Integrated Access and Backhaul (IAB) is a cost-effective and adaptable solution for deploying ultra-dense next-generation (5G and 6G) cellular networks to increase the likelihood of Line-of-Sight (LOS) coverage. This technology allows wireless backhaul connections to be established using the same technology and specifications as available in the access links. However, the absence of a physical testbed or a dataset that can be used for simulation in the millimeter wave (mmWave) band prevents researchers’ validation of the proposed algorithms in the IAB scenario. In this paper, we propose a novel data generator based on a Generative Adversarial Network (GAN), trained on a real dataset from a mobile network that operates in Europe, and maintains a significant market share that returns accurate traffic data for an IAB network. Also, we introduce IAB-CNPos, an intelligent IAB node positioning framework using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that indicates IAB node positions to increase the coverage network with minimal deployment cost. Furthermore, we integrate this data generator with the SeBaSi simulator (an IAB simulator based on Sionna), which obtains accurate, data-consistent, and realistic end-to-end IAB simulation results. The performance results indicate that the data generator successfully passes the Kolmogorov–Smirnov (KS) criterion, so, it could operate as a verified data generator. Furthermore, we use the SeBaSi simulator, integrated with the data generator, to evaluate the performance of an IAB network in the London City scenario.

One of the key requirements for future 5th Generation (5G) Industrial Internet of Things (IIoT) networks will be to deliver low latency to support different production processes. To this end, 5G New Radio (NR) provides Configured Grant (CG) scheduling for periodic traffic, additionally to conventional Grant-Based Scheduling (GBS). However, in view of the complexities introduced by spatio-temporal traffic correlations in IIoT, a fixed scheduler configuration may be suboptimal: GBS introduces excessive signaling overhead, while CG leads to inefficient resource utilization and latency degradation when traffic is not perfectly periodic. To solve these critical issues, we propose Hybrid Centralized Uplink Scheduler (HCUS), a new scheduling framework that dynamically learns the type of traffic generated by User Equipments (UEs), and adapts resource allocation accordingly. HCUS operates per-UE, and dynamically switches between GBS and Configured Grant (CG), optimizing resource allocation while preserving low End-to-End (E2E) latency. We consider both mixed periodic and aperiodic uplink traffic to model different network load conditions and IIoT applications. Extensive simulations show that HCUS achieves up to four times lower latency than GBS and CG while maintaining high reliability, even considering traffic correlations or periodicity changes, making it a robust and scalable solution for next-generation IIoT scenarios.

Although widely employed in mission-critical applications, Underwater Acoustic Networks (UANs) are intrinsically susceptible to Denial of Service (DoS) attacks, due to the broadcast nature of the underwater acoustic channel. These security breaches can severely compromise the UAN’s operational integrity and disrupt the overall network performance, requiring adequate safeguards to be put in place. Reputation-based trust systems represent one of the most effective countermeasures against these threats, protecting from a wide range of attacks by predicting the reliability of nodes’ future behavior based on historical evidence such as their level of participation in network communication. However, this type of communication-based trust evidence, collected by nodes about their 1-hop neighbors, often leads to wrong trust assessments caused by poor link quality that could be avoided by taking channel conditions into account and/or incorporating third-party reputation information when evaluating trustworthiness. In [1], a channel-based reputation system weighing observations based on channel state is presented. Work presented in [2] expands such system by employing Trust- Related Data (TRD) dissemination to make any node in a multihop UAN aware of the trustworthiness of each other. This paper is a substantial extension of [2], introducing first-hand and second-hand TRD integration and rendering the system suitable for deployment by minimizing the amount of transmitted data. Additionally, we present a more comprehensive simulationbased evaluation of the proposed security framework, including a performance analysis in a scenario involving a mobile node, tests against attacks targeting the reputation system itself (such as slandering and spoofing) and a real-world validation conducted through a dedicated sea trial.

Millimeter wave (mmWave) 5th generation (5G) networks offer high data rates but face coverage challenges due to severe path loss and blockage. These problems motivate the use of Integrated Access and Backhaul (IAB) as a flexible wireless backhaul solution that extends connectivity to cell boundaries and unfibered areas, including maritime environments. This paper overviews the latest 3GPP specifications for IAB networks in Releases 16 through 18. Then, it presents an ns-3 module for IAB, featuring a complete end-to-end protocol stack, including the backhaul adaptation protocol (BAP) layer, flexible slot and control configurations, and multiplexing schemes based on both time and frequency division.We test the IAB module via extensive system-level simulations in a custom maritime scenario where vessels, equipped with IAB-nodes, can simultaneously act as access points and relays, forming dynamic multi-hop networks that maintain connectivity via wireless backhaul to shore-based stations. We evaluate different topologies and channel conditions, providing insights into the design and deployment of mmWave IAB networks in offshore environments.

Autonomous driving is a major paradigm shift in transportation, with the potential to enhance safety, optimize traffic congestion, and reduce fuel consumption. Although autonomous vehicles rely on advanced sensors and on-board computing systems to navigate without human control, full awareness of the driving environment also requires a cooperative effort via Vehicle-to-Everything (V2X) communication. Specifically, vehicles send and receive sensor observations to/from other vehicles to extend perception beyond their own sensing range. However, transmitting large volumes of data can be challenging for current V2X communication technologies, so data compression represents a crucial solution to reduce the message size and link congestion. In this paper, we present a statistical characterization of automotive data, focusing on Light Detection and Ranging (LiDAR) sensors. Notably, we provide models for the size of both raw and compressed point clouds. The use of statistical traffic models offers several advantages compared to using real data, such as faster simulations, reduced storage requirements, and greater flexibility in the application design. Furthermore, statistical models can be used for understanding traffic patterns and analyzing statistics, which is crucial to design and optimize wireless networks. We validate our statistical models via a Kolmogorov-Smirnoff (KS) test implementing a Bootstrap Resampling scheme. Moreover, we show via ns-3 simulations that using statistical models yields results in terms of latency and throughput that are comparable to real data, which also demonstrates the accuracy of the models.

One of the most prominent requirements of future Industrial Internet of Things (IIoT) networks will be to provide Ultra-Reliable Low-Latency Communication (URLLC) in support of critical physical processes underlying the production chains. However, standard protocols for allocating wireless resources may not be able to optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized (e.g., grant-based) scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and then granted by the Next Generation Node B (gNB). On the other hand, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the physical resources to transmit uplink data, may lead to potentially many collisions especially when the density of UEs (and so the traffic) increases. Along these lines, in this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal sent from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available physical resources so as to minimize the number of collisions, disaggregated from the network and without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple actions in parallel. We apply DISNETS to the context of IIoT, and demonstrate by extensive simulations the superior performance of the proposed approach in addressing URLLC compared to other baselines.

The integration of Internet of Things (IoT) and Non-Terrestrial Networks (NTNs) has emerged as a key paradigm to provide connectivity for sensors and actuators via satellite gateways in remote areas where terrestrial infrastructure is limited or unavailable. Among other Low-Power Wide-Area Network (LPWAN) technologies for IoT, Long Range (LoRa) holds great potential given its long range, energy efficiency, and flexibility. In this paper, we explore the feasibility and performance of LoRa to support large-scale IoT connectivity through Low Earth Orbit (LEO) satellite gateways. To do so, we developed a new ns3-LoRa-NTN simulation module, which integrates and extends the ns3-LoRa and ns3-NTN modules, to enable full-stack end-to-end simulation of satellite communication in LoRa networks. Our results, given in terms of average data rate and Packet Reception Ratio (PRR), confirm that LoRa can effectively support direct communication from the ground to LEO satellites, but network optimization is required to mitigate collision probability when end nodes use the same Spreading Factors (SFs) over long distances.

In 3GPP New Radio (NR) Vehicle-to-Everything (V2X), the new standard for next-generation vehicular networks, vehicles can autonomously select sidelink resources for data transmission, which permits network operations without cellular coverage. However, standalone resource allocation is uncoordinated, and is complicated by the high mobility of the nodes that may introduce unforeseen channel collisions (e.g., when a transmitting vehicle changes path) or free up resources (e.g., when a vehicle moves outside of the communication area). Moreover, unscheduled resource allocation is prone to the hidden node and exposed node problems, which are particularly critical considering directional transmissions. In this paper, we implement and demonstrate a new channel access scheme for NR V2X in Frequency Range 2 (FR2), i.e., at millimeter wave (mmWave) frequencies, based on directional and beamformed transmissions along with Sidelink Control Information (SCI) to select resources for transmission. We prove via simulation that this approach can reduce the probability of collision for resource allocation, compared to a baseline solution that does not configure SCI transmissions.

Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as delays or errors can lead to significant deviations of the AGVs from the planned trajectory. In this paper, we present a simulation framework based on Gazebo and Robot Operating System 2 (ROS 2) to simulate and visualize, respectively, the complex interaction between the control of AGVs and the underlying communication network. This framework explicitly incorporates communication metrics, such as delay and packet loss, and control metrics, especially the Mean Squared Error (MSE) between the optimal/desired and actual path of the AGV in response to driving commands. Our results shed light into the correlation between the network performance, particularly Packet Reception Ratio (PRR), and accuracy of control.

With the unprecedented diffusion of virtual reality, the number of application scenarios is continuously growing. As commercial and gaming applications become pervasive, the need for the secure and convenient identification of users, often overlooked by the research in immersive media, is becoming more and more pressing. Networked scenarios such as Cloud gaming or cooperative virtual training and teleoperation require both a user-friendly and streamlined experience and user privacy and security. In this work, we investigate the possibility of identifying users from their movement patterns and data traffic traces while playing four commercial games, using a publicly available dataset. If, on the one hand, this paves the way for easy identification and automatic customization of the virtual reality content, it also represents a serious threat to users’ privacy due to network analysis-based fingerprinting. Based on this, we analyze the threats and opportunities for virtual reality users’ security and privacy.

Vehicular Edge Computing (VEC) is a key research area in autonomous driving. As Intelligent Transportation Systems (ITSs) continue to expand, ground vehicles (GVs) face the challenge of handling huge amounts of sensor data to drive safely. Specifically, due to energy and capacity limitations, GVs will need to offload resource-hungry tasks to external (cloud) computing units for faster processing. In 6th generation (6G) wireless systems, the research community is exploring the concept of Non-Terrestrial Networks (NTNs), where satellites can serve as space edge computing nodes to aggregate, store, and process data from GVs. In this paper we propose new data offloading strategies between a cluster of GVs and satellites in the Low Earth Orbits (LEOs), to optimize the trade-off between coverage and end-to-end delay. For the accuracy of the simulations, we consider real data and orbits from the Starlink constellation, one of the most representative and popular examples of commercial satellite deployments for communication. Our results demonstrate that Starlink satellites can support real-time offloading under certain conditions that depend on the onboard computational capacity of the satellites, the frame rate of the sensors, and the number of GVs.

Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.

The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL). Among existing approaches, state-of-the-art bitrate-accuracy tradeoffs have been achieved using stochastic compression methods – in which the client n sends a sample from a client-only probability distribution $q_{\phi^{(n)}}$, and the server estimates the mean of the clients’ distributions using these samples. However, such methods do not take full advantage of the FL setup where the server, throughout the training process, has side information in the form of a global distribution $p_{\theta}$ that is close to the client-only distribution $q_{\phi^{(n)}}$ in Kullback-Leibler (KL) divergence. In this work, we exploit this \emph{closeness} between the clients’ distributions $q_{\phi^{(n)}}$’s and the side information $p_{\theta}$ at the server, and propose a framework that requires approximately $D_{KL}(q_{\phi^{(n)}}|| p_{\theta})$ bits of communication. We show that our method can be integrated into many existing stochastic compression frameworks to attain the same (and often higher) test accuracy with up to 82 times smaller bitrate than the prior work – corresponding to 2,650 times overall compression.

Several sixth generation (6G) use cases have tight requirements in terms of reliability and latency, in particular teleoperated driving (TD). To address those requirements, Predictive Quality of Service (PQoS), possibly combined with reinforcement learning (RL), has emerged as a valid approach to dynamically adapt the configuration of the TD application (e.g., the level of compression of automotive data) to the experienced network conditions. In this work, we explore different classes of RL algorithms for PQoS, namely MAB (stateless), SARSA (stateful on-policy), Q-Learning (stateful off-policy), and DSARSA and DDQN (with Neural Network (NN) approximation). We trained the agents in a federated learning (FL) setup to improve the convergence time and fairness, and to promote privacy and security. The goal is to optimize the trade-off between Quality of Service (QoS), measured in terms of the end-to-end latency, and Quality of Experience (QoE), measured in terms of the quality of the resulting compression operation. We show that Q-Learning uses a small number of learnable parameters, and is the best approach to perform PQoS in the TD scenario in terms of average reward, convergence, and computational cost.

The rapid development of Virtual Reality (VR) technology has led the industry and research community to look at its major challenges with increased interest. The main challenge in ensuring a high Quality of Experience (QoE) for users is represented by cybersickness, a phenomenon similar to motion sickness experienced by many VR users, while at the same time, the high data rates needed by VR require the definition of traffic models for network optimization. These two problems are intertwined, but have never been studied jointly before due to the lack of suitable datasets. In this paper, we present Questset, the first dataset designed for this purpose. Questset contains over 40 hours of VR traces from 70 users playing commercially available video games, and includes both traffic data for network optimization, and movement and user experience data for cybersickness analysis. Therefore, Questset represents an enabler to jointly address the main VR challenges in the near future.

In the last ten years several simulation studies on Autonomous Underwater Vehicle (AUV) swarm fleet formation have been performed, and some preliminary sea demonstrations of proof-of-concept prototypes were carried out. However, their actual realization is hindered by the challenges imposed by the underwater acoustic channel and the difficulties of keeping track of the vehicles’ positions due to the long latency required by traditional Two-Way Travel-Time (TWTT) ranging measurements, that require a specific signalling, hence limiting the throughput of the underwater network. Although One-Way Travel-Time (OWTT) halves the latency, it requires a high precision oscillator, such as an atomic clock or an oven controlled crystal oscillator (OCXO), to be installed in each modem processing unit: while atomic clocks are still very expensive, OCXO are very power demanding, making their application to underwater acoustic networks not always possible, especially in the case of low cost vehicle swarms. In this paper we present a network protocol stack able to perform ranging and localization within the communication task in underwater acoustic networks, limiting the network overhead. Specifically, new layers have been added to the preexisting DESERT Underwater protocol stack to perform the ranging tasks without compromising the correct operation of the communication network. A ranging layer is placed on top of the Medium Access Control (MAC) scheme, allowing the latter to be changed according to the network topology and requirements. This MAC-agnostic ranging layer is further optimized by adapting the amount of data transmitted according to the channel state, and the ranging entries inserted in the data packets according to the least recent information transmitted, hence minimizing the Age of Information. Simulation results obtained with the DESERT Underwater Framework show how this layer allows all AUVs in the swarm to know their distance from every other node in the network, thus limiting the probability of vehicle collisions and allowing better mission coordination.

The remote wireless control of industrial systems is one of the major use cases for 5G and beyond systems: in these cases, the massive amounts of sensory information that need to be shared over the wireless medium may overload even high-capacity connections. Consequently, solving theeffective communicationproblem by optimizing the transmission strategy to discard irrelevant information can provide a significant advantage, but is often a very complex task. In this work, we consider a prototypal system in which an observer must communicate its sensory data to a robot controlling a task (e.g., a mobile robot in a factory). We then model it as a remote Partially Observable Markov Decision Process (POMDP), considering the effect of adopting semantic and effective communication-oriented solutions on the overall system performance. We split the communication problem by considering an ensemble Vector Quantized Variational Autoencoder (VQ-VAE) encoding, and train a Deep Reinforcement Learning (DRL) agent to dynamically adapt the quantization level, considering both the current state of the environment and the memory of past messages. We tested the proposed approach on the well-known CartPole reference control problem, obtaining a significant performance increase over traditional approaches.

Massive Internet of Things systems, e.g., Low Power Wide Area Networks, aim at connecting very large numbers of low-cost devices with multi-year battery life requirements, a goal that is hard to achieve with current technologies. In this paper, a novel asynchronous spread spectrum technique, called Golden Modulation, is introduced. This modulation provides a vast family of equivalent waveforms with very low cross-correlation even in asynchronous conditions, hence enabling naturally massive multiuser operation without the need for inter-user synchronization or interference cancellation receivers. The basic modulation principles, which rely on spectrum spreading via direct Zadoff-Chu sequences modulation, are presented and the corresponding theoretical bit error rate performance in additive white Gaussian noise and multi-path channels is derived and compared by simulation with realistic receiver performance. The demodulation of the Golden Modulation is described, and its performance in the presence of uncoordinated multiple users is characterized and compared against LoRa in a variety of scenarios. Various higher-layer and backward compatibility issues are also discussed.

Within a recent line of research, age of information is supported as an alternate network performance metric with respect to throughput or delay, to evaluate the performance of medium access techniques, especially for remote sensing applications. Analytical investigations based on game theory have shown how selfish players can behave efficiently in random access systems if they are driven by AoI-based objectives. We extend this kind of reasoning to the case of a slotted ALOHA system with capture. We present a fully analytical derivation of the general framework and its main results. We provide a quantitative characterization for the strength of capture in relation to the efficiency of the resulting Nash equilibrium, which provides extremely useful insights for a distributed system management. We apply our analysis to some scenarios of interest, in particular the case of exponentially distributed powers, for which we obtain a closed-form relationship. We highlight the impact of the system parameters, specifically the cost coefficient and the capture threshold, towards achieving an efficient allocation that represents an equilibrium for the network management. It is ultimately shown that, when capture is strong, as quantified through precise conditions (the system is driven towards a Nash equilibrium achieving near-optimal performance).

Over the past few years, the concept of Virtual Reality (VR) has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by the VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation, focusing on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode may have significant frame size fluctuations, impacting NS optimization. We then exploited the models to dynamically determine requirements in an NS scenario, providing the required QoS while minimizing resource usage.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today’s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing Key Performance Indicators (KPIs) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing the average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in latency.

Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost; thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.

Future wireless networks and sensing systems will benefit from access to large chunks of spectrum above 100 GHz, to achieve terabit-per-second data rates in 6th Generation (6G) cellular systems and improve accuracy and reach of Earth exploration and sensing and radio astronomy applications. These are extremely sensitive to interference from artificial signals, thus the spectrum above 100 GHz features several bands which are protected from active transmissions under current spectrum regulations. To provide more agile access to the spectrum for both services, active and passive users will have to coexist without harming passive sensing operations. In this paper, we provide the first, fundamental analysis of Radio Frequency Interference (RFI) that large-scale terrestrial deployments introduce in different satellite sensing systems now orbiting the Earth. We develop a geometry-based analysis and extend it into a data-driven model which accounts for realistic propagation, building obstruction, ground reflection, for network topology with up to 105nodes in more than 85 km2. We show that the presence of harmful RFI depends on several factors, including network load, density and topology, satellite orientation, and building density. The results and methodology provide the foundation for the development of coexistence solutions and spectrum policy towards 6G.

In 5G New Radio (NR), beam management entails periodic and continuous transmission and reception of control signals in the form of synchronization signal blocks (SSBs), used to perform initial access and/or channel estimation. However, this procedure demands continuous energy consumption, which is particularly challenging to handle for low-cost, low-complexity, and battery-constrained devices, such as RedCap devices to support mid-market Internet of Things (IoT) use cases. In this context, this work aims at reducing the energy consumption during beam management for RedCap devices, while ensuring that the desired Quality of Service (QoS) requirements are met. To do so, we formalize an optimization problem in an Indoor Factory (InF) scenario to select the best beam management parameters, including the beam update periodicity and the beamwidth, to minimize energy consumption based on users' distribution and their speed. The analysis yields the regions of feasibility, i.e., the upper limit(s) on the beam management parameters for RedCap devices, that we use to provide design guidelines accordingly.

This paper explores the issue of enabling Ultra-Reliable Low-Latency Communications (URLLC) in view of the spatio-temporal correlations that characterize real 5th generation (5G) Industrial Internet of Things (IIoT) networks. In this context, we consider a common Standalone Non-Public Network (SNPN) architecture as promoted by the 5G Alliance for Connected Industries and Automation (5G-ACIA), and propose a new variant of the 5G NR semi-persistent scheduler (SPS) to deal with uplink traffic correlations. A benchmark solution with a “smart” scheduler (SSPS) is compared with a more realistic adaptive approach (ASPS) that requires the scheduler to estimate some unknown network parameters. We demonstrate via simulations that the 1-ms latency requirement for URLLC is fulfilled in both solutions, at the expense of some complexity introduced in the management of the traffic. Finally, we provide numerical guidelines to dimension IIoT networks as a function of the use case, the number of machines in the factory, and considering both periodic and aperiodic traffic.

We investigate the performance of concurrent remote sensing from independent strategic sources, whose goal is to minimize a linear combination of the freshness of information and the updating cost. In the literature, this is often investigated from a static perspective of setting the update rate of the sources a priori, either in a centralized optimal way or with a distributed game-theoretic approach. However, we argue that truly rational sources would better make such a decision with full awareness of the current age of information, resulting in a more efficient implementation of the updating policies. To this end, we investigate the scenario where sources independently perform a stateful optimization of their objective. Their strategic character leads to the formalization of this problem as a Markov game, for which we find the resulting Nash equilibrium. This can be translated into practical smooth threshold policies for their update. The results are eventually tested in a sample scenario, comparing a centralized optimal approach with two distributed approaches with different objectives for the players.

Accurate channel modeling and simulation tools are vital for studying sub-THz and millimeter (mmWave) wideband communication system performance. To accurately design future high data rate, low latency wireless modems, the entire protocol stack must be appropriately modeled to understand how the physical layer impacts the end-to-end performance experienced by the end user. This paper presents a full stack end-to-end performance analysis in ns-3 using drop-based NYU channel model (NYUSIM) and 3GPP statistical channel model (SCM) in scenarios, namely urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), and indoor hotspot (InH) at 28 GHz with 100 MHz bandwidth. Video data is transmitted at 50 Mbps using User Datagram Protocol (UDP), and we observe that the RMa channel is benign in non-line of sight (NLOS) for NYUSIM and 3GPP SCM as it exhibits no packet drops and yields maximum throughput (48.1 Mbps) and latency of ∼ 20 ms. In NLOS, for NYUSIM, the UMa and RMa channels are similar in terms of throughput and packet drops, and the latency in UMi and InH scenarios is 10 times and 25 times higher respectively compared to UMa. Our results indicate that mmWave bands can support data rates of 50 Mbps with negligible packet drops and latency below 150 ms in all scenarios using NYUSIM.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today’s mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

We consider the problem of communicating a sequence of concepts, i.e., unknown and potentially stochastic maps, which can be observed only through examples, i.e., the mapping rules are unknown. The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data by optimizing a probability distribution over a set of models, i.e., known functions, which can better describe the observed data, and so potentially the underlying concepts. The transmitter then needs to communicate the learned models to a remote receiver through a rate-limited channel, to allow the receiver to decode the models that can describe the underlying sampled concepts as accurately as possible in their semantic space. After motivating our analysis, we propose the formal problem of communicating concepts, and provide its rate-distortion characterization, pointing out its connection with the concepts of empirical and strong coordination in a network. We also provide a bound for the distortion-rate function.

The millimeter-wave (mm-wave) bands enable very large antenna arrays that can generate narrow beams for beamforming and spatial multiplexing. However, directionality introduces beam misalignment and leads to reduced energy efficiency. Thus, employing the narrowest possible beam in a cell may not necessarily imply maximum coverage. The objective of this work is to determine the optimal sector beamwidth for a cellular architecture served by an unmanned aerial vehicle (UAV) acting as a base station (BS). The users in a cell are assumed to be distributed according to a Poisson Point Process (PPP) with a given user density. We consider hybrid beamforming at the UAV, such that multiple concurrent beams serve all the sectors simultaneously. An optimization problem is formulated to maximize the sum rate over a given area while limiting the total power available to each sector. We observe that, for a given transmit power, the optimal sector beamwidth increases as the user density in a cell decreases, and varies based on the height of the UAV. Thus, we provide guidelines towards the optimal beamforming configurations for users in rural areas.

Intelligent reflecting surfaces (IRSs) are being studied as possible low-cost energy-efficient alternatives to active relays, with the goal of improving coverage in millimeter wave (mmWave) and terahertz (THz) network deployments. In the literature, these surfaces are often studied by idealizing their characteristics: notably, it is often assumed that IRSs can tune with arbitrary frequency the phase-shifts induced by their elements, thanks to a wire-like control channel to the next generation node base (gNB). Instead, in this work we investigate an IRS-aided time division multiple access (TDMA) cellular network, where the reconfiguration of the IRS entails an energy or communication cost, and we aim at limiting the number of reconfigurations over time. We propose a clustering-based heuristic scheduling, which optimizes the cell sum-rate subject to a given number of reconfigurations within the TDMA frame. To this end, we first cluster user equipments (UEs) with a similar optimal IRS configuration, determined through a novel beamforming and IRS iterative optimization algorithm. Then, we obtain a single IRS configuration for each cluster of UEs. Numerical results show that our approach is effective in supporting IRSs-aided systems with practical constraints, achieving up to 85% of the sum-rate obtained by an ideal deployment, while reducing by 50% the number of IRS reconfigurations.

millimeter wave (mmWave) and sub-terahertz (THz) communications have the potential of increasing mobile network throughput drastically. However, the challenging propagation conditions experienced at mmWave and beyond frequencies can potentially limit the range of the wireless link down to a few meters, compared to up to kilometers for sub-6GHz links. Thus, increasing the density of base station deployments is required to achieve sufficient coverage in the Radio Access Network (RAN). To such end, 3rd Generation Partnership Project (3GPP) introduced wireless backhauled base stations with Integrated Access and Backhaul (IAB), a key technology to achieve dense networks while preventing the need for costly fiber deployments. In this paper, we introduce SeBaSi, a system-level simulator for IAB networks, and demonstrate its functionality by simulating IAB deployments in Manhattan, New York City and Padova. Finally, we show how SeBaSi can represent a useful tool for the performance evaluation of self-backhauled cellular networks, thanks to its high level of network abstraction, coupled with its open and customizable design, which allows users to extend it to support novel technologies such as Reconfigurable Intelligent Surfaces (RISs).

Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due to the high cost of performing segmentation labeling, many synthetic datasets have been proposed. However, most of them miss the multi-sensor nature of the data, and do not capture the significant changes introduced by the variation of daytime and weather conditions. To fill these gaps, we introduce SELMA, a novel synthetic dataset for semantic segmentation that contains more than 30K unique waypoints acquired from 24 different sensors including RGB, depth, semantic cameras and LiDARs, in 27 different weather and daytime conditions, for a total of more than 20M samples. SELMA is based on CARLA, an open-source simulator for generating synthetic data in autonomous driving scenarios, that we modified to increase the variability and the diversity in the scenes and class sets, and to align it with other benchmark datasets. As shown by the experimental evaluation, SELMA allows the efficient training of standard and multi-modal deep learning architectures, and achieves remarkable results on real-world data. SELMA is free and publicly available, thus supporting open science and research.

Network slicing is a key feature of 5G and beyond networks, allowing the deployment of separate logical networks (network slices), sharing a common underlying physical infrastructure, and characterized by distinct descriptors and behaviors. The dynamic allocation of physical network resources among coexisting slices should address a challenging trade-off: to use resources efficiently while assigning each slice sufficient resources to meet its service level agreement (SLA). We consider the allocation of time-frequency resources from a new perspective: to design a control algorithm capable of learning over the operating network, while keeping the SLA violation rate under an acceptable level during the learning process. For this purpose, traditional model-free reinforcement learning (RL) methods present several drawbacks: low sample efficiency, extensive exploration of the policy space, and inability to discriminate between conflicting objectives, causing inefficient use of the resources and/or frequent SLA violations during the learning process. To overcome these limitations, we propose a model-based RL approach built upon a novel modeling strategy that comprises a kernel-based classifier and a self-assessment mechanism. In numerical experiments, our proposal, referred to as kernel-based RL, clearly outperforms state-of-the-art RL algorithms in terms of SLA fulfillment, resource efficiency, and computational overhead.

With the introduction of Orthogonal Frequency Division Multiple Access (OFDMA) in 802.11ax, the role of the Access Point (AP) in Wi-Fi networks changes significantly, thanks to the opportunity of implementing more complex scheduling logic to handle Downlink (DL) traffic flows and simultaneously act as coordinator of Multi User (MU) Uplink (UL) transmissions. In this context, it becomes necessary to develop reliable network analysis and simulation tools that allow for an in-depth investigation of the trade-offs involved in the usage of OFDMA, especially considering that the standard leaves much of the actual scheduling algorithmic details to vendor-specific implementation. In this work we present a series of results highlighting how several network settings have an impact on throughput and Head-of-Line Delay, in a network that employs multiple 802.11ax features such as OFDMA and the MU Enhanced Distributed Channel Access (EDCA) Parameter Set, while also containing legacy devices. The results are obtained via both the newly re-designed ns-3wifimodule and an original analytical framework, based on the well-established Bianchi 802.11 model.

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum, thus making it possible to wirelessly approach several emerging use cases, such as eXtended Reality (XR), telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service (QoS), that only contention-free scheduling algorithms can guarantee. In this paper, we propose a framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3 (ns-3), a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the trade-offs between contention-based and contention-free access strategies.

We consider a remote contextual multi-armed bandit (CMAB) problem, in which the decision-maker observes the context and the reward, but must communicate the actions to be taken by the agents over a rate-limited communication channel. This can model, for example, a personalized ad placement application, where the content owner observes the individual visitors to its website, and hence has the context information, but must convey the ads that must be shown to each visitor to a separate entity that manages the marketing content. In this remote CMAB (R-CMAB) problem, the constraint on the communication rate between the decision-maker and the agents imposes a trade-off between the number of bits sent per agent and the acquired average reward. We are particularly interested in characterizing the rate required to achieve sub-linear regret. Consequently, this can be considered as a policy compression problem, where the distortion metric is induced by the learning objectives. We first study the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret achieved when Thompson sampling strategy is adopted. In particular, we identify two distinct rate regions resulting in linear and sub-linear regret behavior, respectively. Then, we provide upper bounds for the achievable regret when the decision-maker can reliably transmit the policy without distortion.

In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly. In this context, Reinforce-ment Learning (RL) has come out as a promising approach to perform accurate predictions, and optimize the efficiency and adaptability of wireless networks. Along these lines, in this paper we propose the design of a new entity, integrated at the RAN level that implements PQoS functionalities with the support of an RL framework. Specifically, we focus on the design of the reward function of the learning agent, able to convert QoS estimates into appropriate countermeasures if QoS requirements are not satisfied. We demonstrate via ns-3 simulations that our approach achieves better results in terms of QoS and Quality of Experience (QoE) performance of end users in a teleoperated driving scenario.

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR’s interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

Underwater acoustic networks are often used in mission-critical scenarios, such as military underwater networks and assets deployed for tsunami prevention; hence, an attack performed against these types of networks can easily lead to disastrous consequences. Nevertheless, countermeasures to possible network attacks have not been widely investigated so far. A reputation system, where a node gains trust each time it exhibits a good behavior, and loses trust each time it behaves suspiciously, is an effective way to identify possible attackers in the network. The main challenge when applying a reputation system in an underwater network is to understand whether the network performance degrades because a node is acting maliciously intentionally, or because of the changed channel conditions, causing a large packet drop. For instance, when a ship travels close to an underwater network deployment, it causes an increased packet loss, and so does the change of environmental conditions, such as a drop of temperature, the presence of rain or the increase of the wind speed. This behavior of the acoustic channel can be characterized with a hidden Markov model, whose parameters are obtained observing the time evolution of the acoustic channel in a sea experiment. This article presents a trust model based on the knowledge of the channel state, inferred from the perceived noise and received signal strength, in which misbehavior and correct behavior are considered differently according to the actual channel state. We evaluate the model both analytically and through simulations, implementing the trust mechanism in the DESERT Underwater Network framework.

This paper studies the cross-layer challenges and performance of Hybrid Beamforming (HBF) and Multi-User Multiple-Input Multiple-Output (MU-MIMO) in 5G millimeter wave (mmWave) cellular networks with full-stack TCP/IP traffic and MAC scheduling. While previous research on HBF and MU-MIMO has focused on link-level analysis of full-buffer transmissions, this work reveals the interplay between HBF techniques and the higher layers of the protocol stack. To this aim, prior work on the full-stack evaluation of mmWave cellular networks has been extended by including the modeling of MU-MIMO and HBF. Our results reveal novel relations between the networking layers and the HBF MU-MIMO performance at the physical layer. Particularly, throughput can be increased in 5G networks by means of Space Division Multiple Access (SDMA). However, in order to achieve such benefits it is necessary to take into account certain trade-offs and the implementation complexity of a full-stack HBF solution.

LoRaWAN is a Low Power Wide Area Network technology featuring long transmission ranges and a simple MAC layer, which can support sensor data collection, control applications and reliable services thanks to the flexibility offered by a large set of configurable system parameters. However, the impact of such parameters settings on the system’s performance is often difficult to predict, depending on several factors. To ease this task, in this paper, we provide a mathematical model to estimate the performance of a LoRaWAN gateway serving a set of devices that may or may not employ confirmed traffic. The model features a set of parameters that can be adjusted to investigate different gateway and end-device configurations, making it possible to carry out a systematic analysis of various trade-offs. The results given by the proposed model are validated through realistic ns-3 simulations that confirm the ability of the model to predict the system performance with high accuracy, and assess the impact of the assumptions made in the model for tractability.

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design anad hoctracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. We analyze the trade-off in setting the main parameters of the communication system and Adaptive Data Rate (ADR) scheme, showing how our tracking system can handle large swarms of drones at distances up to 4 km. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a swarm of UAVs, significantly outperforming baseline tracking approaches.

The next generations of mobile networks will be deployed as ultra-dense networks, to match the demand for increased capacity and the challenges that communications in the higher portion of the spectrum (i.e., the mmWave band) introduce. Ultra-dense networks, however, require pervasive, high-capacity backhaul solutions, and deploying fiber optic to all base stations is generally considered to be too expensive for network operators. The 3gpp has thus introduced iab, a wireless backhaul solution in which the access and backhaul links share the same hardware, protocol stack, and also spectrum. The multiplexing of different links in the same frequency bands, however, introduces interference and capacity sharing issues, thus calling for the introduction of advanced scheduling and coordination schemes. This paper proposes a semi-centralized resource allocation scheme for iab networks, designed to be flexible, with low complexity, and compliant with the 3gpp iab specifications. We develop a version of the mwm problem that can be applied on a spanning tree that represents the iab network and whose complexity is linear in the number of iab-nodes. The proposed solution is compared with state-of-the-art distributed approaches through end-to-end, full-stack system-level simulations with a 3gpp-compliant channel model, protocol stack, and a diverse set of user applications. The results show that our scheme can increase the throughput of cell-edge users up to 3 times, while decreasing the overall network congestion with an end-to-end delay reduction of up to 25 times.

