Large models, renowned for superior performance, outperform smaller ones even without billion-parameter scales. While mobile network servers have ample computational resources to support larger models than client devices, privacy constraints prevent clients from directly sharing their raw data. Federated Learning (FL) enables decentralized clients to collaboratively train a shared model by exchanging model parameters instead of transmitting raw data. Yet, it requires a uniform model architecture and multiple communication rounds, which neglect resource heterogeneity, impose heavy computational demands on clients, and increase communication overhead. To address these challenges, we propose FedOL, to construct a larger and more comprehensive server model in one-shot settings (i.e., in a single communication round). Instead of model parameter sharing, FedOL employs knowledge distillation, where clients only exchange model prediction outputs on an unlabeled public dataset. This reduces communication overhead by transmitting compact predictions instead of full model weights and enables model customization by allowing heterogeneous model architectures. A key challenge in this setting is that client predictions may be biased due to skewed local data distributions, and the lack of ground-truth labels in the public dataset further complicates reliable learning. To mitigate these issues, FedOL introduces a specialized objective function that iteratively refines pseudo-labels and the server model, improving learning reliability. To complement this, FedOL incorporates a tailored pseudo-label generation and knowledge distillation strategy that effectively integrates diverse knowledge. Simulation results show that FedOL significantly outperforms existing baselines, offering a cost-effective solution for mobile networks where clients possess valuable private data but limited computational resources.

Native AI support is a key objective in the evolution of 6G networks, with Federated Learning (FL) emerging as a promising paradigm. FL allows decentralized clients to collaboratively train an AI model without directly sharing their data, preserving privacy. Clients train local models on private data and share model updates, which a central server aggregates to refine the global model and redistribute it for the next iteration. However, client data heterogeneity slows convergence and reduces model accuracy, and frequent client participation imposes communication and computational burdens. To address these challenges, we propose FedABC, an innovative client selection algorithm designed to take a long-term view in managing data heterogeneity and optimizing client participation. Inspired by attention mechanisms, FedABC prioritizes informative clients by evaluating both model similarity and each model's unique contributions to the global model. Moreover, considering the evolving demands of the global model, we formulate an optimization problem to guide FedABC throughout the training process. Following the “later-is-better” principle, FedABC adaptively adjusts the client selection threshold, encouraging greater participation in later training stages. Extensive simulations on CIFAR-10 demonstrate that FedABC significantly outperforms existing approaches in model accuracy and client participation efficiency, achieving comparable performance with 32% fewer clients than the classical FL algorithm FedAvg, and 3.5% higher accuracy with 2% fewer clients than the state-of-the-art. This work marks a step toward deploying FL in heterogeneous, resource-constrained environments, thereby supporting native AI capabilities in 6G networks.

The Age of Information (AoI) has emerged as a critical metric for quantifying information freshness; however, its interplay with channel estimation in partially observable wireless systems remains underexplored. This work considers a transmitter-receiver pair communicating over an unreliable channel with time-varying reliability levels. The transmitter observes the instantaneous link reliability through a channel state information acquisition procedure, during which the data transmission is interrupted. This leads to a fundamental trade-off between utilizing limited network resources for either data transmission or channel state information acquisition to combat the channel aging effect. Assuming the wireless channel is modeled as a finite-state Markovian channel, we formulate an optimization problem as a partially observable Markov decision process (POMDP), obtain the optimal policy through the relative value iteration algorithm, and demonstrate the efficiency of our solution through simulations. To the best of our knowledge, this is the first work to aim for an optimal scheduling policy for data transmissions while considering the effect of channel state information aging.

