Sensor-aided wireless edge applications, such as LiDAR-based beam prediction for millimeter-wave communications, demand intelligent on-device processing of high-volume sensor data. However, the computational cost of machine learning models often exceeds the tight power and resource constraints of edge hardware. While quantized neural networks (QNNs) reduce resource requirements, typical FPGA accelerators still rely on power-hungry digital signal processing (DSP) slices and incur avoidable data-movement overheads. To bridge this gap, we propose ArchE-Q, a dataflow accelerator for QNNs combined with efficient data preprocessing. Our design is fundamentally multiplier-less, utilizing: (1) an application-specific first-layer kernel that exploits binarized sensor inputs to remove multipliers; (2) the eXtended Vector Activation Unit (XVAU), fusing convolution, activation, and pooling to reduce buffering and data transfers; and (3) memory-centric buffering for efficient data reuse. Implemented on a Xilinx ZCU104 FPGA, ArchE-Q achieves 13.3% lower latency and up to 28% lower dynamic power than the FINN-R baseline, while eliminating DSP usage.

Millimeter-wave (mm-wave) and sub-terahertz communication are cornerstones for 6G networks, but are hindered by their reliance on highly directional beams and the high latency of conventional beam alignment and beam management procedures. Sensor- and machine learning (ML)-based beam prediction offers a low-latency alternative, but remains constrained by hardware limitations at the wireless edge. This paper presents a systematic, hardware-aware analysis of ML models for sensor-aided beam prediction, specifically focusing on LiDAR, spanning deep learning, classical ML, and a novel class of proposed hybrid models that combine a CNN-based feature extractor with lightweight classical classifiers. Using the public DeepSense 6G dataset, models are benchmarked on accuracy, inference latency, memory footprint, and computational complexity. Our results show that classical ML models, particularly linear and tree-based methods, deliver strong accuracy-efficiency trade-offs, achieving up to 95× lower inference latency and 67× smaller memory footprint than CNNs with minimal accuracy loss. The proposed hybrid models further enhance this trade-off, maintaining CNN-level top-5 accuracy while drastically reducing re-training time by up to 20× and enabling fast, environment-specific adaptation. These findings establish classical and hybrid models, supported by efficient preprocessing, as promising candidates for real-time, resource-efficient beam prediction in future 6G edge systems.

Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel, non-RF sensor inputs and environment awareness can be leveraged to greatly enhance beam management decisions, e.g. via machine learning (ML) techniques. However, the current literature lacks open platforms to gather datasets for the training of such ML techniques and to evaluate novel beam management approaches in real-time, real-world scenarios and full-stack endto-end networks. In this work, we present our SDR-based experimental platform based on OpenAirInterface and are the first to integrate popular low-cost antenna array transceivers, beam sweeping capabilities, and a highly-modular sensor framework and associated interfaces into such a full-stack experimental platform. This enables beam management experimentation in real-world, real-time scenarios and facilitates gathering datasets necessary for developing ML-based beam management protocols that incorporate environment awareness via sensor modalities.

Recent advances in distributed intelligence have driven impressive progress across a diverse range of applications, from industrial automation to autonomous transportation. Nevertheless, deploying distributed learning services over wireless networks poses numerous challenges. These arise from inherent uncertainties in wireless environments (e.g., random channel fluctuations), limited resources (e.g., bandwidth and transmit power), and the presence of coexisting services on the network. In this paper, we investigate a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network. Utilizing device selection, we aim to minimize the convergence time of distributed learning while simultaneously fulfilling the requirements of the URLLC service. We formulate this problem as a Markov decision process and address it via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm that determines each device’s participation decision through distinct branches in the actor’s neural network. We evaluate our solution with a realistic simulator that is compliant with 3GPP standards for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delays of the distributed learning service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all wireless resources.

