Short channel coherence time and oscillator phase noise are two major impairments in millimeter-wave (mmWave) communication systems. Several studies indicate that a substantial fraction of the available bandwidth may be required as overhead to compensate for these impairments, potentially exceeding one third of the total capacity. In this paper, we study Direction-Shift Keying (DSK), a variant of Spatial Modulation (SM), which encodes information in the Direction-of-Arrival (DoA) rather than in the signal amplitude or phase. DSK is implemented over a Distributed Antenna System (DAS), enabling angular resolvability of the transmitted signals. We first derive the structure of the optimal detector for a mobile device equipped withMantennas. We then introduce and characterize the Direction Coherence Time (DCT), defined as the temporal interval over which the DoA remains approximately invariant. Our analysis shows that DCT scales withd/v(transmitter-receiver distance over velocity), whereas the conventional Channel Coherence Time (CCT) scales with λ/v, revealing a coherence-time gain proportional tod/λ, which can exceed several orders of magnitude in mmWave systems. Furthermore, we show that the proposed detector inherently cancels receiver phase noise, eliminating the need for explicit phase-noise tracking. Simulation results validate the analytical findings and demonstrate the robustness of DSK in mobile mmWave environments in the presence of phase noise.

The growing complexity of networks and the variety of future scenarios with diverse and often stringent performance requirements call for a higher level of automation. Intent-based management emerges as a solution to attain high level of automation, enabling human operators to solely communicate with the network through high-level intents. The intents consist of the targets in the form of expectations (i.e., latency expectation) from a service and based on the expectations the required network configurations should be done accordingly. It is almost inevitable that when a network action is taken to fulfill one intent, it can cause negative impacts on the performance of another intent, which results in a conflict. In this paper, we address the challenge of conflict resolution in intent-based networking and propose an online learning approach based on the hierarchical multi-armed bandit framework for autonomous network management. The hierarchical structure enables efficient exploration and exploitation of network configurations while adapting to dynamic network conditions. Our proposed hierarchical multi-armed bandit conflict resolution (MABCR) approach optimizes resource allocation within a partially known system with limited bandwidth. In comparison to other approaches, we show that our algorithm is an effective approach regarding resource allocation and satisfaction of intent expectations.

The Open Radio Access Network (O-RAN) paradigm fosters multi-vendor interoperability, allowing modules from different vendors to cooperatively handle network functions, such as temporary data processing or sensor data collection for network operations optimization. However, this integration agility introduces the risk of selecting suboptimal or adversarial modules, leading to moral hazard. Traditional Moral Hazard testing approaches typically rely on a benchmarking data set in addition to historical performance score. However, they deemed impractical, as vendor-supplied modules may not reveal their outputs before deployment, and the network may lack direct access to reference results for validation. This challenge is further compounded by the dynamic nature of network elements and AI-driven models, whose performance can degrade over time due to malicious tampering, obsolescence, or device deterioration, making historical quality assessments ineffective. In this paper, we address the challenge of identifying legitimate vendor-supplied modules among adversarial ones, with respect to a given network functionality/operation, in the absence of benchmarks. We propose a benchmark-free test framework that detects and eliminates adversarial modules using a methodology inspired by the WereWolf game, combined with zero-knowledge proof techniques. Monte Carlo simulations demonstrate that our approach effectively removes adversarial entities while preserving the privacy of legitimate modules.

The constrained availability of resources on an edge analytics platform prompted the need for a trade-off between accuracy and latency by selecting suitable deep neural network (DNN) models on-the-fly. Earlier efforts either used a single powerful multi-core edge computing device or a distributed cluster of edge nodes. While the former has a high cost and power consumption, the latter incur a high communication overhead. In this paper, we propose a quality-of-service (QoS) aware video analytics platform using an edge-cluster made of low-cost devices. The edge nodes, that constitute the cluster, host heterogeneous DNN models having different configurations and number of layers. The nodes cooperate among themselves to jointly process a streaming video to achieve an optimal QoS. We formulate an optimization problem using penalty as the utility function to minimize the long-term average penalty (LTAP). We first design a DNN model recommender algorithm to minimize the LTAP and then compare it with an Oracle to show that it can achieve an LTAP with an error of 1.6 % and 9.88 % for video resolutions of 720p and 2160p respectively. We also show that the bounds on LTAP are lower and tighter for lower resolution videos compared to the higher resolution videos.

While artificial intelligence (AI) based algorithms have become an epitome of video quality analysis (VQA) for streaming services, the true power of AI is still to be harnessed for situation aware streaming in power constrained wireless networks. In this work, we propose an architecture for situation-aware streaming, that identifies important events from live-feeds of the surveillance cameras, and allocates optimal power that reduces the long-term power consumption. Real-time video surveillance is a crucial technology for smart cities, that requires deployment of large number of cameras; both road-side and aerial. The proposed architecture is designed to (i) reduce the total power consumption of all the surveillance cameras, thus reducing the greenhouse emissions, (ii) improve flight duration of aerial cameras (e.g. drones), (iii) reduce the manual searching of desired events/objects, and (iv) improve the overall QoE. It is imperative that only the important events (example, a car violating a red light) are of interest to a law enforcement officers. Hence, if the important sections of the video are received with high quality, the long-term QoE increases. The architecture has two modules, viz., a tiny neural network (having a small number of hidden layers) at the source, that incurs a small computational resource, albeit at the cost of low accuracy; and a deep neural network (DNN) (with many hidden layers) at the destination that is capable of determining events with high accuracy. We show that there exists an optimal number of frames that provides optimal QoE and is able to reduce the required power consumption of the transmitter compared to when situation awareness is not used.