Achieving flexible and efficient wireless resource sharing across diverse applications and services is among the key goals of the sixth-generation of mobile systems (6G). This work investigates the performance of a real-time system coexisting with a broadband service in a frame-based wireless channel. Specifically, we consider a remote tracking device that monitors an information source and transmits updates to a base station (BS) for real-time source reconstruction, and potential remote actuation. We revise the common idealized assumptions in real-time remote tracking studies, such as instantaneous feedback and pervasive wireless resources, as they do not hold in practical scenarios. We consider a monitoring device and a broadband user communicating with the BS via a grant-free access mechanism over wireless resources defined for either orthogonal or non-orthogonal access, with feedback scheduled at the end of each frame. We analyze system performance using goal-oriented performance metrics for real-time remote reconstruction, alongside throughput and energy efficiency for the broadband user. Our results show that the ‘Idealistic’ model considered in conventional studies achieves better performance but incurs disproportionately high overhead compared to the Frame-Based model. Moreover, within the Frame-Based model, orthogonal resource sharing is preferable for maximizing broadband throughput, while non-orthogonal sharing significantly improves energy efficiency.

6G mobile communication networks are envisioned to be AI-native, that is, the provision of AI services to users as well as the network itself would be one of the most essential aspect for its system architecture and design. To provide these services and in order to make correct design decisions, deep understanding of the relationship between the wireless networks and the different AI paradigms along with their various functional aspects is needed. In this paper, we explore the relationship in one such AI paradigm, namely federated learning (FL). More specifically, we aim to look at the problem of hyper-parameter optimization (HPO) for hierarchical federated learning by exploring two design scenarios: (1) HPO is carried out at intermediary aggregators which are implemented at edge servers and (2) HPO is realized by a global server such as a cloud-based approach. Empirical results show that although offloading the HPO algorithm completely to the edge-servers does lower the costs, the hybridized approach whereby the global server realizes the HPO algorithm and the edge-server offers some assistance, can provide at least twice as much savings on training costs. We further discuss the two approaches for providing FL as a native service from the mobile communication network and the implications on the network architecture in light of the discussed design scenarios.

With the advent of the multimodal immersive communication system, people can interact with each other using multiple devices for sensing, communication and/or application level control either onsite or remotely. As a breakthrough concept, a distributed sensing, computing, communications, and control (DS3C) fabric is introduced in this paper for provisioning 6G services in multi-tenant environments in a unified manner. The DS3C fabric can be further enhanced by natively incorporating intelligent algorithms for network automation and managing networking, computing, and sensing resources efficiently to serve vertical use cases with extreme and/or conflicting requirements. As such, the paper proposes a novel end-to-end 6G system architecture with enhanced intelligence spanning across different network, computing, and business domains, identifies vertical use cases and presents an overview of the relevant standardisation and pre-standardisation landscape.

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the co-ordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.

6G mobile communication system is expected to be intelligence inclusive, that is, its system architecture would be designed to provide AI services to anyone, anywhere, including the network system itself. This mandates a clean-slate approach to its design. In this paper, this broad question is addressed from the perspective of providing distributed learning services, more specifically, using the Federated Learning (FL) paradigm. The relationship between two network metrics and the FL performance in a hierarchical federated learning system is explored, in order to determine the new features that the network architecture needs to support. An estimation of the dependency of bandwidth requirements on different ML models is also provided.

AI/ML is envisioned to play an essential role in 6G mobile communication systems. The privacy-preserving capabil-ities of Federated Learning (FL) make it promising in vertical applications; however, the central server-based system and lack of trusted data management limit its widespread use. To effectively support FL as a service from a network architecture perspective, this work provides a comprehensive design including three key features: First, the network architecture enables transparent and traceable data management based on Distributed Ledger Technology (DLT) platform, and realizes distributed and off-chain data storage by adopting Distributed Data Storage Entity (DDSE). Second, the central aggregator of an FL service is decoupled from the data management scheme mentioned above, and is decentralized through smart contracts for aggregator selection among a set of aggregator candidates, with the selected aggregator subsequently responsible for client selection and model aggregation. Third, a completed set of procedures for FL services operations is defined. A simulation system is developed to verify the feasibility of the proposed architecture and to study the impact of introducing the data management mechanisms on the overall performance overhead. The results show that the impact is related to the FL settings, with a worst-case time overhead of 15% observed in selected test cases, i.e., 15% of the total time spent on the interactions with the DLT platform and DDSE.