In many wireless communication applications, it is desirable to transmit the same data to multiple user equipments (UEs). Physical layer multicasting presents an efficient transmission topology to exploit the beamforming capabilities at the transmitting nodes and broadcast nature of the wireless channel to satisfy the demand for the same content from several UEs. An advantage of multicasting is to avoid unnecessary co-channel interference between UEs requesting the same data. The difficulty is to find the suitable beamforming configuration that guarantees an acceptable minimum data rate, among the receiving UE group, to the multicast transmission. This paper addresses the max-min fair multigroup multicast optimization problem and proposes a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-optimal rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network. The proposed optimization procedure significantly improves computational complexity and spectral efficiency compared to common methods that use SDR followed by some randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The importance of the proposed procedure is that it is applicable to any SDR problem where a low-rank solution is desirable. Further, we propose a low-complexity algorithm that achieves 87% of the optimal rank-1 solution at orders-of-magnitude lower computational time.

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system’s realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a time-slot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

Integrated sensing and communication (ISAC) will be a key new feature in the next-generation wireless networks, with IEEE 802.11bf extending Wi-Fi capabilities to support applications like object localization and activity recognition. However, coexistence with legacy Wi-Fi in densely populated networks poses challenges, as contention for channels can impair both sensing and communication quality. This paper develops an analytical framework and a system-level simulation in ns-3 to evaluate the coexistence of IEEE 802.11bf and legacy 802.11ax in terms of sensing delay and communication throughput. We provide the first coexistence analysis between IEEE 802.11bf and IEEE 802.11ax, supported by link-level simulation in ns-3 to assess impacts on sensing delay and network performance. Our findings reveal key trade-offs between sensing intervals and throughput and the need for balanced sensing parameters to ensure effective coexistence in Wi-Fi networks.

In this paper, we address the problem of scheduling sensing and communication functionality in an integrated sensing and communication (ISAC) enabled base station (BS) operating in an indoor factory (InF) environment. The BS is performing the task of detecting an AGV while managing downlink transmission of ultra-reliable low-latency communication (URLLC) data in a time-sharing manner. Scheduling fixed time slots for both sensing and communication is inefficient for the InF environment, as the instantaneous environmental changes necessitate a higher frequency of sensing operations to accurately detect the AGV. To address this issue, we propose an environment-aware scheduling scheme, in which we first formulate an optimization problem to maximize the probability of detection of AGV while considering the survival time constraint of URLLC data. Subsequently, utilizing the Nash bargaining theory, we propose an adaptive time-sharing scheme that assigns sensing duration in accordance with the environmental clutter density and distributes time to URLLC depending on the incoming traffic rate. Using our own Python-based discrete-event link-level simulator, we demonstrate the effectiveness of our proposed scheme over the baseline scheme in terms of probability of detection and downlink latency.

Millimeter-wave (mm-wave) communications require beamforming and consequent precise beam alignment between the gNodeB (gNB) and the user equipment (UE) to overcome high propagation losses. This beam alignment needs to be constantly updated for different UE locations based on beamsweeping radio frequency measurements, leading to significant beam management overhead. One potential solution involves using machine learning (ML) beam prediction algorithms that leverage UE position information to select the serving beam without the overhead of beam sweeping. However, the highly site-specific nature of mm-wave propagation means that ML models require training from scratch for each scenario, which is inefficient in practice. In this paper, we propose a robust cross-environment transfer learning solution for location-aided beam prediction, whereby the ML model trained on a reference gNB is transferred to a target gNB by fine-tuning with a limited dataset. Extensive simulation results based on ray-tracing in two urban environments show the effectiveness of our solution for both inter- and intra-city model transfer. Our results show that by training the model on a reference gNB and transferring the model by fine-tuning with only 5 % of the target gNB dataset, we can achieve 80 % accuracy in predicting the best beam for the target gNB. Importantly, our approach improves the poor generalization accuracy of transferring the model to new environments without fine-tuning by around 75 percentage points. This demonstrates that transfer learning enables high prediction accuracy while reducing the computational and training dataset collection burden of ML-based beam prediction, making it practical for 5G-and-beyond deployments.