The Sub-6GHz multi-path channel model suggests that the angle of arrivals (AoAs) and angle of departures (AoDs) of dominant rays follow a uniform distribution. Moreover, AoA/AoD varies interdependently from one coherence time/space to another. Several of the recent works on millimeter wave (mmWave) transmissions suggest otherwise, where some presume that it is possible to predict the AoAs/AoDs from nearby users (correlation over space) while others presume that beamforming coherence distance/time is higher than the channel coherence distance/time. The results in existing works, however, are often based on abstract models (two rays model), simulation results (typically rays tracing simulator), and, in many cases, the presence of a Line-of-Sight (LoS) link. In an effort to support or refute such a conjecture, we have carried out a real-world experiment in indoor office environments with high Non-LoS (NLoS) probability. We use statistical Bayesian learning to infer a statistical model on AoAs and extract insightful information.

This study explores the challenges and solutions for managing dynamically changing objects within the Internet of Things (IoT) environment. Given the evolving nature of these configurations, maintaining effective cooperation and safety is a complex task. Our approach utilizes viability theory to develop an optimal control strategy that ensures the agents remain within viable boundaries while respecting and adhering to the system's constraints. An admissible control that minimizes the system cost is obtained through Hamiltonian formulation and solving it via the Minimum Principle. The numerical results demonstrate that the proposed optimal control strategy, combined with effective communication, promotes a cooperative environment where agents can navigate the system reliably.

Cloud and edge computing have proposed different functionalities to enable multiple applications requiring different communication, computing, and caching (3C) resources. The upcoming futuristic applications (e.g., metaverse, holographic, and haptic communication) impose further stringent requirements (e.g., ultra-low latency, ultra-high reliability) on the infrastructure. These requirements call for a paradigm shift in the infrastructure architecture where all resource components and owners collaborate from the cloud up to the edge, creating a cloud-to-edge continuum of integrated resources. Furthermore, we argue that artificial intelligence (AI) and collaborative-based decisions are promising techniques to efficiently manage the highly complex architecture that jointly leverages 3C in the continuum. This article presents a comprehensive survey of existing research, including AI and collaborative-based studies, targeting the effective and seamless provision of 3C resources and services in the cloud-to-edge continuum. Through an extensive analysis of driving use cases, the synergy between these three main services is scrutinized to highlight its crucial role in the next-generation network infrastructures (NGNI). Finally, a discussion on the opportunities and challenges brought by integrating 3C in NGNI from different perspectives, including architectural design as well as the regulatory and business aspects, are presented.

Federated learning (FL) is a global machine-learning model that is trained using several participating nodes, where data is kept private. In this paper, we propose a cost-effective dynamic joint load balancing and pricing algorithm (CELBP) for heterogeneous workers that utilizes the Yardstick Competition Regulation (YCR) to encourage workers to disclose their costs honestly and provide incentives based on relative contributions. In the dynamic case, we aim to minimize the total cost of the server, considering the positive value of subtasks assigned to workers and their limited full load, and a new cost function for workers that accounts for their efficiency level and total communication and computation time required to complete assigned tasks. We show that CELBP outperforms other schemes in terms of accuracy and training time while reducing the server's total cost, using experiments with the MNIST and CIFAR-10 datasets. Additionally, we show that using the proposed online algorithms improves accuracy and reduces latency when compared to other algorithms mentioned in the paper.

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. The main goal is to design content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we propose a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks using the decomposition property of splitting each multi-antenna transmitter into multiple single antenna transmitters. Furthermore, we derive an information-theoretic bound on the optimal NDT of the cache-aided MIMO interference network. Analytical results show that the proposed scheme is within a multiplicative gap of 2 from the derived lower bound independent of all system parameters for any uncoded cache placement scheme. We also derive a novel delivery scheme for the cache-aided Multi-Input-Single-Output (MISO) interference network outperforming our proposed scheme for the cache-aided MIMO interference network. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Our results show that the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain. Our results indicate that the receive-antennas become more effective in reducing the NDT than the transmit-antennas in the presence of caches at the receiver-side. In addition, we show that increasing the number of transmit-antennas has a higher gain in reducing the NDT than adding more transmitters in the cache-aided MISO interference network.

The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.

Video streaming accounts for the most of the global Internet traffic and providing a high user Quality of Experience (QoE) is considered an essential target for mobile network operators (MNOs). QoE strongly depends on network Quality of Service (QoS) parameters. In this work, we use real-world network traces obtained from a major cellular operator in Turkey to establish a mapping from network side parameters to the user QoE. To this end, we use a model-aided deep learning method for first predicting channel path loss, and then, employ this prediction for predicting video streaming MOS. The experimental results demonstrate that the proposed model-aided deep learning model can guarantee higher prediction accuracy compared to predictions only relying on mathematical models. We also demonstrate that even though a trained model cannot be directly transferred from one geographical area to another, they significantly reduce the volume of required training when used for prediction in a new area.