Security is a critical consideration in Underwater Acoustic Networks (UANs) due to the importance of the applications in which these types of networks are often employed, from military applications to marine natural disaster prevention. Furthermore, even simple Denial of Service (DoS) attacks such as jamming can be very effective in disrupting the communication, with significant negative consequences for these critical applications. While jamming has been widely studied in the context of terrestrial networks, the peculiarities of propagation in UANs, such as the low propagation speed, the multipath, and the high delay spread, need to be considered: the relative positions of the jammer, transmitter, and receiver can have a huge impact on the feasibility and impact of reactive jamming, opening the way for the exploitation of other jamming models. In this paper, we analyze the effectiveness of a reactive and a blind jammer through a game theoretical framework, comparing them for different geometries of the scenario. We assess the impact of the different jammers, employing different active and evasive strategies, where the first type of countermeasure implies the use of additional energy to protect the communication, while the second tries to avoid the jamming signals by randomizing the transmission pattern.

Millimeter wave (mmWave) communication represents one of the main innovations of the next generation of wireless technologies, allowing users to reach unprecedented data rates. To overcome the high path loss at mmWave frequencies, these systems make use of directional antennas able to focus the transmit power into narrow beams using BeamForming (BF) techniques, thus making the communication directional. This new paradigm opens up a set of challenges for the design of efficient wireless systems, in which antenna and BF components play an important role also at the higher layer of the protocol stack. For this reason, accurate modeling of these components in a full-stack simulation is of primary importance to understand the overall system behavior.This paper proposes a novel framework for the end-to-end simulation of 5G mmWave cellular networks, including a raytracing based channel model and accurate models for antenna arrays and BF schemes. We showcase this framework by evaluating the performance of different antenna and BF configurations considering both link-level and end-to-end metrics and present the obtained results.

In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role. In this context, new network solutions have to be designed to handle the large volumes of data generated by the rich sensor suite of the cars in a reliable and efficient way. Among all the possible sensors, Light Detection and Ranging (LiDAR) can produce an accurate 3D point cloud representation of the surrounding environment, which in turn generates high data rates. For this reason, efficient point cloud compression is paramount to alleviate the burden of data transmission over bandwidth-constrained channels and to facilitate real-time communications. In this paper, we propose a pipeline to efficiently compress LiDAR observations in an automotive scenario. First, we leverage the capabilities of RangeNet++, a Deep Neural Network (DNN) used to semantically infer point labels, to reduce the channel load by selecting the most valuable environmental data to be disseminated. Second, we compress the selected points using Draco, a 3D compression algorithm which is able to obtain compression up to the quantization error. Our experiments, validated on the Semantic KITTI dataset, demonstrate that it is possible to compress and send the information at the frame rate of the LiDAR, thus achieving real-time performance.

Hybrid analog and digital BeamForming (HBF) is one of the enabling transceiver technologies for millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) systems. This technology offers highly directional communication, which is able to confront the intrinsic characteristics of mmWave signal propagation. However, the small coherence time in mmWave systems, especially under mobility conditions, renders efficient Beam Management (BM) in standalone mmWave communication a very difficult task. In this paper, we consider HBF transceivers with planar antenna panels and design a multilevel beam codebook for the analog beamformer comprising flat top beams with variable widths. These beams exhibit an almost constant array gain for the whole desired angle width, thereby facilitating efficient hierarchical BM. Focusing on the uplink communication, we present a novel beam training algorithm with dynamic beam ordering, which is suitable for the stringent latency requirements of the latest mmWave standard discussions. Our simulation results showcase the latency performance improvement and received signal-to-noise ratio with different variations of the proposed scheme over the optimum beam training scheme based on exhaustive narrow beam search.

The IEEE 802.11ad standard extends WiFi operation to the millimeter wave frequencies, and introduces novel features concerning both the physical (PHY) and Medium Access Control (MAC) layers. The hybrid MAC layer provides for two different kinds of resource allocations: Contention Based Access Periods (CBAPs) and contention-free Service Periods (SPs). In this paper, we propose a Markov Chain model to represent CBAPs, taking into account operation interruptions due to scheduled SPs and the deafness and hidden node problems that directional communication exacerbates. We also propose a mathematical analysis to evaluate the interference among stations and derive analytical expressions to assess the impact of various system parameters on some key performance metrics such as throughput, delay, and packet drop rate. This information may be used to efficiently design a transmission scheduler that allocates contention-based and contention-free periods based on the application requirements.

In this article, we evaluate the performance of a LoRaWAN network in industrial scenarios where different Industrial Internet of Things (IIoT) end nodes communicate to a central controller in order to provide monitoring and sensing information to optimize the efficiency of industrial processes and reduce costs. In particular, we consider confirmed and unconfirmed traffic, multigateway deployments, the usage of different classes of devices, and a nonstandard channel plan. Furthermore, we analyze the higher-layer impact of different models of LoRa PHY layer with industrial channel models. We show that, with proper configuration, LoRaWAN is able to serve IIoT sensing applications with a packet success rate over 90%, providing at the same time limited communication delays.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems. The high propagation loss in this portion of the spectrum calls for the deployment of large antenna arrays to compensate for the loss through high directional gain, thus introducing the need for a spatial dimension in the channel model to accurately represent the performance of a mmWave network. In this perspective, ray tracing can characterize the channel in terms of Multi Path Components (MPCs) to provide a highly accurate model, at the price of extreme computational complexity (e.g., for processing detailed environment information about the propagation), which may limit the scalability of the simulations. In this paper, we present possible simplifications to improve the trade-off between accuracy and complexity in ray-tracing simulations at mmWaves by reducing the total number of MPCs. The effect of such simplifications is evaluated from a full-stack perspective through end-to-end simulations, testing different configuration parameters, propagation scenarios, and higher-layer protocol implementations. We then provide guidelines on the optimal degree of simplification, for which it is possible to reduce the complexity of simulations with a minimal reduction in accuracy for different deployment scenarios.

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental for the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source National Institute of Standards and Technology (NIST) QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.

3GPP NR V2X represents the new 3GPP standard for next-generation vehicular systems which, among other innovations, supports vehicle-to-vehicle (V2V) operations in the millimeter wave (mmWave) spectrum to address the communication requirements of future intelligent automotive networks. While mmWaves will enable massive data rates and low latency, the propagation characteristics at very high frequencies become very challenging, thereby calling for accurate performance evaluations as a means to properly assess the performance of such systems. Along these lines, in this paper we use MilliCar, the new ns-3 module based on the latest NR V2X specifications, to provide an end-to-end performance evaluation of mmWave V2V networks. We investigate the impact of different propagation scenarios and system parameters, including the inter-vehicle distance, the adopted frame numerology, and the modulation and coding scheme, and provide guidelines towards the most promising V2V deployment configurations.

This paper proposes link scheduling and power allocation algorithms for mmWave picocellular integrated access and backhaul networks. The proposed algorithm does not assume that interference is negligible and takes it into account in the solution. The algorithm supports any state of the art multi-user MIMO and hybrid beamforming schemes at the physical layer. The link scheduling and power allocation subproblems are solved using two separate simulated annealing (SA) algorithms which can also be individually combined with other pre-existing suboptimal algorithms for the other subproblem. Particularly, mixed-integer linear programming for fixed power link scheduling, which is optimal in a special interference-free case, is shown to perform much worse than the proposed algorithm. SA link scheduling with water-filling power allocation performs close to the optimal, while reducing power allocation complexity.

Recent developments in robotics and communication technologies are paving the way towards the use of Unmanned Aerial Vehicles (UAVs) to provide ubiquitous connectivity in public safety scenarios or in remote areas. The millimeter wave (mmWave) spectrum, in particular, has gained momentum since the huge amount of free spectrum available at such frequencies can yield very high data rates. In the UAV context, however, mmWave operations may incur severe signal attenuation and sensitivity to blockage, especially considering the very long transmission distances involved. In this paper, we present a tractable stochastic analysis to characterize the coverage probability of UAV stations operating at mmWaves. We exemplify some of the trade-offs to be considered when designing solutions for mmWave scenarios, such as the beamforming configuration, and the UAV altitude and deployment.

Network slicing of multi-access edge computing (MEC) resources is expected to be a pivotal technology to the success of 5G networks and beyond. The key challenge that sets MEC slicing apart from traditional resource allocation problems is that edge nodes depend on tightly-intertwined and strictly-constrained networking, computation and storage resources. Therefore, instantiating MEC slices without incurring in resource over-provisioning is hardly addressable with existing slicing algorithms. The main innovation of this paper is Sl-EDGE, a unified MEC slicing framework that allows network operators to instantiate heterogeneous slice services (e.g., video streaming, caching, 5G network access) on edge devices. We first describe the architecture and operations of Sl-EDGE, and then show that the problem of optimally instantiating joint network-MEC slices is NP-hard. Thus, we propose near-optimal algorithms that leverage key similarities among edge nodes and resource virtualization to instantiate heterogeneous slices 7.5x faster and within 25% of the optimum. We first assess the performance of our algorithms through extensive numerical analysis, and show that Sl-EDGE instantiates slices 6x more efficiently then state-of-the-art MEC slicing algorithms. Furthermore, experimental results on a 24-radio testbed with 9 smartphones demonstrate that Sl-EDGE provides simultaneously highly-efficient slicing of joint LTE connectivity, video streaming over WiFi, and ffmpeg video transcoding.

The recently proposed QUIC protocol has been widely adopted at the transport layer of the Internet over the past few years. Its design goals are to overcome some of TCP's performance issues, while maintaining the same properties and basic application interface. Two of the main drivers of its success were the integration with the innovative Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control mechanism, and the possibility of multiplexing different application streams over the same connection. Given the strong interest in QUIC shown by the ns-3 community, we present an extension to the native QUIC module that allows researchers to fully explore the potential of these two features. In this work, we present the integration of BBR into the QUIC module and the implementation of the necessary pacing and rate sampling mechanisms, along with a novel scheduling interface, with three different scheduling flavors. The new features are tested to verify that they perform as expected, using a web traffic model from the literature.

Security aspects in underwater wireless networks have not been widely investigated so far, despite the critical importance of the scenarios in which these networks can be employed. For example, an attack to a military underwater network for enemy targeting or identification can lead to serious consequences. Similarly, environmental monitoring applications, such as tsunami prevention, are also critical from a public safety point of view. In this article, we assess a scenario in which a malicious node tries to perform a jamming attack, degrading the communication quality of battery-powered underwater nodes. The legitimate transmitter may use packet-level coding to increase the chances of correctly delivering packets. Because of the energy limitation of the nodes, the jammer's objective is twofold: 1) disrupting the communication and 2) reducing the lifetime of the victim by making it send more redundancy. We model the jammer and the transmitter as players in a multistage game, deriving the optimal strategies. We evaluate the performance both in a model-based scenario and using real experimental data, and perform a sensitivity analysis to evaluate the performance of the strategies if the real channel model is different from the one they use.

Bike-sharing systems have become an established reality in cities all across the world and are a key component of the Smart City paradigm. However, the unbalanced traffic patterns during rush hours can completely empty some stations, while filling others, and the service becomes unavailable for further users. The traditional approach to solve this problem is to use rebalancing trucks, which take bikes from full stations and deposit them at empty ones, reducing the likelihood of system outages. Another paradigm that is gaining steam is gamification, i.e., incentivizing users to fix the system by influencing their behavior with rewards and prizes. In this work, we combine the two efforts and show that a joint optimization considering both rebalancing and incentives results in a higher service quality for a lower cost than using simple rebalancing. We use simulations based on the New York CitiBike usage data to validate our model and analyze several schemes to optimize the bike-sharing system.

In a wirelessly-powered communication network (WPCN), an energy access point (E-AP) supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store their received energy in their batteries for possible data transmission. In this paper, we propose an online control policy for energy transfer from the E-AP to the wireless nodes and for data transfer among the nodes. With our proposed control policy, all data queues of the nodes are stable, while the average energy consumption of the network is shown to be within a bounded gap of the minimum energy required for stabilizing the network. Our proposed policy is designed using a quadratic Lyapunov function to capture the limitations on the energy consumption of the nodes imposed by their battery levels. We show that under the proposed control policy, the backlog level in the data queues and the stored energy level in the batteries fluctuate in small intervals around some constant levels. Consequently, by imposing a negligible average data drop rate, the data buffer size and the battery capacity of the nodes can be significantly reduced.

Jamming may become a serious threat in Internet of Things networks of battery-powered nodes, as attackers can disrupt packet delivery and significantly reduce the lifetime of the nodes. In this work, we model an active defense scenario in which an energy-limited node uses power control to defend itself from a malicious attacker, whose energy constraints may not be known to the defender. The interaction between the two nodes is modeled as an asymmetric Bayesian game where the victim has incomplete information about the attacker. We show how to derive the optimal Bayesian strategies for both the defender and the attacker, which may then serve as guidelines to develop and gauge efficient heuristics that are less computationally expensive than the optimal strategies. For example, we propose a neural-network-based learning method that allows the node to effectively defend itself from the jamming with a significantly reduced computational load. The outcomes of the ideal strategies highlight the tradeoff between node lifetime and communication reliability and the importance of an intelligent defense from jamming attacks.

This paper studies the maximum throughput achievable with optimal scheduling in multi-hop mmWave picocellular networks with Multi-user Multiple-Input Multiple-Output (MU-MIMO) radios. MU-MIMO enables simultaneous transmission to multiple receivers (Space Division Multiplexing) and simultaneous reception from multiple transmitters (Space Division Multiple Access). The main contribution is the extension to MU-MIMO of the Network Utility Maximization (NUM) scheduling framework for multi-hop networks. We generalize to MU-MIMO the classic proof that Maximum Back Pressure (MBP) scheduling is NUM optimal. MBP requires the solution of an optimization that becomes harder with MU-MIMO radios. In prior models with one-to-one transmission and reception, each valid schedule was a matching over a graph. However, with MU-MIMO each valid schedule is, instead, a Directed Bipartite SubGraph (DBSG). In the general case this prevents finding efficient algorithms to solve the scheduler. We make MU-MIMO MBP scheduling tractable by assuming fixed power allocation, so the optimal scheduler is the Maximum Weighted DBSG. The MWDBSG problem can be solved using standard Mixed Integer Linear Programing. We simulate multi-hop mmWave picocellular networks and show that a MU-MIMO MBP scheduler enables a 160% increase in network throughput versus the classic one-to-one MBP scheduler, while fair rate allocation mechanisms are used in both cases.

In this work, we face the issue of achieving an efficient dynamic mapping in vehicular networking scenarios, i.e., obtaining an accurate estimate of the positions and trajectories of connected vehicles in a certain area. State-of-the-art solutions are based on the periodic broadcasting of the position information of the network nodes, with an inter-transmission period set by a congestion control scheme. However, the movements and maneuvers of vehicles can often be erratic, making transmitted data inaccurate or downright misleading. To address this problem, we propose to adopt a dynamic transmission scheme based on the actual positioning error, sending new data when the estimate overcomes a preset error threshold. Furthermore, the proposed method adapts the error threshold to the operational context according to an innovative congestion control algorithm that limits the collision probability among broadcast packet transmissions. This threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, and is shown to improve the overall positioning accuracy by more than 20% in realistic urban scenarios.

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation- based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.

The growing penetration of on-board communication units is enabling intelligent vehicles to share their sensor data with cloud computing platforms as well as with other vehicles. Although this unlocks the possibility of a variety of emerging applications, the massive amount of data traffic in vehicular networks is expected to pose a big challenge in the long term. In this paper, we shed light on the potential of value-anticipating networking to tackle this issue. A vehicle sending a piece of information first anticipates the value of that information for potential receivers. When the network is congested, the sender may defer or even cancel transmissions of less valuable information, so that important information can be delivered to receivers more reliably. We investigate the applicability of this concept to cooperative perception, where vehicles exchange processed sensor data over vehicle-to-vehicle (V2V) networks to collaboratively improve coverage and accuracy of environmental perception. Through simulations based on realistic road traffic, we show that value-anticipating V2V communications can significantly improve the performance of cooperative perception under heavy network load.

The automotive industry is rapidly evolving towards connected and autonomous vehicles, whose ever more stringent data traffic requirements might exceed the capacity of traditional technologies for vehicular networks. In this scenario, densely deploying millimeter wave (mmWave) base stations is a promising approach to provide very high transmission speeds to the vehicles. However, mmWave signals suffer from high path and penetration losses which might render the communication unreliable and discontinuous. Coexistence between mmWave and Long Term Evolution (LTE) communication systems has therefore been considered to guarantee increased capacity and robustness through heterogeneous networking. Following this rationale, we face the challenge of designing fair and efficient attachment policies in heterogeneous vehicular networks. Traditional methods based on received signal quality criteria lack consideration of the vehicle's individual requirements and traffic demands, and lead to suboptimal resource allocation across the network. In this paper we propose a Quality-of-Service (QoS) aware attachment scheme which biases the cell selection as a function of the vehicular service requirements, preventing the overload of transmission links. Our simulations demonstrate that the proposed strategy significantly improves the percentage of vehicles satisfying application requirements and delivers efficient and fair association compared to state-of-the-art schemes.

In the last decades, technological developments in wireless communications have been coupled with an increasing demand of mobile services. From real-time applications with focus on entertainment (e.g., high quality video streaming, virtual and augmented reality), to industrial automation and security scenarios (e.g., video surveillance), the requirements are constantly pushing the limits of communication hardware and software. Communications at millimeter wave frequencies could provide very high throughput and low latency, thanks to the large chunks of available bandwidth, but operating at such high frequencies introduces new challenges in terms of channel reliability, which eventually impact the overall end-to-end performance. In this paper, we introduce a proxy that coordinates the physical and transport layers to seamlessly adapt to the variable channel conditions and avoid performance degradation (i.e., latency spikes or low throughput). We study the performance of the proposed solution using a simulated IEEE 802.11ad-compliant network, with the integration of input traces generated from measurements from real devices, and show that the proposed proxy-based mechanism reduces the latency by up to 50% with respect to TCP CUBIC on a 60 GHz link.

The typical workflow for ns-3 users consists of coming up with an experiment, translating that idea to simulation code, running multiple simulations, analyzing the outcomes, and finally plotting results. So far, the ns-3 project has not been providing tools to cover the steps from running simulations to obtaining plots: research teams typically develop their own custom solutions, and often need to learn new tools in order to reproduce results found in the literature. In this work we propose a framework that allows ns-3 users to go from their simulation script to plots in as few lines of code as possible, hiding tedious details about simulation running and result management, and leveraging Python's widely established statistical analysis tools to quickly perform simulations, analyze their outcomes, and plot results. The code and its documentation, which have been in part developed under the Google Summer of Code 2018 program, are publicly available at~\citesem, semdocs.

Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings. In such uses, millimeter wave (mmWave) wireless links can provide high throughput and low latency connectivity between the UAV and a remote command center. However, maintaining reliable aerial communication in the mmWave bands is challenging due to the need to support high speed beam tracking and overcome blockage. This paper provides a simulation study aimed at assessing the feasibility of public safety UAV connectivity through a 5G link at 28 GHz. Real flight motion traces are captured during maneuvers similar to those expected in public safety settings. The motions traces are then incorporated into a detailed mmWave network simulator that models the channel, blockage, beamforming and full 3GPP protocol stack. We show that 5G mmWave communications can deliver throughput up to 1 Gbps with consistent sub ms latency when the base station is located near the mission area, enabling remote offloading of the UAV control and perception algorithms.

The Long Term Evolution (LTE) standard enables, besides cellular connectivity, basic automotive services to promote road safety through vehicle-to-infrastructure (V2I) communications. Nevertheless, stakeholders and research institutions, driven by the ambitious technological advances expected from fully autonomous and intelligent transportation systems, have recently investigated new radio technologies as a means to support vehicular applications. In particular, the millimeter wave (mmWave) spectrum holds great promise because of the large available bandwidth that may provide the required link capacity. Communications at high frequencies, however, suffer from severe propagation and absorption loss, which may cause communication disconnections especially considering high mobility scenarios. It is therefore important to validate, through simulations, the actual feasibility of establishing V2I communications in the above-6 GHz bands. Following this rationale, in this paper we provide the first comparative end- to-end evaluation of the performance of the LTE and mmWave technologies in a vehicular scenario. The simulation framework includes detailed measurement-based channel models as well as the full details of MAC, RLC and transport protocols. Our results show that, although LTE still represents a promising access solution to guarantee robust and fair connections, mmWaves satisfy the foreseen extreme throughput demands of most emerging automotive applications.

The collision resolution mechanism in the Random Access Channel (RACH) procedure of the Long-Term Evolution (LTE) standard is known to represent a serious bottleneck in case of Machine-Type Communication (MTC). Its main drawbacks are seen in the facts that Base Stations (eNBs) typically cannot infer the number of collided User Equipments (UEs) and that collided UEs learn about the collision only implicitly, through the lack of the feedback in the later stage of the RACH procedure. The collided UEs then restart the procedure, increasing the RACH load and making the system more prone to collisions. In this paper, we leverage machine learning techniques to design a system that, besides outperforming the state-of-the-art schemes in preamble detection for the LTE RACH procedure, is able to estimate the collision multiplicity and thus gather information about how many devices chose the same preamble. This data can be used by the eNB to resolve collisions, increase the supported system load and reduce transmission latency. Besides LTE, the presented approach is applicable to novel 3GPP standards that target massive Internet of Things (IoT), e.g., LTE-M and NB-IoT, as well as 5G, since their RACH procedures are based on the same principles.

In heterogeneous cellular networks (HetNets), switching OFF small cells under low user traffic periods has been proved to be an effective energy saving strategy. However, this strategy has strong interactions with interference coordination (IC) mechanisms, making it convenient to address both tasks simultaneously. The motivation of this paper is to develop a self-optimization algorithm capable of jointly controlling energy saving and IC mechanisms using an online learning approach. Our proposal is based on a contextual bandit formulation that, among other challenges, implies discovering the most energy-efficient control actions while satisfying a predefined level of Quality of Service (QoS) for the users. We propose a two-level framework comprising a global controller, in charge of a group of macro cells, and multiple local controllers, one per macro cell. The global controller implements a novel algorithm, referred to as the Bayesian Response Estimation and Threshold Search (BRETS), that is capable of learning, for each control action, its feasibility boundaries in terms of QoS and its energy consumption as a function of the aggregated user traffic. The algorithm comes with a bound on its expected convergence time. The local controllers translate the control actions learned by the global controller into local decisions. Our numerical results show that BRETS is only 1% less efficient than an ideal oracle policy, clearly outperforming other benchmark algorithms.

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission-critical communication services requiring latency in the order of milliseconds or submilliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross-layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of submilliseconds latency. In this paper, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent tradeoffs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

The availability of cheap and easy-to-install sensors is bolstering the development of monitoring applications in IoT scenarios. Although there is a need for periodical measurements of the tracked signals to guarantee an accurate representation at the receiver, choosing an appropriate duration for the reporting window is not trivial. In fact, the energy restrictions of many devices and the interference caused by other users call for longer reporting windows. However, this causes a higher reconstruction error due to the lower sampling resolution, which may be unacceptable in some applications. We propose a probabilistic random channel access scheme for battery-powered devices which monitor time-correlated phenomena and report their measurements to a fusion center. Our goal is to minimize the energy consumption of the sensors, while guaranteeing that the error in the signal estimate at the receiver does not exceed a chosen threshold. We exploit Markov chains and stochastic geometry to characterize the interference caused by the other devices. The numerical evaluation proves that our scheme is scalable and may be used in highly dense scenarios, and that it outperforms other state-of-the-art approaches, which do not consider the impact of interference on both the energy consumption and the accuracy of data representation.

Millimeter-wave (mmWave) bands will play an important role in 5G wireless systems. The system performance can be assessed by using models from stochastic geometry that cater for the directivity in the desired signal transmissions as well as the interference, and by calculating the signal-to-interference-plus-noise ratio (SINR) coverage. Nonetheless, the accuracy of the existing coverage expressions derived through stochastic geometry may be questioned, as it is not clear whether they would capture the impact of the detailed mmWave channel and antenna features. In this paper, we propose an SINR coverage analysis framework that includes realistic channel model and antenna element radiation patterns. We introduce and estimate two parameters, aligned gain and misaligned gain, associated with the desired signal beam and the interfering signal beam, respectively. The distributions of these gains are used to determine the distribution of the SINR which is compared with the corresponding SINR coverage, calculated through the system-level simulations. The results show that both aligned and misaligned gains can be modeled as exponential-logarithmically distributed random variables with the highest accuracy, and can further be approximated as exponentially distributed random variables with reasonable accuracy. These approximations can be used as a tool to evaluate the system-level performance of various 5G connectivity scenarios in the mmWave band.

This paper studies the performance of delay-constrained hybrid automatic repeat request (HARQ) protocols. Particularly, we propose a fast HARQ protocol where, to increase the end-to-end throughput, some HARQ feedback signals and successive message decodings are omitted. Considering quasi-static channels and a bursty communication model, we derive closed-form expressions for the message decoding probabilities as well as the throughput, the expected delay, and the error probability of the HARQ setups. The analysis is based on the recent results on the achievable rates of finite-length codes and shows the effect of the codeword length on the system performance. Moreover, we evaluate the effect of various parameters, such as imperfect channel estimation and hardware on the system performance. As demonstrated, the proposed fast HARQ protocol reduces the packet transmission delay considerably compared with the state-of-the-art HARQ schemes. For example, with typical message decoding delay profiles and a maximum of 2,..., 5 transmission rounds, the proposed fast HARQ protocol can improve the expected delay compared with standard HARQ by 27%, 42%, 52% and 60%, respectively, independently of the code rate/fading model.

Communication at mmWave frequencies is a promising enabler for ultra high data rates in the next generation of mobile cellular networks (5G). The harsh propagation environment at such high frequencies, however, demands a dense base station deployment, which may be infeasible because of the unavailability of fiber drops to provide wired backhauling. To address this issue, 3GPP has recently proposed a Study Item on Integrated Access and Backhaul (IAB), i.e., on the possibility of providing wireless backhaul together with radio access to the mobile terminals. The design of IAB base stations and networks introduces new research challenges, especially when considering the demanding conditions at mmWave frequencies. In this paper we study different path selection techniques, using a distributed approach, and investigate their performance in terms of hop count and bottleneck Signal-to-Noise-Ratio (SNR) using a channel model based on real measurements. We show that there exist solutions that decrease the number of hops without affecting the bottleneck SNR, and provide guidelines on the design of IAB path selection policies.

The next generation of cellular networks (5G) will exploit the mmWave spectrum to increase the available capacity. Communication at such high frequencies, however, suffers from high path loss and blockage, therefore directional transmissions using antenna arrays and dense deployments are needed. Thus, when evaluating the performance of mmWave mobile networks, it is necessary to accurately model the complex channel, the directionality of the transmission, but also the interplay that these elements can have with the whole protocol stack, both in the radio access and in the higher layers. In this paper, we improve the channel model abstraction of the mmWave module for ns-3, by introducing the support of a more realistic antenna array model, compliant with 3GPP NR requirements, and of multiple antenna arrays at the base stations and mobile handsets. We then study the end-to-end performance of a mmWave cellular network by varying the channel and antenna array configurations, and show that increasing the number of antenna arrays and, consequently, the number of sectors is beneficial for both throughput and latency.

This paper addresses the joint problem of energy saving and interference coordination in heterogeneous networks (HetNets) using a contextual bandit formulation. We propose a semi-distributed scheme consisting of a learning agent and local controllers. The learning agent comprises a neural network (NN) classifier and a Multi-Armed Bandit (MAB) algorithm. The NN classifier is dynamically trained to choose a subset of configurations (i.e., feasible configurations in terms of QoS) based on the context information (network state). Then, the MAB algorithm picks one control (i.e., global configuration parameters) among those selected by the NN classifier, with the aim of improving the energy efficiency. These global configurations are interpreted by the local controllers on each network sector. This scheme allows the learning agent to progressively learn the best policy by observing the network state and the performance of the chosen configurations in terms of energy consumption and QoS. Our numerical results show an energy saving close to 20% with respect to a default policy and an improvement of 13% with respect to addressing energy saving and interference coordination separately.

In this paper, the performance of fifth generation (5G) cellular networks under two promising technologies, namely dual connectivity and coordinated multi-point transmission (CoMP) is investigated. In this regard, a joint access and fronthaul radio resource allocation for a two tier downlink heterogeneous cloud radio access network (H-CRAN) is formulated. The main aim of the proposed problem formulation is to maximize the system energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul. The proposed optimization is a mixed integer nonconvex problem with a high computational complexity solution. Therefore, existing convex optimization methods can not be used directly to solve this problem. To solve this issue, an iterative algorithm based on a successive convex approximation (SCA) approach with low complexity is exploited. As the numerical results show, via dual connectivity and CoMP EE is improved by approximately 90% compared to using only μW subcarriers.

Large antenna arrays and millimeter- wave (mmWave) frequencies have been attracting growing attention as possible candidates to meet the high requirements of future 5G mobile networks. In view of the large path loss attenuation in these bands, beamforming techniques that create a beam in the direction of the user equipment are essential to perform the transmission. For this purpose, in this paper, we aim at characterizing realistic antenna radiation patterns, motivated by the need to properly capture mmWave propagation behaviors and understand the achievable performance in 5G cellular scenarios. In particular, we highlight how the performance changes with the radiation pattern used. Consequently, we conclude that it is crucial to use an accurate and realistic radiation model for proper performance assessment and system dimensioning.

Monitoring applications are gaining a lot of momentum in the Internet of Things (IoT), bolstered by the availability of cheap and easy-to-install sensors. Often, random access schemes are preferred to coordinated ones because they are more flexible and have no synchronization overhead. The possibility of collisions with other packets and the limited energy availability of the battery-powered nodes demand strategies to reduce the number of transmissions. This, however, is counterbalanced by the need to accurately monitor the process of interest, which requires a sufficient amount of sensed data. In this study, we propose a novel compression and transmission strategy with the objective of prolonging sensors' lifetime while guaranteeing a desired reconstruction accuracy of the tracked data. We consider the effect of both sensing and transmissions on the energy consumption, and adapt the sampling and transmission rates based on the target estimation accuracy and on the probability of packet losses caused by the interference from other sensors.

Recently, the millimeter wave (mmWave) band has been investigated as a means to support the foreseen extreme data rate demands of emerging automotive applications, which go beyond the capabilities of existing technologies for vehicular communications. However, this potential is hindered by the severe isotropic path loss and the harsh propagation of high-frequency channels. Moreover, mmWave signals are typically directional, to benefit from beamforming gain, and require frequent realignment of the beams to maintain connectivity. These limitations are particularly challenging when considering vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of the vehicular scenarios, and pose new challenges for proper vehicular communication design. In this paper, we conduct simulations to compare the performance of IEEE 802.11p and the mmWave technology to support V2V networking, aiming at providing insights on how both technologies can complement each other to meet the requirements of future automotive services. The results show that mmWave-based strategies support ultra-high transmission speeds, and IEEE 802.11p systems have the ability to guarantee reliable and robust communications.

So far, research on Smart Cities and self-organizing networking techniques for fifth-generation (5G) cellular systems has been one-sided: a Smart City relies on 5G to support massive machine-to-machine (M2M) communications, but the actual network is unaware of the information flowing through it. However, a greater synergy between the two would make the relationship mutual, since the insights provided by the massive amount of data gathered by sensors can be exploited to improve the communication performance. In this paper, we concentrate on self-organization techniques to improve handover efficiency using vehicular traffic data gathered in London. Our algorithms exploit mobility patterns between cell coverage areas and road traffic congestion levels to optimize the handover bias in heterogeneous networks and dynamically manage mobility management entity (MME) loads to reduce handover completion times.

In this paper, fifth-generation (5G) cellular networks under three promising technologies, namely, dual connectivity, coordinated multi-point transmission (CoMP), and power domain non orthogonal multiple access (PD-NOMA) are investigated. The main aim is to maximize the downlink energy efficiency (EE) by using both millimeter wave (mmW) and micro wave (μW) links in access and fronthaul, while employing CoMP and PD-NOMA. In this regard, joint access and fronthaul radio resource allocation for a downlink heterogeneous cloud radio access network is considered. The proposed optimization is a mixed integer non-convex problem with a high computational complexity solution, and hence, the alternate search method based on a successive convex approximation approach using fractional programming is exploited. Furthermore, the convergence of the proposed iterative resource allocation method is proved and its computational complexity is investigated. As the numerical results show, via dual connectivity through receiving signals from both mmW and μW transmitters, the system EE is improved by approximately 50%, in contrast to using only μW subcarriers (e.g., as in local thermal equilibrium). In addition, by applying both PD-NOMA and CoMP technologies on the μW subcarriers, the EE of the system increases by approximately 45%.

We consider a monitoring application where sensors periodically report data to a common receiver using time division multiplexing. The sensors are constrained by the limited and unpredictable energy availability provided by energy harvesting (EH), and by the channel impairments. To maximize the quality of the reported data, the packets transmitted contain newly generated data blocks together with up to r -1 previously unsuccessfully delivered ones, where r is a design parameter. These data blocks are compressed, concatenated, and encoded with a channel code. The scheme applies lossy compression, such that the fidelity of the individual blocks is traded off with the reliability provided by the channel code. We show that the proposed strategy outperforms the one in which retransmissions are not allowed. We also investigate the tradeoff between the value of r, the compression and the coding rates, under the constraints of the energy availability, and, once r has been decided, use a Markov decision process (MDP) to optimize the compression/coding rates. Finally, we implement a reinforcement learning algorithm, through which devices can learn the optimal transmission policy without knowing a priori the statistics of the EH process, and show that it indeed reaches the performance obtained via MDP.

We focus on the detection of sporadic low-power acoustic/seismic signals of unknown structure and statistics, such as the detection of sound produced by marine mammals, low-power underground signals, or the discovery of events such as volcano eruptions. In these cases, since the ambient noise may be fast time varying and may include many noise transients, threshold-based detection may lead to a significant false alarm rate. Instead, we propose a detection scheme that avoids the use of a decision threshold. Our method is based on clustering the samples of the observed buffer according to a binary hidden Markov model to discriminate between “noise” and “signal” states. Our detector is a modification of the Baum-Welch algorithm that takes into account the expected continuity of the desired signal and obtains a robust detection using the complex but flexible general Gaussian mixture model. The result is a combination of a constrained expectation-maximization algorithm with the Viterbi algorithm. We evaluate the performance of our scheme in numerical simulations, in a seimic test, and in an ocean experiment. The results are close to the hybrid Cramér-Rao lower bound and show that, at the cost of some additional complexity, our proposed algorithm outperforms common benchmark methods in terms of detection and false alarm rates, and also achieves a better accuracy of the time of detection. To allow reproducibility of the results, we publish our code.