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

Communication at millimeter-wave (mm-wave) enables unprecedented data rates for 5G-and-beyond cellular networks but relies on precise beam alignment to overcome the challenging propagation characteristics at higher frequencies. Leveraging additional sensor-aided context information for beam management, such as location data, has been proposed to reduce the search space of feasible directional beam-pair-links and reduce signaling overhead. So far, evaluation of such location-aided schemes has largely been based on simplified and limited-scope simulation scenarios, without accounting for the complexity and performance implications in real end-to-end cellular networks. In this work, we present REMLAB, a first-of-its-kind framework for REM-based location-aided beam management in the full-stack 5G-NR-compliant network simulator ns-3 and make our code publicly available. Our framework provides access to network performance metrics and parameters that can only be obtained in realistic end-to-end network simulation, such as the signaling overhead required for beam management. We evaluate REMLAB in an example urban mobility scenario and show an improvement in average IP throughput of at least 24% and up to 46% over default 5G-NR by eliminating the need for periodic control signaling. Furthermore, towards the evaluation of practical REM-based schemes, we implement a realistic location error model and show that localization errors increase the radio link failure duration. This highlights that the real-world feasibility of context-based beam management in 5G-and-beyond networks depends on the development and detailed evaluation of sensor-error-aware schemes.

5G-NR in FR2 relies on beamforming and accurate beam alignment between the gNodeB (gNB) and the user equipment (UE) to address challenging channel conditions. Maintaining beam alignment for a mobile UE requires frequent updates via beam-sweeping and directional radio frequency measurements, entailing high delay and signaling overhead. Location-aided beam management has emerged as a promising solution for reducing this overhead by combining UE location with historical directional beam training information to construct radio environment maps (REMs) for effective beam alignment. However, the highly site-specific nature of millimeter-wave (mm-wave) propagation makes REM-based beam management vulnerable to performance degradation due to errors in UE location information, e.g., obtained via global navigation satellite system (GNSS), affecting the accuracy of both REM construction and querying. This paper proposes a 5G-NR standard-compliant solution to mitigate GNSS location error in REM-based beam management, by constructing our rank-REM from multiple UE directional channel measurements and multi-beam monitoring for connected UEs. Our results, based on ray-tracing in an urban environment, show that our proposed REM construction and querying strategy effectively mitigates realistic GNSS location error, substantially enhancing link stability for a mobile UE by reducing radio link failures (RLFs) and handovers, thus enabling effective REM-based beam management for 5G-and-beyond.

Hybrid beamforming (HBF) multi-user multiple-input multiple-output (MU-MIMO) is a key technology for unlocking the directional millimeter-wave (mm-wave) nature for spatial multiplexing beyond current codebook-based 5G-NR networks. In order to suppress co-scheduled users' interference, HBF MU-MIMO is predicated on having sufficient radio frequency chains and accurate channel state information (CSI), which can otherwise lead to performance losses due to imperfect interference cancellation. In this work, we propose IABA, a 5G-NR standard-compliant beam pair link (BPL) allocation scheme for mitigating spatial interference in practical HBF MU-MIMO networks. IABA solves the network sum throughput optimization via either a distributed or a centralized BPL allocation using dedicated CSI reference signals for candidate BPL monitoring. We present a comprehensive study of practical multi-cell mm-wave networks and demonstrate that HBF MU-MIMO without interference-aware BPL allocation experiences strong residual interference which limits the achievable network performance. Our results show that IABA offers significant performance gains over the default interferenceagnostic 5G-NR BPL allocation, and even allows HBF MU-MIMO to outperform the fully digital MU-MIMO baseline, by facilitating allocation of secondary BPLs other than the strongest BPL found during initial access. We further demonstrate the scalability of IABA with increased gNB antennas and densification for beyond-5G mm-wave networks.