Mobile opportunistic networks (MONs) are characterized by frequently changing network topology that rely on multi-copy transmission schemes to ensure the delivery of messages. However, the limited buffer capacity of mobile nodes leads to buffer overflow and head-of-line blocking. Head-of-line-blocking is caused due to short contact duration, which causes the older messages to be replicated at a faster rate than the newer messages waiting behind them. This unfair replication decreases the message delivery ratio and increases the average delivery delay. For implementation, we categorize a message as new or old and calculate the necessary maximum number of replicas required to deliver a message to the destination. If the instantaneous number of replicas in the network is less than this number, we schedule a message for replication and vice-versa. We demonstrate that our proposed scheme achieves a delivery ratio comparable to a utility-based optimal message drop algorithm with significantly lower overhead.

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively learn a global model by sharing local updates on the model parameters rather than their datasets, with the help of a mobile base station (MBS). We optimize the resource allocation among MUs to reduce the communication latency in learning iterations. Observing that the performance in this centralized setting is limited due to the distance of the cell-edge users to the MBS, we introduce small cell base stations (SBSs) orchestrating FEEL among MUs within their cells, and periodically exchanging model updates with the MBS for global consensus. We show that this hierarchical federated learning (HFL) scheme significantly reduces the communication latency without sacrificing the accuracy.

Device-to-device (D2D) communication helps in enhancing the capacity of the cellular network. However, the provision of video streaming in a D2D underlay network is challenging due to the dynamic and limited availability of resources especially under high mobility. Scalable video coding (SVC) allows for dynamic adjustment of video quality level according to the instantaneous network conditions, e.g., achievable data rate, player butter occupancy and user preferences.In this paper, we propose an optimal decision theory (ODT) based scheme to fill the buffers with appropriate video quality levels so as to minimize the absolute distortion under constrained stall percent. A setup with one-pair of D2D user and other cellular users (CUs) sharing the same bandwidth with different mobility patterns is used to evaluate the proposed scheme. The scheme is compared with two other schemes viz., the random allocation scheme and the greedy allocation scheme to observe the performance of the ODT based scheme.

The recent evolution of ambient backscattering technology has the potential to provide long-range and low-power wireless communications. In this work, we study the unmanned aerial vehicle (UAV)-assisted backscatter networks where the UAV acts both as a mobile power transmitter and as an information collector. We aim to maximize the number of successfully decoded bits in the uplink while minimizing the UAV's flight time by optimizing its altitude. Power-domain NOMA scheme is employed in the uplink. An optimization framework is presented to identify the trade-off between numerous network parameters, such as UAV's altitude, number of backscatter devices, and backscatter coefficients. Numerical results show that an optimal altitude is computable for various network setups and that the impact of backscattering reflection coefficients on the maximum network throughput is significant. Based on this optimal altitude, we also show that an optimal trajectory plan is achievable.

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. Our objective is to propose content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we design a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks by using the decomposition property. The numerical results show the superiority of our proposed scheme over the state-of-the-art schemes in the literature. Furthermore, we show that increasing the receiver-cache sizes achieves a higher gain than increasing the number of receive-antennas. In other words, the coded caching gain has a more significant contribution in reducing the transmission latency than the spatial multiplexing gain.

We consider a fog radio access network (F-RAN) with multiple transmitters and receivers, where each transmitter is connected to the cloud via a fronthaul link. Each network node has a finite cache, where it fills its cache with portions of the library files in the off-peak hours. In the delivery phase, receivers request each library files according to an arbitrary popularity distribution. The cloud and the transmitters are responsible for satisfying the requests. This paper aims to design content placement and coded delivery schemes for minimizing both the expected normalized delivery time (NDT) and the peak NDT which measures the transmission latency. We propose achievable transmission policies, and derive an information-theoretic bound on the expected NDT under uniform popularity distribution. The analytical results show that the proposed scheme is within a gap of 2.58 from the derived bound for both the expected NDT under uniform popularity distribution and the peak NDT. Next, we investigate the expected NDT under an arbitrary popularity distribution for an F-RAN with transmitter-side caches only. The achievable and information-theoretic bounds on the expected NDT are derived, where we analytically prove that our proposed scheme is optimal within a gap of two independent of the popularity distribution.

Cache capacity-delay trade-off is studied for cooperative coded caching among small-cell base stations (SBSs) considering mobile users. First, a delay-aware coded caching policy is introduced, taking into account the popularity of the files and the maximum re-buffering delay constraint, which minimizes the average re-buffering delay of a mobile user under a given cache capacity constraint. Subsequently, a given average re-buffering delay constraint is considered to ensure a certain quality-of-service (QoS) target, and certain files are served by the macro-cell base station (MBS) when the cache capacity of the SBSs is not sufficient to store all the files in the library. A coded caching policy that minimizes the average amount of data served by the MBS is proposed for the latter scenario.

In this paper, we consider a class of wireless powered communication devices using hybrid automatic repeat request (HARQ) protocol to ensure reliable communications. In particular, we analyze the trade-off between accumulating mutual information and harvesting RF energy at the receiver of a point-to-point link over a time-varying independent and identically distributed (i.i.d.) channel. The transmitter is assumed to have a constant energy source while the receiver relies, solely, on the RF energy harvested from the received signal. At each time slot, the incoming RF signal is split between information accumulation and energy accumulation with the objective of minimizing the expected number of re-transmissions. A major finding of this work is that the optimal policy minimizing the expected number of re-transmissions utilizes the incoming RF signal to either exclusively harvest energy or to accumulate mutual information. This finding enables achieving an optimal solution in feasible time by converting a two dimensional uncountable state Markov decision process (MDP) with continuous action space into a countable state MDP with binary decision space.