Designing decentralized policies for wireless communication networks is a crucial problem, which has only been partially solved in the literature so far. In this paper, we propose a Decentralized Markov Decision Process (Dec-MDP) framework to analyze a wireless sensor network with multiple users which access a common wireless channel. We consider devices with energy harvesting capabilities, that aim at balancing the energy arrivals with the data departures and with the probability of colliding with other nodes. Over time, an access point triggers a SYNC slot, wherein it recomputes the optimal transmission parameters of the whole network, and distributes this information. Every node receives its own policy, which specifies how it should access the channel in the future, and, thereafter, proceeds in a fully decentralized fashion, with no interactions with other entities in the network. We propose a multi-layer Markov model, where an external MDP manages the jumps between SYNC slots, and an internal Dec-MDP computes the optimal policy in the short term. We numerically show that, because of the harvesting, stationary policies are suboptimal in energy harvesting scenarios, and the optimal trade-off lies between an orthogonal and a random access system.

In the last years, the advancements in signal processing and integrated circuits technology allowed several research groups to develop working prototypes of in-band full-duplex wireless systems. The introduction of such a revolutionary concept is promising in terms of increasing network performance, but at the same time poses several new challenges, especially at the MAC layer. Consequently, innovative channel access strategies are needed to exploit the opportunities provided by full-duplex while dealing with the increased complexity derived from its adoption. In this direction, this paper proposes RTS/CTS in the Frequency Domain (RCFD), a MAC layer scheme for full-duplex ad hoc wireless networks, based on the idea of time-frequency channel contention. According to this approach, different OFDM subcarriers are used to coordinate how nodes access the shared medium. The proposed scheme leads to efficient transmission scheduling with the result of avoiding collisions and exploiting full-duplex opportunities. The considerable performance improvements with respect to standard and state-of-the-art MAC protocols for wireless networks are highlighted through both theoretical analysis and network simulations.

The presence of many battery-powered sensors in the Internet of Things paradigm calls for the design of energy-aware protocols. Source coding techniques make it possible to save some energy by compressing the packets sent over the network, but at the cost of poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining a scheduling strategy with the twofold goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a time division multiple access-based access scheme that efficiently allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry energy costs. We consider both full and statistical knowledge of the wireless channels and derive communication policies for the two cases. The overall problem is structured in modules and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters taken from real sensors.

While acoustic communications are still considered the most prominent technology to communicate under water, other technologies are being developed based, e.g., on optical and radio-frequency electromagnetic waves. Each technology has its own advantages and drawbacks: for example, acoustic signals achieve long communication ranges at order-of-kbit/s rates, whereas optical signals offer order-of-Mbit/s transmission rates, but only over short ranges. Such diversity can be leveraged by multimodal systems, which integrate different technologies and provide the intelligence required to decide which one should be used at any given time. In this paper, we address a fundamental part of this intelligence by proposing optimal multimodal routing (OMR), a novel routing protocol for underwater networks of multimodal nodes. OMR makes distributed decisions about the flow in each link and over each technology, at any given time, in order to advance a packet toward its destination; in doing so, it prevents bottlenecks and allocates resources fairly to different nodes. We analyze the performance of OMR via simulations and in a field experiment. The results show that OMR successfully leverages all technologies to deliver data, even in the presence of imperfect topology information. To permit the reproduction of our results, we share our simulation code.

The millimeter-wave (mm-wave) frequencies offer the potential of orders of magnitude that increases in capacity for next-generation cellular systems. However, links in mm-wave networks are susceptible to blockage and may suffer from rapid variations in quality. Connectivity to multiple cells at mm-wave and/or traditional frequencies is considered essential for robust communication. One of the challenges in supporting multi-connectivity in mm-waves is the requirement for the network to track the direction of each link in addition to its power and timing. To address this challenge, we implement a novel uplink measurement system that, with the joint help of a local coordinator operating in the legacy band, guarantees continuous monitoring of the channel propagation conditions and allows for the design of efficient control plane applications, including handover, beam tracking, and initial access. We show that an uplink-based multi-connectivity approach enables less consuming, better performing, faster and more stable cell selection, and scheduling decisions with respect to a traditional downlink-based standalone scheme. Moreover, we argue that the presented framework guarantees: 1) efficient tracking of the user in the presence of the channel dynamics expected at mm-waves and 2) fast reaction to situations in which the primary propagation path is blocked or not available.

Proactive caching will enable the 5G networks of the future to meet the challenge of continuously increasing wireless data traffic. Thanks to proactive caching, we are observing a shift from standard cellular networks, with one base station providing wireless connectivity to all the users in the cell, to heterogeneous networks, where several small base stations can assist the macro base station. Going one step further, users can also share their local content via device-to-device communications, avoiding multiple requests to the base station. In this complex network scenario, we propose a proactive caching policy to exploit all these communication opportunities and reduce congestion on the backhaul link, with the goal of minimizing the system cost, e.g., in terms of energy or bandwidth wastage. We provide a closed-form expression for the average system cost in the case of user mobility. In this scenario, we also consider heterogeneous file popularity profiles for different users. We present a robust optimization framework and show significant performance gains compared to a static caching policy, in which the caching decisions do not depend on the evolution of the network scenario.

The role of mobile devices, like smartphones or tablets, is becoming more and more important in everyday life, at the point that their unavailability due to early or unexpected battery discharge is perceived as a serious issue. Therefore, there is an urge for smart and efficient battery management algorithms that can prolong the duration of the battery charge. To this end, a reliable prediction of the battery discharging process would represent a precious tool to enable energy-efficiency optimization mechanisms. In this paper, we address this challenge by considering different machine learning techniques to provide an accurate and user-dependent prediction of the discharging time of a mobile device and, eventually, we propose a Deep Neural Network model that provides the best performance. Unlike previous solutions proposed in the literature, our method exploits space-time data from the device operating system (Android) to learn the specific battery usage pattern of the user, thus offering a customized prediction of the discharge process. We show that such model outperforms the other machine-learning methods considered in this study, and achieves much better performance than the deterministic linear fitting methods widely used in commercial devices.

Millimeter-wave (mmWave) bands have been attracting growing attention as a possible candidate for next- generation cellular networks, since the available spectrum is orders of magnitude larger than in current cellular allocations. To precisely design mmWave systems, it is important to examine mmWave interference and SIR coverage under large-scale deployments. For this purpose, we apply an accurate mmWave channel model, derived from experiments, into an analytical framework based on stochastic geometry. In this way we obtain an analytical expression for the SIR coverage probability in mmWave cellular networks.

The Internet of Things (IoT) is being used to monitor a wide range of physical phenomena. In this paper, we are concerned with the extraction of features from the gathered IoT signals and, specifically, with the online estimation of their rate-distortion relationship. This information is in fact key to the configuration and adaptation of data compression and in-network processing protocols and, in turn, is deemed a prime functionality for IoT networks. The point is that lossy compression can be often applied at the sources to save transmission energy, while meeting application requirements in the reconstruction quality. This task is however signal- and time-dependent as different signals are usually characterized by different relations and the signal statistics may also change as a function of time. Here, we first formulate the rate-distortion estimation task, framing it as a classification problem. Hence, we consider the following clustering algorithms from the literature: multilayer perceptron, support vector machine, random forest and linear discriminant analysis, and use them to automatically assess rate-distortion curves in an online fashion and from a small number of signal samples. These algorithms are compared in terms of classification accuracy, training time and memory footprint. Numerical results reveal that, although the problem is inherently complex, the careful combination of feature extraction and classification tools makes it possible to reach high classification accuracies using only a few signal features (e.g., from one to four). The best algorithm (random forest) also entails a short training time and, if properly tuned, has a modest memory footprint.

We characterize time and power allocations to optimize the sum-throughput of a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). In our setup, an Energy Rich (ER) source broadcasts wireless energy to several devices, which use it to simultaneously transmit data to an Access Point (AP) on the uplink. Differently from most prior works, in this paper we consider a generic scenario, in which the ER and AP do not coincide, i.e., are two separate entities. We study two NOMA decoding schemes, namely Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). For each scheme, we formulate a sum-throughput optimization problem over a finite horizon. Despite the complexity of the LCD optimization problem, due to its non-convexity, we recast it into a series of geometric programs. On the other hand, we establish the convexity of the SICD optimization problem and propose an algorithm to find its optimal solution. Our numerical results demonstrate the importance of using successive interference cancellation in WPCNs with NOMA, and show how the energy should be distributed as a function of the system parameters.

Energy harvesting is a promising technology for the Internet of Things (IoT) towards the goal of self-sustainability of the involved devices. However, the intermittent and unreliable nature of the harvested energy demands an intelligent management of devices' operation in order to ensure a sustained performance of the IoT application. In this work, we address the problem of maximizing the quality of the reported data under the constraints of energy harvesting, energy consumption and communication channel impairments. Specifically, we propose an energy-aware joint source-channel coding scheme that minimizes the expected data distortion, for realistic models of energy generation and of the energy spent by the device to process the data, when the communication is performed over a Rayleigh fading channel. The performance of the scheme is optimized by means of a Markov Decision Process framework.

The approaching 5G era of cellular communications is posing stringent performance requirements. New groundbreaking applications can be enabled only by means of multi-Gbps data rates and ultra-low latencies. The spectrum scarcity at frequencies below 6 GHz stimulated a new wave of wireless research that focuses on higher bands, namely mmWave. Directionality and high penetration loss represent the key challenges when operating with such carriers. The resulting intermittent connectivity makes the user association problem even more complex and critical than in previous generations of cellular systems, where the channel was better behaved. In this paper, we aim at deriving an optimal and fair cell selection policy that encapsulates the reallocation cost of potential handovers, and captures the erratic nature of the mmWave channel. An important conclusion is that (i) if there is no, or minimal, reallocation cost, each user associates with a single BS, while (ii) for higher handover cost values, users tend to connect to multiple base stations simultaneously.

The millimeter wave (mmWave) bands offer the possibility of orders of magnitude greater throughput for fifth-generation (5G) cellular systems. However, since mmWave signals are highly susceptible to blockage, channel quality on any one mmWave link can be extremely intermittent. This paper implements a novel dual connectivity protocol that enables mobile user equipment devices to maintain physical layer connections to 4G and 5G cells simultaneously. A novel uplink control signaling system combined with a local coordinator enables rapid path switching in the event of failures on any one link. This paper provides the first comprehensive end-to-end evaluation of handover mechanisms in mmWave cellular systems. The simulation framework includes detailed measurement-based channel models to realistically capture spatial dynamics of blocking events, as well as the full details of Medium Access Control, Radio Link Control, and transport protocols. Compared with conventional handover mechanisms, this paper reveals significant benefits of the proposed method under several metrics.

Wireless powered communication networks are becoming an effective solution for improved self-sustainability of mobile devices. In this context, a hybrid access point transfers energy to a group of nodes, which use the harvested energy to perform computation or transmission tasks. While the availability of the wireless energy transfer mechanism opens up new frontiers, an appropriate choice of the network parameters (e.g., transmission powers, transmission duration, and amount of transferred energy) is required in order to achieve high performance. In this paper, we study the throughput optimization problem in a system composed of an access point, which recharges the batteries of two devices at different distances. In the literature, the main focus so far has been on slot-oriented optimization, in which all the harvested energy is used in the same slot in which it is harvested. However, this approach is strongly suboptimal, because it does not exploit the possibility to store the energy and use it at a later time. Thus, instead of considering the slot-oriented case, we address the long-term maximization. This assumption greatly increases the optimization complexity, as it requires to consider, e.g., the channel state statistics and the batteries evolution. Our objective is to find the best scheduling scheme, both for the energy transferred by the access point and for the data sent by the two nodes. We discuss how to perform the maximization with optimal as well as approximate techniques and show that the slot-oriented policies proposed so far are strongly suboptimal in the long run.

This paper introduces a new metric, referred to as delay-sensitive area spectral efficiency (DASE), to analyze the performance of delay-constrained wireless networks. We study DASE in different point-to-point, spectrum sharing, interference, and Poisson-point process-based dense network configurations and with different levels of channel state information (CSI) at the transmitters. Also, we determine the optimal rates/powers optimizing DASE. Finally, we use some recent results on finite block-length codes to analyze the effect of the codewords length on the network DASE. As demonstrated, DASE is a useful metric for analyzing delay-sensitive green networks. Moreover, adaptive power allocation and partial CSI feedback are powerful tools for performance improvement in green networks.

We consider a multihop network where a source node must reliably deliver a set of data packets to a given destination node. To do so, the source applies a fountain code and floods the encoded packets through the network, until they reach their destination or are lost in the process. We model the probability that the destination can recover the original transmissions from the received coded packets as a function of the network topology and of the code redundancy, and show that our analytical results predict the outcome of simulations very well. These results are employed to design distributed forwarding policies that achieve a good tradeoff between the success probability and the total number of transmissions required to advance a packet toward the destination. We finally develop in detail the case where intermediate relays can inject additional redundancy in the network, provided that they have successfully decoded the source packets.

In this paper, we study the achievable rate and the energy efficiency of analog, hybrid, and digital combining (AC, HC, and DC) for millimeter wave (mmW) receivers. We take into account the power consumption of all receiver components, not just analog-to-digital converters (ADCs), determine some practical limitations of beamforming in each architecture, and develop performance analysis charts that enable comparison of different receivers simultaneously in terms of two metrics, namely, spectral efficiency (SE) and energy efficiency (EE). We present a multi-objective utility optimization interpretation to find the best SE-EE weighted tradeoff among AC, DC, and HC schemes. We consider an additive quantization noise model to evaluate the achievable rates with low resolution ADCs. Our analysis shows that AC is only advantageous if the channel rank is strictly one, the link has very low SNR, or there is a very stringent low power constraint at the receiver. Otherwise, we show that the usual claim that DC requires the highest power is not universally valid. Rather, either DC or HC alternatively results in the better SE versus EE tradeoff depending strongly on the considered power consumption characteristic values for each component of the mmW receiver.

We present a spatial reuse resource allocation scheme for underwater acoustic networks that organizes communications so as to avoid destructive collisions. One prime source of collisions in underwater acoustic networks is the so called near-far effect, where a node located farther from the receiver is jammed by a closer node. While common practice considers such a situation a challenge, in this paper we consider it a resource, and use it to increase the network throughput of spatial-reuse time-division multiple access. Our algorithm serves two types of communications: (1) contention-free and (2) opportunistic. Our objective is to maximize the time slot allocation, while guaranteeing a minimum per-node packet transmission rate. The result is an increase in the number of contention-free packets received, and a decrease in the scheduling delay of opportunistic packets. Numerical results show that, at a slight cost in terms of fairness, our scheduling solutions achieve higher throughput and lower transmission delay than benchmark spatial-reuse scheduling protocols. These results are verified in a field experiment conducted in the Garda Lake, Italy, where we demonstrated our solution using off-the-shelf acoustic modems. To allow the reproducibility of our results, we publish the implementation of our proposed algorithm.

Wireless transmissions in water are mostly carried out via long-range (but low-rate) underwater acoustic communications, or short-range (but high-rate) underwater optical communications. In this paper, we are interested in finding out whether a statistical relationship exists between underwater acoustics and optics. Besides the theoretical interest of such a relationship, predicting the quality of the optical link through acoustics is also relevant in the context of a multimodal system with both acoustics and optics. Our study is based on a large data set acquired during the NATO ALOMEX 2015 expedition. During this experiment, we simultaneously measured several characteristics of the acoustic and optical links at multiple locations, reflecting a diversity of sea environments. Our results show a strong correlation between the properties of the acoustic link and the reliability of optical communications. This correlation makes it possible to predict the state of the underwater optical link at a certain depth and range. Due to the complexity of the acoustic and optical channels, we could not find the source of this correlation. This paper is, therefore, aimed to stimulate a theoretical study of the mutual properties of underwater acoustic and optical communication links. For reproducibility, we share the processed data from the experiment.

The millimeter-wave (mmWave) frequencies have attracted considerable attention for fifth generation (5G) cellular communication as they offer orders of magnitude greater bandwidth than current systems. However, the medium access control (MAC) layer may need to be significantly redesigned to support the highly directional transmissions, and the demand for ultra-low latencies and high peak rates expected in mmWave communication. To address these challenges, we present a novel mmWave MAC layer frame structure with a number of enhancements, including flexible, highly granular transmission times, dynamic control signal locations, extended messaging, and the ability to efficiently multiplex directional control signals. Analytic formulas are derived for the utilization and control overhead as a function of control periodicity, number of users, traffic statistics, signal-to-noise ratio, and antenna gains. Importantly, the analysis can incorporate various front-end MIMO capability assumptions-a critical feature of mmWave. Under realistic system and traffic assumptions, the analysis reveals that the proposed flexible frame structure design offers significant benefits over designs with fixed frame structures similar to current 4G long-term evolution. It is also shown that the fully digital beamforming architectures offer significantly lower overhead compared with analog and hybrid beamforming under equivalent power budgets.

Recent advancements related to device to device (D2D) communication make it possible for a transmitting node to dynamically select the interface to be used for data transfers locally, without traversing any network infrastructure. In this scenario, a controller is identified, whose goal is to manage the D2D connection after its establishment. The software defined networking paradigm makes it possible to select this controller node via software: a device becomes the master node of a Wi-Fi-direct network, whereas the remaining units, i.e., the clients, can exchange data with other devices through the master. This paper develops a machine learning-based prediction algorithm for the aforementioned scenario, in which multiple elements, while receiving data from the controller, require an accurate on-the-fly estimation of the remaining transmission time, i.e., the estimated time of arrival. Different machine learning approaches are considered for this task, with the goal of exploiting only the information available at each client, without modifying any standard communication protocol. This information is critical when, for instance, a mobile user needs to decide whether or not to delay a data transfer, based on the load of the network and on the residual time under radio coverage from an access point.

In this paper, we study the Energy Efficiency (EE) and Area Spectral Efficiency (ASE) of green random cellular networks which utilize the Cell Zooming (CZ) techniques. To this aim, using stochastic geometry, we derive a tractable expression for the Ergodic Capacity in a Poisson Voronoi Tessellation (PVT) random cellular network in which both Base Station (BS) and Mobile User (MU) locations are drawn randomly from two independent Poisson Point Processes (PPPs). The performance of this network is examined under different MU densities and two CZ algorithms. Numerical evaluations show that there is an optimum transmission power, which maximizes EE in PVT random cellular networks. On the other hand, increasing the transmission power and the cell size does not improve ASE much more after passing a threshold. The tradeoff between EE and ASE is also presented.

Nowadays, most mobile devices are equipped with multiple wireless interfaces, causing an emerging research interest in device to device (D2D) communication: the idea behind the D2D paradigm is to exploit the proper interface to directly communicate with another user, without traversing any network infrastructure. A first issue related to this paradigm is the need for a coordinator, called controller, able to decide when activating a D2D connection is appropriate and to manage such connection. In this view, the paradigm of Software Defined Networking (SDN) can be exploited both to handle the data flows among the devices and to interact directly with every device. This work is focused on a scenario where a device is selected by the SDN controller, in order to become the master node of a WiFi-Direct network. The remaining nodes, called clients, can exchange data with other nodes through the master. The objective is to infer, through different Machine Learning approaches, the number of nodes actively involved in receiving data, exploiting only the information available at the client side and without modifying any standard communication protocol. The information about the number of client nodes is crucial when, e.g., a user desires a precise prediction of the transmission estimated time of arrival (ETA) while downloading a file.

In satellite networks, the design of MAC schemes which achieve high performance is an important and widely studied problem. Random access is recognized as an appealing solution for these scenarios. In the literature so far, two main research lines have been followed: diversity slotted Aloha-like protocols and spread spectrum Aloha schemes. The goal of this work is to analyze the performance of these two paradigms and introduce a hybrid algorithm which combines both packet replicas and code division to further enhance the probability of successfully decoding a packet.

In this paper, decentralized heuristic access policies are designed for two secondary users (SUs) in an underlay cognitive radio network in which the PU employs Type-I Hybrid ARQ. Exploiting the redundancy in PU retransmissions, each SU receiver applies interference cancellation (IC) to remove a successfully decoded PU message in the subsequent PU retransmissions. There is no central control unit and we consider two different scenarios. In the first scenario, each SU transmitter only knows the PU message knowledge state of its corresponding SU receiver. In the second scenario, the SU transmitters are unaware of any PU message knowledge states at the SU receivers. We design access policies in both offline and online modes, where in an online mode each SU can learn from its own local information. Using heuristic approaches for the considered scenarios, decentralized access policies are proposed to maximize the average sum throughput of SUs under a PU throughput degradation constraint. The results also show that heuristic access policies using the online mode have a performance gain close to that obtained using the offline mode, while providing increased robustness and flexibility.

Recently, several working implementations of inband full-duplex wireless systems have been presented, where the same node can transmit and receive simultaneously in the same frequency band. The introduction of such a possibility at the physical layer could lead to improved performance but also poses several challenges at the MAC layer. In this paper, an innovative mechanism of channel contention in full-duplex OFDM wireless networks is proposed. This strategy is able to ensure efficient transmission scheduling with the result of avoiding collisions and effectively exploiting full-duplex opportunities. As a consequence, considerable performance improvements are observed with respect to standard and state-of-the-art MAC protocols for wireless networks, as highlighted by extensive simulations performed in ad hoc wireless networks with varying number of nodes.

The increase in wireless data traffic is encouraging a shift from standard cellular networks, with one base station providing wireless connectivity to all the users in the cell, to heterogeneous networks, where several small base stations can assist the macro base station in providing service. Furthermore, with device-to-device communications the users can also share local content without multiple requests to the base station. In this complex network scenario, we propose a proactive caching policy to exploit all these communication opportunities and reduce congestion at the backhaul link. The goal is to minimize the system cost, in terms of energy or bandwidth wastage. We provide a closed form expression for the average system cost in the case in which we consider user mobility and different classes of user's interests. We present a robust optimization framework, and we show significant performance gains compared to a static caching policy.

Several studies assert that the random access procedure of the Long Term Evolution (LTE) cellular standard may not be effective whenever a massive number of simultaneous connection attempts are performed by terminals, as may happen in a typical Internet of Things or Smart City scenario. Nevertheless, simulation studies in real deployment scenarios are missing because many system-level simulators do not implement the LTE random access procedure in detail. In this paper, we propose a patch for the LTE module of ns-3, one of the most prominent open-source network simulators, to improve the accuracy of the routine that simulates the LTE Random Access Channel (RACH). The patched version of the random access procedure is compared with the default one and the issues arising from massive simultaneous access from mobile terminals in LTE are assessed via a simulation campaign.

In this paper, statistical Quality of Service provisioning in next generation heterogeneous mobile cellular networks is investigated. To this aim, any active entity of the cellular network is regarded as a queuing system, whose statistical QoS requirements depend on the specific application. In this context, by quantifying the performance in terms of effective capacity, we introduce a lower bound for the system performance that facilitates an efficient analysis. We exploit this analytical framework to give insights about the possible improvement of the statistical QoS experienced by the users if the current heterogeneous cellular network architecture migrates from a Half Duplex to a Full Duplex mode of operation. Numerical results and analysis are provided, where the network is modeled as a Matérn point processes with a hard core distance. The results demonstrate the accuracy and computational efficiency of the proposed scheme, especially in large scale wireless systems.

Recent studies indicate the feasibility of in-band full-duplex (FD) wireless communications, where a wireless radio transmits and receives simultaneously in the same band. Due to its potential to increase the capacity, analyzing the performance of a cellular network that contains full-duplex devices is crucial. In this paper, we consider maximizing the weighted sum-rate of downlink and uplink of a single cell OFDMA network which consists of an imperfect FD base-station (BS) and a mixture of half-duplex and imperfect full-duplex mobile users. To this end, the joint problem of sub-channel assignment and power allocation is investigated and a two-step solution is proposed. A heuristic algorithm to allocate each sub-channel to a pair of downlink and uplink users with polynomial complexity is presented. The power allocation problem is convexified based on the difference of two concave functions approach, for which an iterative solution is obtained. Simulation results demonstrate that when all the users and the BS are perfect FD nodes the network throughput could be doubled, Otherwise, the performance improvement is limited by the inter-node interference and the self-interference. We also investigate the effect of the self-interference cancellation capability and the percentage of FD users on the network performance in both indoor and outdoor scenarios.

Active sensing refers to the process of choosing or tuning a set of sensors in order to track an underlying system in an efficient and accurate way. In a wireless environment, among the several kinds of features extracted by traditional sensors, the information carried by the communication channel about the state of the system can be used to further boost the tracking performance and save energy. A joint tracking problem which considers sensor measurements and communication channel together for tracking purposes is set up and solved. The system is modeled as a partially observable Markov decision problem and the properties of the cost-to-go function are used to reduce the problem complexity. Numerical results show the advantages of our proposal.

In this paper, we target single-anchor localization schemes for millimeter wave (MMW) systems. The schemes are designed to be lightweight, so that even computationally-constrained devices can support them. We identify the main propagation properties of MMW signals that have an impact on localization and design three algorithms that exploit these, namely a triangulation-validation procedure, an angle difference-of-arrival approach, and a scheme based on location fingerprinting. We evaluate the algorithms by means of simulations, and draw conclusions on their robustness. We then validate our results via measurements involving commercial pre- standard 60- GHz MMW hardware. Our experiments confirm that, by relying only on a single anchor and without requiring complex signal processing at the receiver, the algorithms can localize a node with high probability, and in many cases with sub-meter accuracy. We conclude by discussing how these algorithms complement each other in terms of robustness and localization success probability.

Wireless powered communication networks are becoming an effective solution for improving self sustainability of mobile devices. In this context, a hybrid access point transfers energy to a group of nodes, which use the harvested energy to perform computation or transmission tasks. While the availability of the wireless energy transfer mechanism opens up new frontiers, it also requires an appropriate choice of the network parameters (e.g., transmission powers, transmission duration, amount of transferred energy, etc.) in order to achieve high performance. In this work, we study the throughput optimization problem in a system composed of an access point which recharges the batteries of two devices at different distances. In the literature, the main focus so far has been on slot-oriented optimization, in which all the harvested energy is used in the same slot in which it is harvested. However, this approach may be strongly suboptimal because it does not exploit the possibility to store the energy and use it at a later time. Thus, instead of considering the slot-oriented case, we address the long-term maximization. This assumption greatly increases the optimization complexity, as it requires to consider, e.g., the channel state realizations, its statistics and the batteries time evolution. Our objective is to find the best scheduling scheme, both for the energy transferred by the access point and for the data sent by the two nodes. We discuss how to perform the optimization and show that the slot-oriented policies proposed so far are strongly sub-optimal in the long-term case. Our scenario can be considered as a first step toward the study of more complex and distributed schemes in wireless energy-transfer scenarios in the presence of battery-powered nodes.

In this paper, we develop a green joint power and information transfer system. To guarantee reliable communication, we implement hybrid automatic repeat request (HARQ) feedback both in the power and information transmission phases. Moreover, with a limit on the packet transmission delay, we derive the optimal power allocation minimizing the energy-constrained outage probability. The simulation and analytical results indicate that the diversity gain of the joint power and information transfer system is the same as the diversity gain of the conventional non-green information transfer systems. Also, the implementation of HARQ improves the power efficiency significantly. For instance, consider a Rayleigh fading channel, outage probability 10-3, code rate 1 nats-per-channel-use and the case where the receiver requires to receive 0.1 Joule-per-channel-use energy per codeword length from the transmitter to process the information. Then, with a maximum of two retransmissions in the power and information transfer phases, the HARQ reduces the average required energy by 4 dB, compared to the open-loop communication setup.

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

The secrecy rate represents the amount of information per unit time that can be securely sent on a communication link. In this work, we investigate the achievable secrecy rates in an energy harvesting communication system composed of a transmitter, a receiver and a malicious eavesdropper. In particular, because of the energy constraints and the channel conditions, it is important to understand when a device should transmit and to optimize how much power should be used to improve security. Both full knowledge and partial knowledge of the channel are considered under a Nakagami fading scenario. We show that high secrecy rates can be obtained only with power and coding rate adaptation. Moreover, we highlight the importance of optimally dividing the transmission power in the frequency domain, and note that the optimal scheme provides high gains in secrecy rate over the uniform power splitting case. Analytically, we explain how to find the optimal policy and prove some of its properties. In our numerical evaluation, we discuss how the maximum achievable secrecy rate changes according to the various system parameters. Furthermore, we discuss the effects of a finite battery on the system performance and note that, in order to achieve high secrecy rates, it is not necessary to use very large batteries.

This paper investigates the extent to which spectrum sharing in millimeter-wave (mmWave) networks with multiple cellular operators is a viable alternative to traditional dedicated spectrum allocation. Specifically, we develop a general mathematical framework to characterize the performance gain that can be obtained when spectrum sharing is used, as a function of the underlying beamforming, operator coordination, bandwidth, and infrastructure sharing scenarios. The framework is based on joint beamforming and cell association optimization, with the objective of maximizing the long-term throughput of the users. Our asymptotic and non-asymptotic performance analyses reveal five key points: 1) spectrum sharing with light on-demand intra- and inter-operator coordination is feasible, especially at higher mmWave frequencies (for example, 73 GHz); 2) directional communications at the user equipment substantially alleviate the potential disadvantages of spectrum sharing (such as higher multiuser interference); 3) large numbers of antenna elements can reduce the need for coordination and simplify the implementation of spectrum sharing; 4) while inter-operator coordination can be neglected in the large-antenna regime, intra-operator coordination can still bring gains by balancing the network load; and 5) critical control signals among base stations, operators, and user equipment should be protected from the adverse effects of spectrum sharing, for example by means of exclusive resource allocation. The results of this paper, and their extensions obtained by relaxing some ideal assumptions, can provide important insights for future standardization and spectrum policy.

In this paper, we propose and analyze a wireless energy and information transfer system. To reduce the outage probability, compared with open-loop communication, we implement retransmission protocols both in the energy and in the information transmission phases. We use some recent results on finite block-length codes to analyze the effect of the energy and information signals lengths on the system outage probability/throughput. Finally, under a packet transmission delay constraint, we derive the optimal power allocation and time sharing between the energy and information signals, such that the energy-constrained outage probability is minimized. The simulation and analytical results demonstrate that the retransmission-based protocols are efficient techniques to reduce the energy-limited outage probability of wireless energy and information transmission systems.

Distributed generators (DGs), coupled with suitable control and communication infrastructures, are expected to play a key role in improving the efficiency of electricity grids. In this paper, we focus on low-voltage and single-phase microgrids exploring the interplay of distributed power loss reduction and communication. We select representative power-loss reduction algorithms from the state of the art and provide design rules for the required networking strategies in the presence of lossy communication links, assessing the impact of communication as well as electrical grid features. Toward this end, we devise a novel statistical cosimulation (electricity grid, communication, and control) framework that faithfully mimics the characteristics of real-world microgrids in terms of communication and grid topologies, power demand, and distributed generation from solar sources. Our numerical results highlight the role of communication procedures and the differences among the selected optimization techniques for power loss reduction, assessing their convergence rate and quantifying the impact of communication failures, line impedance estimation error, communication and electricity grid topologies, network size, and number of DGs.

This paper introduces a novel technique that enables access by a cognitive secondary user (SU) to a spectrum occupied by an incumbent primary user (PU) that employs Type-I hybrid automatic retransmission request (ARQ). The technique allows the SU to perform selective retransmissions of SU data packets, whose transmission previously failed. The temporal redundancy introduced by the PU ARQ protocol and by the selective retransmission process of the SU can be exploited by the SU receiver to perform interference cancellation (IC) over multiple transmission slots, thus creating a “clean” channel for the decoding of the concurrent SU or PU packets. The chain decoding (CD) technique is initiated by a successful decoding operation of an SU or a PU packet and proceeds by an iterative application of IC as previously buffered packets become decodable and their interference can be removed, thus making it possible to recover the concurrent data packets, and so on, until no more packets are decodable. Based on this scheme, an optimal policy is designed that maximizes the SU throughput under a constraint on the average long-term PU performance. The optimality of the CD protocol is proved, which determines which packet the SU should send at any given time, based on four basic rules. Moreover, a decoupling principle is proved, which establishes the optimality of decoupling the secondary access strategy from the CD protocol. Specifically, first, the SU access policy, optimized via dynamic programming, specifies whether the SU should access the channel or remain idle, based on a compact state representation of the protocol, and second, the CD protocol embeds four basic rules that are used to select the packet transmitted by the SU. It is shown numerically that CD outperforms by up to 35% other schemes considered in the literature, which do not employ retransmissions at the SU pair and thus do not exploit the full potentiality of IC.

Cooperative Spectrum Sharing (CSS) is an appealing approach for primary users (PUs) to share spectrum with secondary users (SUs) because it increases the transmission range or rate of the PUs. Most previous works are focused on developing complex algorithms which may not be fast enough for real-time variations such as channel availability and/or assume perfect information about the network. Instead, we develop a learning mechanism for a PU to enable CSS in a strongly incomplete information scenario with low computational overhead. Our mechanism is based on a Markovian variant of multi-armed bandits (MABs) called superprocess, enhanced with the concept of Upper Confidence Bound (UCB) from stochastic MABs. By means of Monte-Carlo evaluations we show that, despite its low computational overhead, it converges to a low regret solution outperforming baseline approaches such as epsilon-greedy. This algorithm can be extended to include more sophisticated features while maintaining its desirable properties such as low computational overhead and fast speed of convergence.

Millimeter wave (mmWave) bands have attracted considerable recent interest for next-generation cellular systems due to the massive available spectrum at these frequencies. However, a key challenge in designing mmWave cellular systems is initial access-the procedure by which a mobile device establishes an initial link-layer connection to a cell. MmWave communication relies on highly directional transmissions and the initial access procedure must thus provide a mechanism by which initial transmission directions can be searched in a potentially large angular space. Design options are compared considering different scanning and signaling procedures to evaluate access delay and system overhead. The channel structure and multiple access issues are also considered. The results of our analysis demonstrate significant benefits of low-resolution fully digital architectures in comparison with single stream analog beamforming.

Next generation cellular systems are expected to entail a wide variety of wireless coverage zones, with cells of different sizes and capacities that can overlap in space and share the transmission resources. In this scenario, which is referred to as Heterogeneous Networks (HetNets), a fundamental challenge is the management of the handover process between macro, femto and pico cells. To limit the number of handovers and the signaling between the cells, it will hence be crucial to manage the user's mobility considering the context parameters, such as cells size, traffic loads, and user velocity. In this paper, we propose a theoretical model to characterize the performance of a mobile user in a HetNet scenario as a function of the user's mobility, the power profile of the neighboring cells, the handover parameters, and the traffic load of the different cells. We propose a Markov-based framework to model the handover process for the mobile user, and derive an optimal context-dependent handover criterion. The mathematical model is validated by means of simulations, comparing the performance of our strategy with conventional handover optimization techniques in different scenarios. Finally, we show the impact of the handover regulation on the users performance and how it is possible to improve the users capacity exploiting context information.