Cooperative multi-monostatic sensing enables accurate positioning of passive targets by combining the sensed environment of multiple base stations (BS). In this work, we propose a novel fusion algorithm that optimally finds the weight to combine the time-of-arrival (ToA) and angle-of-arrival (AoA) likelihood probability density function (PDF) of multiple BSs. In particular, we employ a log-linear pooling function that fuses all BSs’ PDFs using a weighted geometric average. We formulated an optimization problem that minimizes the Reverse Kullback–Leibler Divergence (RKLD) and proposed an iterative algorithm based on the Monte Carlo importance sampling (MCIS) approach to obtain the optimal fusion weights. Numerical results verify that our proposed fusion scheme with optimal weights outperforms the existing benchmark in terms of positioning accuracy in both unbiased (line-of-sight only) and biased (multipath-rich environment) scenarios.

Physical layer multicasting is an efficient transmission technique that exploits the beamforming potential at the transmitting nodes and the broadcast nature of the wireless channel, together with the demand for the same content from several UEs. This paper addresses the max-min fair multigroup multicast beamforming optimization, which is an NP-hard problem. We propose a novel iterative elimination procedure coupled with semidefinite relaxation (SDR) to find the near-global optimum rank-1 beamforming vectors in a cell-free massive MIMO (multiple-input multiple-output) network setup. The proposed optimization procedure shows significant improvements in computational complexity and spectral efficiency performance compared to the SDR followed by the commonly used randomization procedure and the state-of-the-art difference-of-convex approximation algorithm. The significance of the proposed procedure is that it can be utilized as a rank reduction method for any problem in conjunction with SDR.

6G networks will be highly dynamic, reconfigurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AI in networks will likely require distributed AI deployments with resilient connectivity, e.g., for communication between RL agents and environment. Such approaches need to be validated in realistic network environments. In this demo, we use ContainerNet to emulate AI-capable and autonomic networks that employ the routing protocol KIRA to provide resilient connectivity and service discovery. As an example AI application, we train and infer deep RL agents learning medium access control (MAC) policies for a wireless network environment in the emulated network.

Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate local-ization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echoes. Existing lo-calization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FRl) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.

Joint communication and sensing (JCAS) networks are envisioned as a key enabler for a variety of applications which demand reliable wireless connectivity along with accurate and robust sensing capability. When sensing and communication share the same spectrum, the communication links in the JCAS networks experience interference from both sensing and communication signals. Therefore, it is crucial to analyze the interference caused by the uncoordinated transmission of either sensing or communication signals, so that effective interference mitigation techniques could be put in place. We consider a JCAS network consisting of dual-functional nodes operating in radar and communication modes. To gain access to the shared communication channel, each node follows carrier sense multiple access (CSMA)-based protocol. For this setting, we study the radar and communication performances defined in terms of maximum unambiguous range and aggregated network throughput, respectively. Leveraging on the stochastic geometry approach, we model the interference of the network and derive a closed-form expression for both radar and communication performance metrics. Finally, we verify our analytical results through extensive simulation.

Co-channel interference poses a challenge in any wireless communication network where the time-frequency resources are reused over different geographical areas. The interference is particularly diverse in cell-free massive multiple-input multiple-output (MIMO) networks, where a large number of user equipments (UEs) are multiplexed by a multitude of access points (APs) on the same time-frequency resources. For realistic and scalable network operation, only the interference from UEs belonging to the same serving cluster of APs can be estimated in real-time and suppressed by precoding/combining. As a result, the unknown interference arising from scheduling variations in neighboring clusters makes the rate adaptation hard and can lead to outages. This paper aims to model the unknown interference power in the uplink of a cell-free massive MIMO network. The results show that the proposed method effectively describes the distribution of the unknown interference power and provides a tool for rate adaptation with guaranteed target outage.

This paper considers a mmWave cell-free massive MIMO (multiple-input multiple-output) network composed of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. We address UE mobility in the downlink (DL) with imperfect channel state information (CSI) and pilot training. Aiming at extending traditional handover concepts to the challenging AP-UE association strategies of cell-free networks, distributed algorithms for joint pilot assignment and cluster formation are proposed in a dynamic environment considering UE mobility. The algorithms provide a systematic procedure for initial access and update of the serving APs and assigned pilot sequence to each UE. The principal goal is to limit the necessary number of AP and pilot changes, while limiting computational complexity. We evaluate the performance, in terms of spectral efficiency (SE), with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithms effectively identify the essential AP-UE association refinements with orders-of-magnitude lower computational time compared to the state-of-the-art. It also provides a significantly lower average number of pilot changes compared to an ultra-dense network (UDN). Moreover, we develop an improved pilot assignment procedure that facilitates massive access to the network in highly loaded scenarios.