This paper studies the decentralized coded caching for a Fog Radio Access Network (F-RAN), whereby two edge-nodes (ENs) connected to a cloud server via fronthaul links with limited capacity are serving the requests of K r users. We consider all ENs and users are equipped with caches. A decentralized content placement is proposed to independently store contents at each network node during the off-peak hours. After that, we design a coded delivery scheme in order to deliver the user demands during the peak-hours under the objective of minimizing the normalized delivery time (NDT), which refers to the worst case delivery latency. An information-theoretic lower bound on the minimum NDT is derived for arbitrary number of ENs and users. We evaluate numerically the performance of the decentralized scheme. Additionally, we prove the approximate optimality of the decentralized scheme for a special case when the caches are only available at the ENs.

This paper characterizes the performance of a generic -tier cache-aided heterogeneous network (CHN), in which the base stations (BSs) across tiers differ in terms of their spatial densities, transmission powers, pathloss exponents, activity probabilities conditioned on the serving link and placement caching strategies. We consider that each user connects to the BS which maximizes its average received power and at the same time caches its file of interest. Modeling the locations of the BSs across different tiers as independent homogeneous Poisson Point processes (HPPPs), we derive closed-form expressions for the coverage probability and local delay experienced by a typical user in receiving each requested file. We show that our results for coverage probability and delay are consistent with those previously obtained in the literature for a single tier system.

This work considers a system with two energy harvesting (EH) nodes transmitting to a common destination over a random access channel. The amount of harvested energy is assumed to be random and independent over time, but correlated among the nodes possibly with respect to their relative position. A threshold-based transmission policy is developed for the maximization of the expected aggregate network throughput. Assuming that there is no a priori channel state or EH information available to the nodes, the aggregate network throughput is obtained. The optimal thresholds are determined for two practically important special cases: i) at any time only one of the sensors harvests energy due to, for example, physical separation of the nodes; ii) the nodes are spatially close, and at any time, either both nodes or none of them harvests energy.

In this paper, we investigate an optimal resource on-off switching framework that minimizes the energy consumption of a heterogeneous cellular network. Specifically, our goal is to minimize the energy consumption of the cellular network while satisfying a desired level of buffer starvation probability, which can be considered as a quality of experience (QoE) term. For an ON/OFF bursty arrival process, we introduce recursive equations to obtain the buffer starvation probability of a mobile device (MD) for streaming services. The MD is in the coverage area of a femtocell base station (FBS) which is implemented at the cell edge of a macrocell base station (MBS), and when its buffer gets empty, the media player of the MD restarts the service after a certain amount of packets are prefetched (this event is known as start-up delay in the literature). Numerical simulations illustrate how our system significantly reduces the overall energy consumption of the network while guaranteeing a target starvation probability in comparison to the case where the MD is covered only by one MBS.

Energy harvesting (EH) is a promising technology for realizing energy-efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this paper is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I, which has unlimited energy supply and Type II, which is solely powered by an RF energy harvesting circuit. The transmissions of a Type I node are recycled by a Type II node to replenish its battery. We characterize an inner bound on the stable throughput region under half-duplex and full-duplex energy harvesting paradigms as well as for the finite capacity battery case. Additionally, we analyze the case where RF energy harvesting serves as a backup for an unlimited energy source. We present numerical results that validate our analytical results, and demonstrate their utility for the analysis of the exact system.

We consider the problem of resource allocation and control of multihop networks in which multiple source-destination pairs communicate confidential messages, to be kept confidential from the intermediate nodes. We pose the problem as that of network utility maximization, into which confidentiality is incorporated as an additional quality of service constraint. We develop a simple, and yet provably optimal dynamic control algorithm that combines flow control, routing and end-to-end secrecy-encoding. In order to achieve confidentiality, our scheme exploits multipath diversity and temporal diversity due to channel variability. Our end-to-end dynamic encoding scheme encodes confidential messages across multiple packets, to be combined at the ultimate destination for recovery. We first develop an optimal dynamic policy for the case in which the number of blocks across which secrecy encoding is performed is asymptotically large. Next, we consider encoding across a finite number of packets, which eliminates the possibility of achieving perfect secrecy. For this case, we develop a dynamic policy to choose the encoding rates for each message, based on the instantaneous channel state information, queue states and secrecy outage requirements. By numerical analysis, we observe that the proposed scheme approaches the optimal rates asymptotically with increasing block size. Finally, we address the consequences of practical implementation issues such as infrequent queue updates and de-centralized scheduling. We demonstrate the efficacy of our policies by numerical studies under various network conditions.

We study the impact of a full-duplex secondary node on a cognitive cooperative network with Multipacket Reception (MPR) capabilities at the receivers. Motivated by recent schemes that make full-duplex communication feasible, we study a model with one primary and one secondary transmitter-receiver pair, where the secondary transmitter is able to relay primary unsuccessful packets. Cooperation between primary and secondary users has been previously shown to be beneficial for the primary and the secondary users in terms of stable throughput. Our model assumes an imperfect full-duplex secondary node that can transmit and receive simultaneously, cancelling self-interference to a certain extent. Furthermore, we assume that the secondary transmitter chooses between cooperating with the primary user and transmitting secondary packets probabilistically according to some optimized probabilities that depend on both the channels in the network and the state of the primary user. We determine these probabilities by formulating a constrained optimization problem with the secondary throughput as the objective function and the stability of the primary queues as constraints. Using the dominant system approach, we show that the optimization problem has a quasi-concave structure, to which the optimal solution can be easily found. Using Numerical results, we characterize the cases where the full-duplex capability is beneficial to the system, namely, we show that the full-duplex secondary node greatly increases both the secondary throughput and the primary maximum stable throughput in channels with receivers that have strong MPR capability.