This paper considers a wireless network composed of a pair of sensors powered by energy harvesting devices (EHDs), which transmit data to a receiver over a shared wireless channel. At any given time, based on the energy levels of the two rechargeable batteries of the sensors, a central controller (CC) decides on the amount of energy to be drawn from the two batteries and used for transmission. The problem considered is the maximization of the long-term average reward associated with data transmission, by optimizing the transmission strategy of the two nodes, in the case of a collision channel model and both i.i.d. and correlated energy arrivals. In addition, contrary to the traditional assumption that the amount of energy available to the sensors can be easily estimated, we derive the optimal policy in the cases where the state of charge (SOC) may not be perfectly known by the central controller, analyzing the performance degradation caused by this imperfect knowledge of the SOC. For this second scenario, supposing that the CC is only aware that each SOC is “LOW” or “HIGH,” we show that the impact of imperfect knowledge decreases with the two battery capacities and is negligible in most cases of practical interest.

In this paper, we investigate the achievable secrecy rates in an Energy Harvesting communication system composed of one transmitter and multiple receivers. In particular, because of the energy constraints and the channel conditions, it is important to understand when a device should transmit or not and how much power should be used. We introduce the Optimal Secrecy Policy in several scenarios. We show that, if the receivers demand high secrecy rates, then it is not always possible to satisfy all their requests. Thus, we introduce a scheme that chooses which receivers should be discarded. Also, we study how the system is influenced by the Channel State Information and, in particular, how the knowledge of the eavesdropper's channel changes the achievable rates.

In this work we address the problem of optimal resource allocation in the uplink of a wireless cellular network with Rayleigh fading channels, where the aim is to minimize the average total energy spent for packet delivery. The devices are assumed to transmit over orthogonal resources where the total energy used for each resource is modeled as the sum of the transmit energy and an overhead circuit energy. We first derive the optimal allocation for the single-user case when varying our assumptions on both the Channel State Information available at the transmitter and the Automatic Repeat reQuest capabilities. Then, we generalize the analysis to the multi-user case and compare the results obtained for the different scenarios.

We investigate the ability of the physical layer approaches of constellation shaping and forward error correction to enhance the performance of full duplex (FD) wireless radio systems. We use low density parity check and optimized shaping codes to transmit non-equiprobable constellation points from an amplitude phase shift keying modulation with imperfect power amplifier and in the presence of FD residual self interference. A simple approach to the design of optimized shaping codes for a given signal to noise ratio and residual self interference power is presented. Simulation results show that a FD transceiver with bit interleaved coded modulation, optimized shaping and iterative demodulation and decoding noticeably improves the performance in comparison to the same system without constellation shaping.

Cognitive Network (CN) paradigm and Machine Learning (ML) techniques are increasingly becoming popular in Wireless network design. CN allows to efficiently arrange the network stack parameters among the nodes involved in the data transmission. It can be divided into two modules: the former permits to retrieve the ISO/OSI protocol stack parameters and the latter optimizes the transmission through a Cognitive Engine entity. This engine usually adopts ML algorithms to extract important features regarding the network, with the objective of either maximizing the global throughput or predicting relevant network behaviors. This paper analyzes how common ML algorithms are able to model transmissions occurring in a typical wireless network scenario. In particular, we test the algorithms with our CN testbed to predict transmission estimated time of arrivals (ETAs) in different network setups. We compare these results with those obtained via scp, which is the typical Unix/Linux shell program used to exchange files. We show that all learning techniques significantly improve the goodness of ETA prediction, thus suggesting to embed such algorithms in future scp revisions.

The spreading of video streaming services in the last few years is presenting new challenges in wireless networking; Video Rate Adaptation (VRA) is a technique that optimizes the bandwidth usage by adapting video quality as network conditions change. We propose two Quality of Experience (QoE) aware algorithms that perform VRA while guaranteeing user satisfaction.

The increasing demand for higher data rates has accelerated research on the next generation of mobile cellular networks (5G). One of the key factors of 5G is the use of a larger bandwidth allocated in the millimeter wave (mmWave) frequency spectrum. In particular, one of the candidate bands is the portion of spectrum between 17 and 30 GHz that is currently used by other technologies such as fixed satellite services (FSS) and the cellular network backhaul. In this paper, we analyze the coexistence between mobile services and FSS considering the main characteristics of the mmWave spectrum recently investigated in the literature. Moreover, we present a novel cooperative scheduling algorithm based on a game theoretic framework that exploits the use of analog beamforming at the base stations (BS). Finally, we show that adopting this algorithm ensure that the system meets the regulatory recommendation concerning the interference level at the FSS and at the same time provides a good user spectral efficiency.

The use of a larger bandwith in the millimeter wave (mmWave) spectrum is one of the key components of next generation cellular networks. Currently, part of this band is allocated on a co-primary basis to a number of other applications, such as the fixed satellite services (FSSs). In this paper, we investigate the coexistence between a cellular network and FSSs in a mmWave scenario. In light of the parameters recommended by the standard and the recent results presented in the literature on the mmWave channel model, we analyze different BSs deployments and different antenna configurations at the transmitters. Finally, we show how, exploiting the features of a mmWave scenario, the coexistence between cellular and satellite services is feasible and the interference at the FSS antenna can be kept below recommended levels.

Cooperative Spectrum Sharing (CSS) is an appealing approach for primary users (PUs) to share spectrum with secondary users (SUs) because it increases the transmission range or rate of the PUs. Most previous works are focused on developing complex algorithms which may not be fast enough for real-time variations such as in channel availability. Instead, we develop a learning mechanism for a PU to enable CSS in a strongly incomplete information scenario with low computational overhead. We model the learning mechanism of the PU to discover which SU to interact with and what offer to make to it with a combination of a Multi-Armed Bandit (MAB) and a Markov Decision Process (MDP). By means of Monte-Carlo simulations we show that, despite its low computational overhead, our proposed mechanism converges to the optimal solution and significantly outperforms the ε-greedy heuristic. This algorithm can be extended to include more sophisticated features while maintaining its desirable properties such as the fast speed of convergence.

This paper studies the throughput of spectrum sharing networks utilizing rate adaptation. We use some recent results on the achievable rates of finite block-length codes to analyze the secondary user (SU) throughput with a constraint on the primary user (PU) codeword drop probability. With codewords of finite length, we derive closed-form expressions for the SU activation probability and throughput. The results are obtained in different scenarios with no channel state information at the SU transmitter. As demonstrated numerically and analytically, using finite-length codewords there is considerable potential for the data transmission of unlicensed secondary users under different quality-of-service requirements of the licensed primary users.

The idea that we are moving towards a future networked society is widely accepted. The increase of network access, in particular to the Internet, and the wide heterogeneity of devices and components that access the network, and occasionally communicate with each other, pose great design challenges, but at the same time open unprecedent opportunities. In order to be able to fully exploit the new opportunities, a uniform and standardized software architecture must be adopted. The aim is to regulate and organize the multidisciplinary nature of the Internet-of-Things (IoT) applications. In this article we first introduce the REpresentational State Transfer (REST) architecture style as one of the de-facto standards in a totally-IP-connected world. Then the main focus will move to a new M2M RESTful framework for the IoT, developed following the REST paradigm, but enhancing its interface to introduce new web functionalities that are still accessed via the usual interface of the Internet Web sites.

One of the main concerns in traditional wireless sensor networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is ambient energy harvesting (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is wireless energy transfer (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.

The papers in this special issue presents cutting-edge research results in the emerging area of energy harvesting wireless communications and wireless energy transfer. This first issue starts with a review article coauthored by the guest editors that summarizes recent results in the broad area of energy harvesting communications, in particular, in information-theoretic, offline and online schedulingtheoretic, medium access, networking approaches to energy harvesting communications, as well as in energy cooperation and simultaneous wireless energy and information transfer.

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.

In many wireless communication networks a common channel is shared by multiple users who must compete to gain access to it. The operation of the network by self-interested and strategic users usually leads to the overuse of the channel resources and to substantial inefficiencies. Hence, incentive schemes are needed to overcome the inefficiencies of non-cooperative equilibrium. In this work, we consider a slotted-Aloha-like random access protocol and two incentive schemes: pricing and intervention. We provide some criteria for the designer of the protocol to choose one scheme between them and to design the best policy for the selected scheme, depending on the system parameters. Our results show that intervention can achieve the maximum efficiency in the perfect monitoring scenario. In the imperfect monitoring scenario, instead, the performance of the system depends on the beliefs of the different entities and, in some cases, there exists a threshold for the number of users such that, for a number of users lower than the threshold, intervention outperforms pricing, whereas for a number of users higher than the threshold pricing outperforms intervention.

The millimeter-wave (mmWave) frequency band is seen as a key enabler of multigigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which leads to high directivity gains, fully directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. This paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.

In this paper, an underlay cognitive radio network that consists of an arbitrary number of secondary users (SU) is considered in which the primary user (PU) employs type-I hybrid automatic repeat request (HARQ). Exploiting the redundancy in PU retransmissions, each SU receiver applies forward interference cancelation to remove a successfully decoded PU message in the subsequent PU retransmissions. The knowledge of the PU message state at the SU receivers and the ACK/NACK message from the PU receiver are sent back to the transmitters. With this approach and using a constrained Markov decision process (CMDP) model and constrained multi-agent MDP (CMMDP), centralized and decentralized optimum access policies for SUs are proposed to maximize their average sum throughput under a PU throughput constraint. In the decentralized case, the channel access decision of each SU is unknown to the other SU. Numerical results demonstrate the benefits of the proposed policies in terms of sum throughput of SUs. The results also reveal that the centralized access policy design outperforms the decentralized design especially when the PU can tolerate a low average long term throughput. Finally, the difficulties in decentralized access policy design with partial state information are discussed.

In this paper, we analyze the impact of relays on the uplink performance of frequency-division multiple-access cellular networks. We focus our analysis on decode-and-forward techniques, with the aim of measuring the improvements that can be achieved in terms of throughput and energy saving. We apply a stochastic-geometry-based approach to a scenario with intercell interference and full frequency reuse. The first goal of this work is to observe what is the impact of various relay features, such as transmission power, location, and antenna pattern, when a half-duplex constraint is imposed. The second goal is to determine how beneficial relaying can be also for mobiles who are not at the cell edge and who can therefore use a direct link toward the base station. We show that if more refined decoding techniques, such as Successive Interference Cancellation and Superposition Coding, are properly used, considerable gains can be obtained for these mobiles as well.

This paper studies the throughput of spectrum sharing networks utilizing rate adaptation. We use some recent results on the achievable rates of finite block-length codes to analyze the secondary user (SU) throughput with a constraint on the primary user (PU) codeword drop probability. With codewords of finite length, we derive closed-form expressions for the SU activation probability and throughput. The results are obtained in different scenarios with perfect or no channel state information at the SU transmitter. As demonstrated numerically and analytically, using finite-length codewords and rate adaptation, there is considerable potential for the data transmission of unlicensed secondary users under different quality-of-service requirements of the licensed primary users.

Wireless sensors can integrate rechargeable batteries and energy-harvesting (EH) devices to enable long-term, autonomous operation, thus requiring intelligent energy management to limit the adverse impact of energy outages. This work considers a network of EH wireless sensors, which report packets with a random utility value to a fusion center (FC) over a shared wireless channel. Decentralized access schemes are designed, where each node performs a local decision to transmit/discard a packet, based on an estimate of the packet's utility, its own energy level, and the scenario state of the EH process, with the objective to maximize the average long-term aggregate utility of the packets received at the FC. Due to the non-convex structure of the problem, an approximate optimization is developed by resorting to a mathematical artifice based on a game theoretic formulation of the multiaccess scheme, where the nodes do not behave strategically, but rather attempt to maximize a common network utility with respect to their own policy. The symmetric Nash equilibrium (SNE) is characterized, where all nodes employ the same policy; its uniqueness is proved, and it is shown to be a local maximum of the original problem. An algorithm to compute the SNE is presented, and a heuristic scheme is proposed, which is optimal for large battery capacity. It is shown numerically that the SNE typically achieves near-optimal performance, within 3% of the optimal policy, at a fraction of the complexity, and two operational regimes of EH-networks are identified and analyzed: an energy-limited scenario, where energy is scarce and the channel is under-utilized, and a network-limited scenario, where energy is abundant and the shared wireless channel represents the bottleneck of the system.

This paper studies the effect of optimal power allocation on the performance of communication systems utilizing automatic repeat request (ARQ). Considering Type-I ARQ, the problem is cast as the minimization of the outage probability subject to an average power constraint. The analysis is based on some recent results on the achievable rates of finite-length codes and we investigate the effect of codewords length on the performance of ARQ-based systems. We show that the performance of ARQ protocols is (almost) insensitive to the length of the codewords, for codewords of length ≥ 50 channel uses. Also, optimal power allocation improves the power efficiency of the ARQ-based systems substantially. For instance, consider a Rayleigh fading channel, codewords of rate 1 nats-per-channel-use and outage probability 10-3. Then, with a maximum of 2 and 3 transmissions, the implementation of power-adaptive ARQ reduces the average power, compared to the open-loop communication setup, by 17 and 23 dB, respectively, a result which is (almost) independent of the codewords length. Also, optimal power allocation increases the diversity gain of the ARQ protocols considerably.

In this paper, we address an important problem in mobile ad hoc networks, namely, the intrinsic inefficiency of the standard transmission control protocol (TCP), which has not been designed to work in these types of networks. After an initial training phase, we predict the mobility status of the network through a probabilistic approach, and we propose a series of ad hoc strategies to counteract the TCP inefficiency based on this prediction. Via simulation, we show the performance improvements in various wireless scenarios, in terms of increased average throughput and decreased length of the outage intervals. The significant performance improvements shown here will be verified in a future work by implementing our approach in a real testbed.

Mobile Content Delivery Networks (MCDNs) are designed to effectively support the delivery of continuous and discrete media to mobile consumers over cellular networks. Enabling large scale content distribution at a reasonable cost and without overloading the mobile core network is a crucial design goal for network operators. Nowadays, a key task for network operators is the development of efficient MCDNs due to the day-by-day video traffic increase in the network. In this paper, a novel concept of MCDN is proposed with the target of flexibly adapting the video caching and transport in the cellular network to the mobility of the users. By means of simulation we show that there exists a video chunk length which provides the best tradeoff between the traffic volume generated in the network and the size of the cache in the CDN nodes. New challenges are discussed and practical considerations for wide-scale deployments in next generation cellular networks are discussed.

In the context of dynamic spectrum access, spectrum sharing among multiple operators has recently emerged as a promising paradigm to improve the efficiency of resource usage. Several theoretical evaluations have proven the benefits offered by pooling the available frequencies so as to tune the capacity offered by the operators according to their different needs, especially the service demands from their users. However, practical aspects concerning the application of sharing techniques are rarely studied, and deserve more detailed investigations. This paper aims at tackling this problem, in particular investigating the impact of asymmetries and dynamics of the user demands on the implementation of spectrum sharing techniques and the resulting performance, especially in terms of fairness among the users, which seems to be often neglected by many studies. We show that in variable traffic conditions, a constantly monitored and updated sharing of frequency bands performs much better than a static allocation simply based on average traffic loads. However, it is possible to choose the update rate of the spectrum allocation so that it does not represent a heavy computational and signaling burden, while retaining most of the improvements brought by the spectrum sharing paradigm.

The exponential growth of video traffic in mobile networks calls for the deployment of advanced video admission control (VAC) and resource management (RM) techniques in order to provide the best quality of experience (QoE) to the end user according to the available network resources. The degradation of the QoE perceived by the user when reducing the source rate of a video typically depends on the content of the video itself. In this paper, we analyzed the QoE of a group of test video sequences encoded with H.264 advanced video codec at different rates, i.e., quality levels. The QoE is objectively expressed in terms of the average structural similarity (SSIM) index. Based on empirical results, we propose a 4-degree polynomial approximation of the SSIM as a function of the coded video rate. We hence propose to tag each video with these polynomial coefficients that provide a compact description of its specific SSIM behavior, and to use this information in VAC and RM algorithms to optimally manage a shared transmission medium. As a proof of concept, we report selected simulation results that compare QoE-aware and QoE-agnostic algorithms in a scenario with a single link shared by multiple concurrent video flows.

Several techniques for wireless networking, such as opportunistic spectrum access, or self-healing networks, may be seen as using a form of cognition, meaning that they mimic reasoning processes of intelligent beings. We propose to expand this cognition-based process by exploiting the parallel processing power of the infrastructure, so as to go beyond cognition as is meant by these approaches. We leverage novel approaches, taken from cognitive science and artificial intelligence, involving not only supervised but also unsupervised learning, and we envision their application to systems for video over wireless. The transmission of multimedia content, and its adaptation to the condition of the communication infrastructure, i.e., the wireless channel or the content delivery network, are envisioned as particularly critical steps for the development of latest generation mobile networks. For this scenario, we propose and evaluate a video classifier based on a Restricted Boltzmann Machine that tries to extract abstract features of videos from the analysis of the sizes of a few coded frames. These features can then be exploited by the communication network itself to optimize video transmission based on its content.

“Smart City” is a powerful paradigm that applies the most advanced communication technologies to urban environments, with the final aim of enhancing the quality of life in cities and provide a wide set of value-added services to both citizens and administration. A fundamental step towards the practical realization of the Smart City concept consists in the development of a communication infrastructure capable of collecting data from a large variety of different devices in a mostly uniform and seamless manner, according to the Internet of Things (IoT) paradigm. While the scientific and commercial interest in IoT has been constantly growing in the last years, practical experimentation of IoT systems has just begun. In this paper, we present and discuss the Padova Smart City system, an experimental realization of an urban IoT system designed within the Smart City framework and deployed in the city of Padova, Italy. We describe the system architecture and discuss the fundamental technical choices at the base of the project. Then, we analyze the data collected by the system and show how simple data processing techniques can be used to gain insights on the functioning of the monitored system, public traffic lighting in our specific case, as well as other information concerning the urban environment.

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.

In this paper, we consider an underlay cognitive radio paradigm with a primary packet system which implements automatic repeat request (ARQ)-based error control. We propose cognitive transmission strategies which provision for minimum primary quality of service (QoS) requirements. The proposed strategies take advantage of opportunities that arise during ARQ retransmissions of the primary system at the link layer, and adapt channel fading variations by employing adaptive modulation and coding (AMC) and power control at the physical layer. In this cross-layer formulation, the cognitive throughput is optimized subject to 1) a packet-loss-rate (PLR) constraint on the cognitive link, and 2) a minimum required throughput and a PLR constraint on the primary link. We first derive analytical expressions for the link-layer throughput of the cognitive link defined in terms of the data rate successfully received at the receiver side. Next, we present optimized AMC and power control schemes for the cognitive transmitter. Numerical results show significant improvement in terms of the cognitive throughput and PLR performance. Namely, when the cognitive transmitter employs both power and rate control the throughput-maximizing strategy is determined based on a tradeoff between the primary PLR and delay (in terms of the number of retransmission rounds) QoS metrics.

Wireless sensors can be integrated with energy harvesting (EH) devices to enable long-term, autonomous operation, necessitating efficient energy management. Existing research assumes knowledge of the state-of-charge (SOC) of the rechargeable battery; however, accurate SOC estimation in real-world devices is typically costly or impractical. This paper investigates the impact of imperfect SOC knowledge and the design of policies to cope with such uncertainty. The optimization complexity is reduced by decoupling the different time scales of the system: first, the short-term average performance is optimized with respect to fast-varying exogenous state variables, under an average energy consumption constraint, but neglecting battery dynamics; then, the policy dictating the average energy consumption as a function of state variables evolving over longer time scales is optimized, based on the detailed battery dynamics. A local search algorithm is presented to determine a locally optimal policy. The performance degradation compared to the scenario with perfect SOC knowledge is shown to decrease with increasing storage capacity and decreasing uncertainty in the EH source, and is within 5% for most cases of practical interest. Moreover, near-optimal performance is achieved by only a loose SOC knowledge, which distinguishes between high/low SOC levels. Finally, the impact of time correlation in the EH source is investigated. EH state knowledge is shown to be more critical than SOC knowledge, hence precise knowledge of the former can obviate the need for accurate information about the latter.

This paper presents ALBA-R, a protocol for convergecasting in wireless sensor networks. ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The protocol is localized and distributed, and adapts efficiently to varying traffic and node deployments. Through extensive ns2-based simulations, we show that ALBA-R significantly outperforms other convergecasting protocols and solutions for dealing with connectivity holes, especially in critical traffic conditions and low-density networks. The performance of ALBA-R is also evaluated through experiments in an outdoor testbed of TinyOS motes. Our results show that ALBA-R is an energy-efficient protocol that achieves remarkable performance in terms of packet delivery ratio and end-to-end latency in different scenarios, thus being suitable for real network deployments.

ABSTRACT A novel iterative algorithm for the efficient computation of the intersection areas of an arbitrary number of circles is presented. The algorithm, on the basis of a trellis structure, hinges on two geometric results, which allow the existence check and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. The presented algorithm is a powerful tool for the performance analysis of wireless networks and finds many applications, ranging from sensor to cellular networks. As an example of practical application, an insightful study of the uplink outage probability in a wireless network with cooperative access points as a function of the transmission power and access point density is presented. Copyright © 2012 John Wiley & Sons, Ltd.

Multimedia traffic is expected to be widespread in next generation wireless networks, which will be likely based on Orthogonal Frequency Division Multiple Access. While multimedia content is heavily demanding in terms of network resources, it is also inherently adaptable at the application layer, thereby imposing soft QoS constraints, rather than strict requirements on a specific data rate. In this paper, we specifically investigate the suitability of such a medium access control rationale for this kind of traffic. It turns out that, if properly managed, next generation networks can accommodate several multimedia users, thanks to a proper exploitation of user and frequency diversity. However, on the application side a great deal of attention should be paid to take advantage of scalability of the video flows and adaptability of this kind of traffic, to exploit the network capacity at its fullest.

Long Term Evolution (LTE) represents the cutting-edge broadband wireless access technology in providing ubiquitous and simultaneous connectivity to many users. This paper evaluates the impact that packets breaking the 1500 bytes legacy value, called Jumboframes, have in LTE networks, by exploiting and extending the network stack in the ns-3 simulator. We first provide an overview about the key features of LTE starting from a physical layer perspective, to logical functions like the adaptive modulation and coding scheme, together with a detailed description of the Radio Link Control (RLC) segmentation capabilities. A comparative evaluation is performed based on diverse network configuration criteria, such as user position, density and mobility. We aim at assessing the benefits and caveats that derive from Jumboframes usage in LTE networks. Moreover, a novel cross-layer approach is proposed to mitigate the effect of rapid buffer saturation, due to the transmission of oversized packets with scarce radio resources. To conclude, we test our framework through the analysis of realistic video traces.

Harvesting-Based Wireless Sensor Devices are increasingly being deployed in today's sensor networks, due to their demonstrated advantages in terms of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of harvesting-based battery-powered sensor devices, focusing on the impact of the battery discharge policy on the irreversible degradation of the storage capacity. A general framework based on Markov chains which captures the battery degradation process is proposed. Based on such model, it is shown that a degradationaware policy significantly improves the lifetime of the sensor compared to "greedy" operation policies, while guaranteeing the minimum required QoS.

Spectrum sharing has been recently proposed as a promising paradigm to improve the efficiency of resource usage in next generation mobile networks. In particular, non orthogonal spectrum sharing allows the operators to re-use the available frequencies at the cost of higher interference at the receivers. In this paper, we mathematically analyze the performance of this technique and how it is statistically related to the channel coefficients. Moreover, we compare different kinds of schedulers that exploit various aspects of non orthogonal spectrum sharing. Finally, the resulting system performance is assessed, first through an exact statistical framework and then by simulating the schedulers in an LTE scenario with the open-source network simulator ns3.

We investigate the optimization of video transmissions over cellular networks by using the H.264 Scalable Video Coding (SVC) at the application layer and an Adaptive Modulation and Coding (AMC) scheme at the physical layer. We analyze how the cross-layer optimization (XLO) of these two techniques together performs compared to a sequential and independent selection of video packets and Modulation and Coding Schemes (MCS) with no cross-layer optimization (NXLO), in terms of goodput and packet delivery delay. We formulate an analytical model based on a Markov chain representing the wireless channel, where each state is associated to a different channel quality corresponding to a set of possible choices of video layer and MCS. Our numerical results show that XLO significantly outperforms NXLO for video transmissions, thereby pointing out the strong need for cross-layer solutions in video transmission.

Current spectrum allocation policies, imposing exclusive usage of a licensed operator, may lead to inefficient management and waste of resources. Spectrum sharing, i.e., usage by the same frequency band by multiple operators, can improve the efficiency of the allocation. We analyze a scenario where two mobile operators managing neighboring cells also share a fraction of their available spectrum and quantify the performance gain. To this end, we propose a framework based on the definition of the Interference Suppression Ratio, which models effects such as beamforming or directional antennas. Depending on its value, mutual interference among the operators is reduced and sharing gains can be achieved. We implemented this framework in the well known open-source simulator ns-3 and we ran a parametric analysis of the impacting factors, including noise and cell radius. Simulation results confirm that significant gains can be achieved in terms of network capacity and throughput, provided that the Interference Suppression Ratio is above a given value.

This paper introduces a novel technique for access by a cognitive Secondary User (SU) using best-effort transmission to a spectrum with an incumbent Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the primary ARQ protocol to perform Interference Cancellation (IC) at the SU receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward IC, where SUrx, after decoding the PU message, cancels its interference in the (possible) following PU retransmissions of the same message, to improve the SU throughput; Backward IC, where SUrx performs IC on previous SU transmissions, whose decoding failed due to severe PU interference. Secondary access policies are designed that determine the secondary access probability in each state of the network so as to maximize the average long-term SU throughput by opportunistically leveraging IC, while causing bounded average long-term PU throughput degradation and SU power expenditure. It is proved that the optimal policy prescribes that the SU prioritizes its access in the states where SUrx knows the PU message, thus enabling IC. An algorithm is provided to optimally allocate additional secondary access opportunities in the states where the PU message is unknown. Numerical results are shown to assess the throughput gain provided by the proposed techniques.

This paper discusses a new perspective for the application of game theory to wireless relay networks, namely, how to employ it not only as an analytical evaluation instrument, but also in constructively deriving practical network management policies. We focus on the problem of medium sharing in wireless networks, which is often seen as a case where game theory just proves the inefficiency of distributed access, without proposing any remedy. Instead, we show how, by properly modeling the agents involved in such a scenario, and enabling simple but effective incentives towards cooperation for the users, we obtain a resource allocation scheme which is meaningful from both perspectives of game theory and network engineering. Such a result is achieved by introducing throughput redistribution as a way to transfer utilities, which enables cooperation among the users. Finally, a Stackelberg formulation is proposed, involving the network access point as a further player. Our approach is also able to take into account power consumption of the terminals, still without treating it as an insurmountable hurdle to cooperation, and at the same time to drive the network allocation towards an efficient cooperation level.

This paper studies the interaction between a designer and a group of strategic and self-interested users who possess information the designer does not have. Because the users are strategic and self-interested, they will act to their own advantage, which will often be different from the interest of the designer, even if the latter is benevolent and seeks to maximize (some measure of) social welfare. In the settings we consider, the designer and the users can communicate (perhaps with noise), the designer can observe the actions of the users (perhaps with error) and the designer can commit to (plans of) actions - interventions - of its own. The designer's problem is to construct and implement a mechanism that provides incentives for the users to communicate and act in such a way as to further the interest of the designer - despite the fact that they are strategic and self-interested and possess private information. To address the designer's problem we propose a general and flexible framework that applies to many scenarios. To illustrate the usefulness of this framework, we discuss some simple examples, leaving further applications to other papers. In an important class of environments, we find conditions under which the designer can obtain its benchmark optimum - the utility that could be obtained if it had all information and could command the actions of the users - and conditions under which it cannot. More broadly we are able to characterize the solution to the designer's problem, even when it does not yield the benchmark optimum. Because the optimal mechanism may be difficult to construct and implement, we also propose a simpler and more readily implemented mechanism that, while falling short of the optimum, still yields the designer a "good" result.

Most congestion control schemes are based on user cooperation, i.e., they implicitly assume that users are willing to share their private information and to take actions such that the network operates efficiently. However, a self-interested and strategic user might exploit such schemes to obtain an individual gain at the expenses of the other users, misrepresenting its private information and overusing the resources. We first quantify the inefficiency of the network in the presence of selfish users for two different scenario: in the complete information case - in which the users have no private information - and in the incomplete information case - in which the users have private information. Then, we ask whether the congestion control scheme can be designed to be robust to self-interested strategic users. To reach this objective, we use an intervention scheme. For the complete information scenario we describe a scheme that is able to give the users an incentive to optimally use the resources. For the incomplete information scenario we describe two schemes that provide the users with an incentive to report truthfully and to use the resources efficiently, although not always optimally. Illustrative results show that the considered schemes can considerably improve the efficiency of the network.

Energy Harvesting Wireless Sensor Devices are increasingly being considered for deployment in sensor networks, due to their demonstrated advantages of prolonged lifetime and autonomous operation. However, irreversible degradation mechanisms jeopardize battery lifetime, calling for intelligent management policies, which minimize the impact of these phenomena while guaranteeing a minimum Quality of Service (QoS). This paper explores a mathematical characterization of these devices, focusing on the interplay between the battery discharge policy and the irreversible degradation of the storage capacity. We propose a stochastic Markov chain framework, suitable for policy optimization, which captures the degradation status of the battery. We present a general result of Markov chains, which exploits the timescale separation between the communication time-slot of the device and the battery degradation process, and enables an efficient optimization. We show that this model fits well the behavior of real batteries for what concerns their storage capacity degradation over time. We demonstrate that a degradation-aware policy significantly improves the lifetime of the sensor compared to "greedy" policies, while guaranteeing the minimum required QoS. Finally, a simple heuristic policy, which never discharges the battery below a given threshold, is shown to achieve near-optimal performance in terms of battery lifetime.

This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain ("GOOD" and "BAD"), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves near-optimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.

Relay sharing has been recently investigated to increase the performance of coexisting wireless multi-hop networks. In this paper, we analyze a scenario where two wireless ad hoc networks are willing to share some of their nodes, acting as relays, in order to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian network analysis is exploited to compute the probabilistic relationships between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through the use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

In this paper, we introduce Underwater Selective Repeat (USR), a Selective Repeat Automatic Repeat reQuest (SR-ARQ) mechanism for multiuser underwater acoustic networks. Our scheme exploits the typically large round-trip time (RTT) of underwater acoustic links to interlace the transmission of data and acknowledgment (ACK) packets, such that the transmitter never starts sending data packets when it should receive ACKs. No specific synchronization mechanism is required to do so. It is shown that the timing of point-to-point communications can be adjusted to optimize the performance of multiuser networks of a given size. Moreover, it is shown that the proposed strategy can be made robust to mobility, hence to time-varying RTTs. We provide detailed simulation results that assess the performance of USR as a function of the protocol parameters, both in static and in mobile networks. Based on these results, we propose an adaptive version of USR, whereby a node can modify its behavior (e.g., it can pack data transmissions more tightly or more loosely within one RTT) by reacting to packet errors induced by multiple-access interference.

We propose a theoretical framework to evaluate the expected throughput of underwater networks over an ensemble of node topologies and propagation environments. The analysis is based on the assumptions that the transmitters are spatially distributed according to a Poisson point process, and that the channel follows a Rayleigh fading distribution, with a mean that is determined by spreading loss and frequency-dependent absorption. We evaluate the probability of a successful transmission, i.e., the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a given threshold, and determine the maximum network throughput density over the transmitter density and the operating frequency. The theoretical results are validated using a realistic underwater channel simulator based on ray tracing. It is demonstrated that, for a number of practical scenarios, the theoretical and simulated throughput match provided that the spreading-loss exponent is appropriately fitted to the simulation scenario. Overall, the proposed framework provides easy-to-obtain network throughput results, which can be used as a complement or an alternative to time-costly, deployment-dependent network simulations.

ABSTRACT In this paper, we extend previous work on geographic random forwarding (GeRaF) by considering a more efficient paradigm whereby on/off cycles are exploited and every node can participate to packet relaying, as long as it wakes up during any handshake procedure. The protocol is fully distributed, as routing decisions are made hop by hop based on geographic metrics and using a joint channel access/routing procedure, like in the first version of GeRaF. In addition, a previously defined analytical model for GeRaF is used to analyze the protocol, after extending it to cover the new channel access and relay selection procedure. The model is employed to find optimal choices for the parameters of the protocol, before comparing analytical results with ns2 simulations. Results confirm that the geographic paradigm remains a very good solution for energy‐constrained, density‐constrained, and latency‐constrained networks, and that small yet very effective protocol optimizations can achieve remarkable performance improvements. Copyright © 2011 John Wiley & Sons, Ltd.

The general feeling about ecology is changing worldwide. People all around the world are more careful about problems such as energy provisioning and consumption. New ideas, such as the Smart Grid concept, are trying to give solutions to the energy problems by means of a modern power grid with intelligent management capabilities. In such scenarios, networking solutions, e.g., the Internet Protocol version 6 (IPv6), will allow appliances to be remotely coordinated in order to optimize the total power consumption, and balance the power demand. This paper presents an innovative implementation of the IPv6 stack over the 6LoWPAN adaptation layer. Our 6LoWPAN implementation aims to provide a support platform for IPv6 over interoperable constrained-resources devices, such as those embedded in the electrical appliances. Our main goals are to optimize the memory management and to address the portability problems of the stack. In particular we want to develop a stack which is not tied to a particular data-link protocol. Comparison with existing solutions is also provided.

As Energy Harvesting Devices (EHD) become more widely deployed in sensor network platforms, the need arises for "smart" operation policies which can ensure long-term, autonomous and reliable operation. Existing research has relied on the implicit assumption of perfect knowledge of the energy available in the EHD. However, estimating the energy level of the batteries or super-capacitors employed in real-world EHDs, commonly known as State-Of-Charge (SOC), is a non-trivial task. In this paper, we design operation policies that maximize the long-term reward under imperfect knowledge of the SOC. Through an array of simulation results, we quantify the performance degradation due to imperfect SOC knowledge, and show that it increases with decreasing storage capacity and increasing variance in the energy arrival process. In the particular case of a two-state controller, i.e., a controller which knows only if the SOC is HIGH or LOW, we prove that, for a linear reward function, there is no performance loss, while, for a logarithmic reward function, simulations show that the loss is typically less than 5%.

Infrastructure sharing has been recently investigated as a viable solution to increase the performance of coexisting wireless networks. In this paper, we analyze a scenario where two wireless networks are willing to share some of their nodes to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian Network analysis is exploited to compute the correlation between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

While network coding can potentially provide significant throughput benefits by combining packets prior to forwarding them, the achievable gains are directly related to the coding opportunities at a relay that performs encoding. If the relay does not have packets destined for distinct destinations, that can be encoded together, the network coding gains could be marginal. Towards increasing the opportunities for network coding, in this paper we propose a queue management scheme, that arbitrates the rate at which distinct transmitters send packets to a common relay which applies network coding. Our queue management approach prioritizes the channel access of nodes that do not have enough enqueued packets at the common relay, thereby essentially attempting to balance the number of packets from the distinct senders at the relay. We perform extensive simulations of our approach (built as a wrapper on top of the popular network coding approach COPE) in multi-rate scenarios. We find that our approach yields throughput gains of up to 57% compared to COPE due to enhanced opportunities towards encoding packets.