Millimeter-wave and terahertz systems rely on beam-forming/combining codebooks to determine the best beam directions during the initial access and data transmission. Existing approaches suffer from large codebook sizes and high beam searching overhead in the presence of mobile devices. To address this issue, we utilize the similarity of the channel in adjacent locations to divide the user trajectory into a set of separate regions and maintain a set of candidate beams for each region in a database. Due to the tradeoff between the number of regions and the signalling overhead, i.e., the greater number of regions results in a higher signal-to-noise ratio (SNR) but also a larger signalling overhead for the database, we propose an optimization framework to find the minimum number of regions based on the trajectory of a mobile device. Using a ray tracing tool, we demonstrate that the proposed method provides high SNR while being more robust to the location information accuracy in comparison to the lookup table baseline and fixed size region baseline.

This paper addresses mobility management in the downlink of a mmWave cell-free massive MIMO (multiple-input multiple-output) network with imperfect channel knowledge obtained from pilot training. The network consists of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) via coherent joint transmission. The objective is to extend traditional handover concepts to the challenging AP-UE association strategies of cell-free networks. To this end, we propose a distributed algorithm for joint pilot assignment and cluster formation in a dynamic environment considering UE mobility. The primary goal is to limit the necessary number of AP and pilot changes, with reasonable computational complexity. We evaluate the performance in terms of the spectral efficiency with maximum ratio and regularized zero-forcing precoding. Results show that our proposed distributed algorithm effectively identifies the essential AP-UE association refinements. Moreover, it provides a significantly lower average number of pilot changes compared to an ultra-dense network.

Ultra-reliable low latency communications (URLLC) service is envisioned to enable use cases with strict reliability and latency requirements in 5G. One approach for enabling URLLC services is to leverage Reinforcement Learning (RL) to efficiently allocate wireless resources. However, with conventional RL methods, the decision variables (though being deployed at various network layers) are typically optimized in the same control loop, leading to significant practical limitations on the control loop’s delay as well as excessive signaling and energy consumption. In this paper, we propose a multi-agent Hierarchical RL (HRL) framework that enables the implementation of multi-level policies with different control loop timescales. Agents with faster control loops are deployed closer to the base station, while the ones with slower control loops are at the edge or closer to the core network providing high-level guidelines for low-level actions. On a use case from the prior art, with our HRL framework, we optimized the maximum number of retransmissions and transmission power of industrial devices. Our extensive simulation results on the factory automation scenario show that the HRL framework achieves better performance as the baseline single-agent RL method, with significantly less overhead of signal transmissions and delay compared to the one-agent RL methods.

In this paper, we develop an algorithm for joint handover and beam tracking in millimeter-wave (mmWave) networks. The aim is to provide a reliable connection in terms of the achieved throughput along the trajectory of the mobile user while preventing frequent handovers. We model the association problem as an optimization problem and propose a reinforcement learning-based solution. Our approach learns whether and when beam tracking and handover should be performed and chooses the target base stations. In the case of beam tracking, we propose a tracking algorithm based on measuring a small spatial neighbourhood of the optimal beams in the previous time slot. Simulation results in an outdoor environment show the superior performance of our proposed solution in achievable throughput and the number of handovers needed in comparison to a multi-connectivity baseline and a learning-based handover baseline.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Diversity techniques and power control are the main approaches to reduce latency and enhance the reliability of wireless communications at the expense of redundant transmissions and excessive resource usage. Focusing on the application layer reliability key performance indicators (KPIs), we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize these KPIs. Furthermore, to address the scalability issue that emerges in the per-device orchestration problem, we develop a new branching soft actor-critic framework in which a separate branch represents the action space of each industrial device. Our orchestrator enables near real-time control and can be implemented in the edge cloud. We test our solution with a 3GPP-compliant and realistic simulator for factory automation scenarios. Compared to the state-of-the-art, our solution offers significant scalability gains in terms of computational time and memory requirements. Our extensive experiments show significant improvements in our target KPIs, over the state-of-the-art, especially for 5th percentile user availability. To achieve these targets, our framework requires substantially less total energy or spectrum, thanks to our scalable RL solution.