In this letter, we investigate the trade-off between the quality of multi-sensor fusion decision and the delay of making that decision in a random access sensor network in a particular setting where the correlation between two sensor observations decreases with the increasing distance between these sensors. We propose a system operation wherein the nodes located in a small neighborhood of a sensor node that has successfully transmitted its decision to the fusion center are deactivated to reduce the contention over the random access channel. We propose a utility function that involves the quality and timeliness of the decision and determine the optimal size of this neighborhood analytically under a Markovian system operation model and numerically by simulating the actual system operation. These experiments demonstrate how our model can be used to set this neighborhood size before data collection to achieve a desired trade-off between decision quality and timeliness.

We consider the problem of cross-layer resource allocation with information-theoretic secrecy for uplink transmissions in time-varying cellular wireless networks. Particularly, each node in an uplink cellular network injects two types of traffic, confidential and open at rates chosen in order to maximize a global utility function while keeping the data queues stable and meeting a constraint on the secrecy outage probability. The transmitting node only knows the distribution of channel gains. Our scheme is based on Hybrid Automatic Repeat Request (HARQ) transmission with incremental redundancy. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

Playback delay control is an important mechanism to avoid jitter in video streaming systems. This paper introduces a playback delay minimization problem for multiuser video streaming systems providing a jitter-free video streaming service to end users in the system. In particular, a necessary condition on the playback delay for jitter-free streaming is obtained. Then, based on the derived necessary condition, an optimum rate splitting algorithm that splits available rate to all users is proposed. The proposed algorithm is optimum in the sense that it achieves the minimum system delay, which is defined as the maximum of all initial playback delays, while ensuring jitter-free streaming service to all users. Finally, using these results, an expression for the minimum system delay as a function of system parameters such as total rate and playback curves of requested video files is also derived.

In this paper, optimal resource allocation policies are characterized for wireless cognitive networks under the spectrum leasing model. We propose cooperative schemes in which secondary users share the time-slot with primary users in return for cooperation. Cooperation is feasible only if the primary system's performance is improved over the non-cooperative case. First, we investigate a scheduling problem where secondary users are interested in immediate rewards. Here, we consider both infinite and finite backlog cases. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long-term basis, in return for cooperation. Finally, we present a power allocation problem where the goal is to maximize the expected net benefit defined as utility minus cost of energy. Our proposed scheduling policies are shown to outperform non-cooperative scheduling policies, in terms of expected utility and net benefit, for a given set of feasible constraints. Based on Lyapunov optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

We consider the problem of cross-layer resource allocation in time-varying cellular wireless networks and incorporate information theoretic secrecy as a quality-of-service constraint. Specifically, each node in the network injects two types of traffic, private and open, at rates chosen in order to maximize a global utility function, subject to network stability and secrecy constraints. The secrecy constraint enforces an arbitrarily low mutual information leakage from the source to every node in the network, except for the sink node. We first obtain the achievable rate region for the problem for single- and multiuser systems assuming that the nodes have full channel state information (CSI) of their neighbors. Then, we provide a joint flow control, scheduling, and private encoding scheme, which does not rely on the knowledge of the prior distribution of the gain of any channel. We prove that our scheme achieves a utility arbitrarily close to the maximum achievable utility. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

It is well known that Max-Weight type scheduling algorithms are throughput optimal since they achieve the maximum throughput while maintaining the network stability. However, the majority of existing works employing Max-Weight algorithm require the complete channel state information (CSI) at the scheduler without taking into account the associated overhead. In this work, we design a Scheduling and Selective Feedback algorithm (SSF) taking into account the overhead due to acquisition of CSI. SSF algorithm collects CSI from only those users with sufficiently good channel quality so that it always schedules the user with the highest queue backlog and channel rate product at every slot. We characterize the achievable rate region of SSF algorithm by showing that SSF supports 1 + ϵ fraction of the rate region when CSI from all users are collected. We also show that the value of ϵ depends on the expected number of users which do not send back their CSI to the base station. For homogenous and heterogeneous channel conditions, we determine the minimum number of users that must be present in the network so that the rate region is expanded, i.e., ϵ > 0. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by collecting CSI from only less than 50% of all users in a CDMA based cellular network utilizing high data rate (HDR) protocol.

It is well known that opportunistic scheduling algorithms are throughput optimal under dynamic channel and network conditions. However, these algorithms achieve a hypothetical rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. In this work, we design a joint scheduling and channel probing algorithm by considering the overhead of obtaining the channel state information. We adopt a correlated and non-stationary channel model, which is more realistic than those used in the literature. We use concepts from learning and information theory to accurately track channel variations to minimize the number of channels probed at every slot, while scheduling users to maximize the achievable rate region of the network. Simulation results show that with the proposed algorithm, the network can carry higher user traffic.

We consider the problem of resource allocation in a wireless cellular network, in which nodes have both open and private information to be transmitted to the base station over block fading uplink channels. We develop a cross-layer solution, based on hybrid ARQ transmission with incremental redundancy. We provide a scheme that combines power control, flow control, and scheduling in order to maximize a global utility function, subject to the stability of the data queues, an average power constraint, and a constraint on the privacy outage probability. Our scheme is based on the assumption that each node has an estimate of its uplink channel gain at each block, while only the distribution of the cross channel gains is available. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable utility given the available channel state information.