Nowadays, many devices in wireless sensor networks are provided with energy harvesting capability to allow for their continuous operation over long periods of time. In principle, the energy level within each sensor should be managed optimally to ensure the best performance. Network engineers, however, often consider optimality under the idealized assumption of perfect knowledge about the State-of-Charge (SOC) of the device. This information is not always realistic or accurate. In our previous work [1], we showed that optimal policies for sensing, transmission, and battery usage should rather consider uncertainty on the SOC of the device. In this paper, we extend that investigation, therein performed in the idealized scenario of i.i.d. energy arrivals, by considering a correlated energy generation process. We show that the knowledge of the SOC and that of the energy generation process are useful in a complementary manner, that is they can be traded for each other. Moreover, the knowledge on the state of the energy generation process can obviate the need for acquiring accurate SOC information. This investigation paves the road for a new line of research in wireless sensor networks, allowing a tighter interaction between the designers of energy harvesting and battery storage mechanisms on the one hand, and the engineers of network operation and control policies on the other.

In this work we present a link abstraction model for the simulation of downlink data transmission in LTE networks. The purpose of this model is to provide an accurate link performance metric at a low computational cost by relying solely on the knowledge of the SINR and of the modulation and coding scheme. To this aim, the model combines Mutual Information-based multi-carrier compression metrics with Link-Level performance curves matching, to obtain lookup tables that express the dependency of the Block Error Rate on the SINR values and on the modulation and coding scheme being used. In addition, we propose a 3GPP-compliant Channel Quality Indicator evaluation procedure, based on the proposed Link Abstraction Model, to be used as part of the LTE Adaptive Modulation and Coding mechanisms. Finally, we discuss how these contributions have been tested, validated and integrated in the ns-3 simulator. The link abstraction model described in this paper has been included in the official ns-3 distribution since release 3.14.

In this paper we design a robust opportunistic scheduler to be implemented at the base station in a cellular network for broadcast media streaming applications. The scheduling mechanism operates based on information on both the average and the instantaneous user distributions, and on radio link channel quality, obtained through the cellular uplink channel. Video streams are encoded into multiple scalable video layers, that are split into video packets opportunistically scheduled at the base station with the goal of minimizing the wireless resource usage while keeping the overall target Quality of Service (QoS) in the cell. We finally discuss potential extensions of this framework to more sophisticated scheduling solutions for broadcast video delivery over next generation cellular networks.

Cooperative techniques have been shown to significantly improve the performance of wireless networks. Despite being a mature technology in single communication link scenarios, their implementation in wider, and practical, networks poses several challenges which have not been fully identified and understood so far. In this paper, the implementation of cooperative communications in non-centralized ad hoc networks with sensing-based channel access is extensively discussed. Both analysis and simulation are employed to provide a clear understanding of the mutual influence between the link layer contention mechanism and relaying protocols. Results show that sensing-based channel access significantly hampers the effectiveness of cooperation by biasing the spatial distribution of available relays, and by inducing a level of spatial and temporal correlation of interference that diminishes the diversity improvement on which cooperative gains are founded. Moreover, the efficiency reduction entailed by several practical protocol issues related to carrier sense multiple access which are typically neglected in the literature is thoroughly investigated.

In this paper, we address the problem of computing the probability that r out of n interfering wireless signals are "captured," i.e., received with sufficiently large Signal to Interference plus Noise Ratio (SINR) to correctly decode the signals by a receiver with multi-packet reception (MPR) and Successive Interference Cancellation (SIC) capabilities. We start by considering the simpler case of a pure MPR system without SIC, for which we provide an expression for the distribution of the number of captured packets, whose computational complexity scales with n and r. This analysis makes it possible to investigate the system throughput as a function of the MPR capabilities of the receiver. We then generalize the analysis to SIC systems. In addition to the exact expressions for the capture probability and the normalized system throughput, we also derive approximate expressions that are much easier to compute and provide accurate results in some practical scenarios. Finally, we present selected results for some case studies with the purpose of illustrating the potential of the proposed mathematical framework and validating the approximate methods.

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this paper, the optimal transmission policy for the secondary user when the primary user adopts a retransmission-based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: 1) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; 2) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

The effective capacity (EC) for a wireless system expresses the maximum arrival source rate that the system can transmit over the wireless channel by fulfilling certain probabilistic delay constraints. This paper presents an EC analysis for a relay-assisted cooperative protocol exploiting adaptive transmission at physical and data-link layers. In the considered model, a source employs adaptive modulation and coding (AMC) in conjunction with a cooperative ARQ protocol, to transmit delay-constrained traffic to the destination node. To achieve adaptive cooperative diversity, a relay node, also equipped with AMC, performs cooperation when it decodes the source packet correctly and the destination requested a packet retransmission. We derive a tight closed-form upper-bound expression for the EC of the queue service process at the source node when wireless links are subject to time-correlated fading. To this end, a Markov model for the relay channel is used. We also present a cross-layer approach to delay-aware AMC design for the relay channel which provides the maximum system throughput subject to a probabilistic delay constraint. The numerical and simulation results show that the proposed adaptive cooperative protocol can dramatically improve the EC and the system throughput over time-correlated fading channels when compared to a direct transmission system.

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

Random access techniques have traditionally been used for the design of efficient medium access control protocols for wireless networks where the entire bandwidth is provided to the users as a single channel to be accessed for communication. In this paper we analyze the broadcast performance of several random access techniques in a multi channel system, where users can dynamically access portions of the available bandwidth at a given time and location. In order to compare these protocols we focus on metrics of interest such as the receiving probability, the system throughput and the network blocking probability. Our results show that these protocols can offer significant improvements in terms of increased system throughput provided that their operational parameters are carefully tuned to achieve near-optimal behavior in a multi channel setting.

This paper proposes a novel method for solving localization problems leveraging on node position constraints. This consists in mapping n wireless nodes onto n predefined positions in a map. The problem may be solved by first applying standard localization algorithms to get an initial estimate of the node positions in the area and, successively, mapping each estimated position to the closest admissible point in the map. Results can be improved by applying algorithms that are explicitly designed to manage the available information for the constrained problem. In this study, we propose three algorithms, based on a greedy, multi dimensional scaling, and belief propagation approach, respectively. The algorithms are analyzed and compared by using synthetic data. Results reveal that the belief propagation approach, suitably modified to account for the position constraints, outperforms the other algorithms in all the considered settings.

Cooperative Network-Coded Hybrid ARQ (Coop-NC HARQ) has recently been proposed as a smart way to improve Decode & Forward (D&F) relaying. According to this idea, cooperators send HARQ redundancy on behalf of a source coded together with a data packet intended for the destination they are helping and taken from their own queue. This paper applies for the first time such a technique in ad hoc networks with Multi-User Detection (MUD) capabilities, allowing several terminals to simultaneously act as relays. In this context, we introduce a novel communication paradigm that leverages network-coded retransmissions to allow more efficient data exchanges among all terminals involved in a cooperative process, enabling inter-relay communications as well. Simulation results show that the proposed solution offers important gains over D&F as well as Coop-NC HARQ in both single- and multi-hop networks in terms of throughput and energy consumption.

In this paper we address the problem of provisioning Quality of Service (QoS) to Voice over IP applications in a Wireless LAN scenario based on the IEEE 802.11 standard. We propose the use of a Cognitive Network approach to design a Call Admission Control (CAC) scheme, according to which each user stores relevant information on its past network experience and then uses such information to build a Bayesian Network (BN), a probabilistic graphical model to describe the statistical relationships among network parameters. The BN is exploited to predict the voice call quality, as a function of the Link Layer conditions in the particular scenario considered. Such prediction on the present and future values of the QoS provided is directly exploited to design the cognitive CAC scheme, which is shown to significantly outperform state of the art CAC techniques in a realistic scenario.

In this paper, we derive fundamental performance limits of underwater (UW) networks via an analysis of the average behavior of random deployments. In particular, we consider an UW network that consists of a Poisson point process of transmitters, each with a receiver at a given distance. The link channel model accounts for path-loss, frequency-dependent absorption, and Rayleigh fading. We evaluate the probability of a successful transmission, defined as the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a predetermined threshold, over different network and channel realizations. We then determine the network throughput density and the transmission capacity, defined as the maximum throughput density such that a constraint on the success probability is satisfied. The dependence of these metrics on the operating frequency and other system parameters is quantified through the proposed framework.

In the past few years, a large number of networking protocols for data gathering through aggregation, compression and recovery in Wireless Sensor Networks (WSNs) have utilized the spatio-temporal statistics of real world signals in order to achieve good performance in terms of energy savings and improved signal reconstruction accuracy. However, very little has been said in terms of suitable spatio-temporal models of the signals of interest. These models are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering algorithms. In this paper, we address this gap by devising a mathematical model for real world signals that are correlated in space and time. We thus describe a method to reproduce synthetic signals with tunable correlation characteristics and we verify, through analysis and comparison against large data sets from real world testbeds, that our model is accurate in reproducing the signal statistics of interest.

Aiming at improving the QoS performance over fading channels, this paper presents an analysis of the effective capacity for a relay-assisted communication system which utilizes a cooperative packet retransmission protocol at the link-layer in conjunction with AMC-based multi-rate transmission at the physical layer. In the considered model, a source (S) node employs AMC to transmit delay-constrained packet traffic to the destination (D) node. To achieve adaptive cooperative diversity, a relay node, also equipped with AMC, forwards the packet received from node S to node D if it was able to decode the source packet correctly and node D requested a packet retransmission. For this protocol we derive a closed-form expression for the effective capacity of the queue service process at node S. Numerical results reveal that the proposed cooperative protocol can dramatically increase the effective capacity with respect to a direct transmission system with AMC.

In this paper, we address the problem of computing the probability that r out of n interfering signals can be correctly received in a random access wireless system with capture, capable of performing up to K successive interference cancellation iterations. We provide an expression for the probability distribution of the number of captured packets that is scalable with n and r. We also provide an approximate expression for the mean number of captured signals, which gives the system throughput as a function of K and n. The approximation is much easier to compute than the exact mean and provides good results for reasonable values of K. Finally, we present some selected results for a case study with the purpose of illustrating the potential of the proposed mathematical framework, and validate the accuracy of the approximate method for the computation of the system throughput.

Video streaming is considered as one of the most important and challenging applications for next generation cellular networks. Current infrastructures are not prepared to deal with the increasing amount of video traffic. The current Internet, and in particular the mobile Internet, was not designed with video requirements in mind and, as a consequence, its architecture is very inefficient for handling video traffic. Enhancements are needed to cater for improved Quality of Experience (QoE) and improved reliability in a mobile network. In this paper we design a novel dynamic transport architecture for next generation mobile networks adapted to video service requirements. Its main novelty is the transport optimization of video delivery that is achieved through a QoE oriented redesign of networking mechanisms as well as the integration of Content Delivery Networks (CDN) techniques.

Cooperation in wireless networks is an important means to improve the resource utilization efficiency. It finds an interesting application in the context of spectrum sharing, where multiple wireless users put their licensed frequency bands in common in order to achieve a better resource usage. Due to the complexity of the problem, mathematical analysis is typically focused on simple scenarios. However, we believe that, in order to obtain a concrete proof of concept of the sharing paradigm, it is mandatory to assess its performance in realistic situations, i.e., with a larger number of nodes and a wider range of applications. Therefore, the support of a proper simulation environment is fundamental for high-quality applied research. In this paper we present and evaluate an original extension of the well known ns-3 network simulator which focuses on multiple operators of the most up-to-date cellular scenarios, i.e., the Long Term Evolution of UMTS employing OFDMA multiplexing. We describe the software architecture that enables the spectrum sharing and, in particular, allows operators to interact in order to agree on a spectrum division. A sample sharing policy is given as well, and a detailed simulation campaign is run to validate the proposed architecture, assess its efficiency, and evaluate the simulation time related to scenarios with an increasing number of nodes.

We propose an analytical framework to study broadcasting performance in multi channel wireless networks in the presence of adversary attacks. In order to reduce the effect of such attacks on the dissemination performance we use network coding and show that it can bring significant benefits to the broadcasting process. We investigate the impact that different medium access, transmission schemes and channel conditions have on the information exchange among all nodes. We analyze such impact in terms of reception delay and robustness with respect to malicious interference generated by adversary nodes. In order to do so, we model the process of data broadcasting as a coupon collector's problem. We derive the average delay required to retrieve partial and complete information by all nodes in the network, and quantify the gains obtained when using a multi channel system. We take into account the presence of different types of adversaries and find the optimum number of channels that nodes have to access in order to minimize the data reception delay.

In this paper, we address the problem of packet forwarding in multi-hop wireless networks of nodes equipped with multiple antennas. Through the use of MIMO (multiple-input multiple-output) technology, the physical layer is able to superimpose multiple concurrent information flows, thereby increasing the communications parallelism in the network. We propose a cooperative cross-layer scheme that integrates distributed incremental redundancy hybrid automatic retransmission request (HARQ) error control with routing in order to increase the efficiency of the transmissions and decrease the interference load in the network. In the proposed scheme, next hop relays are opportunistically selected during the HARQ process in order to avoid overloaded receivers and guarantee the geographical advancement of the packets. Thus, cooperation is used to both strengthen weak links and select the path to the destination. Numerical results are presented for a specific instantiation where a layered space-time multiuser detection (LASTMUD) transmitter-receiver architecture with spread-spectrum signals is implemented at the physical layer and packets are encoded with a linear erasure code (LEC).

A licensed multihop network that coexists with a set of unlicensed nodes is considered. Coexistence is regulated via a spectrum leasing mechanism that is based on cooperation and opportunistic routing. Specifically, the primary network consists of a source and a destination communicating via a number of primary relay nodes. In each transmission block, the next hop is selected in an on-line fashion based on the channel conditions (and thus the decoding outcome) in the previous transmissions, according to the idea of opportunistic routing. The secondary nodes may serve as extra relays, and hence potential next hops, for the primary network, but only in exchange for spectrum leasing. Namely, in return for their forwarding of primary packets, secondary nodes are awarded spectral resources for transmission of their own traffic. Secondary nodes enforce Quality-of-Service requirements in terms of rate and reliability when deciding whether or not to cooperate. Four policies that exploit spectrum leasing via opportunistic routing in different ways are proposed. These policies are designed to span different operating points in the trade-off between gains in throughput and overall energy expenditure for the primary network. Analysis is carried out for networks with a linear geometry and quasi-static Rayleigh fading statistics by using Markov chain tools. Different multiplexing techniques are considered for multiplexing of the primary and secondary traffic at the secondary nodes, namely orthogonal multiplexing (such as time, frequency or orthogonal code division multiplexing) and superposition coding. The optimality in terms of both throughput and primary energy consumption of superposition coding over all possible multiplexing strategies, for the given routing techniques, is proved. Finally, numerical results demonstrate the advantages of the proposed spectrum leasing solution based on opportunistic routing and illustrate the trade-offs between primary throughput and energy consumption.

This paper presents performance analysis and cross-layer design approaches for hybrid ARQ (HARQ) protocols in wireless networks, which employ adaptive modulation and coding (AMC) in conjunction with adaptive cooperative diversity and are subject to time-correlated fading channels. We first consider a point-to-point scenario, i.e., non-cooperative HARQ with AMC. Utilizing a Markov channel model which accounts for the temporal correlation in the successive transmission of incremental redundancy by the HARQ protocol, we derive the system throughput and the packet loss probability based on a rate compatible punctured convolutional code family. Next, we consider a cooperative HARQ (CHARQ) scheme in which a relay node, also equipped with AMC, retransmits redundancy packets when it is able to decode the source information packet correctly. For this scenario, we also derive the throughput and packet loss performance. Finally, we present a cross-layer AMC design approach which takes into account the hybrid ARQ protocol at the link layer. The results illustrate that including AMC in the HARQ protocols leads to a substantial throughput gain. While the performance of the AMC with HARQ protocol is strongly affected by the channel correlation, the CHARQ protocol provides noticeable performance gains over correlated fading channels as well.

This paper explores the relationship between cooperation and two very different medium access strategies: CSMA (epitomized by IEEE 802.11 DCF) and one instance of TDMA (represented by IEEE 802.15.3). By cooperation we mean basic decode-and-forward relaying as well as more advanced forms thereof based on network coding. The essential features that make each system more or less suitable to support the potentially high performance gains of hybrid ARQ are analyzed and studied by means of extensive simulations. The discussion carried out in this paper shows that centralized systems are able to reap far higher gains from a cooperative paradigm than their distributed counterparts, and investigates in depth the reasons that lead to such different behaviors, prompting also some interesting insights for proper design of advanced hybrid ARQ protocols.

Opportunistic routing is a well-known technique that exploits the broadcast nature of wireless transmissions and path diversity to form the route in an adaptive manner based on current channel conditions. This paper studies the throughput advantages of opportunistic routing over conventional multihop routing for linear multihop wireless networks with type-I Hybrid Automatic Repeat reQuest (HARQ) and quasi-static Rayleigh fading channels. The end-to-end throughput of opportunistic routing is derived using Markov chain tools and accounting for fading statistics. Both fixed-rate and optimal-rate transmissions are considered. Moreover, an investigation of the throughput using standard information-theoretic performance metrics for asymptotic signal-to-noise ratio regimes is provided. Specifically, the multiplexing gain and energy efficiency (i.e., minimum energy per bit) of both opportunistic and multihop routing are analyzed. Numerical results are given to corroborate the analysis.

Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios that apply cognition only at the physical layer. Designing a cognitive network is challenging since learning the relationship between network protocol parameters in an automated fashion is very complex. We propose to use Bayesian Network (BN) models for creating a representation of the dependence relationships among network protocol parameters. BN is a unique tool for modeling the network protocol stack as it not only learns the probabilistic dependence of network protocol parameters but also provides an opportunity to tune some of the cognitive network parameters to achieve desired performance. To the best of our knowledge, this is the first work to explore the use of BNs for cognitive networks. Creating a BN model for network parameters involves the following steps: sampling the network protocol parameters (Observe), learning the structure of the BN and its parameters from the data (Learn), using a Bayesian Network inference engine (Plan and Decide) to make decisions, and finally effecting the decisions (Act). We have proved the feasibility of achieving a BN-based cognitive network system using the ns-3 simulation platform. From the early results obtained from our cognitive network approach, we provide interesting insights on predicting the network behavior, including the performance of the TCP throughput inference engine based on other observed parameters.

This paper presents performance analysis and cross-layer design approaches for hybrid ARQ (HARQ) protocol in wireless networks which employ adaptive modulation and coding (AMC) at the physical layer and are subject to time-correlated fading channels. Utilizing a Markov channel model which accounts for the temporal correlation in successive parity transmissions by the adaptive rate HARQ protocol, we derive the system throughput and the packet loss probability based on a rate compatible punctured convolutional (RCPC) code family. As an application, we then present a cross-layer AMC design which takes into account the performance gain of the HARQ protocol at the link layer. The results illustrate that including AMC in the HARQ protocol leads to a substantial throughput gain, but the channel correlation strongly diminishes the performance of the HARQ protocol in terms of throughput and packet loss rate.

Past research has shown that Carrier Sense Multiple Access (CSMA) is not a particularly favorable environment for cooperative networks. This observation suggests that other medium access schemes, like scheduled TDMA, may enable cooperation to yield more substantial improvements over conventional techniques. From this point of view, a hybrid cooperative-network coding protocol (called Phoenix, and so far only proposed for CSMA) has been adapted for Personal Area Network communications. The improvements brought by cooperative-NC techniques in this setting have roughly doubled with respect to the CSMA scenario, with gains of over 20% with respect to standard cooperative systems.

Video transmission is expected to be the next big thing in personal communication systems. While text messaging applications have already experienced an explosive growth, the exchange of multimedia content is still lacking architectural solutions which enable it to become the next killer application. The EU project MEDIEVAL (MultimEDia transport for mobIlE Video AppLications) aims at filling this gap. In this paper we describe the approaches envisioned by the project for what concerns the data link layer, with special emphasis on wireless access and its related cross-layer issues. After presenting some general project aspects, we will review the state of the art and enumerate several open issues, concerning the identification of the most suitable radio technologies for video support, as well as their integration and required enhancements to enable efficient video transport.

The first step required by the process of cognition is the intelligent observation of the environment that surrounds the actors of such a process. We present the CalRAdio-Based advanced Spectrum Scanner (CRABSS), an open platform developed to monitor the ISM 2.4--2.499 GHz band and reveal opportunities for a better utilization of the available spectrum resources. CRABSS is built through a modular approach by integrating the development platform CalRadio 1 with the Unified Link Layer API (ULLA) framework. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it takes advantage of the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs.

The main contribution of this paper is the implementation and experimental evaluation of a signal reconstruction framework for Wireless Sensor Networks (WSNs). We design WSN-Control, an architecture to control a WSN from an external server connected to the Internet. Within such architecture, we implement a compression and recovery technique that combines Principal Component Analysis (PCA) and Compressive Sensing (CS) to reconstruct signals with many components from a sensor field through the collection of a relatively small number of samples, i.e., through incomplete representations of the actual signal. Overall, our experimental results show that a careful use of CS recovery is effective and can lead to a fully automated system for data gathering and reconstruction of real world and non-stationary signals in WSNs. In detail, WSN-Control effectively recovers signals showing some temporal and/or spatial correlation, from a relatively small number of samples, even below 20%, keeping the relative reconstruction error smaller than 5 · 10-3. Signals with more irregular and quickly varying statistics are also recovered, even though the reconstruction error becomes highly dependent on the number of collected samples. CS minimization is obtained through the recently proposed NESTA optimization algorithm. Our implementation of CS recovery is available.

A novel framework for the analysis and optimization of cognitive wireless networks with unslotted time operations and reactive primary users is proposed. In the considered network setting, primary users' channel access is regulated by a carrier sense-based contention mechanism. As the sensing mechanism cannot distinguish between primary and secondary signals, secondary users' activity may interfere with primary users' channel contention, thus biasing the statistics of the stochastic process modeling primary users' transmissions. In fact, a primary user which wakes up during a transmission from a secondary user may sense a busy channel and enter backoff or generate a collision. The proposed framework considers these effects and optimizes the fraction of time a secondary user is allowed to transmit according to a constraint on the minimum throughput achieved by the primary users. Numerical results are presented which illustrate fundamental behaviors and tradeoffs in a network with one primary and one secondary user. Extension to more general scenarios is also discussed.

We present a framework, based on Markov models, for the analysis of error control techniques in video transmission over wireless channels. We focus on retransmission-based techniques, which require a feedback channel but also enable to perform adaptive error control. Traditional studies of these methodologies usually consider a uniform stream of data packets. Instead, video transmission poses the non-trivial challenge that the packets have different sizes, and, even more importantly, are incrementally encoded; thus, a carefully tailored model is required. We therefore proceed on two different sides. First, we consider a low-level description of the system, where two main inputs are combined, namely, a video packet generation process and a wireless channel model, both described by Markov Chains with a tunable number of states. Secondly, from a highlevel perspective, we represent the whole system evolution with another Markov Chain describing the error control process, which can feed the packet generation process back with retransmissions. The framework is able to evaluate hybrid automatic repeat request with selective retransmission, but can also be adapted to study pure automatic repeat request or forward error correction schemes. In this way, we are able to comparatively evaluate different solutions for video transmission, as well as to quantitatively assess their performance trends in a variety of scenarios. Thus, our framework can be used as an effective tool to understand the behavior of error control techniques applied to video transmission over wireless, and eventually identify design guidelines for such systems.

This paper presents SYNAPSE++, a system for over the air reprogramming of wireless sensor networks (WSNs). In contrast to previous solutions, which implement plain negative acknowledgment-based ARQ strategies, SYNAPSE++ adopts a more sophisticated error recovery approach exploiting rateless fountain codes (FCs). This allows it to scale considerably better in dense networks and to better cope with noisy environments. In order to speed up the decoding process and decrease its computational complexity, we engineered the FC encoding distribution through an original genetic optimization approach. Furthermore, novel channel access and pipelining techniques have been jointly designed so as to fully exploit the benefits of fountain codes, mitigate the hidden terminal problem and reduce the number of collisions. All of this makes it possible for SYNAPSE++ to recover data over multiple hops through overhearing by limiting, as much as possible, the number of explicit retransmissions. We finally created new bootloader and memory management modules so that SYNAPSE++ could disseminate and load program images written using any language. At the end of this paper, the effectiveness of SYNAPSE++ is demonstrated through experimental results over actual multihop deployments, and its performance is compared with that of Deluge, the de facto standard protocol for code dissemination in WSNs. The TinyOS 2 code of SYNAPSE++ is available at http://dgt.dei.unipd.it/download.

In this paper we consider practical dissemination algorithms exploiting network coding for data broadcasting in ad hoc wireless networks. For an efficient design, we analyze issues related to the use of network coding in realistic network scenarios. In detail, we quantify the impact of random access schemes, as used by IEEE 802.11, on the performance of network coding. In such scenarios, deadlock situations may occur where the delivery process stops and some of the nodes never gather the required packets. To tackle this problem, we propose a proactive mechanism (called proactive network coding) which adapts its transmission schedule according to the decoding status of neighboring nodes. This scheme can detect when nodes need additional packets in order to decode and acts accordingly. We finally investigate the behavior of network coding schemes in multi-rate environments, where we propose a distributed heuristic approach for the selection of data rates.

In this paper we propose a Dynamic Spectrum Access scheme which allows the users to opportunistically and efficiently access the channels available for communications. It addresses the following important aspects of opportunistic spectrum access: 1) implementation of the control channel, 2) multi-channel medium access control, 3) primary user detection, and 4) secondary reuse of spectrum unused by primary users. The main features of the scheme are that it is completely distributed, it does not need dedicated spectrum resources for control purposes, but rather leverages on a virtual control channel which is implemented using Network Coding techniques, and it exploits a cooperative detection strategy to identify unused spectrum. Due to these aspects, our proposal represents a significant improvement with respect to existing Dynamic Spectrum Access solutions. We carry out an evaluation study of the proposed solution to assess its performance with respect to different system and scenario parameters; the obtained results show that the proposed solution is feasible, capable of providing satisfactory performance, and suitable for implementation in real systems.

In this paper, we evaluate the performance of a Medium Access Control (MAC) protocol for MIMO ad hoc networks under imperfect channel estimation. To this end, we also present an analysis of channel estimation errors using correlator-based and Minimum Mean-Square Error (MMSE) channel estimators. Unlike similar works, we specifically focus on a scenario where the presence of several simultaneous, symbol-asynchronous signals makes the problem more complicated than in traditional channel estimation. In particular, we show that there is direct dependence of the channel estimation error on the instantaneous channel matrix. The model we propose in this paper makes it possible to quickly evaluate the performance of channel estimation schemes as a function of the system parameters. In this light, we include the effect of channel estimation errors in an ad hoc networking protocol simulator and thoroughly evaluate their impact. Our results show that there exists a significant interplay between the performance of MAC protocols for MIMO networks and the accuracy of channel estimation. Moreover, we show that interesting tradeoffs arise between MAC- and physical level-related parameters.

Abstract In this paper we present TinyNET, a modular framework allowing development and quick integration of protocols and applications for Wireless Sensor Networks (WSNs) in TinyOS. The motivation behind TinyNET is two‐fold: on one hand it allows to adopt a divide‐and‐conquer approach in the development of any TinyOS application; on the other hand it provides a flexible administration of network protocols. As a sample development using TinyNET, we consider an environmental monitoring application, and test it over a floor‐wide WSN testbed. Data are converge‐casted toward a sink node, which gathers all data collected by the sensors. Routing toward the sink is achieved by means of a hop count (HC) based algorithm. Our framework also integrates support for the 6LowPAN standard (providing, e.g., per‐sensor queries and pings). Thanks to TinyNET, s these messages will make transparent use of the underlying network protocols. Also, TinyNET transparently manages the network components and related messages, allowing different applications to share the same network stack; furthermore, it translates TinyOS interfaces so that any previously developed application can be easily ported. These features make it possible to have a global vision over any application, as well as to focus on each of its separate components. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The first step required by the process of cognition is the intelligent observation of the environment that surrounds the actors of such a process. We present the CalRAdio‐Based advanced Spectrum Scanner (CRABSS), an open platform developed to monitor the ISM 2.4–2.499 GHz band and reveal opportunities for a better utilization of the available spectrum resources. CRABSS is built through a modular approach by integrating the development platform CalRadio 1 with the Unified Link Layer API (ULLA) framework. This solution provides sensing capabilities while preserving the 802.11b standard compatibility on the CalRadio 1 platform. Moreover, it takes advantage of the ULLA framework to export spectrum occupancy information to prospective cognitive radio manager engines, through a standardized set of sensing APIs. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

This paper presents an analytical framework for the study of hybrid ARQ techniques aimed at the transmission of multimedia content with differential encoding. We propose a Markov model of a selective repeat hybrid ARQ transmission scheme, where we assume that packets have different properties in terms of size, information content, and retransmission limit, so as to capture the differential encoding which characterizes such multimedia data. We consider a non-zero round-trip time channel modeled through a discrete-time Markov chain. We provide an analytical tool for the evaluation of two main performance metrics, namely, throughput and goodput. The model is extremely flexible and allows evaluation of several channel conditions and comparison of different types of ARQ (plain ARQ, Type I and II hybrid ARQ). Our results can be used as an effective tool to define design guidelines of multimedia transmission systems and to understand their performance trends.

In this paper, we present an application of the cognitive networking paradigm to the problem of dynamic channel selection in infrastructured wireless networks. We first discuss some of the key challenges associated with the cognitive control of wireless networks. Then we introduce our solution, in which a Neural Network-based cognitive engine learns how environmental measurements and the status of the network affect the performance experienced on different channels, and can therefore dynamically select the channel which is expected to yield the best performance for the mobile users. We carry out performance evaluation of the proposed system by experimental measurements on a testbed implementation; the obtained results show that the proposed cognitive engine is effective in achieving performance enhancements with respect to state-of-the-art channel selection strategies.

This paper proposes a method to improve the robustness of the carrier sense system for opportunistic MAC (OMAC). This carrier sense method is specific for MIMO and the new version here illustrated is robust to noise and interference. Network simulations prove that this modified MIMO carrier sense correctly estimates whether a new communication would harm other ongoing transmissions for a wide range of system parameters.

We present a novel optimization framework based on stochastic control and Markov theory for wireless networks where users concurrently access the channel and implement retransmission-based error control. In order to let users transmit at the same time, we consider an interference mitigation, rather than a collision avoidance approach. Our focus is on the interaction between the stochastic processes modeling the various individual sources of the network. Due to retransmissions, transmission by a user does not only instantaneously interfere with other simultaneous communications, but also biases the future evolution of the stochastic processes describing the other users. We, thus, define a novel interference measure called process distortion, that takes this effect into account. We investigate the optimization of access and power control for a network with two groups of users where transmission by the second group is constrained by the process distortion generated to the first group. We present algorithms to solve the unconstrained and constrained infinite horizon average cost per stage problems modeling this scenario. We discuss in detail the application of this framework to cognitive networks.

In this paper, we address the problem of computing the probability that r out of n interfering signals can be correctly received in a random access wireless system with capture. We extend previous results on the capture probability computation, and provide an expression for the distribution of the number of captured packets that is scalable with n and r. We also provide an approximate expression, that is much easier to compute and provides good results for r = 0 and r = n. Finally, we study the dependence of the system throughput performance on the multi-packet reception capabilities of the receiver.

In this paper we propose a Multi-Channel Medium Access scheme with the aim to enable efficient Dynamic Spectrum Access. Two are the remarkable features of our solution: first, it is completely distributed, and second, it does not need dedicated spectrum resources for control purposes, but rather it leverages on a virtual control channel which is implemented by having users exchange control information whenever they meet in a particular channel, and by retrieving the control information using Network Coding techniques. We carry out an evaluation study of the proposed solution to assess its performance with respect to different system and scenario parameters; the obtained results show that the proposed solution is feasible and suitable for implementation in real Dynamic Spectrum Access systems.

In this paper we present TinyNET, a modular framework that allows development and quick integration of protocols and applications for Wireless Sensor Networks in TinyOS. As a sample experience of software development using TinyNET, we employ an environmental monitoring application. We organize our network testbed in a converge-casting topology, where the sink gathers the data collected by all sensors. Routing toward the sink is achieved based on a hop-count based algorithm. Our framework also integrates support for the 6LowPAN standard, which becomes therefore integrated with other network components and allows, e.g., to query single sensors within the network using ping messages. Thanks to TinyNET, these messages will make transparent use of the underlying network protocols. The main advantages yielded by TinyNET are found in the modularity of network components, in specific code that transparently manages the interfaces among different modules and translates standard TinyOS interfaces into new TinyNET ones, and in the readily available infrastructure to multiplex different applications over the same network stack. This allows a global vision of the system as well as the chance to focus on the design of separate components.

CalRadio 1 is an open 802.11b-compatible development platform, designed and developed at UCSD with the aim of providing the research community with an open and fully reprogrammable board for experimental purposes. In this work we describe the hardware and software architecture of the board and we provide an accurate analysis of the limiting performance achieved by CalRadio 1 in comparison with commercial 802.11b wireless interfaces. The analysis offers a clear vision of the real potential and limitations of the CalRadio 1 board, pointing out the aspects of major concern for prospective developers.

In this paper, we investigate the effectiveness of cooperative techniques in non-infrastructured ad hoc networks based on carrier sense access control. We point out, via analysis and detailed network simulations, that CSMA biases some fundamental statistics of geographical positioning of terminals available for cooperation. Not only may relay nodes refrain from cooperating due to the carrier sense mechanism, but also the spatial correlation of interference in the network is likely to compress potential cooperators within an area in the proximity of the source. Furthermore, our discussion highlights how issues that affect carrier sense based medium access strategies, such as hidden terminals, reduce the effectiveness of relaying strategies when implemented in large non-centralized networks. The combination of the aforementioned factors is shown to significantly diminish the performance gain granted by cooperative schemes widely studied in the literature from a theoretical perspective.

Network coding is a technique which is particularly suitable for the dissemination of data in distributed ad hoc networks. The definition of a mathematical model that describes the interactions among nodes and, in particular, their relationship in terms of buffer subspaces is still an open and challenging problem. The contribution of this paper is an analysis of the relationship between the network topology and the subspace overlap among nodes. This analysis can be used to establish criteria for the design of packet combination policies in diverse networking scenarios. Differently from previous studies, we will explicitly take the overlap among subspaces into account through a framework comprising networks with fixed as well as mobile nodes.

The computation of the intersection area of a large number of circles with known centers and radii is a challenging geometric problem. Nevertheless, its resolution finds several applications in the analysis and modeling of wireless networks. Prior literature discusses up to three circles and even in this case there are many possible geometric configurations, each leading to a different involved close-form expression for the intersection area. In this paper, we derive two novel geometric results, that allow the check of the existence and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. Based on these results, we construct an iterative algorithm based on a trellis structure that efficiently computes the intersection areas of an arbitrary number of circles. As an example of practical application of our results, we derive the probability that a fixed number of mobiles can be reliably allocated to a set of base stations in code division multiple access-based cellular networks.