Wind speed interval prediction plays an important role in wind power generation. In this article, a new interval construction model based on error prediction is proposed. The variational mode decomposition is used to decompose the complex wind speed time series into simplified modes. Two types of GRU models are built for wind speed prediction and error prediction. Prediction error for each mode is given a weight and accumulated to obtain the width of the prediction interval. The particle swarm optimization algorithm is applied to search for the optimal weights of the prediction errors. Experiments considering eight cases from two wind fields are conducted by using methods of interval construction in the literature for comparison with the proposed model. The result shows that the proposed model can obtain prediction intervals with higher quality.

Within the ongoing activities devoted to the definition of 5G networks, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet QoS constrained, mIoT services will require adhoc end- to-end (E2E) solutions, i.e., featuring access and core network enhanced Control and User planes (CP/UP) mechanisms. This paper presents and evaluates a novel connectivity solution to manage massive number of devices. The paper presents an analytical model developed to evaluate the performance of the proposed solution. Quantitative results derived from the model demonstrate the effectiveness of the solution proposed in this paper, compared to 4G systems, and its ability to reduce CP signaling and optimize UP resource utilization for massive device deployment.

This paper reports a study on the characterization of in-home Internet activity behavior based on application usage logs. We collected online activity data from 86 Belgium households for 60 days. We analyzed the activity traces to gain insights on the temporal traffic distribution, interaction regularity, and activity correlations. This analysis is then used to develop a generic method to segment households into designated groups showing similar behavioral profiles. Our technique combines interaction frequencies and regularities across activities for segmentation, and is able to reveal interesting time-slotted profile for each segment. These profiles aim to show the strength of routine behaviors in Internet usage, based on which we present a novel algorithm to predict future Internet activities of a household. Our algorithm shows that 60% of the households online activities can be predicted accurately 70% of times.

Software Defined Networking (SDN) is considered one of the major driving factors to bring radical changes on Mobile Core Networks (MCN) design. There are many proposals on how to advocate SDN methodology, however, most of them stop at “vision” level without providing details about key performance contributors. In this paper, we tackle this gap and present a study of latency in SDN based MCN. We identify the major factors and elements in an SDN MCN that contribute to the overall latency. Two types of latency are considered: the processing delay inside the SDN control plane and the transmission delay of SDN control messages between controller and the managed switches. We use a realistic system setup with implementations of SDN controller and SDN application, as well as in-band transfer of control messages among switches and controller. We compare the latency obtained from SDN based MCN with the Evolved Packet Core (EPC). The observed overall latency in our experiments for SDN based MCN is within the EPC requirements. The direct comparison of the SDN versus EPC based MCN in the proposed scenarios show the first can reduce the overall latency. We also identified that the calls to controller APIs can be the major key contributors to the overall latency in SDN MCN, but the act of transmitting more control messages from the controller to the switches to setup longer flowpaths is not a key contributor for the overall latency.

Telco operators started to apply the SDN technologies also in the design of Mobile Core Networks (MCNs). In this change towards SDNized Mobile Core Network, it is crucial to understand how conventional interfaces among different mobile network entities should evolve. The issues derived from this change have not been tackled by current research approaches. This paper presents the first initiative to close this research gap. We tackle the key problem of how to identify which mobile SDN applications (APPs) should be invoked once a PACKET_IN (the OpenFlow message that transport information from the data plane to the control plane) is received at the control level. We propose data structures, a model, and detailed examples of three important PACKET_IN context interpretation for MCNs. Initial experiments, based on Floodlight controller and Mininet emulation environment were carried out. The results indicate that it is feasible to use our proposed approach to dispatch PACKET_INs to the right SDN APP. The delay introduced due to invocation of such mechanism to interpret the context of the PACKET_IN and activate the appropriate mobile SDN APPs is only in the order of microseconds. Our proposal can be used to simplify current Mobile Core Network interface design by exploiting the SDN mechanisms. We believe, this work helps to pave the way towards fully SDNized Mobile Core Networks.

