Throughout our experiments, we target traditional performance metrics such as delivery rate, number of transmissions per packet, and delay, and our results confirm, both qualitatively and quantitatively, that multi-hop communication can provide a reliable and robust approach for data collection within a car.