We consider a network where each route comprises a backlogged source, a number of relays and a destination at a finite distance. The locations of the sources and the relays are realizations of independent Poisson point processes. Given that the nodes observe a TDMA/ALOHA MAC protocol, our objective is to determine the number of relays and their placement such that the mean end-to-end delay in a typical route of the network is minimized. We first study an idealistic network model where all routes have the same number of hops, the same distance per hop and their own dedicated relays. Combining tools from queueing theory and stochastic geometry, we provide a precise characterization of the mean end-to-end delay. We find that the delay is minimized if the first hop is much longer than the remaining hops and that the optimal number of hops scales sublinearly with the source-destination distance. Simulating the original network scenario reveals that the analytical results are accurate, provided that the density of the relay process is sufficiently large. We conclude that, given the considered MAC protocol, our analysis provides a delay-minimizing routing strategy for random, multihop networks involving a small number of hops.

In this paper, we present a methodology to obtain a channel description tailored on performance evaluation for incremental redundancy hybrid automatic repeat request schemes. Such techniques counteract channel errors by using data coding and transmitting parts of the codeword over different channel realizations. We focus on coding performance models where the error probability is asymptotically zero if the channel parameters of these realizations fall within a given region. To map this region in a compact but still precise manner, we adopt a finite-state channel model. This approach is quite common in the literature; however, differently from existing work, we propose a novel method to derive efficient channel partitioning rules, i.e., a code-matched quantization of the channel state. Such a representation enables the use of accurate Markov models to study the system performance. Compared to existing channel representation methods, our proposed technique leads to a more accurate evaluation of higher layer statistics while at the same time keeping the computational complexity low.

We consider a network with an arbitrary number of nodes, where transmission of data packet is enhanced by a medium access control protocol that allows cooperation among nodes and solves transmission failures by an automatic repeat request (ARQ) protocol. Nodes operate in half-duplex mode and may interfere with each other as no coordination is assumed. In this scenario, we first derive the outage probability for a given number of interfering nodes and then relate the interference level to the single node behavior. We analyze the network behavior with a steady state analysis that matches the number of interfering nodes with the number of ARQ retransmissions. Two alternatives for cooperation are considered: decode and forward (DF) cooperation and multiple input-single output (MISO) cooperation, where for each packet transmission either one node transmits at a given time or two cooperating nodes transmit simultaneously. In our steady state analysis we also include an opportunistic version of DF, where cooperation is activated only when the quality of the cooperator-destination link is better than that of the source-destination link. Moreover, we investigate cooperator selection based on its distance to the destination.

The paper presents an original integrated MAC and routing scheme for wireless sensor networks. Our design objective is to elect the next hop for data forwarding by jointly minimizing the amount of signaling to complete a contention and maximizing the probability of electing the best candidate node. Towards this aim, we represent the suitability of a node to be the relay by means of locally calculated and generic cost metrics. Based on these costs, we analytically model the access selection problem through dynamic programming techniques, which we use to find the optimal access policy. Hence, we propose a contention-based MAC and forwarding technique, called cost and collision minimizing routing (CCMR). This scheme is then thoroughly validated and characterized through analysis, simulation and experimental results.

We consider a scenario in which wireless users want to connect to the Internet using one of several available network access opportunities, possibly using different radio technologies. We propose a distributed cognitive network access scheme with the aim of providing the best quality of service with respect to both radio link and core network performance and user application requirements. Knowledge of the service quality experienced by active connections is shared, and prospective users use Fuzzy Logic techniques to process cross-layer communication quality metrics and to estimate the expected transport-layer performance. These estimates are compared to the Quality of Service requirements of the application using Fuzzy Decision Making techniques to choose the most suitable access opportunity. This scheme naturally fits into the recently proposed Cognitive Network paradigm in that it defines a cognition process leveraging on end-to-end and cross-layer performance evaluation techniques as well as information sharing among users; moreover, it offers a significant amount of flexibility and extensibility, thanks to its modularity and its independence from the particular technology and application being used. The proposed scheme is shown to outperform state-of-the-art solutions in several multi-technology and multi-application scenarios, while at the same time achieving similar performance to application-specific omniscient schemes that we introduce in this paper as a benchmark.

In this paper, we present a novel analytical framework for the study of a multiple access scheme for an ad hoc code division multiple access (CDMA) network with hybrid automatic repeat request (HARQ) error control and matched filter linear successive interference cancellation receivers. Unlike most prior work on Multi-User Detection (MUD), we directly address networking issues. In particular, we develop an approach based on renewal and semi-Markov processes, that accurately accounts for the statistical dependencies between interference, coding rate, and performance in a multiple access system. We show that our approximate approach can accurately predict throughput and failure rate values, and present results for a specific system, providing useful insight on issues related to the use of HARQ error control and MUD receivers in CDMA ad hoc networks.

This paper introduces a novel MAC protocol for wireless networks, called Phoenix, that employs ideas from network coding to enhance decode and forward cooperation. A relay is allowed to code data of its own together with a corrupted packet during a retransmission at no additional cost in bandwidth. Therefore, while in conventional cooperative protocols a node becomes a relay only to assist other terminals, with our proposal a cooperator can also serve its own traffic. We evaluate Phoenix's performance by means of a theoretical model and extensive simulation campaigns. We show that Phoenix is especially beneficial in multihop settings and interesting gains over benchmark protocols can be achieved.

Beamforming is regarded as a key element for multiantenna ad hoc networks. However, it cannot simultaneously provide the omnidirectional and long-range coverage required by broadcast transmissions, a problem known as the Asymmetry in Gain. We propose a scheme for control packet exchange over an extended radio coverage based on a Space Time Code technique. This solution is shown to solve the Asymmetry in Gain issue, and is proposed as part of a MAC protocol for MIMO ad hoc networks, whose performance benefits include increased throughput and reduced delay and energy consumption.

The estimation of the communication performance achievable with respect to environmental factors and configuration parameters plays a key role in the optimization process performed by a Cognitive Radio according to the original definition by Mitola [1]. In this paper we propose the use of Multilayered Feedforward Neural Networks as an effective technique for real-time characterization of the communication performance which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.

In this paper, we propose an analytical technique to evaluate the statistics of the channel estimation error in a simple multi-user ad hoc networking scenario. This problem is very relevant in situations where advanced PHY techniques are used (e.g., MIMO or interference cancellation) and channel state information may be needed. The presence of several simultaneous and non-orthogonal signals makes the problems significantly more complicated than in traditional channel estimation. In particular, there is direct dependence of the channel estimation error on the instantaneous channel matrix. The proposed model makes it possible to quickly evaluate the performance of channel estimation schemes as a function of the system parameters. In this light, we include the effect of channel estimation errors in an ad hoc networking protocol simulator and thoroughly evaluate their impact. Our results show that there exists a significant interplay between the performance of MAC protocols for MIMO networks and the accuracy of channel estimation. Moreover, we show that relevant tradeoffs arise between MAC- and PHY-level parameters which lead to the definition of design guidelines.

In this paper we present APOS, a method for dynamically adapting the parameters of IEEE 802.11 g to the estimated system state, with the aim of enhancing the quality of a voice communication between a mobile station and a remote peer node. The system state is estimated based on a number of counters that are collected by the MAC layer of the mobile station, regarding the number of successful and unsuccessful transmission/reception events, channel busy periods and idle slots. These statistics are processed to estimate the collision probability and the signal to noise ratio at the receiver side. Hence, a mathematical model is used to get the expected end-to-end network performance in terms of throughput, delay and packet error rate, for different settings of some PHY and MAC parameters, such as the modulation/coding scheme and the retransmission limit. The setting that is estimated to maximize the quality of service for the end user is then selected. Unlike other optimization mechanisms proposed in literature, APOS is totally stand-alone and standard compliant. In fact, APOS makes use of local information that can be collected from the Network Interface Card, and no explicit interactions with the other devices in the network is required.

Rate adaptation for 802.11 has been deeply investigated in the past, but the problem of achieving optimal rate adaptation with respect not only to channel-related errors but also to contention-related issues (i.e., collisions and variations in medium access times) is still unsolved. In this paper we address this issue by proposing (1) a practical definition of the medium status in a multi-user 802.11 scenario in terms of channel errors, MAC collisions and packet service times, and a method for its estimation based on measurements; (2) an analytical model of the goodput performance as a function of the Medium Status; (3) a rate adaptation algorithm, called goodput optimal rate adaptation (GORA), which is based on this model. Unlike other rate adaptation schemes proposed in literature, which require either modifications to the IEEE 802.11 standard or cooperation among nodes, GORA is totally stand-alone and standard compliant. In fact, the Medium Status Estimation used by GORA is obtained by using standard MAC counters that are commonly collected by commercial MAC drivers, and no explicit interactions with the other devices in the network is required. Therefore, GORA offers the advantage of being readily deployable on real devices. The performance of GORA is evaluated through NS2 simulations which reveal that, as expected, GORA outperforms other well- known rate adaptation algorithms in several scenarios and can be used as a new reference benchmark.

A joint congestion control, channel allocation and scheduling algorithm for multi-channel multi-interface multi- hop wireless networks is discussed. The goal of maximizing a utility function of the injected traffic, while guaranteeing queue stability, is defined as an optimization problem where the input traffic intensity, channel loads, interface to channel binding and transmission schedules are jointly optimized by a dynamic algorithm. Due to the inherent NP-Hardness of the scheduling problem, a simple centralized heuristic is used to define a lower bound for the performance of the whole optimization algorithm. The behavior of the algorithm for different numbers of channels, interfaces and traffic flows is shown through simulations.

There is wide consensus that properly taking into account wireless interference is necessary to design high performance link scheduling algorithms for multi-hop networks. However, most approaches in the literature use simplified models, which significantly abstract from the physical behavior of wireless links. Indeed, the main problem in representing the wireless propagation conditions with a proper level of detail is the very large amount of information needed, that includes the wireless link gains between all node pairs. In this paper, we propose a greedy, centralized scheduler which is based on the physical interference model but aims at exploiting local information available at each node, in order to reduce global information exchange and therefore the overhead as well as the computational complexity of the algorithm. We prove the effectiveness of our approach by extensive simulation results. We also show that our system outperforms the most up-to-date benchmark in realistic interference aware schedulers for wireless multi-hop networks.

This paper is meant to discuss the practical application of cooperative techniques in wireless UMTS cellular networks. In recent years cooperation in wireless networks has been the focus of a considerable research effort and several techniques have been proposed and investigated. Our feeling is that the many theoretical studies existing in the literature should now be applied to more realistic environments to pave the way to practical applications. In this paper we consider well-known cooperative techniques, such as decode & forward and amplify & forward, and we investigate and discuss the benefits their use can grant to UMTS cellular networks. In particular, we evaluate the performance of these two schemes in terms of coverage range extension and we discuss how cooperative nodes characterization influences their effectiveness.

In this paper, we investigate how to apply MIMO_NC in order to increase the efficiency of cooperative communications. MIMO_NC is a recent strategy to combine, in an integrated system, MIMO detection techniques and network coding. MIMO_NC is robust to channel errors, as it can exploit the spatial diversity provided by multiple transmissions of the same information, and throughput effective as it is based on network coding. In this paper we design a cooperative MAC protocol based on MIMO_NC which can outperform classical cooperative schemes. We also prove its effectiveness by means of analytical modelling and simulations in different network configurations.

Wireless reprogramming is a key functionality in wireless sensor networks (WSNs). In fact, the requirements for the network may change in time, or new parameters might have to be loaded to change the behavior of a given protocol. In large scale WSNs it makes economical as well as practical sense to upload the code with the needed functionalities without human intervention, i.e., by means of efficient over the air reprogramming. This poses several challenges as wireless links are affected by errors, data dissemination has to be 100% reliable, and data transmission and recovery schemes are often called to work with a large number of receivers. State-of-the-art protocols, such as Deluge, implement error recovery through the adaptation of standard automatic repeat request (ARQ) techniques. These, however, do not scale well in the presence of channel errors and multiple receivers. In this paper, we present an original reprogramming system for WSNs called SYNAPSE, which we designed to improve the efficiency of the error recovery phase. SYNAPSE features a hybrid ARQ (HARQ) solution where data are encoded prior to transmission and incremental redundancy is used to recover from losses, thus considerably reducing the transmission overhead. For the coding, digital fountain codes were selected as they are rateless and allow for lightweight implementations. In this paper, we design special fountain codes and use them at the heart of SYNAPSE to provide high performance while meeting the requirements of WSNs. Moreover, we present our implementation of SYNAPSE for the Tmote Sky sensor platform and show experimental results, where we compare the performance of SYNAPSE with that of state of the art protocols.

In this paper we study the performance of the ambient networks (AN) access selection architecture. We consider heterogeneous wireless networks, where mobile terminals (MTs) own multiple radio technologies and need to remain connected while on the move. We would like to provide the MTs with seamless IP services, such as video/audio streaming, so that changes in their point of attachment will not affect the experienced streaming quality. In the first part of this paper, we present the AN architecture for access selection, along with its functional entities (FEs), their interrelations and the algorithms that are to be run within each FE. Hence, we describe our ns2 simulation framework and detail two simulation scenarios. We finally discuss, through extensive simulation results, the effectiveness of the AN architecture in reaching the above goals.

In this paper, we present a mathematical framework that permits an accurate performance analysis, both in terms of average throughput and energy efficiency, of Bluetooth link performance in fading channels. Conversely to most part of the literature concerning Bluetooth performance, this analysis takes into consideration the microscopic level power-saving mechanisms introduced in Bluetooth specifications to reduce the energy consumption during active mode. The analysis makes use of a two-state Markov chain to distinguish the transmission of useful and duplicate packets by the master. Then, the average energy spent, the amount of data exchanged and the time elapsed for each transition of the Markov chain, are derived. Hence, we apply the renewal reward theory to derive the average throughput and energy efficiency of the data link for different data packet formats and transmission rates, both in AWGN and fading channels. A proof of concept is provided by applying the mathematical framework to a Bluetooth v2.0 with Enhanced Data Rate data link. The analysis permits to appreciate the advantage, in terms of throughput and energy efficiency, of the enhanced data rate packet formats over the basic rate.

The 13 papers in this special issue focus on underwater wireless communication networks.

Interest in underwater acoustic networks has grown rapidly with the desire to monitor the large portion of the world covered by oceans. Fundamental differences between underwater acoustic propagation and terrestrial radio propagation may call for new criteria for the design of networking protocols. In this paper, we focus on some of these fundamental differences, including attenuation and noise, propagation delays, and the dependence of usable bandwidth and transmit power on distance (which has not been extensively considered before in protocol design studies). Furthermore, the relationship between the energy consumptions of acoustic modems in various modes (i.e., transmit, receive, and idle) is different than that of their terrestrial radio counterparts, which also impacts the design of energy-efficient protocols. The main contribution of this work is an in-depth analysis of the impacts of these unique relationships. We present insights that are useful in guiding both protocol design and network deployment. We design a class of energy-efficient routing protocols for underwater sensor networks based on the insights gained in our analysis. These protocols are tested in a number of relevant network scenarios, and shown to significantly outperform other commonly used routing strategies and to provide near optimal total path energy consumption. Finally, we implement in ns2 a detailed model of the underwater acoustic channel, and study the performance of routing choices when used with a simple MAC protocol and a realistic PHY model, with special regard to such issues as interference and medium access.

This paper presents an analytical model for the study of hybrid ARQ techniques on discrete time Markov channels by means of an appropriate Markov chain, which tracks the transmission outcome and can be used to evaluate several performance metrics, including throughput, loss probability, number of retransmissions, and delay. The analysis is carried out with the assumptions that the information frame is encoded by the source with a linear block code and hard decoding is used at the receiver side. We finally present numerical evaluations for the performance of a truncated type II hybrid ARQ technique based on Reed Solomon erasure codes.

For a network where each node has multiple antennas, we propose a transmission mode and a cooperation protocol, with the aim of maximizing the network throughput. The distinctive feature of the work is that the focus in both the design and the evaluation is at the network level, rather than on a single link. To this end, we propose the use of spatial multiplexing and code division multiple access (CDMA) to increase the parallelism of transmissions in the network, thus improving throughput. Cooperation is also implemented by spatial multiplexing and CDMA, together with an adaptive hybrid automatic repeat request mechanism that adapts the retransmissions to the actual channel conditions. Spatial multiplexing allows frame-asynchronous transmissions and a flexible cooperation protocol that minimizes the signaling overhead. The resulting scheme is named layered coded cooperative system (LCCS). We propose an implementation of LCCS based on linear erasure packet codes, where cooperation is transparent to the receiver, and we assess the performance of LCCS both by analyzing a simple network with three nodes and by simulating a more complex network.

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called congestion-aware routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.

In this letter we discuss the exploitation of aggregated mobility patterns in mobile networks including heterogeneous multiple access techniques. We advocate the use of knowledge about neighboring devices to create routing groups (RGs) of adjacent nodes in order to optimize radio resource management. Basically, RGs consist of aggregated logical structures which are built and maintained at the application layer. Their use allows decreased signaling overhead between groups of nodes and access points (AP) and, at the same time, improved connectivity, which is achieved through the exploitation of technology diversity and relaying schemes. We illustrate a simple yet effective analytical model, and validate it through accurate simulation results. Finally, we show the effectiveness of the RG approach in terms of resource efficiency, throughput and multiple access performance.

In this paper we analyze the provisioning of multimedia services over a wireless LAN hot-spot, based on the IEEE 802.11 protocol. We address the issue of defining proper pricing strategies, from the perspective of both evaluating the technical performance and quantifying the economic revenues. We take into account a model for users' behavior that describes all users' choices in a decentralized manner, so that the transmission rate of each node is driven both by multimedia service requirements and by the customer's willingness to pay. The multiple users' medium access mechanism is studied through a simulation analysis based on ns-2. Within this model, the network performance is evaluated and discussed, presenting numerical results which can provide practical insight for pricing setup in a wireless LAN hot-spot. We observe that the impact of the pricing policy on the provider's income and on the satisfaction of the users is critical and especially depends on the shape of the pricing function (flat, linear or hybrid). Additionally, we investigate the provider's task of having a suitable price policy which properly tunes the tradeoff between the two contrasting factors of achieving high revenue and obtaining high satisfaction of the users.

In this paper, we present a novel MAC approach for ad hoc networks in which Multiple Input-Multiple Output (MIMO) techniques are used at the physical level (PHY). The use of MIMO in PHY point-to-point as well as multiuser communications has been extensively studied in the recent literature. Here we go one step further, extending the approach to also include protocol design for decentralized and infrastructureless ad hoc networks. First, we study the impact of MIMO on packet transmission in an ad hoc network setting. Then, following a crossiquestlayer design paradigm, we deploy a distributed access control protocol and characterize its performance, with the aim to improve spatial reuse and convey more information on a single ad hoc link. We also explore the interaction between the Medium Access Control (MAC) and PHY layers, and use this knowledge to implement proper policies for distributed traffic control and robustness against interference. Important tradeoffs that arise when managing radio resources are highlighted, and extensive simulation results are presented and discussed.

In this letter, we are concerned with adaptive ARQ techniques combined with backoff strategies that exploit the bursty nature of wireless links for improved energy savings. Specifically, we propose an adaptive Go-back-N/Stop-and-Wait (GBN/SAW) protocol with backoff and present a renewal reward analysis of the throughput and energy efficiency performance of the proposed scheme. We show that the GBN protocol with a linear backoff (LBO) strategy results in energy savings of about 2 dB compared to GBN with no backoff (NBO) on highly correlated fading channels when the round-trip delays (RTD) are small, and this energy saving decreases as RTD increases. With adaptive GBN/SAW protocol with LBO, however, the energy saving is retained high even for large RTDs. In fact, the proposed adaptive GBN/SAW protocol with LBO performs quite close to that of an ideal (though not practical) backoff scheme which assumes a priori knowledge of the channel status in each slot.

Abstract This paper explores the logical device aggregation of terminals in future generation networks, where the availability of several different radio access techniques is integrated by means of common radio resource management algorithms. In particular, we investigate the creation of routing groups (RGs) among adjacent nodes, which might be beneficial in order to improve connectivity, decrease signaling overhead and increase transmission efficiency. A simple analytical approach is proposed, which allows the performance evaluation of device aggregation algorithms. We measure the performance of establishing RGs with special focus on two metrics of interest: the connectivity of the nodes and the energy consumption. Within this framework, many detailed insights are obtained and presented throughout the paper. In particular, we focus on the effectiveness of these aggregation techniques in improving network connectivity and on the cost incurred in getting the extra information needed to build and maintain group structures. In the final part of the paper, we provide simulation results which further validate our discussion and highlight additional aspects that are to be considered in real scenarios. Our work is a first step in the investigation of the effectiveness of in‐network aggregation of terminals equipped with multiple radio technologies. The results derived in the paper are encouraging and motivate further research on the topic. Copyright © 2007 John Wiley & Sons, Ltd.

The search for the ultimate architecture for crosslayer optimization in cognitive radio networks is characterized by challenges such as modularity, scalability, complexity and interpretability constraints. In this paper we propose Fuzzy Logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.

This paper investigates routing strategies for mobile and heterogeneous multi-hop wireless networks. We leverage the knowledge about users mobility to improve the efficiency of route discovery and of the following data forwarding phase. In particular, we exploit group mobility behaviors, which allow us to apply a distributed on-line algorithm for the recognition of aggregated mobility patterns. Hence, we adopt a novel routing strategy that uses the aggregate structure formed within this algorithm to simplify the exchange of signaling and data messages. Finally, we demonstrate and quantify the benefits obtained with the proposed technique by means of a simulator for heterogeneous wireless networks.

In recent years, network coding has proved to be an efficient tool to disseminate data through a network. A number of practical schemes have been proposed to implement network coding also in wireless environments. Most of them are based on reactive and probabilistic random network coding and their effectiveness has been investigated under the assumption of idealized network conditions. However, recent work has shown that the benefits of such strategies decrease when applied in realistic network environments. In this paper, we propose an algorithm to efficiently disseminate data through network coding in realistic wireless networks by using a proactive approach, named ProNC. We develop a distributed and self-adaptable protocol which substantially increases the performance of network coding in practical scenarios and achieves full reliability with both low protocol overhead and low delay. We show the effectiveness of ProNC via ns-2 simulations and compare it with previously proposed schemes.

Cooperation has proved to be an effective technique for improving the performance and the efficiency of wireless networks. Most of the existing work on cooperation focuses on the physical layer, with the aim of enhancing the capacity and the quality of a single link. In this paper we propose a cooperative protocol that melds physical, MAC and routing layers to increase the performance of a MIMO-BLAST ad hoc network, where nodes are allowed to transmit simultaneously. Nodes try to resolve in-range delivery of packets with an adaptive distributed Hybrid ARQ scheme to counteract interference from simultaneously active communications, while dynamic route selection is implemented for avoiding transmissions over links with harsh fading conditions. We assess the performance of our scheme through detailed simulations.

Cognitive radio networking research today mainly focuses on finding efficient ways to let secondary users access radio spectrum that is licensed to primary users, with minimal interference to the license owners. Physical layer cognition, though very important, is complex, expensive, and can provide only limited information about the higher layer traffic. We argue that, even in the absence of physical layer cognitive capability, higher layer traffic information can still be used to generate sufficient cognitive networking information to improve system performance. In this paper, we present an architecture for cognitive networking and an early prototype and experimental setup of a cognitive network access point (CogNet AP), and we describe our observations and lessons learned from this experimental activity. The CogNet AP gathers, processes, analyzes, and stores information available through its monitoring interface in order to build a cognitive local repository which holds the spatio-temporally tagged network traffic information. The inexpensiveness of the components used for building the CogNet AP shows the flexibility of building Cognitive Network elements compared to cognitive radio devices. The proposed cognitive networking architecture and prototype point to the many possible application scenarios and research potential for such systems which use temporal patterns of higher layer traffic information. From our experiments we found that the use of cognitive information derived from higher networking layers resulted in achieving better system throughput.

Physical carrier sense for wireless multihop networks has attracted a certain degree of attention since it is a simple yet important mechanism for distributed channel access. Some work has modelled interference as a Gaussian process, but in this paper we show that for IEEE 802.11 this assumption is not accurate. We propose an alternative analytical model for the optimization of the carrier sense threshold for these networks that correctly predicts the best threshold value.

In this paper, we consider a scenario in which wireless users want to connect to the Internet using one of several available network access opportunities, possibly using different radio technologies. We propose a distributed cognitive network access scheme with the aim of providing the best quality of service with respect to both radio link and core network performance and user application requirements. Knowledge of the service quality experienced by active connections is shared, and prospective users use Fuzzy Logic techniques to process cross-layer communication quality metrics and to estimate the expected transport-layer performance. These estimates are compared to the Quality of Service requirements of the application, and Fuzzy Decision Making is used to choose the most suitable access opportunity. The proposed scheme is compared by simulation to commonly used algorithms as well as omniscient decision schemes in a variety of scenarios, and is shown to have superior performance and much better flexibility.

Geographic forwarding in wireless sensor networks (WSN) has long suffered from the problem of bypassing "dead ends," i.e., those areas in the network where no node can be found in the direction of the data collection point (the sink). Solutions have been proposed to this problem, that rely on geometric techniques leading to the planarization of the network topology graph. In this paper, a novel method alternative to planarization is proposed, termed ALBA-R, that successfully routes packets to the sink transparently to dead ends. ALBA-R combines nodal duty cycles (awake/asleep schedules), channel access and geographic routing in a cross-layer fashion. Dead ends are dealt with by enhancing geographic routing with a mechanism that is distributed, localized and capable of routing packets around connectivity holes. An extensive set of simulations is provided, that demonstrates that ALBA-R is scalable, generates negligible overhead, and outperforms similar solutions with respect to all the metrics of interest investigated, especially in sparse topologies, notoriously the toughest benchmark for geographic routing protocols.

The present paper investigates practical algorithms to efficiently exploit random network coding for data delivery in multi-hop wireless networks. In the past few years, a great deal of work has been carried out to derive analytical results about network coding. However, only recently have researchers started to utilize the theoretical findings in practical settings. Network coding is a new paradigm for data delivery which proved to be very efficient. It is particularly suitable for wireless networks due to the inherent broadcast nature of the channel. Even though previous work dealt with practical schemes exploiting these new techniques, many issues concerning the coexistence of network coding and channel access mechanisms are still unsolved. In addition, it is still unclear how packets should be combined in order to get the highest benefits in terms of throughput, delay, and energy efficiency. Our work presents an accurate investigation of these aspects. In particular, we couple several MAC and scheduling schemes together with different network coding strategies, and compare them via extensive ns2 simulation. Finally, we propose a new timing strategy for the combination of data packets in random network coding.

In this paper we design a network based on cooperative spatial multiplexing (SM), capable to adaptively support terminals with multiple antennas and multiple cooperating nodes. Our proposal overcomes limitations of existing cooperative networks based on space-time block codes (STBC), i.e., their need for symbol synchronization and signalling overhead for cooperation setup. Furthermore, the presented cooperation scheme is designed with the aim of improving the overall network performance, while most of the proposals in the literature focus on single link performance. SM is integrated with decision- feedback multiuser receivers for both the non-cooperative and the cooperative phases in order to maximize the use of radio resources. The cooperation efficiency is further enhanced by using a hybrid automatic repeat request (ARQ) mechanism for error control, implemented with linear erasure packet codes. Also in this case, the packet-based coding allows to have no signalling overhead for cooperation. Extensive results are provided, obtained by simulation of the complete PHY and MAC layers for a realistic network scenario with several nodes.

We propose the novel concept of cooperation for deafness prevention in directional antenna ad hoc networks, along with a low-complexity multiuser detector specialised for these systems. We call a generic MAC protocol that uses these techniques a cooperative-MAC (CMAC) and we define an upper and a lower bound on the performance of this class of protocols. Several simulations compare these bounds against the circular RTS/CTS protocol (CRCM), which is one of the finest deafness- suppression systems proposed so far, and show that our combined techniques achieve significant gains over CRCM in terms of throughput and energy consumption.

The paper presents a novel integrated MAC/routing scheme for wireless sensor networking. Our design objective is to elect the next hop for data forwarding by minimizing the number of messages and, at the same time, maximizing the probability of electing the best candidate node. To this aim, we represent the suitability of a node to act as the relay by means of locally calculated and generic cost metrics. Based on these costs, we analytically model the access selection problem through dynamic programming techniques thereby devising the optimal access policy. We subsequently derive a contention-based MAC and forwarding scheme, named cost and collision minimizing routing (CCMR). Both analytical and simulation results are given to demonstrate the effectiveness of our technique by comparing its performance against state of the art solutions.

In this paper, we investigate some of the issues that arise when mobile nodes engage voice connections with remote peers by using a wireless ad hoc network (MANET-cell) to access the distribution core network. We focus on a specific network scenario where all communications are directed to a special node, called gateway, which in turn bridges traffic to and from the core network. We first derive a mathematical expression to estimate the maximum number of sustainable voice sessions in a single-hop cell, with multi-rate terminals. Then, we assess the accuracy of the formula through ns2 simulations, which allow us to determine the voice capacity of the system also in presence of hidden terminals. Hence, we analyze the impact of multi-hop paths on the voice capacity of the system. From this analysis, we propose a new cost metric that we apply to a modified version of the OLSR routing algorithm, named HOLSR. This algorithm has proved to be able to preserve stability in some scenarios that were unstable under other routing algorithms.

We derive the expressions of the outage probability for a wireless network that integrates hybrid automatic repeat request (HARQ) and coded cooperation among nodes. The medium access control (MAC) provides a HARQ protocol where the source node, upon a decoding failure at the destination node, transmits additional coded bits for the same data packet. If a neighbor node is able to decode the first transmission, it cooperates with the source by sending additional coded bits. In order to keep nodes simple, we assume half-duplex, single band asynchronous transmissions, and receivers with single user detection techniques. Various configurations of cooperation are considered and the analysis includes key characteristics of wireless communications and interference.

In this paper, we consider a wireless sensor network (WSN) where packets are forwarded to a central collector node (the sink). As all network traffic converges to this point, congestion builds up. We deal with this problem by assuming that the nodes around the sink are allowed to use a different, more efficient protocol for advancing data, namely a method designed to yield more throughput and reduce collisions, at the expense of some coordination efforts. We carry out extensive simulations, and compare our solution (which is composed of two protocols, one for the sink and its neighbors and one for the rest of the network) to a simpler collision avoidance approach used throughout the whole network. This allows to highlight the tradeoff between coordinating and interfacing two protocols (which requires better design, but achieves higher effectiveness) and using just one protocol (simpler, but more prone to congestion and losses). We show how our two-protocol solution outperforms the simpler one.

Wireless sensor network technologies enable a wide variety of applications (e.g., environmental monitoring). Such sensor networks are often deployed in regions that make it difficult to collect and redistribute the nodes for maintenance. However, there is often a need to reprogram all of the nodes in the network, either during application test phases on deployed networks, or to support software upgrades. Therefore, a reliable method of sending a relatively large amount of data to each node in the network is required to support these functions.

A well-known concept to adapt the transmitted pay- load to the current channel capacity is to perforin segmentation of the uncoded user payload. A new cross layer approach to hybrid ARQ was introduced that performs first channel coding and then segmentation of coded bits. In this paper, we denote the traditional approach as radio centric, while the latter is denoted as packet centric. By changing the order of coding and segmentation, it is possible to overcome the conflicting first requirements of link adaptation and hybrid ARQ in terms of packet size. Although the packet centric concept is not introduced here for the first time, a detailed performance analysis has been lacking so far. This paper presents a study that explores the characteristics of the packet centric approach in more detail and provides performance results and a comparison with the traditional approach. The new method is compared to the radio centric approach by means of a theoretical model and a cellular network simulator. It is shown that users with a weak channel can benefit from the former, leading to some gain in capacity.

This paper presents integrated MAC/routing solutions for wireless sensor networks. At the MAC layer, every node accesses the channel according to its own cost by means of properly defined cost-dependent access probabilities. Costs are used to capture the suitability of a node to act as the relay and may depend on several factors such as residual energies, link conditions, queue state, etc. Our cost-aware MAC discriminates nodes right in the channel access phase by therefore assisting the forwarding decisions to be made at the routing level. In fact, nodes with high costs are ruled out from channel contention and are not considered when making routing decisions. This provides the routing layer with better relay candidates and, at the same time, decreases the number of in-range devices contending for the channel, thereby reducing interference. The proposed MAC scheme is coupled with routing over hop count (HC) coordinates. To this end, we introduce a set of rules designed to perform HC routing by exploiting first and second order neighborhood information. These are then integrated with our MAC scheme according to a cross-layer approach and their effectiveness is demonstrated by means of analysis and simulation

We present a queueing model for performance analysis of go-back-N (GBN) and selective repeat (SR) automatic repeat request (ARQ) protocols in wireless networks using dynamic radio link adaptation with non-instantaneous feedback. Link adaptation technique allows multi-rate transmission which is assumed to be achieved through adaptive modulation and coding. The radio link level queueing models for these two ARQ protocols are formulated in discrete time where the exact queue length and the delay statistics are obtained by using matrix geometric methods under different feedback delay values, channel and system parameters. The link layer delay statistics are useful in many ways, for example, to perform packet level admission control under statistical delay constraints. We validate the analysis by simulation and discuss useful implications of the analytical model on system performance. For dynamic link adaptation, the mode switching thresholds for the received signal-to-noise ratio (SNR) can be chosen to obtain very good link level delay performance. This SNR partitioning is shown to achieve significant cross-layer design gain compared to the case where the mode switching thresholds are chosen to maximize the physical layer throughput.

In this paper, we consider a MAC protocol for ad hoc networks where nodes are equipped with multiple antennas, and communications are spatially multiplexed using the Bell labs LAyered space time (BLAST) system. The contribution of this paper is twofold. First, we introduce two different analytical models aimed at predicting the propagation of detection errors within the BLAST receiver, the first based on a Gaussian approximation of the detection errors, and the second on the weighed enumeration of error configurations. A simplification of the latter, with lower complexity, is also obtained and compared to the original model. We then use these analytical tools to assess the performance of a cross–layer MAC protocol, and compare it with fully detailed simulations. Since the analytical tools replace the simulation of the physical layer, the proposed semianalytical approach is much faster than the bit-by-bit simulations. Numerous results are provided for the network performance assessment, showing that our semianalytical approach is able to predict network behavior with very good accuracy, but much lower complexity.

This paper presents an analytical model for the analysis of hybrid ARQ techniques on discrete time Markov channels by means of Markov chains. The first contribution is an original proposal to track the outcome of the packet transmissions in a generalized hybrid ARQ transmission system, deriving an appropriate Markov chain. Also, an example is given of how to put this Markov chain in relationship with a discrete-time Markov channel description. This framework can be used to evaluate from the theoretical point of view the performance of truncated type II hybrid ARQ techniques, deriving in general some useful insight on the behavior of such systems.