Industrial and academic focus in recent years has been shifting towards the Fifth Generation Mobile Networks (5G). In the horizon 2020, the key improvements will rely on technical breakthroughs in the radio access networks, mainly to bring increased bandwidth to the user devices. However, there is still much discussion ongoing about the evolution required in the core network to accommodate increased demands from existing services (e.g. in the number of devices) and services which are not feasible or efficiently implemented with today's architecture. Disruptive approaches can even be considered if the horizon for such changes is extended to 2025. Due to the advance of virtualization and SDN technologies, it is very promising that the current mobile core network could gain more flexibility, support multiple radio access network generations in parallel, and meanwhile enrich its ecosystem to generate new revenue opportunities. In this paper, we would like to discuss the feasibility to design a flexible and adaptive mobile core network based on functional decomposition and network slicing concepts. This paper will provide a guideline and enlighten strategic directions for future mobile core network research.

Network functions virtualization is one of the most promising networking technology trends for mobile operators. Such functions often are considered as virtualized version of physical devices. They follow the same implementation, deployment, and operation and management model as such physical entities. However, some key characteristics of the underlying cloud technologies, such as the elastic provisioning of resources, the different hardware failure rates and the increasing programmability of the underlying network enable new design paradigms. Thus, virtual network functions should leverage on IT technologies like Software-as-a-Service (SaaS) to reduce operational costs and enable new business models. We introduce a classification of different design paradigms, detail how a SaaS implementation can be performed for mobile network gateways, and provide a comparison of the operation cost implications of employing such paradigms to create network nodes in a mobile network.

The gateways in mobile cellular networks are capable to handle the traffic from millions of subscribers at the same time, while providing high availability guarantee. Such entities are not only expensive, but also inflexible in terms of system deployment and maintenance. In this work, we investigate the feasibilities to virtualize gateway entities used in the Long Term Evolution (LTE) Evolved Packet Core (EPC). We propose a novel PDN Gateway (PGW) architecture, which decouples the control plane from the user plane by using Software Defined Network (SDN) technology. Moreover, we propose to run the user plane forwarding function on low cost IT hardware. The major advantages of our proposal are two-fold. First, our proposal lowers down the expenditure of operating services in LTE networks. Second, fine-grained resource management policies can be implemented in our virtualized gateway, which brings agility and reliability for the mobile cellular network.

Exploring the semantic contents of mobile traffic has significant meaning for Telco operators to gain a better understanding of the traffic generated by their subscribers. The mobile traffic dynamics do not only provide guidance for operators in terms of network planning and resource management, they also help operators to innovate new business models by providing value-added services. The mobile traffic dynamics are not static features. They are developing with the evolution of radio access technologies, subscribers' adoption of mobile services, etc. In this paper, we analyze the mobile traffic from multiple dimensions based on a large-scale dataset collected by a major European mobile operator in 2012. Within this work, we investigate the traffic dynamics and the application features used by different mobile devices. Moreover, for the first time, we also present a methodology about how to link these metrics to the operating system of mobile devices. We observed a noticeable impact of the operating system on some of the evaluated features.

To minimize cost, Virtual Network Operators (VNOs) need to consider the required network availability already at the network design stage. One generic approach to reach the availability target is to select only high-quality physical network elements that offer high availability and consequently demand high expenses per element. The other generic approach to achieve high availability is to add protection capacity on the level of the virtual network based on lower cost components. In this paper, we analyze both alternatives with a simulation tool to answer the fundamental question how quality can be traded against capacity. For this purpose, we consider different network topologies and the influence of different parameters and provide a framework to find an optimal strategy between Mean Time Between Failures (MTBF) targets for the physical infrastructure and the usage of additional backup paths on the virtual network level.

Even though 60 GHz frequency band has limited coverage, due to the availability of higher bandwidth worldwide (unlicensed, approximately 5 GHz between 57–62 GHz), it is one of the promising candidates for future broadband employing both Fiber and Wireless (Fi-Wi) technology. In this article we briefly explain an in-home radio over fiber (RoF) architecture employing 60 GHz spectrum band. The main challenge for designers of such networks is the additional delay introduced by the optical distribution network. Later in the light of this increased delay, we provide an investigation on the feasibility of applying the IEEE 802.15.3c MAC protocol in such a RoF system at 60 GHz. Mainly, we analyse various ACK mechanisms in the IEEE 802.15.3c when it is used in conjunction with RoF network. The analytical results show that the fiber optic network causes a slight drop in the throughput but it could still be easily used in home networks. This gives us an opportunity to sustain high bandwidth at low cost for home networking, also supporting mobility. Modelling of a RoF system with IEEE 802.15.3c and numerical results thereof are the contributions of this article.