It is well known that Max-Weight scheduling provides queue stability whenever this is possible. However, Max-Weight scheduling requires the complete channel state information (CSI) to make the best transmission decision at every time slot. The common assumption in this line of research assumes that the network controller has full CSI at every decision time without taking into account the overhead associated with channel probing. In practice, however, acquiring CSI is not cost-free and requires certain amount of resources. In this work, we design a Scheduling and Dynamic Feedback algorithm, named SDF, by considering the overhead of obtaining the channel state information. We first establish a bound on the achievable rate region of SDF algorithm by proving that SDF supports 1+ ϵ fraction of of the full rate region (the rate region when all users are probed) where ϵ only depends on the expected number of users which are not probed. Then, for homogenous channel, we show that when the number of users in the network is greater than 3, ϵ >;0, i.e., we guarantee to expand the rate region. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDMA based cellular network utilizing high data rate protocol under normal channel conditions.

Optimal transmission scheduling in wireless cognitive networks is considered under the spectrum leasing model. We propose a cooperative scheme in which secondary nodes share the time slot with primary nodes in return for cooperation. Cooperation is feasible only if the system's performance is improved over the non-cooperative case. First, we investigate a scenario where secondary users are interested in immediate rewards. Then, we formulate another problem where the secondary users are guaranteed a portion of the primary utility, on a long term basis, in return for cooperation. In both scenarios, our proposed schemes are shown to outperform non-cooperative scheduling schemes, in terms of both individual and total expected utility, for a given set of feasible constraints. Based on Lyapunov Optimization techniques, we show that our schemes are arbitrarily close to the optimal performance at the price of reduced convergence rate.

Recent work on cooperative communications has demonstrated benefits in terms of improving the reliability of links through diversity and/or increasing the reach of a link compared to a single transmitter transmitting to a single receiver (single-input single-output or SISO). In one form of cooperati

The user association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper we design a new association framework in order to provide optimal association and network performance. In this framework we propose a new channel-quality based user association mechanism inspired by the operation of the infrastructure-based WLANs. Besides, we enforce our framework by proposing an airtime-metric based association mechanism that is aware of the uplink and downlink channel conditions as well as the communication load. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer, in order to fit it in the operation of wireless mesh networks. Lastly, we design a hybrid association scheme that can be efficiently applied in real deployments to improve the network performance. We evaluate the performance of our system through simulations and we show that wireless mesh networks that use the proposed association mechanisms are more capable in meeting the needs of QoS-sensitive applications.

We consider source-initiated broadcast session traffic in an ad hoc wireless network operating under a hard constraint on the end-to-end delay between the source and any node in the network. We measure the delay to a given node in the number of hops data travels from the source to that node, and our objective in this paper is to construct an energy-efficient broadcast tree that has a maximum depth Δ, where Δ; represents the end-to-end hop constraint in the network. We characterize the optimal solution to a closely related problem in massively dense networks using a dynamic programming formulation. We prove that the optimal solution can be obtained by an algorithm of polynomial time complexity O(Δ²). The solution to the dynamic program indicates that there is a single optimal policy applicable to all massively dense networks. Elaborating on the insights provided by the structure of the problem in massively dense networks, we design an algorithm for finding a solution to the hop constrained minimum power broadcasting problem in general networks. By extensive simulations, we demonstrate that our proposed optimization-based algorithm generates broadcast trees within 20% of optimality for general dense networks.

In sensor networks, data traffic is concentrated towards a small number of base stations, and those nodes close to the base station have to relay large amount of data for the rest of the network. This would deplete the batteries of these nodes very quickly, and reduce the network lifetime. In order to alleviate this problem, a novel solution is proposed in this paper, where the transmission powers of the nodes in the network are determined based on their distance from the base station. This solution promises to balance the energy consumption in the network without periodically collecting network state information or using nodes with special capabilities. The optimal affine distance-based routing strategy is analytically determined, and it is shown that the proposed method can increase the network lifetime by more than twice of that in a network employing minimum energy routing scheme with constant transmission ranges. Finally, the optimal transmission ranges of the nodes are characterized with respect to the network size and the energy costs due to transmit amplifier and transceiver electronics.

A major objective in wireless sensor networks is to find optimum routing strategies for energy efficient use of nodes. Routing decision and transmission power selection are intrinsically connected since the transmission power of a node is adjusted depending on the location of the next hop. In this paper, we propose a location-based routing framework to control the energy distribution in a network where transmission ranges, hence powers, of nodes are determined based on their locations. We show that the proposed framework is sufficiently general to investigate the minimum-energy and maximum-lifetime routing problems. It is shown that via the location based strategy the network lifetime can be improved by 70% and the total energy consumption can be decreased to three-fourths to one-third of the constant transmission range strategy depending on the propagation medium and the size of the network.

We present a novel approach in characterizing the optimal reliablemulti-hopvirtualmultiple-inputsingle-output(vMISO)routingin ad hoc networks. Under a high node density regime, we determine the optimal cardinality of the cooperation sets at each hop on a path minimizing the total energy cost per transmitted bit. Optimal cooperating set cardinality curves are derived, and they can be used to determine the optimal routing strategy based on the required reliability, transmission power, and path loss coefficient. We design a new greedy geographical routing algorithm suitable for vMISO transmissions, and demonstrate the applicability of our results for more general networks.