In this paper, we address the impact of different access strategies in ad hoc networks with multiple antennas and MIMO communications. We employ a cross-layer designed MAC protocol that allows both for multiple simultaneous access to the radio medium and for proper exploitation of multiuser detection at the receiver for interference cancellation purposes. Still, the network is subject to early deadlock if no access strategy is employed that reduces transmission persistency. To this aim, we study two types of exponential backoff, namely node- wise and destination-wise, and combine them with a form of cooperative agreement on who takes the role of transmitter or receiver. The impact of all schemes is assessed and a comparison is pursued, focusing on typical network metrics (such as throughput and transmission delay among others) and showing when and why one technique performs better than the others.

The paper presents IRIS, an Integrated Routing and Interest dissemination System for wireless sensor networks. The proposed protocols are designed to work under very low duty cycle operations and are jointly optimized for improved efficiency. Routing towards the sink is achieved by exploiting hop count information which is proactively distributed during the interest dissemination phase. Node densities are locally and dynamically estimated at each node and exploited at the MAC layer by means of a cost based probabilistic scheme. A cross-layer routing/MAC scheme is defined where relays to the sink are selected based on nodes' resources (including energy and queue occupancy). The proposed solution is a step towards the definition of complete, self-adapting and autonomous sensor network systems.

In this paper, we focus on a vehicular ad hoc network (VANET) that makes use of 802.11-like wireless interfaces for Inter Vehicular Communication (IVC). We propose a distributed position-based broadcast protocol, named Smart Broadcast (SB), that aims at i) maximizing the progress of the message along the propagation line, and ii) minimizing the re-broadcast delay. The protocol is analyzed through a mathematical model that permits to determine the optimal parameter setting for a given scenario. Simulations are then used to validate the mathematical model and to compare SB with other broadcast algorithms.

In this paper, we consider a layered space-time multiuser detection technique and propose an analytical approximation for its performance. Our work is useful in two different stages of network design. On one hand, the approximation may be used to evaluate the bit and packet error performance of communications among a group of terminals making use of multiuser detection. On the other hand, it can also be seen as a very valuable tool from the networking point of view, as it may help in designing radio access control protocols based on multiuser detection, as analytical formulas are very fast to evaluate, in contrast to bit-level simulations that may need a long time to complete. Thus, an analytical formulation is important, because it helps discriminating among different protocol alternatives, speeding up considerably the protocol design phase and the development of new radio access policies for multiuser networks.

In this paper, the problem of broadcast in ad hoc networks with smart antennas in a general propagation scenario is investigated. The conventional models proposed in the literature are surveyed and their limiting assumptions on the array at the receiver are identified. The connection between this point and the gain asymmetry for the broadcast of control packets is explored. As a first result, the impossibility to solve this problem in a conventional setting is proved. Then, a solution is mathematically deduced based on the consideration that a true MIMO (rather than MISO) system is required. The scheme can simultaneously send these packets farther, omnidirectionally and with very limited delay. Finally, a comparison with the Circular RTS scheme [1] is carried out with interesting results.

In this paper, we analyze a wireless LAN hot-spot, based on the IEEE 802.11b protocol, and more specifically we address the issue of defining proper pricing strategies, from both perspectives of evaluating technical performance and quantifying the economic revenues. We take into account a model for users' behavior that considers the trade-off between perceived QoS and paid price. This allows us to describe all users' choices in a decentralized manner, so that the transmission rate of each node is driven both by service requirements and by the customer's willingness to pay. After this setup, the multiple users' medium access mechanism is considered through simulation based on ns-2. Within this model, the network performance is evaluated and discussed. First, we investigate the provider's task of having a suitable price policy that gives a satisfactory income. This is connected with the goal of achieving high throughput, but is also dependent on a price setting that is accepted by the users and optimizes resource usage. Finally, we present numerical results which can provide practical insight for pricing setup in a wireless LAN hot-spot.

In this work, we analyze the effects of a distributed transmission coordination scheme that is particularly suited for ad hoc networks with the ability to exploit spatially--multiplexed communications over MIMO links. Following previous work where we discussed the performance of this kind of networks and deployed a fast yet reliable approximation for physical layer behavior, we now employ this knowledge to analyze the performance of a different access scheme, meant to outperform previous results by the use of distributed coordination of transmissions and receptions without adding any further redundancy in communications. Furthermore, we analyze the effect of tuning two parameters on the overall network performance.

The focus of this paper is on routing over heterogeneous networks. We consider a scenario involving both infrastructure and infrastructureless wireless networks, where a set of mobile users are interested in communicating with several access points (APs). Multi-hop routing, possibly over heterogeneous technologies, is exploited to extend the coverage for those users that are not within the transmission range of any APs. We propose a proactive tree-based approach for the dissemination of routing information and the subsequent data forwarding towards the APs. Subsequently, we compare its performance against reactive routing algorithms for ad hoc networks. The network performance is obtained via simulation through careful modeling of the considered radio interfaces. The results indicate the superiority of proactive schemes under moderate/high traffic conditions and motivate further research

No abstract available.

We consider the application of spatial multiplexing to ad hoc networks where nodes have multiple antennas. At the physical level, we suppose that layered space-time multiuser detection (LAST-MUD) is applied to separate multiple streams arriving at the receiver simultaneously. Our contributions here consist first in the reproduction of the multiuser detection process performance by a simple analysis, where we also specify when the analytical results are expected to be accurate and why. Second, we use this approximation to perform a cross-layer design of a MIMO ad hoc network where physical layer and medium access control strategies are integrated to maximize the network throughput. We finally corroborate our conclusions, by comparing analysis with simulation results both at the link and the network level

Existing cooperative diversity techniques for wireless ad hoc networks mostly consider space-time block codes for cooperation. In this paper we propose a cross-layer design of ad hoc wireless networks based on spatial multiplexing (SM). Each node is equipped with multiple antennas and the spatial dimensions of the channel are exploited to support multiple simultaneous transmissions. Diversity is then provided only for failed transmissions by means of selective cooperation among nodes that still use SM for transmission. Moreover, to increase the efficiency of the system, a hybrid automatic repeat request (HARQ) protocol integrated with packet coding is used at the medium access control layer. We analyze the proposed network architecture with a Markov chain description of the decoding process and we derive a closed form expression for the achieved throughput in Rayleigh fading channels.

The problem of gain asymmetry in MIMO ad-hoc networks is outlined and the solutions proposed in literature are surveyed. The approach in R. Korakis et al. (2003) is analyzed and its delay quantified. A new solution, based on the ideas in F. Rossetto and M. Zorzi (2006), is studied. Its performance advantages over . Korakis et al. (2003) are quantified by physical layer and network level simulations

In this paper we investigate the limiting properties, in terms of capacity and delay, of an ad hoc network employing a topology-transparent scheduling scheme. In particular, we focus on Time-Spread Multiple Access (TSMA) protocols, which are able to offer, in a distributed fashion, a deterministic upper bound on the access delay. The analysis is based on some asymptotic (focusing) properties of geometric random graphs. The analytical framework is applied to both static and mobile networks. The obtained results are compared with the results present in the literature for the case of an optimum (centralized) scheduling scheme.

The present paper replies to a comment by Nguyen et al. (IEEE Trans. Sel. Areas Commun., vol.24, no.12, p.2340-1, December 2006) on the original paper by Zorzi and Rao (IEEE Trans. Sel. Areas Commun., vol.12, no.8, p.1289-98, October 1994)

In this letter we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the m round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular the packet arrival rate, on the delay statistics

In this paper we address the issue of optimizing the performance of data-link and transport protocols running on a system including multiple transmit and receive antennas with a V-BLAST architecture. More specifically, we explore the possibility of adaptively selecting which transmit antennas and which per antenna rates to use as well as the antenna transmit powers in order to maximize the rate of data packets successfully delivered through the system. This is a novel approach with respect to the recent literature which is generally focused on channel capacity optimization. We show that the use of this rate/power adaptation technique, when a data transmission protocol is running over the link, may lead to significant throughput improvements especially at small signal-to-noise ratios.

In this paper the packet delay statistics of a fully reliable selective repeat ARQ (SR ARQ) scheme is investigated. An N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be non zero, i.e., ACK/NACK messages are received at the transmitter m channel slots after the packet transmission started. The ARQ packet delay statistics is evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, procedures to derive a Markov channel description of a Rayleigh fading process are discussed and the delay statistics obtained from the Markov analysis is compared with that estimated by simulation of the SR ARQ protocol over the actual fading process. The accuracy of the delay statistics obtained from the Markov Channel representation of the actual fading process is investigated by explicitly addressing the effect of the number of states considered in the Markov channel model and the impact of the Doppler frequency. Finally, besides giving a new analysis to obtain link layer statistics over N-state Markov channels, the paper provides important considerations on the adequacy of the widely used Markov modeling approach for the characterization of higher layer performance

This paper addresses the issues of distributed dynamic resource allocation (DRA) in the downlink of a SDMA broadband wireless packet network with multiple access points and adaptive antennas. Packet access and downlink beamforming make the intercell interference hard to predict, and how to handle it in order to efficiently perform slot and power allocation is still an open issue. We propose here new DRA strategies for efficient allocation in a distributed environment, which jointly consider spatial compatibility of users and an intercell interference estimation based on a fraction of the worst case interference measurements. The latter is independent of the actual allocation. We consider a low mobility TDD system with synchronized base stations and we assume perfect channel knowledge. The algorithms which are introduced and compared are the distributed max-min fit (DMMF) and the distributed reverse fit (DRF), which are based on the max-min fit criterion, the DMMF combined with the novel concept of nulls preallocation, where each AP reserves some beamforming nulls for the most interfered users of neighboring cells, and the power shaping technique (PS-DRA for SDMA), which efficiently exploits static power preallocation on time slots. The numerical results show that our techniques are able to significantly reduce the gap between a greedy centralized strategy that fully coordinates all the access ports and the baseline case of random distributed allocation, previously proposed for packet access. However, distributed power control in a packet switched environment with downlink beamforming still remains a hard task: nevertheless, PS-DRA is an efficient solution for joint slot-power allocation

In this paper we investigate the packet delay statistics of a fully reliable selective repeat ARQ scheme by considering a discrete time Markov channel with non-instantaneous feedback and assigned round-trip delay m. Our focus is on studying the impact of the arrival process on the delay experienced by a packet. An exact model is introduced to represent the system constituted by the transmitter buffer, the round-trip slots, and the channel state. By means of this model, we evaluate and discuss the delay statistics and we analyze the impact of the system parameters, in particular of the packet arrival rate, on the delay statistics

In this paper we focus on routing strategies for wireless sensor networks over hop count (HC) virtual coordinates. We consider the problem of optimally delivering data packets by means of multi-hop forwarding techniques where we assume that each node in the network, upon the execution of a proper distribution algorithm, can obtain a hop count number, i.e., the minimum number of transmissions needed to get to the sink (destination) node on the shortest path. We exploit HCs in place of commonly considered geographical coordinates as a valuable indication of the direction towards the sink. Within this framework, we present localized greedy routing schemes and compare them against globally optimal solutions, where the objective is to minimize a properly defined cost function. Further, we present novel routing algorithms where the statistical knowledge of the minimum costs of second order (two hops away) neighboring nodes is used as an aid to drive the forwarding process. These statistically enhanced schemes are found to outperform both hop count greedy approaches and geographical routing of up to one order of magnitude in terms of goodness of the selected path

The paper compares the performance of the AODV, DSR, OESR classic routing protocols for ad hoc networks, when an IEEE 802.11b MAC protocol is used. The investigation, performed by means of simulations, evaluates the energy efficiency of these protocols and the effect of the 802.11b rate adaptation capability on the performance. The paper evaluates the effectiveness of a distributed power control (DPC) scheme (P. Bergamo et al., 2002) applied to the classic AODV, DSR and OESR routing algorithms. The DPC scheme, acting both at MAC and network layers, has the aim to reduce the overall power consumption of the network. In particular, DPC evaluates, hop-by-hop, the suitable transmit power level and then selects, end-to-end, for any destination, the path which minimizes the total transmit power required. A final comparison between classic and DPC routing protocols is also reported

Backoff algorithms can be employed on point-to-point wireless fading links to improve energy efficiency., particularly when the link experiences long deep fades and bursty errors. A backoff scheme at the link layer (LL), applying an appropriate backoff rule upon each LL packet loss event due to channel errors, can intentionally leave the channel idle (i.e., not transmit) for some specified number of slots, thereby reducing the possible energy wastage due to packet transmissions in error. Our new contribution in this paper is that we consider the use of backoff algorithms on wireless fading links with large round-trip delays, propose a go-back-N (GBN) protocol with backoff, and present a renewal-reward analysis of the throughput and energy efficiency performance of the proposed scheme. We show that the GBN protocol with a linear backoff (LBO) strategy results in energy savings of about 2 dB compared to GBN with no backoff (NBO), even in the case of large round-trip delays. In addition, we also propose and analyze an adaptive go-back-N/stop-and-wait (GBN/SAW) ARQ scheme with LL backoff. We show that this scheme with LBO achieves energy efficiency performance quite close to that of an ideal (though not practical) backoff scheme which assumes a priori knowledge of the channel status in each slot.

In this paper, we shed some light on the implications that using a recently proposed layered space-time multiuser detection technique has on MAC protocol design for ad hoc networks with multiple antennas. From this point of view, our work relates to both physical layer and network layer studies. In fact, on the one hand physical layer aspects are important to characterize the behavior at the receiver, especially in terms of bit and packet error rates, but are rarely considered in conjunction with networking scenarios; on the other hand, networking aspects are typically studied using drastically simplified physical layer models that, while allowing easier networking analysis, are often too restrictive or unrealistic. Also, this disconnects between physical and networking layer studies may severely limit the possibilities for cross-layer optimization, which appears to be the right approach for efficient wireless network design. Our paper is then an effort to establish some connection between the "physical" and "network" approaches, highlighting some interesting capabilities that ad hoc networks with multiple antennas are endowed by the use of multiuser detection

In this paper, we present a joint economic and technical analysis of a wireless LAN, where we model the user behavior as aimed at the choice of the transmission rate. User rate allocation requests are determined in a decentralized manner by considering the tradeoff between service requirements and willingness to pay the service price. Then, the multiple users' medium access is considered and the resulting allocation is evaluated and discussed. We provide results for what concerns the allocated resource, the number of satisfied users and the revenue coming from the assignment. All these metrics are shown to be connected, since they are strongly influenced by the service appreciation rate; however, they are also dependent on the price setting, which has to be acceptable for the users and allow efficient resource usage. Moreover, we investigate in particular the sensitivity to the number of possible system customers. These considerations are finally extended to gain general insights on the performance of the distributed coordination function of IEEE 802.11b.

In this paper, we propose a novel technique for the provisioning of multicast streaming flows in 3G W-CDMA cellular systems. Our focus here is on the transmission of a downlink multicast streaming flow to the interested users in a 3G cell. For error control, we propose packet-based forward error correction (FEC) and with the transmission of a certain amount of redundancy over parallel common channels. In practice, we exploit the temporal diversity over multiple channels to strengthen the FEC scheme thereby increasing the QoS. To increase error resilience, in every channel we exploit well-known packet-based Reed Solomon like coding techniques. Moreover, appropriate time shifts of the information sent across the parallel common channels are also introduced to increase the robustness against error bursts. The performance evaluation is carried out through an analytical framework. The obtained results show the substantial benefits deriving from the usage of a multiple channel transmission technique.

In this paper we investigate radio resource management strategies for multimedia networks driven by economic aspects such as users' utility and service pricing. To this end, we discuss a framework in which the impact of both QoS and pricing is accounted for in the users' acceptance rate of the service. The model is general enough to be adapted to different situations and optimization goals. Thus, we discuss possible objectives for the network management under the constraints of meeting quality of service requirements, by satisfying at the same time technical conditions of feasibility. We employ utility functions, in order to account for the additional characteristic represented by the traffic elasticity, and consider the effect of price to have a realistic characterization of economic quantities. The resulting optimization problem is then discussed and analyzed, to derive general insight and identify possibilities for enhancement.

Video services are expected to account for a large portion of the traffic in future wireless networks. Therefore, realistic traffic sources are needed to investigate the network performance of future communication protocols. Previously, we provided a publicly available library of frame size traces of long MPEG-4 and H.263 encoded videos in the QCIF format resulting in low bandwidth video streams. These traces can be used in 3G network simulations. Some future communication systems, such as WLAN systems, offer high data rates and therefore high quality video can be transmitted over such higher speed networks. We now present an addition to our existing trace library. For this addition we collected over 100 pre-encoded video sequences from the Web, generated the trace files, and conducted a thorough statistical evaluation. Because the pre-encoded video sequences are encoded by different users, their video settings differ in terms of codec, quality, format, and length. The advantage of user diversity for encoding is that it reflects very well the traffic situation in upcoming WLANs. Thus, the new traces are very suitable for the network performance evaluation of future WLANs.

The packet delay statistics of a fully reliable selective repeat (SR) ARQ scheme are investigated. A N-state discrete time Markov channel model is used to describe the packet error process and the channel round trip delay is considered to be finite, i.e., ACK/NACK messages are received at the transmitted m channel slots after the packet transmission is started. The ARQ packet delay statistics are evaluated by means of an exact analysis by jointly tracking packet errors and channel state evolution. Furthermore, a procedure to derive a Markov channel description of a Rayleigh fading process is presented and the delay statistics obtained from the Markovian analysis is compared with those estimated by simulation of the ARQ protocol over the actual fading process. Finally, some discussion on the accuracy of the delay statistics obtained from the Markov channel representation of the actual fading process is reported.

We introduce error control algorithms for multicast delivery in 3G cellular networks. For efficiency reasons, the delivery of multicast flows is usually achieved by allocating a common channel in the forward direction. This enables multiple users to be served by a single physical resource. However, different users are affected by independent channel error processes and new techniques, different from plain ARQ, have to be found to perform error recovery. These new algorithms are needed to deliver the multicast flow in an efficient manner and to enable a reliable, performant and network operator inexpensive, multicast service. Different hybrid ARQ algorithms for the error recovery of multicast flows over common channels are proposed and their performance is evaluated both analytically and by simulation. The proposed solutions have been found to be effective and advantageous over plain ARQ techniques. Results on the achievable video quality are reported for the multicast video streaming case by considering the H.263 video coding format.

This paper addresses dynamic resource allocation (DRA) for the downlink of a SDMA broadband wireless packet network with multiple access ports and adaptive antennas. The main issue for distributed DRA is how to manage the intercell interference, which is very difficult to be handled in an uncoordinated environment, due to packet access and downlink beamforming. Here, we investigate the performance and the feasibility of some distributed DRA strategies to gain insight on the impact of the lack of coordination among cells. We propose to estimate the intercell interference on the basis of cell load and worst case (taking into account adaptive antennas) measured interference and implement some greedy strategies suitable for a distributed implementation. We show that the gap between random allocation (previously proposed for distributed packet allocation) and a centralized algorithm can be significantly reduced, although the implementation of an efficient distributed power control still appears a hard task.

In this paper, we consider a novel forwarding technique based on geographical location of the nodes involved and random selection of the relaying node via contention among receivers. A new collision avoidance scheme based on this idea is described in detail, and an approximate analysis is provided. The proposed scheme is compared with a similar solution based on busy tones, as well as with STEM, and is shown to perform well for sufficient node density. Compared to a previously proposed protocol, the one presented here only needs one radio, thereby greatly simplifying the hardware.

In this paper we study the multihop performance of two energy efficient forwarding schemes (GeRaF and GAF) in a Rayleigh fading propagation scenario. Specifically, we evaluate the number of hops which are necessary to reach a destination at distance D, as a function of the density of available relay nodes. Analytical and simulation results show that GeRaF significantly outperforms GAF from the multihop point of view. GeRaF's energy/latency performance is also found to be very robust with respect to propagation impairments.

In this paper we discuss utility functions models to study Radio Resource Management. Our goal is to identify the characteristics of the wireless systems which make such theoretical models, though challenging, very useful, as they allow to quantify the Quality of Service and to analytically investigate the users ’ satisfaction. Moreover, we show how, within a utility-based framework, it is possible to also study economic issues, besides more conventional technical aspects such as throughput or system capacity. Thus, when economics are taken into account by considering the financial needs of the provider and the users ’ reaction to prices, we are able to study wireless systems in a more realistic and appropriate way. Another key contribution of this paper is a discussion on how utility functions should be applied to the particular case of the radio resource. To this end, we extend classic economic concepts with an original proposal, better able to model the nature of the wireless services. Finally, by giving both analytical insight and numerical results, we compare different classes of RRM strategies and explore the relationships between Radio Resource Allocation, pricing, provider’s revenue, network capacity and users ’ satisfaction. Categories and Subject Descriptors C.2.1 [Network Architecture and Design]: network communications, wireless communication; I.6.5 [Model Development]: modeling

A performance investigation of UDP and TCP in ad hoc networks is presented, by comparing the standard DSR routing algorithm, with a new algorithm implementation based on a distributed power control (DPC). The proposed approach tries to minimize the network power consumption in order to increase the network life time. To investigate the impact of DPC on the system, some UDP and TCP sessions has been simulated by using the network simulator 2, with different mobility scenarios.

The success of 3G networks depends on the possibility to attract customers to this new technology. Therefore, new services using the spectrum in an efficient manner while satisfying the customers are needed. We introduce link layer algorithms that are suitable for multicast transmission in 3G cellular systems and we present their impact on video application in terms of play-out buffer requirements. By our approach we show that hybrid ARQ solutions can be successfully employed to perform error control in the multicast transmission case. Using these schemes, performance can be increased thereby increasing system capacity and lowering the cost per served user. In the final part of the paper, these solutions are extended for the important multicast video streaming case, where new schemes are devised to avoid the throughput inefficiencies of fully reliable error recovery algorithms.

We consider the design of error control schemes in wireless sensor networks. Unlike in traditional communications systems, in which coding is almost always beneficial, in sensor networks, the relatively short communications range and the lack of energy resources may lead to different conclusions. In particular, the energy spent for decoding may actually exceed that saved due to the coding gain. In this environment, interesting tradeoffs can be identified and non-traditional ways to perform error control may be proposed.

This paper aims at introducing possibilities of self-organization for packet-switched networks at the scheduling management level. By discussing already existing scheduling strategies, we identify several contrasting needs that should be jointly addressed. The employment of the same algorithm for the whole network leads to low performance. On the other hand, adjusting the management cell-by-cell is not feasible and requires coordination. Hence, we propose a general framework for the scheduler, which can be easily tuned by means of a neural network. In this way, cells are grouped and self-similarities are identified, so that the differentiation in the management is lighter and a significant performance improvement can be achieved. Moreover, a general tunability of the system is introduced, allowing to cut the right trade-off between system complexity and QoS in a simple way.

We consider distributed dynamic slot and power allocation for the downlink of a TD/SDMA broadband wireless packet network with multiple access ports and adaptive antennas. The still open issue for packet multicell SDMA is how to manage the intercell interference, which is very difficult to predict in an uncoordinated environment, due to packet access and downlink beamforming. For this reason, doing distributed allocation efficiently as well as improving the performance by means of power control results in a very hard task. We propose a greedy SDMA algorithm exploiting the power shaping technique, which is based on a static preallocation of the transmit power to each slot of the frame. This permits to obtain a partial predictability of intercell interference, allowing different levels of estimated intercell interference and available power for each slot. We show that our greedy SDMA algorithm with power shaping increases system capacity with respect to the same algorithm without power shaping and reduces the performance gap with respect to a greedy centralized strategy, thus limiting the need of coordination among cells. Both centralized and distributed algorithms are compared, as reference, to the baseline case of random allocation, previously proposed for packet access.

In this paper, we present an accurate analytical model for transport control protocol (TCP) over correlated channels (e.g., as induced by multipath fading) taking into account a finite round-trip delay. In particular, we develop models and analysis for studying four versions of TCP, namely, Old Tahoe, Tahoe, Reno, and New Reno. We focus on a single wireless TCP connection by modeling the correlated packet loss/error process as a Discrete Time first-order Markov chain. Our model explicitly incorporates important aspects such as slow start, congestion avoidance, fast retransmit and fast recovery. The main findings of this study are that: 1) an increasing round-trip time may significantly affect the throughput performance of TCP, especially when an independent channel is considered; 2) New Reno performs better than Reno and Tahoe when the channel is uncorrelated, whereas Tahoe's recovery strategy is the most efficient when the channel correlation is high; and 3) the maximum window size does not play a determinant role in increasing throughput performance in both correlated and independent channels. While some of these conclusions confirm what other authors have observed in simulation studies, our analytical approach sheds some new light on TCP's behavior.

We propose and investigate a new resource allocation technique for the downlink of time-division multiple access (TDMA)-based fixed broadband wireless access systems (FBWA) with full frequency reuse. This technique, named power-shaped advanced resource assignment exploits an appropriate set of power profiles which limit (or shape) the power transmitted in each slot of the frame and are suitably reused among the cells, in order to efficiently distribute the intercell and intersector interference inside the frame and to make it partially predictable. In systems where base stations assign radio resources in an uncoordinated fashion, this allows the allocation algorithm to assign time slots to users on the basis of the power required to fulfill a predefined carrier-to-interference ratio, since worst case interference can be suitably estimated; moreover, different degrees of protection against interference are provided across the slots to efficiently accommodate users with different location-dependent channel conditions. Simulation results for a typical cellular FBWA system show that this technique significantly improves the capacity with respect to other techniques recently proposed, e.g., the enhanced staggered resource assignment, even when they use power control. Moreover, an analytical framework useful to understand the concept of power shaping and to discuss some guidelines for the design of power profiles is provided.

Over the years, radio resource management has been benchmarked mostly by its technical merits. A service provider, however, must also reckon with economics. When the financial needs of the provider and the satisfaction of the users are considered, common objectives in radio resource management, like maximising throughput or meeting various quality constraints, may no longer be sufficient. We analyse next generation communication systems by including models of economics, which have been presented in the literature, and reasonable considerations to depict the users/provider relationship in a generalised multimedia environment. In particular, we develop a model of users' satisfaction, in which both requested quality of service and price paid are taken into account. The model enables us to investigate how resource allocation dynamics affect operator revenues and to derive some useful insights. Radio resource management can be shown to be highly dependent on economic considerations. The provider's task to determine the best usage of the network capacity is heavily affected by the users' service demand and their reactions to the pricing policy. Thus, the economic scenario needs to be taken into account to exploit the constrained radio resource efficiently. The model is applied to a CDMA cellular system.

Routing protocols for ad hoc networks are currently a hot research topic and when a lot of metrics are jointly considered, some nontrivial conclusions can be drawn. In this work, we make a comparison of link state, AODV and DSR protocols for two different traffic classes, in a selected environment. The classic Dijkstra is also reported as comparison term. As performance metric, packet delivery fraction, throughput, average delay and energy are considered. We show that AODV and DSR perform well when the network load is moderate, while, if the traffic load is heavy, simple link state outperforms the reactive protocols and becomes a good candidate to be used.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. It is assumed that the sender continuously transmits packets whose error process is characterized by means of a two-state discrete time Markov channel. At the receiver these packets are checked for errors and ACK/NACK messages (assumed error-free) are sent back to the sender accordingly. The feedback message is known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been developed in order to find the exact statistics of the delays experienced by ARQ packets after their first transmission.

We study a novel forwarding technique based on the geographical location of the nodes involved and random selection of the relaying node via contention among receivers. We focus on the multihop performance of such a solution, in terms of average number of hops to reach a destination as a function of the distance and of the average number of available neighbors. An idealized scheme (in which the best relay node is always chosen) is discussed, and its performance is evaluated by means of both simulation and analytical techniques. A practical scheme to select one of the best relays is shown to achieve performance very close to that of the ideal case.

In this paper, we present a study of admission control in 3G systems. In particular, the behavior of algorithms already presented in the literature is analyzed, with respect to their implementation in UMTS-like systems, and a model of trade-off between the QoS metrics, blocking and dropping probability, is presented. The obtained performance is discussed and analyzed under different points of view. Finally, possibilities to improve fairness and generality of these results open up when a more detailed model for mobility, data rate and discontinuous transmission (DTX) is considered.

The distributed power control (DPC) proposed and tested in various works has been applied to an ad hoc network using AODV as routing scheme. To test the performance of the power controlled version of AODV several simulations have been performed by considering two different kinds of traffic sources and different mobility conditions. Even if in the Dijkstra and Link State case. DPC showed good results in terms of delivery percentage, delivery time and energy saving in several system conditions, in this paper we outline that in the AODV case, its effectiveness is only performed in some operative conditions as a consequence of the intrinsic structure of the routing protocol.

In this paper the packet delay statistics of a fully reliable selective-repeat ARQ scheme is investigated. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. It is assumed that the feedback message is known with no errors at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find exact statistics of the delays experienced by ARQ packets. In this work, in order to reduce the computational complexity of such analysis, an appropriate model is presented. The results obtained from the approximate approach are shown to be in excellent agreement with the ones derived from the exact analysis.

In this paper, we investigate the delay statistics of an aggregate of fully reliable selective-repeat ARQ packets. The sender transmits packets whose error process is characterized by means of a two-state discrete time Markov channel (DTMC). At the receiver, these packets are checked for errors and ACK/NACK messages are sent back to the sender accordingly. No errors are accounted for in the reverse channel. The feedback message is assumed to be known at the transmitter m channel slots (round-trip delay) after the packet transmission started. An appropriate Markov model has been previously developed in order to find the exact statistics of the delays experienced by ARQ packets. This work presents an extension of the analysis that computes the delay statistics of an aggregate of ARQ packets. This is achieved without increasing the model complexity and allows useful considerations from the point-of-view of higher level protocols.

In this paper, we describe a novel forwarding technique based on geographical location of the nodes involved and random selection of the relaying node via contention among receivers. A collision avoidance scheme based on this idea is described in detail, and an approximate analysis is provided. The proposed scheme is compared with STEM, and is shown to perform significantly better for sufficient node density.

In this paper, we propose a novel forwarding technique based on geographical location of the nodes involved and random selection of the relaying node via contention among receivers. We focus on the multihop performance of such a solution, in terms of the average number of hops to reach a destination as a function of the distance and of the average number of available neighbors. An idealized scheme (in which the best relay node is always chosen) is discussed and its performance is evaluated by means of both simulation and analytical techniques. A practical scheme to select one of the best relays is shown to achieve performance very close to that of the ideal case. Some discussion about design issues for practical implementation is also given.

In this paper, we study a novel forwarding technique based on geographical location of the nodes involved and random selection of the relaying node via contention among receivers. We provide a detailed description of a MAC scheme based on these concepts and on collision avoidance and report on its energy and latency performance. A simplified analysis is given first, some relevant trade offs are highlighted, and parameter optimization is pursued. Further, a semi-Markov model is developed which provides a more accurate performance evaluation. Simulation results supporting the validity of our analytical approach are also provided.

Energy efficient communications in ad hoc and sensor wireless networks is a very important topic. We study the problem of creating spanning trees of low cost, where low cost can be viewed in terms of global or local energy efficiency. We refer to the algorithms and techniques presented in the literature, and we show an extension to them, called TDPC (time division path changing), which takes into account both global and local efficiency. We show that these techniques can be applied to existing algorithms, improving the performance, and addressing both needs. We show how it is possible, in general, to cut a trade-off between the two contrasting requests of global and local energy efficiency, by using TDPC with a particular class of algorithms.

This paper investigates a new resource allocation technique for TDMA-based fixed broadband wireless access systems (FBWA) with full frequency reuse where base stations assign radio resources in an uncoordinated fashion. This technique, named Power-Shaped Advanced Resource Assignment (PSARA) exploits an appropriate set of power profiles that limit (or shape) the power transmitted in each slot of the frame with the aim of helping the allocation algorithm to efficiently distribute the intercell and intersector interference inside the frame. Simulation results for a typical cellular FBWA system show that this technique significantly improves the capacity with respect to other techniques recently proposed, e.g. the Enhanced Staggered Resource Assignment (ESRA), even when they use power control. Moreover, an analytical framework useful to understand the concept of power-shaping and to discuss some guidelines for the design of power profiles is provided.

We investigate the possibility of using Markov chains to model the error process in the data blocks delivered by the physical layer of a WCDMA (wideband code division multiple access) cellular system. Starting from the results on the error statistics obtained from a suitable simulation tool, which jointly performs system and link level analysis, we first classify the users on the basis of performance level and burstiness, then we provide some guidelines for the design of Markov models in the different system and channel conditions. The performance of an ARQ (go-back N) protocol at the link layer is taken as a reference to test the reliability of the proposed models. It is shown that the perspective of using simple error models in the analysis of upper-layer protocol is feasible in many cases.

A new approach to model a fading process (e.g., as generated by Jakes' simulator) is proposed and investigated. This is based on a finite-state Markov chain, with a two-dimensional state given by the quantized values of the amplitude and its speed of variation. This results in better accuracy with respect to classical one-dimensional schemes. The validity of the proposed approach is shown by first comparing the autocorrelation function of the resulting process with that of the simulated process. Furthermore, a simple go-back-N protocol has been used for the same purpose, in order to verify the impact of modeling accuracy on upper layers. Some investigations have also been performed to evaluate the sensitivity to parameter selection. Finally, the widely used model with only two values of envelope amplitude has been studied, in the case of both one-dimensional and two-dimensional Markov chain states.

We carry out a comparative study of different dynamic greedy frame-by-frame packet scheduling strategies with QoS guarantees. The considered environment is the downlink of a locally centralized (or bunched) CDMA/TDD wireless communication system. In particular, in order to address the trade-off between throughput and fairness, we first analyze the system performance using system-level metrics, and then proceed with a "user by user" investigation. We show that an appropriate design of the packet scheduling heuristics may lead to significant performance improvements in terms of total user level of satisfaction, at the price of a small degradation of the system throughput.

Two TCP/IP header compression (HC) schemes for wireless networks are proposed and investigated on a WCDMA air interface. These novel schemes are compared with the classical ones by means of a TCP/IP New Reno simulator taking as input WCDMA channel traces. These channel traces are derived by using a simulator built according to the UMTS standard requirements. The RLC (radio link control) level operates in transparent mode, while overhead is added to simulate the presence of a MAC level. Some new performance metrics are introduced to evaluate the performance of HC schemes.

We apply dynamic slot allocation with power-shaping to TDMA-based (pure or hybrid) mobile radio systems. We show that this technique, originally proposed by the Authors for fixed broadband wireless access systems, where interference is mitigated by the use of highly directional antennas, is able to increase the capacity even in full frequency reuse cellular systems with omnidirectional antennas. By using a set of power profiles, suitably defined here for hexagonal cells with three sectors, that limit (or shape) the power transmitted in each slot of the frame, this allocation technique partially organizes the intercell and intersector interference in the available slots and makes it partially predictable. This allows an efficient use of radio resources, when the base stations assign radio resources in an uncoordinated fashion without the need of centralized strategies requiring heavy signaling load.