This paper considers a cell-free massive multiple-input multiple-output (MIMO) system that consists of a large number of geographically distributed access points (APs) serving multiple users via coherent joint transmission. The downlink performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, under two optimization objectives for power allocation: sum spectral efficiency (SE) maximization and proportional fairness. We present iterative centralized algorithms for solving these problems. Aiming at a less computationally complex and also distributed scalable solution, we train a deep neural network (DNN) to approximate the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation, based on large-scale fading (LSF) parameters, as the pre-processed input to the DNN. We train the DNN to refine the heuristic scheme, thereby providing higher SE, using only local information at each AP. Another distributed DNN that exploits side information assumed to be available at the central processing unit is designed for improved performance. Further, we develop a clustered DNN model where the LSF parameters of a small number of APs, forming a cluster within a relatively large network, are used to jointly approximate the power coefficients of the cluster.

Distributed artificial intelligence (AI) has recently accomplished tremendous breakthroughs in various communication services, ranging from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wireless connected devices, random channel fluctuations, and the incumbent services simultaneously running on the same network affect the performance of distributed learning. In this paper, we investigate the interplay between distributed AI workflow and ultra-reliable low latency communication (URLLC) services running concurrently over a network. Using 3GPP compliant simulations in a factory automation use case, we show the impact of various distributed AI settings (e.g., model size and the number of participating devices) on the convergence time of distributed AI and the application layer performance of URLLC. Unless we leverage the existing 5G-NR quality of service handling mechanisms to separate the traffic from the two services, our simulation results show that the impact of distributed AI on the availability of the URLLC devices is significant. Moreover, with proper setting of distributed AI (e.g., proper user selection), we can substantially reduce network resource utilization, leading to lower latency for distributed AI and higher availability for the URLLC users. Our results provide important insights for future 6G and AI standardization.

Due to the large bandwidth available, millimeter-Wave (mmWave) bands are considered a viable opportunity to significantly increase the data rate in cellular and wireless networks. Nevertheless, the need for beamforming and directional communication between the transmitter and the receiver increases the complexity of the channel estimation and link establishment phase. Location-aided beamforming approaches have the potential to enable fast link establishment in mmWave networks. However, these are often very sensitive to location errors. In this work, we propose a beamforming algorithm based on tracking spatial correlation of the available strong paths between the transmitter and the receiver. We show that our method is robust to uncertainty in location information, i.e., location error and can provide a reliable connection to a moving user along a trajectory. The numerical results show that our approach outperforms benchmarks on various levels of error in the location information accuracy. The gain is more prominent in high location error scenarios.

Communication systems supporting cyber-physical production applications should satisfy stringent delay and reliability requirements. Violation of these requirements may result in faulty behavior of the system and cause significant economic losses. Although wireless communications enable mobility and easy maintenance to industrial networks, it introduces many challenges to high-performance control systems due to interference and harsh environments (e.g., vibrations and many metallic objects). Diversity techniques and power control are powerful approaches to reduce latency and enhance reliability at the expense of excessive resource usage due to redundant transmissions. In this paper, we adopt fundamental metrics from reliability literature to wireless communications and provide critical indicators to measure reliability key performance indicators (KPIs) of cyber-physical systems. Then, we design a deep reinforcement learning orchestrator for power control and hybrid automatic repeat request retransmissions to optimize our reliability KPIs. Our orchestrator enables near real-time control and can be implemented on the edge cloud. We implement our framework on 3GPP compliant simulator on a factory automation scenario. Our comprehensive experiments show that, compared to the state-of-the-art, our solution can substantially improve the performance, especially for 5th percentile availability.