In IEEE 802.11 wireless networks, users associate with access points that can provide the best service quality. In this paper, we analyze the convergence and steady state performance of a practically well performing load-based user association scheme. The analysis is based on a novel game theoretical model, which extends the results on atomic congestion games. We prove the existence of and convergence to a Nash equilibrium for this game. The bounds on the efficiency of equilibrium compared to centralized optimum solutions are established under different system costs.

The cooperative transmissions emulate multi-antenna systems, and can be used to reduce the total energy consumption. In this paper, our objective is to quantify the advantages of using cooperative transmissions in extending the network lifetime of the energy-constrained wireless networks. We develop a detailed energy consumption model suitable for systems with cooperative transmissions taking into account the effects of channel fading. We then design a new cooperating node set selection and routing algorithm. The algorithm greedily chooses the cooperating nodes and the paths that has the minimum cost. We use a new cost metric which takes into account the transmission and reception energy costs, residual node energies, the rate of the space time block codes used, and the diversity gain provided by the cooperative transmissions. Our simulation results indicate that the network lifetime with cooperative transmissions is on the average 50% more than the lifetime with traditional transmissions. We also show that cooperative transmissions provide the maximum benefit in medium density networks and the lifetime increases when the network gets more clustered.

In IEEE 802.11-based wireless mesh networks a user is associated with an access point (AP) in order to communicate and be part of the overall network. The association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper, we propose an association mechanism that is aware of the uplink and downlink channel conditions. We introduce a metric that captures the channel conditions and the load of the APs in the network. The users use this metric in order to optimally associate with the available APs. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer. The novelty of the mechanism is that the routing QoS information of the back haul is available to the end users. This information can be combined with the uplink and downlink channel information for the purpose of supporting optimal end-to-end communication and providing high end-to-end throughput values. We evaluate the performance of our system through simulations and we show that 802.11-based mesh networks that use the proposed association mechanism are more capable in meeting the needs of QoS-sensitive applications.

Wireless multimedia home servers are the next generation of home entertainment systems. From a single broadband connection entering home, the multimedia data is transmitted to TV and other peripherals by using only wireless links. Due to the high attenuation and multi-path fading environment caused by the walls as well as the contention of the channel by other users, the provision of service guarantees to time-critical services such as video streaming is extremely challenging. In our project, we use wireless mesh networks to combat with attenuation, and provide high end-to-end throughput to all users. We also use Enhanced Distributed Channel Access to combat with the channel contention for users with time critical services, e.g., video streaming. We use the latest video encoding standard H.264, which classifies the video packets according to their importance. In our prioritization technique, we use this classification in order to give a fair share of the channel to the ongoing data traffic, while providing a high quality video to the end users.

Space-time communications can help combat fading and, hence, can significantly increase the capacity of ad hoc networks. Cooperative diversity or virtual antenna arrays facilitate spatio-temporal communications without actually requiring the deployment of physical antenna arrays. Virtual MISO entails the simultaneous transmission of appropriately encoded information by multiple nodes to effectively emulate a transmission on an antenna array. We present a novel multilayer approach for exploiting virtual MISO links in ad hoc networks. The approach spans the physical, medium access control and routing layers, and provides 1) a significant improvement in the end-to-end performance in terms of throughput and delay and 2) robustness to mobility and interference-induced link failures. The key physical layer property that we exploit is an increased transmission range due to achieved diversity gain. Except for space-time signal processing capabilities, our design does not require any additional hardware. We perform extensive simulations to quantify the benefits of our approach using virtual MISO links. As compared to using only SISO links, we achieve an increase of up to 150 percent in terms of the end-to-end throughput and a decrease of up to 75 percent in the incurred end-to-end delay. Our results also demonstrate a reduction in the route discovery attempts due to link failures by up to 60 percent, a direct consequence of the robustness that our approach provides to link failures

This paper considers the problem of multiple video streaming over wireless local area networks. In particular, we propose video streaming techniques to improve the quality of video streams over the link from an access point to multiple wireless clients. The proposed video streaming techniques decide on clients to be served, packets to be (re)transmitted, and forward error correction (FEC) rates by considering video, channel, and network properties in a rate-distortion optimized manner. Multi-client rate distortion optimized video streaming is the first technique operated by an access point to jointly optimize client, packet, and FEC rate selection for video streaming. Second, we propose multi-client sub-optimal video streaming technique that optimizes packet and FEC rate, but not client. The client is selected by using the results of optimization to reduce the computational complexity. The simulation results show that the proposed video streaming techniques achieve signal-to-noise ratio (SNR) improvement up-to 5 dB as compared with time-division multiple access (TDMA) and packet adaptive TDMA.

Space-time communications can help combat fading and hence can significantly increase the capacity of ad hoc networks. Cooperative diversity or virtual antenna arrays facilitate spatio-temporal communications without actually requiring the deployment of physical antenna arrays. Virtual MISO entails the simultaneous transmission of appropriately encoded information by multiple nodes to effectively emulate a transmission on an antenna array. We present a novel multi-layer approach for exploiting virtual MISO links in ad hoc networks. The approach spans the physical, medium access control and routing layers and provides: (a) a significant improvement in the end-to-end performance in terms of throughput and delay and, (b) robustness to mobility and interference induced link failures. The key physical layer property that we exploit is an increased transmission range due to achieved the diversity gain. Except for space-time signal processing capabilities, our design does not require any additional hardware. We perform extensive simulations to quantify the benefits of our approach using virtual MISO links. As compared to using only SISO links, we achieve an increase of up to 150% in terms of the end-to-end throughput and a decrease of up to 75% in the incurred end-to-end delay. Our results also demonstrate a reduction in the route discovery attempts due to link failures by up to 60%, a direct consequence of the robustness that our approach provides to link failures.