In this work we investigate and discuss the behavior of a wideband CDMA cellular system from the point of view of the power efficiency and the quality level at the physical layer. More precisely, special attention is given to the role played by the SIR-based closed loop power control when it tries to compensate for channel loss variations in different conditions of fading velocity. It is shown that, for a heavily loaded system in a slow fading environment, the largest amount of power is required by those users experiencing the worst quality level. This aspect should be taken into account in the management of the system resources. The results have been obtained from a suitable simulation tool which joins the system level analysis with the link level analysis and includes many features of the WCDMA air interface of UMTS.

In this paper we investigate the performance of data-link and transport protocols running on a transmission system including multiple transmit and receive antennas with a BLAST architecture. More specifically, by exploring the possibility of adaptively selecting the set of transmit antennas to improve protocol performance, we propose and discuss a novel strategy for antenna selection which is suited to the protocols considered. We show that the use of these adaptive techniques leads to an increase of the link throughput, which becomes significant even at small values of the signal-to-noise ratio if the proposed strategy is adopted. Moreover, when applied to TCP, our selection method significantly outperforms the more general channel-capacity-based selection rules.

In this letter, we present a study of the signal-to-interference-plus-noise ratio (SINR) and throughput efficiency achievable on the uplink of a multicellular environment in the presence of smart antennas at the base station receivers. Both intracell and intercell interferers are explicitly taken into account, as is the SINR gain yielded by optimal beamforming. Different cases are compared, and in particular the tradeoff involved in choosing the frequency reuse plan is considered. It is seen that the system operates most efficiently with full frequency reuse, even though this requires intercell synchronization.

In a typical wireless system, communications between a transmitter-receiver pair is subject to impairments such as intracell and intercell interference, as well as multipath fading. For practical wireless data transmission systems, e.g., UMTS or GPRS, it has been demonstrated by simulation and supported by real measurements that these impairments can be adequately modeled by hidden Markov models (HMMs). It has also been demonstrated that various types of wireless data arrival process can be modeled by a batch Markov arrival process (BMAP). This paper presents analytical methods for evaluating the packet queue length and packet delay probability distributions assuming that packet arrivals are modeled by a BMAP and packets are transmitted over channels with bursts of errors which are modeled by HMMs. In contrast with simulations, the analytical approach allows a system designer to find and test proper diversity, source and channel coding schemes, and communication protocols more efficiently. Analytical and simulation results are compared to determine the accuracy of the presented methods.

Welcome to the second issue of Volume 2 of Wireless Communications and Mobile Computing/ (WCMC). We have been receiving a large number of great articles and we would like to assure you that we are grateful for your contributions. Our fast review process and publication turnaround has caught the attention of our authors and readers. We are working hard with the John Wiley staff to make the review process more efficient by introducing a new system soon. Once that is implemented, we will be able to process more manuscripts in a short span of time. In the meantime, we appreciate receiving your quality manuscripts and would like to invite you to sit back and enjoy reading the articles in this issue. As always, we welcome your contributions, whether by submitting manuscripts and/or special issues. We also appreciate any comments/feedback you have regarding WCMC. Send your comments to any of the editors and/or to the Wiley office. Multicast protocols in mobile ad-hoc networks have been an area of active research for the past couple of years. In the first paper, Papavassiliou and An discuss the issues, challenges, and protocols related to multicasting in mobile ad-hoc wireless networks. They survey several existing multicasting protocols in mobile ad-hoc networks and summarize the activities of recent advances in the field. A qualitative comparison of these protocol characteristics according to several distinct features and performance parameters is presented. Finally, they present an overview of research and development efforts in the area of group mobility modeling in mobile ad-hoc networks. Next, Cui and Bassiouni present an analysis of hierarchical cellular networks with mobile base stations, termed as totally mobile wireless networks (TMWNs). Their performance evaluation concentrated on comparing the results of a two-tier system's throughput, handoff blocking rate and new call success rate with those obtained by a one-tier model. Under all load conditions, their results show that the two-tier system outperforms the one-tier system if the number of channels is kept the same. Other conditions have been studied and results have been presented. An analytical model to compute the new call and handoff blocking probabilities in TMWN is given and evaluated. Extensive results are presented. In the third article, a software architecture for GPRS session management (SM) is presented by Haung and Lin. This software architecture is designed to accommodate 3G wireless networks. It is compatible, modular, and flexible. The aim of the architecture is to support a GPRS support node. In a situation where single-band and dual-band PCS handsets co-exist, the channel assignment becomes a concern. In the fourth article, Chang and Li propose load-balancing channel assignment schemes to improve the system performance. To further improve the system capacity, a channel reassignment scheme is also presented. Modeling and simulation results are carried out. The results indicate that both load-balancing and channel re-assignment techniques significantly increase the system capacity as the percentage of dual-band handsets increases. Furthermore, the load-balancing with channel re-assignment scheme that combines both techniques achieves the best system performance, even when the percentage of dual-band handsets is as low as 25%. To reduce the peak-to-average power ratio (PAR) of an orthogonal frequency division multiplexing (OFDM) signal, a set of fixed permutations are used by Jayalath and Tellambura. This set of permutations is applied on K − 1 interleavers are used to produce K − 1 permuted sequences from the same information sequence. To reduce the technique's complexity, an adaptive approach is adapted. Simulation results show that random interleavers and odd-even symmetric interleavers are performing equally well in reducing the PAR. Other results for the out-of-band radiation and the bit error rate performance of interleaved OFDM (IOFDM) and conventional OFDM are also presented and discussed. In the sixth paper, Liu and Li propose a new modulation scheme to improve the performance of DS-CDMA systems in fast time-dispersive fading channels called differential space–time modulation for DS-CDMA (DST-CDMA). They study three different types of receivers using this new scheme. Then, they compare the three types under different design combinations. Finally, Sheikh and Shah present an application of LMS algorithm with optimum step size (µopt-LMS) for fading channel estimation. The expression for optimum step size of the LMS algorithm is modified for use in the fading channel estimation problem. The robustness of the proposed µopt-LMS algorithm is demonstrated via simulation in different channel conditions. Thanks to all the authors and the reviewers. We appreciate your support and hope that you continue your dedication to WCMC.

Welcome to the fourth issue of the second volume of Wireless Communications and Mobile Computing (WCMC). Our Journal has been getting a lot of attention from many experts in the field and therefore many new subscriptions but we still need your continuous support as always. Even though we have enough submissions and special issues to fill all the slots for next year, we would like to encourage all readers to send us their Special Issue proposals and manuscripts in their fields of expertise to enrich the rest of the community of their new findings. I extend a special invitation to all members of the editorial board to step in and contribute at this junction, now that we are ready to take WCMC to the next level of excellence. I would like to extend special thanks to the authors who have worked hard to produce the fine articles you are about to read in this issue. Thanks also to those who have contributed whether running a special issue or publishing a regular manuscript. Of course, the continuous help from the staff of John Wiley is always very much appreciated. We welcome any comments/feedback you have regarding WCMC. Send your comments to any of the editors and/or to the Wiley office. The first article by Mähönen et al. reviews and analyzes the standards currently available and describes the European two-layer trial system developed in 1996–2000. The authors show why further development towards IP-based LMDS is useful in the future. Most of their recommendations are based on results derived from the European Union supported research project CABSINET. It had the aim of demonstrating the viability of a 40 GHz cellular digital television system with a return channel to offer interactive services. Two systems were tested: a line of sight link using QPSK, and a non-line of sight with COFDM modulation scheme. Then, they present the architectures of the transmitters, nomadic terminals, and the design of the IF/RF subsystems for both types of modulations. The discussion is focused on system engineering and selections required in order to build a full two-layer LMDS system. Jaseemuddin et al., in the second article, present a study of profiled handoff for DiffServ-based mobile nodes. They investigate the effects of handoff on service quality of mobile nodes. Several experiments are conducted using various packet metering and marking schemes with or without transferring profiles to the new router. Results indicate the relative instability period following handoff, loss of packets and delay encountered by packets in profiled or un-profiled handoffs, leading to determining suitable mix of metering and marking schemes with or without context transfer. The third and fourth articles are two parts of the same topic prepared by Nassar et al. The first part (third article) is entitled ‘Multi-carrier platform for wireless communications: High-performance, high-throughput TDMA and DS-CDMA via multi-carrier implementations.’ In part I, the authors demonstrate how a multi-carrier platform can be adopted by TDMA and DS-CDMA systems. Then, they show how, in both systems, the proposed multi-carrier platform is able to: (1) outperform existing time-based-processing receiver structures in multipath fading channels, due to better exploitation of the channel diversity and a minimization of the MAI (multi-access interference); (2) achieve increases in network capacities (measured by numbers of users or, equivalently, throughput per user); and (3) reduce receiver complexity. In part II, OFDM and MC-CDMA systems with high-performance, high-throughput via innovations in spreading, the authors demonstrate how the CI (carrier interferometry), which brought far reaching benefits to TDMA and DS-CDMA, is also able to enhance existing multi-carrier systems, namely existing OFDM and MC-CDMA architectures. By application of the CI approach to these systems, they demonstrate significant performance benefits (measured in terms of probability of error performance) and notable network capacity gains (measured by numbers of users or throughput per user). Moreover, in the case of OFDM, the CI approach eliminates the PAPR (peak to average power ratio) problem, and in the case of MC-CDMA, a simple procedure is available to resolve all PAPR concerns. Combining the results of part I and part II, the authors succeeded in presenting a common multi-carrier platform for the most popular architectures in use today. Hence, by use of the CI approach, a simple software code will switch a CI processor from one mode to another, where modes include CI/TDMA, CI/DS-CDMA, CI/MC-CDMA, and CI/OFDM. Each mode demonstrates improved performances and network capacities relative to its traditional counterpart. Next, Al-Hussaini et al. propose and analyze parallel co-channel interferers (CCI) multistage cancellation by combining RAKE and selection diversity. In order to account for channel variations, adaptive implementation of decision thresholds at the RAKE output is suggested. It is shown to provide significant improvement over either hard or soft decision techniques especially in the near-far situation. Investigation of the system robustness to imperfect channel parameter estimation is also presented. The communication channel is modeled as slowly varying Rayleigh fading discrete multipath channel. Finally, Annamalai and Srivastava outline a general numerical procedure for computing the probability of outage of a cellular mobile radio system that is equipped with a smart antenna to suppress a few strongest co-channel interferers (CCI) out of a total of NI active interferers by adaptive null-steering. Aside from the interference-limited case, refined outage criterion that either treat receiver noise as CCI or consider a minimum detectable receiver signal threshold are studied. Exact closed-form expressions for both the basic and refined outage criterion are also derived by assuming that all the CCI signals are subject to Nakagami-m fading with a positive integer fading severity index. Selected numerical examples are provided to illustrate the application of the theory which includes the investigation into the effects of fade distribution of the CCI signals and traffic loading on the outage probability, and also the study of spectrum utilization efficiency improvement using a selective co-channel interference cancellation technique.

Abstract In this paper, we present a study on the performance of TCP, in terms of both throughput and energy consumption, in the presence of a wideband CDMA radio interface typical of third generation wireless systems. The results show that the relationship between throughput and average error rate is largely independent of the network load, making it possible to introduce a universal throughput curve, empirically characterized, which gives throughput predictions for each value of the user error probability. Furthermore, the study of the energy efficiency shows the possibility to select an optimal power control threshold to maximize the trade‐off between throughput and energy, thereby potentially achieving very significant energy gains. Copyright © 2001 John Wiley & Sons, Ltd.

We develop a new media access control strategy, called plane cover multiple access (PCMA), that provides a means of allocating wireless bandwidth in a packet-based cellular system. PCMA seeks to maximize the number of parallel transmissions among cells by defining virtual cells in which users transmit using a given reuse factor. By keeping the reuse factors low, system throughput can be maximized. We show that the throughput of a simple system designed using PCMA is up to 82% more efficient than capture division packet access (CDPA), the best known alternative for the cellular mobile environment.

Use of an adaptive antenna array or a space-time processor at each base station of a wireless network can substantially abate the effects of multipath fading and co-channel interference. Among the expected benefits are higher data rates, greater frequency reuse factors, and overall higher capacity systems as needed to enable wireless multimedia services. Media access control (MAC) protocols which facilitate the deployment of such a processor have been previously proposed and studied. These MAC protocols invoke the delivery of a pilot tone from each packet access unit in the network as needed, so that the array antenna at the associated base station may rapidly adjust its weighting coefficients, or its per branch equalization coefficients, thereby ensuring subsequent reliable communication between the access unit and the base station. This paper considers two modifications to these earlier protocols, both based upon the notion of piggybacking information requests on to the actual information messages and both intended to improve utilization efficiency and mean delay performance. Results show that a maximum link utilization efficiency of 97% is readily achieved with either modification, and that this maximum utilization efficiency is independent of the number of remote users in the network. Note that the utilization efficiency refers to the throughput at maximum loading, i.e., when the remotes always have queued requests. The modifications also help in achieving a considerable reduction in the average delay in low-load regimes: for typical system parameters, the average delay at low load is only about 10% of that produced by the original schemes.

Welcome to the fourth issue of the journal Wireless Communications and Mobile Computing (WCMC). We have been fortunate so far that we are able to attract quality papers and also meet all our publication deadlines. I am sure that you have been enjoying reading the fine articles in the previous three issues and hope that you continue to do so in the future ones. Special thanks to the authors who worked hard to produce the fine articles that we present to you in this issue. Thanks to those who have contacted us praising the efforts and quality of this publication. We welcome any comments/feedback you have regarding WCMC. Please send your comments to any of the editors and/or to the Wiley office. The first article in this issue reviews mobility management for the third-generation mobile networks. The authors, Y-B. Lin, Y-R. Haung, Y-K. Chen, and I. Chlamtac, focus on the evolution from General Packet Radio Service (GPRS) to Universal Mobile Telecommunication System (UMTS). In this evolution, the radio access network UTRAN has been introduced, and radio-related management is moved from the core network to UTRAN. They elaborate on how this architecture change affects the mobility management functionality, including the attach and detach procedures, location update, serving radio network controller relocation and intersystem change between GPRS and UMTS. Existing research in ad hoc mobile networks is mainly concerned with unicast routing. There is a growing interest in supporting multicast communication in an ad hoc mobile environment. C-K. Toh and S. Bunchua present an ad hoc mobile multicast routing using the concept of long-lived routes. They propose a new routing protocol called ABAM (associativity-based ad hoc multicast) routing protocol. They show that ABAM has many advantages in route reconstruction. Then, they demonstrate that ABAM is robust since the repair can be triggered by a node in the tree or by the migrated node itself. They also show that ABAM is capable of handling multicast group dynamics when mobile hosts decide to join and leave an existing multicast group. Their simulation results reveal that under different mobility scenarios and multicast group size, ABAM has low communication overhead and yields better throughput performance. Next, A. Andreadis, G. Benelli, G. Giambene, and B. Marzucchi discuss the analysis of the WAP protocol over SMS in GSM networks based on the PALIO Project within the fifth Research Framework of the European Commission. This deals with an information tourist service accessible through mobile phones by means of the Wireless Application Protocol (WAP). They considered a Global System for Mobile communications (GSM) network where the WAP traffic is transported by the Short Message Service (SMS) on a logical channel that also conveys signaling messages. Suitable models have been considered for both WAP browsing traffic and signaling traffic. Analytical predictions have been validated through comparisons with simulation results. Quality of Service (QoS) requirements in terms of the maximum transmission delay that is guaranteed in 95 per cent of cases was also considered. They concluded that this study proves the feasibility for the envisaged mobile service based on WAP and it also allows dimensioning WAP pages so that each user experiences reasonable browsing delays. A review of reduced-trellis equalization using the M-BCJR Algorithm is presented by C. Fragouli, N. Seshadri, and W. Turin. They review the complexity of channel equalization using the BCJR algorithm and how it grows exponentially with the channel memory. They also discuss how the M-BCJR algorithm provides a method for reduced-trellis channel equalization, but its performance varies significantly with the distribution of a channel energy to its taps. Then, they use the implementation of the BCJR algorithm in the logarithmic domain to propose a variation of the M-algorithm, which (they show) can offer robust performance. They discuss how the designer can make decision regarding delay, maximum and minimum phase transformation, and selection of states. Simulation is carried out to demonstrate the effect of different parameters on the proposed algorithm's performance. The broadcast nature of WDM star couplers makes the handoff scheme easier and reduces the number of reconnections. This is needed as the radio cells becomes gradually smaller and smaller, the procedures for mobile terminals call setup and control become complicated due to the high handoff frequency. H-C. Chao, R-C. Wang, and J-Y. Hong propose a channel allocation algorithm, called pre-empted assigned offset (PAO) scheme, for the WDM- and SCM/WDM-based personal communication network architectures. The main idea is to keep the offset conflict probability lower. Simulation results are provided to show that the proposed scheme outperforms those previously introduced schemes, especially in offset conflict probability under a moderate roaming rate for WDM and even under heavy roaming situations for SCM/WDM. Finally, A. Annamalai, Jr. discusses the effect of Gaussian error in Selection Diversity Combiners (SDC). SDC is one of the simplest and most commonly implemented diversity mechanisms for mitigating the detrimental effects of deep fades experienced on wireless channels. SDC main advantage is the reduction of the probability of deep fades. The effect of Gaussian errors in the branch gain estimates on the SDC receiver performance is investigated by deriving new closed-form expressions for the probability density function, cumulative distribution function and the moment generating function of the combiner output SNR statistic. Mathematical expressions for quantifying the degradation in the mean combined SNR, outage probability and the average symbol error rate of a broad class of binary and multilevel modulation schemes owing to imperfect branch SNR estimates in Rayleigh fading are also derived. The author then shows that combiner errors affect the mean combined SNR negligibly in comparison to their effect on the deep fades.

This is the first issue of the new journal Wireless Communications and Mobile Computing (WCMC) published by John Wiley & Sons, Ltd. WCMC promises to bring its readers outstanding published work that will enrich their horizons in their areas of expertise related to all aspects of wireless systems. It also promises authors speedy publication under the guidance of an international Advisory Board and Editorial Board drawn from academia and industry. The objective and strategy of WCMC is to carry high calibre survey papers, and a rapid publication time with a strong focus on new trends, developments, emerging technologies and industrial standards. We realize that we have a long way to go in order to attain these goals and objectives, but we have already made an excellent start. We are very proud of the highly qualified individuals who have joined our Advisory and Editorial Boards. They represent an international panel of well-known experts from around the world. In addition, we have instituted three region editors, under the leadership of a Senior Editor, in order to minimize workloads and encourage speedy manuscript processing. We have already reached a wide range of audiences around the world who have submitted and/or are in the process of submitting manuscripts to WCMC. We at WCMC are aware of the number of fine publications already in this field and the tight competition to attract the best manuscripts. Nevertheless, we feel that WCMC will target a different pool of contributions. The focus of WCMC will be towards running more special issues on hot topics that describe industry projects/standards, publishing at least one review/tutorial paper in each issue, and bridging the gap between wireless communications and mobile computing. This fresh approach to international publication will enlarge the pool of quality publications, rather than draw from the existing pool. WCMC's publication process will be fully electronic, administered by dedicated editors, which we firmly believe will contribute to a speedy review process and reduce the waiting time for authors. Finally, we would like to offer special thanks to all of those who have encouraged and contributed in the initial stages of this project, in particular the staff at John Wiley & Sons, Ltd. We also would like to thank those authors who have already sent their contributions to WCMC that you will read in this issue and in the next two issues. Furthermore, we welcome all authors and readers of WCMC and ask for their involvement and participation by submitting their manuscripts and welcome their personal subscriptions and that of their libraries. At the outset it was our priority to make sure both personal and institutional subscriptions to WCMC are affordable. For more details about the topics and how to make your electronic submission/subscription, we invite you to visit the WCMC web site at: www.interscience.wiley.com/jpages/1530-8669/ We also welcome any comments to any of the four editors listed below. Again, we welcome all of you to contribute and encourage your colleagues and libraries to subscribe to this fine publication.

This is the second issue of Wireless Communications and Mobile Computing (WCMC) published by John Wiley & Sons, Ltd. The first issue was published ahead of time and presented some excellent review papers from experts in the field. We received great feedback from a number of colleagues about its content and we promise to continue producing excellent issues in the future. If you did not receive your sample copy of the inaugural issue, please let me or any of the regional editors know to arrange to send you a copy. We have selected seven papers to appear in this second issue. These papers span a good range of topics in the area. The first is a review paper by Ralph and Aghvami. They discuss the Wireless Application Protocol (WAP) architecture and characteristics. Then, they investigate the devices that are supported by WAP as well as the drivers and services. These services include quality of service, security, billing, interoperability, and performance engineering. The variety of media types combined with the diversity of Internet connection characteristics raises momentous challenges to the achievement of ubiquitous access to Internet multimedia content. The second paper, by Margaritidis and Polyzos, addresses issues related to this topic. They identify the importance of adaptation to this category. Then, they examine several important factors that influence the design and optimization of the adaptation architecture. In conclusion, they describe current commercial solutions and future trends in application adaptation in conjunction with recent developments towards wireless access to the Internet. The third paper, by Havinga and Smit, discusses also wireless multimedia networking, but focuses more on energy efficiency. The authors identify three key problems and provide possible solutions to overcome them. The efficient management of power-controlled cellular wireless systems is necessary for their deployment success. Power control can be used as a platform for radio resource management and is important for channel fading as well as providing QoS to individual users. Xiao, Shroff and Chong review the developments of distributed power control and related resource management problems in cellular wireless systems. The discussion is concentrated on achieving high efficiency for a power-controlled system while preventing the system from failure. They then review power and rate control schemes proposed for wireless data, and present a framework for utility-based power control as a possible candidate for distributed power control of multimedia wireless systems. In the fifth paper, Arslan and Bottomley review commonly used approaches to channel estimation. This is necessary in adaptive receiver designs used in narrowband and wireless digital communication systems. They consider both time-invariant and time-varying channels in their discussion. They also provide a number of applications that will make use of this standard. On the other hand, Tepedelenliolu, Abdi, Giannakis and Kaveh talk about estimation of Doppler spread and signal strength in wireless systems. They concentrate on estimating the received signal strength, mobile velocity, and other related statistical channel parameters that apply to handoff and adaptive transmission. They provide comparison of the presented schemes based on their modeling and simulation results. Finally, Annamalai and Tellambura present a research paper discussing three new exponential-type bounds derived using the Cauchy–Schwarz bounding technique. They show that these new bounds are found to be tight and useful for a number of applications. They compare these bounds to other well-known exponential type bounds and prove that these perform at least comparable or tighter. We hope that you will enjoy reading this issue and welcome any comments to any of the four editors listed below. Again, we welcome all of you to contribute and encourage your colleagues and libraries to subscribe to this fine publication.

We present a study of the signal-to-interference-plus-noise ratio and throughput efficiency achievable in a multicellular environment in the presence of smart antennas at the base station receivers. Interference is explicitly taken into account, and different cases are compared. It is seen that the system operates most efficiently with full frequency reuse, even though this requires network-wide synchronism. The effect of lack of synchronization is also studied.

In this paper the behavior of the UTRA-FDD radio interface of UMTS, based on wideband CDMA technology, is investigated at different conditions of Doppler bandwidth, network load and power control threshold. To this aim, a general simulation setup has been developed, which includes a cellular environment with hexagonal layout and uniform user distribution, a propagation channel with path-loss, log-normal shadowing and Rayleigh fast fading, a transmission system with SIR-based closed loop power control, convolutional coding with interleaving and RAKE receiver. Among the simulation results, the SIR process, the frame error probability process and the energy efficiency of the transmitters are analyzed. It is shown that power control provides a good level of fairness across the system and, even though an increasing network load reduces the fraction of users fulfilling the performance requirements, the behavior of the satisfied users remains unchanged. Moreover, the robustness to network load improves as the fading changes faster.

The wireless local loop (WLL) is an emerging technology that allows rapid connection to the wired network from remote locations. A crucial issue for the WLL is the system design so that the number of subscribers can be maximized while providing the required quality of service. In this paper, we consider a broadband wireless local loop with fixed point users and we evaluate the subscriber capacity for a slotted CDMA scheme which accommodates integrated voice, data, and video traffic. The impact of the system parameters on the WLL performance is studied via simulation and provides a useful insight on how to efficiently design the network.

A general setup for simulating a WCDMA cellular system has been developed to estimate the performance, in terms of both the signal-to-interference ratio (SIR) and error burst statistic. Here, the analysis has been focused on closed loop power control in order to characterize the performance as a function of the control delay and the step size in different conditions. The simulator takes into account a hexagonal geometry with spatially uniform user distribution and a propagation channel characterized by distance attenuation, log-normal slow fading, and Rayleigh fast fading. The system is based on a wideband CDMA radio interface with RAKE receiver, convolutional coding with Viterbi decoding, and interleaving to counteract burst error occurrences. A relationship between the power control loop and the Doppler frequency trend is underlined in the numerical investigation. In particular, a power control delay increase could be related to an "equivalent" Doppler frequency increase. Furthermore, the sensitivity of the performance to the power control step size as a function of the Doppler frequency is shown.

We investigate the performance of TCP Tahoe and NewReno on wireless links with a FEC/ARQ protocol at the link layer. The effect of link layer (LL) parameters like the LL packet size, number of LL transmission attempts, and FEC code rate on the TCP throughput is evaluated. It is shown that the retransmissions at the link layer must persist long enough to outlast the average bad state duration of the Rayleigh fading process in order to achieve performance better than TCP without an ARQ at the link layer. It is further shown that with a suitable link layer in place, TCP Tahoe offers as high a throughput as offered by other enhanced versions like NewReno in the considered wireless environment.

In this paper, we study the performance of a simple retransmission-based error-control strategy for delay-constrained data communications over a bursty channel. Correlated errors are modeled as a two state Markov process. A retransmission algorithm is used to correct errors, and the probability that a packet is not successfully delivered within D slots of its arrival is computed. In the presence of a smoothing buffer at the receiver, this is the probability that the jitter experienced by a packet is too large to be absorbed by the buffer itself. The cases of zero and nonzero roundtrip delay are studied separately, as are the conditions of perfect and imperfect feedback. Our results relate the achievable quality of service and the amount of traffic that can be served to the packet-error process parameters, which in turn are induced by the physical layer specifications. This relationship between the traffic and the channel parameters can be useful in making admission control decisions and in assessing the effect of physical layer design on the performance of higher layer protocols.

In this paper, the bulk throughput performance of TCP NewReno over wireless fading links having memory is studied. Like TCP Tahoe, the NewReno version of TCP implements a fast retransmit procedure, but it uses a different congestion window adaptation algorithm. In this study, we show that, for the default parameters of the BSD implementation of TCP over a 1.5 Mbps wireless link having a very small bandwidth-delay product, and as long as sufficiently large advertised window sizes are used, the burstiness in packet errors caused by slow multipath fading (experienced by slow moving users) significantly benefits NewReno compared to i.i.d. packet errors (experienced at vehicular user speeds). We further show that, in such slow fading conditions, NewReno performs no better than Tahoe, mainly due to the high degree of correlation in the fading process.

When user terminals powered by a finite battery source are used for wireless communications, energy constraints are likely to influence the design/choice of media access protocols. We analyze the energy efficiency of a multichannel wireless access protocol using a finite energy source in a mobile radio environment. The average number of correctly transmitted packets for a given amount of allocated energy is used as the appropriate energy efficiency metric. The mobile radio channel itself is characterized by a correlated Rayleigh fading process, whose memory parameters depend on the speed of the user terminal, the data rate, and the physics of the channel. We show that the protocol which recovers erroneous data packets through retransmission is more energy efficient at low channel correlations.

Studies of the capacity of cellular systems, stated in terms of the admissible number of remote users, have generally been limited to voice telephony. We address the problem of comparing the interference-limited performance of CDMA and TDMA systems in a packet switched environment. The objective is to determine whether the capacity advantages claimed for circuit-switched CDMA still apply in a packet-switched environment, where the natural time diversity of bursty transmission may be a significant factor. Under a set of specific assumptions about the wireless environment (including path loss, shadow fading, multipath delay spread, co-channel interference, power control, coding), we evaluate the number of users which can be admitted to the system while maintaining some desired quality-of-service level. Four different classes of users with different characteristics and requirements are considered. The system capacity is found to significantly depend on the QoS objectives, which might be stated in terms of availability of some specified signal to interference level, packet loss rate, or mean tolerable delay. The main finding is that strict requirements imposed on the radio access level tend to favor CDMA, whereas if some form of packet recovery at the higher layers is allowed (implying a relaxed set of requirements on the radio interface), then a somewhat higher capacity may be achieved by TDMA.

There has been an emerging interest in using ATM for wireless transmissions. Because ATM is primarily designed for a rather benign error free environment, in the wireless context the sources of errors and their consequences must be thoroughly understood. While this concern is valid in any network, it takes on a new more central role in the mobile wireless environment, where error bursts are expected to be a very significant source of degradation. These concerns motivate this study of the impact of errors on ATM cell transfer in the wireless environment. In this paper, we examine the impact of burst errors on the direct transfer of ATM cells taking into account the cell framing format. We examine the consequences of interleaving as well as the impact of fading. We find that fragmenting the data in cells could result in a much higher error rate as seen by the higher layers relative to the bit error rate on the raw channel.

As mobile terminals are powered by a finite battery source, energy constraints play a major role in the design of wireless communications systems. In this letter, based on a general analytical framework, we study the energy efficiency of a class of multiple access schemes, using the average number of correctly transmitted packets for a given amount of allocated energy as an appropriate metric. We show that a good choice of the protocol rules can significantly improve the energy efficiency.

The throughput performance of a wireless media access protocol taking into account the effect of correlated channel fading, capture, and propagation delay is analyzed. For efficient access on the uplink (mobile-to-base-station link), the protocol makes use of the uplink channel status information which is conveyed to the mobiles through a busy/idle flag broadcast on the downlink (base-station-to-mobile link). A first-order Markov model is used to describe the correlation in the packet success/failure process on a Rayleigh-fading channel. The analytical results obtained through the first-order Markov approximation of the channel are compared to those obtained from an independent and identically distributed (i.i.d.) channel model. The Markovian-fading channel model is shown to provide better performance results than the i.i.d. channel model. Simulations show that a first-order Markov approximation of the Rayleigh-fading process is quite accurate. An enhanced version of the access protocol to take advantage of the memory in the fading channel behavior is proposed and analyzed. The effect of retransmission of erroneous data packets and propagation delay on the throughput is also analyzed. It is shown that the access protocol with an error detect (ED) feature is efficient in slow fading (e.g., pedestrian user speeds), whereas a retransmission protocol is more efficient in fast fading (e.g., vehicular user speeds).

In this paper the outage concept is discussed. Classic definitions, only based on the marginal statistics, are found not to be adequate for a proper characterization of the effect of channel impairments at the application level. A more flexible definition, which incorporates time parameters and appears suitable for the characterization of the performance of packet communications systems, is proposed. An analytical framework for the computation of various performance metrics is also presented. A Markov description is assumed for the channel, which allows closed-form solutions and can effectively model many situations of interest. Outage events are defined as being triggered by T/sub b/ consecutive channel errors, and being ended by T/sub g/ consecutive successes. A numerical example of application is given and extensions of the analysis to more general definitions are discussed.

We investigate the behavior of block errors which arise in data transmission on fading channels. Our approach takes into account the details of the specific coding/modulation scheme and tracks the fading process symbol by symbol. It is shown that a Markov approximation for the block error process (possibly degenerating into an identically distributed (i.i.d.) process for sufficiently fast fading) is a good model for a broad range of parameters. Also, it is observed that the relationship between the marginal error rate and the transition probability is largely insensitive to parameters such as block length, degree of forward error correction and modulation format, and depends essentially on an appropriately normalized version of the Doppler frequency. This relationship can therefore be computed in the simple case of a threshold model and then used more generally as an accurate approximation. This observation leads to a unified approach for the channel modeling, and to a simplified performance analysis of upper layer protocols.

The outage concept is discussed. Classic definitions, only based on the marginal statistics, are found not to be adequate for a proper characterization of the effect of channel impairments at the application level. A more flexible definition, which incorporates time parameters and appears suitable for the characterization of the performance of packet communications systems, is proposed. An analytical framework for the computation of various performance metrics is also presented. A Markov description is assumed for the channel, which allows closed-form solutions and can effectively model many situations of interest. Outage events are defined as being triggered by /spl tau//sub b/ consecutive channel errors, and being ended by /spl tau//sub g/ consecutive successes. A numerical example of application is given, and extensions of the analysis to more general definitions are discussed.

We consider the problem of communications over a wireless channel in support of data transmissions from the perspective of small portable devices that must rely on limited battery energy. We model the channel outages as statistically correlated errors. Classic ARQ strategies are found to lead to a considerable waste of energy, due to the large number of transmissions. The use of finite energy sources in the face of dependent channel errors leads to new protocol design criteria. As an example, a simple probing scheme, which slows down the transmission rate when the channel is impaired, is show? to be more energy efficient, with a slight loss in throughput. A modified scheme that yields slightly better performance but requires some additional complexity is also studied. Some references on the modeling of battery cells are discussed to highlight the fact that battery charge capacity is strongly influenced by the available "relaxation time" between current pulses. A formal approach that can track complex models for power sources, including dynamic charge recovery, is also developed.

We provide a general analytical framework for the computation of the interference statistics in mobile radio cellular systems. This approach, which takes into account log-normal shadowing, distance path loss, best cell site selection and power control based on signal strength, applies to various cellular environments, including narrowband systems (e.g., IS-54 and Group Special Mobiles GSM) and code-division multiple-access (CDMA) systems. Some examples of application are provided.

In the development of encoding algorithms for image, video, and other mixed media transmissions, it is important to note that the channel "seen" by the applications is the physical channel as modified by the error-correcting mechanisms used at the physical level. Therefore, the statistics of the residual error process is relevant to the design of encoding algorithms. In this paper, we study the second- and third-order statistics of the residual error process when block transmissions are performed over a bursty channel. The effect of interleaving is explicitly studied. The conditions under which a Markovian model for the block errors is adequate are identified. Derivations of the parameters of the block error process are then presented in terms of the parameters of the bit/symbol error process. At higher data speeds an effective interleaving strategy is found to require a very large buffer.

The paper compares the performance of two channel-access schemes suitable for the cellular environment which, in particular allow the packet capture and can deal with inter-cell interference. The first scheme is the well known S-ALOHA while the second one is the capture division packetized access (CDPA). The comparison is analytically performed over a common system with a common analytical model. Despite the many analyses that have appeared on S-ALOHA, the one we develop is new because a throughput density uniformly distributed on the plane is considered in a multiple cell environment. The analysis clearly shows the effect of intra-cell and inter-cell interference on the ALOHA system and proves that a superior throughput is achieved by CDPA, which completely avoids intra-cell interference. Our analysis also provides an insight into the effectiveness of power control on both systems.