Network redundancy and protection have become an even more serious issue after the last tsunami in Japan. Future networks need to be planned and operated keeping such unprecedented failures in mind. In this regard, we consider Virtual Network Embedding (VNE) techniques as an useful tool to realize an optimization between redundancy and resource consumption. However, conventional VNEs mostly concentrate on single network embedding. As VNE is a computationally intractable problem, relaxation methods are often used to offer a workable polynomial time solution. Some of such relaxation methods like flow-splitting do not fit well in real network operation. In this paper, we propose a heuristic VNE algorithm for network site protection without flow splitting. We also provide a solution with joint site and link embedding to achieve better bandwidth consumption. Evaluation results show that our VNE algorithm performs close to theoretical thresholds and consumes less link resources in delivering a VNE solution in polynomial time compared to the conventional algorithm.

Network reliability and survivability are important features for hosting high-demanding services in virtual network environments. Since multiple virtual networks can share the physical resources of the underlying substrate, even a single failure in the substrate can affect a large number of virtual networks and the services they offer. To overcome the physical link failure impact on virtual network operations, backup path mechanisms are investigated in many related works for path protection. However, this one to one path protection cannot handle explicit path availability requirements. In this work, we present a virtual network embedding algorithm which provides path protection with explicit virtual link availability constraints. Evaluation results show that our algorithm performs close to theoretical thresholds and consumes less link resources when delivering a virtual network embedding solution in polynomial time compared to a conventional path backup algorithm.

The study of human activity patterns traditionally relies on the continuous tracking of user location. We approach the problem of activity pattern discovery from a new perspective which is rapidly gaining attention. Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their location and the venues they visit. In this paper, we present automated techniques for filtering, aggregating, and processing combined social networking traces with the goal of extracting descriptions of regularly-occurring user activities, which we refer to as “user routines”. We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful venue information about the locations we observe. We analyze and combine the two datasets to highlight their properties and show how the emergent features can enhance our understanding of users' daily schedule. Finally, we evaluate and discuss the potential of routine descriptions for predicting future user activity and location.

Abstract The newer context‐aware applications require many inputs and amongst them the location information is one of the most important. In the near future, we see the potential in using wireless sensors deployed inside buildings to support in generating the location information besides their other routine tasks. This paper records the efforts involved in designing and prototyping a centralized indoor positioning system for tracking a target's position. We present an in‐depth discussion on the RSSI based positioning algorithms both, range‐based and range‐free. Considering that the signal strength can be distorted heavily due to multi‐path and shadowing, we have proposed the weighing schemes to leverage the credibility of the measured RSSI values. For online tracking, we have also proposed boundary selection and local grid scanning to lower the searching time, and the RSSI data dissemination and collection schemes to reduce traffic overhead. Evaluations have been done in both the field measurements and with an RSSI generator. The generator has been developed to simulate and replace the real measurements for the ease of algorithm design and testing, thus avoiding repetitive field measurements. The results show that positioning systems with adequate accuracy can be built with our proposed schemes. We expect that these proposed schemes to be integrated in multitude of systems with context‐aware applications, which use location information. Copyright © 2010 John Wiley & Sons, Ltd.

With the convergence between phone and data networks in LTE and 4G, cellular signaling traffic is increasingly carried over IP. Control plane functions, once performed by dedicated machinery, are evolving into large-scale network applications with strict requirements on delay, availability, and processing throughput as mandated by 3GPP standards. In this paper we present DMME, a distributed architecture that implements mobility management for next-generation cellular systems. DMME is a scalable and cost-effective drop-in replacement for the LTE mobility management entity (MME). We evaluate the DMME scheme via analysis and simulation under several deployment scenarios, using mobility patterns drawn from synthetic models and from traces collected in a production network. We also report preliminary performance figures by our DMME prototype implementation. Our results confirm that distributed architectures are a viable choice to reliably support high-throughput, latency-sensitive control plane functions.