In this paper, we investigate rate distortion optimized streaming of H.264 coded video sequences over time-varying wireless channels. Our main objective is to minimize average end-to-end distortion to satisfy a certain quality of service (QoS) by considering the media and channel characteristics such as packet importance, packet dependencies, decoding deadlines, channel state information and channel capacity. In particular, we propose a sender-driven optimized joint ARQ-FEC scheme that decides on packet transmissions, re-transmissions as well as the FEC rate used in each transmission. The optimized joint ARQ-FEC scheme along with packet scheduling aims to minimize a weighted sum of distortion and total transmission cost under capacity constraints. The other objective in this paper is to satisfy the QoS of the optimized joint ARQ-FEC scheme without minimizing end-to-end distortion. We improved two approximate algorithms that use the media and channel characteristics to transmit the best selected and FEC coded packet. The real-time simulation results with H.264 sequences demonstrate the efficacy of the all proposed algorithms over the classical error recovery scheme uses ARQ and FEC.

Web sites disseminate some of their information to surrogate caches in order to reduce the latency observed during the delivery of the information. The surrogates classify publishers under several classes with respect to their willingness-to-pay. Surrogate partitions the total cache capacity among different classes to provide loose version of quality of service. In our model, publishers try to get as large cache space as possible, while the surrogate is required to achieve fair allocation among the publishers. Specifically, each publisher should be charged the same if they receive equal share of caching space. We determine the optimal pricing strategy of the surrogate maximizing its revenue. We also analyzed the competition between surrogates under this model and determined the condition that leads to a Nash equilibrium. We showed that at equilibrium surrogates peer with each other as if there is a single combined surrogate server.

Caches have been used extensively to store the most popular/recent requested data to improve the user latency and reduce the network load. Recently, a more systematic approach to caching has been developed within the framework of content delivery networks (CDN). A CDN is the network of caches, where the caches are geographically distributed and serve user requests on behalf of the subscriber Web sites. Users receive the requested information from the caching servers, which are closer to the users and usually much less loaded than the origin server. The objective is to minimize the user latency by intelligently distributing the content and serving the user requests from the most efficient sites. We realistically model the agents in a CDN with selfish self-maximizing behaviors and define the problem as a noncooperative game. We separate the distribution and routing subproblems and use games to solve each. We show that the subproblems have equilibrium solutions and, if the equilibrium of a subproblem is unique, we achieve the global optimum for that subproblem. We also determine that a unique equilibrium is reached if the content providers are not willing to pay high amounts and the cache sizes are sufficiently small. We noticed that the global system optimum requires the content providers to pay very high amounts, which in practice may prohibit the applicability of the distributed method. Thus, we consider an Investment strategy for the content providers, which maximizes the publishers' net benefits and leads to a near-optimum system solution. We also show that the joint distribution and routing game has an equilibrium and demonstrate its performance by numerical examples.

One of the prominent objectives of National/Global Information Infrastructure is to provide all types of users global access to information. Satellite broadcast data delivery has inherent advantages, such as scalability and location independent availability, in achieving this objective. However, users need expensive and cumbersome equipment to receive and transmit satellite signals. Furthermore, as the amount of information being broadcast increases, average user latency increases as well. Often, users in a geographical locality have similar interests, which can be better served by employing a local broadcast schedule. In this context, a two stage satellite terrestrial wireless broadcast system can provide more efficient service in terms of lower average user latency and cheaper and more convenient user equipment. In such a system, the main server broadcasts information via satellite to the geographically distributed local ground stations. Every ground station has limited buffer capacity to store the data broadcast by the satellite. According to their buffer content, and the interests of their users, local stations deliver the information to their users via terrestrial wireless channel. We develop novel methods for the joint cache management and scheduling problem encountered in these systems. Our results demonstrate that such two stage systems are feasible and they can provide more efficient data delivery compared to the single stage systems.

Low Earth orbit satellite networks can augment terrestrial wireless networks to provide global broadband services to users regardless of the users' locations. Delivering QoS guarantees to the users of LEO satellite networks is complicated since the footprints of the LEO satellites move as the satellites traverse their orbits, and thus, causing frequent user handovers between the satellites. Traffic on inter-satellite links of a particular satellite change as the user traffic served by the satellite changes with the satellite's mobility. The change in user traffic on the inter-satellite links may cause violation of QoS requirements of on-going calls. We propose a novel routing algorithm called the predictive routing protocol (PRP), that exploits the predictive nature of the LEO satellite topology to maximize the total number of users served by the system, while maintaining each user's QoS requirements. The PRP predicts the user traffic load on the inter-satellite links up to a short time in the future by using the deterministic knowledge of the LEO satellite topology, and user location information. The PRP determines multiple paths for a particular connection that effectively help avoid possible future bottlenecks as predicted by estimated future traffic on the inter-satellite links. The algorithm is compared with other non-predictive routing protocols such as IP routing by extensive simulations and it is shown that PRP can deliver deterministic QoS guarantees (such as delay jitter), without over-reserving channel bandwidth. An admission control curve has also been obtained which may be used to ensure that the desired QoS metrics may be guaranteed.