60 GHz radio is a very attractive technology for short-range wireless communication due to its capability to provide Gbps data rate. To address the neighbor discovery (ND) problem in 60 GHz networks, we propose a novel analytical framework to investigate the ND performance. The main difficulty in modeling the ND process in a 60 GHz network is the involvement of the directional antennas with gain differences between the antenna's main lobe and side lobes. Different antenna modes - directional or omni-directional - coupled with different ND mechanisms make the analytical study demanding. In this article, we propose a comprehensive theoretical model to demonstrate the performance of ND processes using one-way ND and handshake-based ND mechanisms. Moreover, we combine them with different antenna modes, i.e., directional transmitting with omni-directional listening (DO) and directional transmitting with directional listening (DD) modes. The impact of antenna modes on the ND process is analyzed. Since 60 GHz radio is prone to co-channel interference, we examine a realistic interference-aware link model and antenna pattern via simulation studies. Our work is specifically beneficial to provide guidelines for applying directional neighbor discovery process within 60 GHz wireless networks.

Wireless personal area networks (WPANs) usually have very limited coverage. In this paper, we focus on methods to extend this range through multi-hop communication crossing several inter-connected WPANs called piconets. Multi-hop communication between piconets is possible through the bridge devices. While extending the range of WPANs, bridge devices become the capacity bottlenecks for these connected piconets. Therefore, an unoptimized resource allocation mechanism involving the bridges could degrade system performance severely. In this work we propose a novel resource allocation mechanism that outperforms other schemes proposed so far. Moreover, our scheme is QoS aware due to its cross-layer design. In our proposed scheme, resource allocation cooperates with the route discovery process to mitigate collisions due to inter-piconet resource reservations. Simulation results show that our approach significantly increases network capacity. This work is expected to pave the way for QoS support in WPANs.

Millimeter wave (mmWave) wireless personal area networks (WPANs) could provide multi-Gbps based data rate for short range communications. However, this ultra high data rate is sensitive to the co-channel interference (CCI). In this paper we use synchronization frame (sync frame) to mitigate possible interference between WPANs. Sync frames could be simply considered as the copies of beacons, which are relayed via the devices associated with the piconet. By using sync frames, the possible CCI could be reduced by increasing per WPAN coverage range, but meanwhile, the spatial reuse capability is also reduced. Therefore, in this work, we are motivated to investigate the optimized beacon/sync frame range at different link distances and its effect to throughput. Simulation results in 60 GHz system for a 2.5 m link indicate that, sync frames can double the guaranteed throughput. For guaranteed throughput of 1.4 Mbps at a 3.5 m link, beacon range can be 35\% reduced. Another contribution of the paper is investigating the effect of CCI in 60 GHz systems with spreading 2 and 4. Our results indicate a limited increase in robustness at high CCI situation compared to non spreading cases.

60 GHz radio is a promising technology which can handle the data rate of the order of gigabits-per-second. Hence, it is an attractive candidate for wireless personal area networks (WPANs). Due to the high path loss, directional antennas are recommended for use in 60 GHz systems. To set-up directional communications, it is essential to obtain the knowledge of direction of the surrounding devices. However, a neighbor discovery process using directional antennas is nontrivial. In this paper, we provide a comprehensive investigation of using directional antenna for neighbor discovery process. We demonstrate the performance of neighbor discovery using different antenna modes combining different neighbor discovery mechanisms. Our results may aid in designing directional antennas for 60 GHz WPANs.

In this paper, we propose a new resource allocation scheme for wireless personal area networks (WPANs). The novelty of this scheme is in its capability to combine, in a dynamic way, QoS with fairness in resource allocation. This makes our scheme a suitable solution for supporting heterogeneous services in WPANs. In order to be QoS-aware we make use of preassigned service priorities. Further, in order to add fairness, our scheme is capable of assigning higher medium access priorities to the backlogged traffic flows. Using OPNET, extensive simulations have been done to compare the performance of the proposed policy with two QoS-unaware and unfair scheduling algorithms: First-come-first-served (FCFS) and directional transmission scheduling (DTS). Simulation results indicate that our proposal can substantially improve system performance in terms of the delay-based fairness and job failure ratio.

Gossiping is a probabilistic approach to facilitate routing and information dissemination in ad hoc and sensor networks. In this paper, we provide a comprehensive evaluation of gossip-based protocols using directional antennas. We evaluate different aspects of the directional gossiping protocols: directional gossiping mechanism, network graph type, transceiver antenna mode, et cetera. For instance, we analyze the gossiping performance by using different antenna modes: directional-transmitting with omnidirectional-receiving (D-O) and directional-transmitting with directional-receiving (D-D). We show the appropriate antenna beamwidth range for using different antenna modes. Moreover, we also discuss and compare several directional gossiping extension algorithms. Our work provides novel and valuable insights for using directional antennas in ad hoc and sensor networks.

To achieve ultra high data rate communications in 60 GHz Wireless Personal Area Networks (WPANs), frame aggregation is devised in IEEE 802.15.3c to increase the MAC transmission efficiency. In this work, we propose a novel statistical model to analyze the behavior of two types of frame aggregation mechanisms: standard frame aggregation and low latency frame aggregation. Through our model, we could fully understand the performance of the frame aggregation mechanisms in terms of transmission delay and system capacity. The accuracy of our model is validated by extensive simulations. Our work provides valuable insights for ultra high performance transmission using 60 GHz radio.

In this paper, we propose a solution to resolve link blockage problem in 60 GHz WPANs. Line-of-Sight (LOS) link is easily blocked by a moving person, which is concerned as one of the severe problems in 60 GHz systems. Beamforming is a feasible technique to resolve link blockage by switching the beam path from LOS link to a Non-LOS (NLOS) link. We propose and evaluate two kinds of Beam Switching (BS) mechanisms: instant decision based BS and environment learning based BS. We examine these mechanisms in a typical indoor WPAN scenario. Extensive simulations have been carried out, and our results reveal that combining angle-of-arrival with the received signal to noise ratio could make better decision for beam switching. Our work provides valuable observations for beam switching during point-to-point communication using 60 GHz radio.

This paper investigates the interference mitigation capability of a millimeter-wave wireless personal area network (WPAN) in a realistic 60 GHz channel. The interference mitigation mechanism employs farther-reaching beacon signals and device-relayed synchronization command frames (sync frames) to further extend the protection range of the victim (a.k.a. primary) network. Firstly, it is found that the sync frame is capable of extending the 'interference-free' region as a function of network-controller-to-device distance and the location of the interferer. Secondly, it is found that in the interference-free region extended by the beacon/sync frame, the probability of interference is near zero. Finally, interference, when taking place, impacts high speed (3~5 Gbps) links 5~8 dB more significantly than their low speed (1~3 Gbps) counterparts.

The IEEE 802.15.3 standard defines a widely-accepted MAC layer protocol for high data rate WPANs. However, resource allocation schemes are not specified in the IEEE 802.15.3 MAC. In this paper, an enhanced IEEE 802.15.3 MAC is proposed for 60 GHz based WPANs. The enhancement relies on a novel resource management scheme. By exploiting the advanced features of the 60 GHz technology, e.g. the usage of direction antennas, our resource management scheme could realize spatial reuse TDMA in 60 GHz based WPANs. We have implemented our protocol in OPNET. Based on this simulation platform we have evaluated the advantages of using our additions to the IEEE 802.15.3 MAC. We show that our enhanced MAC protocol significantly improves the throughput and delay characteristics in 60 GHz based WPANs.

Recently, up to 7 GHz license-free spectrum around 60 GHz has been allocated worldwide for high data rate wireless communications. This enables the deployment of WPANs at 60 GHz for short-range multimedia applications up to gigabits per second. In this paper we propose a new scheme to increase the efficiency of MAC layer protocol for WPANs at 60 GHz when directional antennas are used. Our scheme is based on an adaptation of the current IEEE 15.3 standard MAC protocol for WPANs on two aspects. Firstly, we propose a rate-adaptation based scheme to coordinate the directional and omni-directional transmissions in WPANs. Secondly, we propose a novel channel time allocation algorithm, which enables spatial reuse TDMA. The analytical results reveal that our algorithm significantly increases the system capacity.