Increasingly more wireless devices adopt CSMA at the MAC layer, primarily due to the ubiquitous utilization of Wi-Fi, but also due to the development of new standards that implement CSMA variants, like LAA-LTE. It is thus critical to use reliable modelling tools that accurately evaluate the performance of large-scale CSMA deployments, as expected in practice. This paper compares the accuracy and computational efficiency of stochastic geometry modelling (SGM) and our proposed hybrid model, for estimating the MAC-layer throughput and signal-to-interference-and-noise ratio (SINR) of large-scale CSMA wireless deployments. Using ns-3 simulations as the baseline reference, we show that our hybrid model is significantly more accurate and at least one order of magnitude faster to compute than SGM. SGM underestimates the SINR, yet as it neglects the sensing overhead, it usually overestimates the MAC-layer throughput substantially. We thus argue that our hybrid model is preferable to SGM for modelling large-scale CSMA deployments.

Increasingly more unlicensed bands accommodate multiple broadband technologies, e.g. Wi-Fi and LTE in the 5 GHz band and Wi-Fi 6E and 5G NR-U in the new 6 GHz unlicensed band. In these bands, the technologies coexist primarily via distributed MAC spectrum sharing mechanisms, so it is crucial to evaluate their performance using the right tools that reliably and accurately model the coexisting schemes. CSMA and duty cycle are representative schemes in this context, but the few modelling tools that capture their coexistence performance are yet to be properly evaluated in terms of accuracy and computational efficiency. This paper is the first to compare three such modelling tools - stochastic geometry modelling (SGM), ns-3, and our hybrid model - which comprise different time granularities and abstraction levels for the MAC mechanisms. We compare both the accuracy of their estimated SINR and throughput versus ns-3 and their computational efficiency, for CSMA and duty cycle coexistence. Furthermore, we make recommendations pertinent to future broadband coexistence cases. Our results show that ns-3 captures important effects of the CSMA sensing time on the SINR, but its computation time may be prohibitive for modelling coexisting dense networks. This tool is thus useful primarily for studying dynamic MAC features in small to medium-sized coexisting deployments. SGM is the least accurate and also requires a long computation time, so we argue it is the least attractive approach to model coexisting CSMA and duty cycle technologies. Finally, our hybrid model yields the time-average per-link SINR with reasonable accuracy and the throughput with good accuracy and is at least two orders of magnitude faster to compute than SGM and ns-3, making it very well suited for extensive Monte Carlo simulations and large networks.

Millimeter-wave (mmWave) communication is a promising solution to the high data rate demands in the upcoming 5G and beyond communication networks. When it comes to supporting seamless connectivity in mobile scenarios, resource and handover management are two of the main challenges in mmWave networks. In this paper, we address these two problems jointly and propose a learning-based load balancing handover in multi-user mobile mmWave networks. Our handover algorithm selects a backup base station and allocates the resource to maximize the sum rate of all the users while ensuring a target rate threshold and preventing excessive handovers. We model the user association as a non-convex optimization problem. Then, by applying a deep deterministic policy gradient (DDPG) method, we approximate the solution of the optimization problem. Through simulations, we show that our proposed algorithm minimizes the number of the events where a user's rate is less than its minimum rate requirement and minimizes the number of handovers while increasing the sum rate of all users.

Cyber-physical control applications impose strict requirements on the reliability and latency of the underlying communication system. Hence, they have been mostly implemented using wired channels where the communication service is highly predictable. Nevertheless, fulfilling such stringent demands is envisioned with the fifth generation of mobile networks (5G). The requirements of such applications are often defined on the application layer. However, cyber-physical control applications can usually tolerate sparse packet loss, and therefore it is not at all obvious what configurations and settings these application level requirements impose on the underlying wireless network. In this paper, we apply the fundamental metrics from reliability literature to wireless communications and derive a mapping function between application level requirements and network level parameters for those metrics under deterministic arrivals. Our mapping function enables network designers to realize the end-to-end performance (as the target application observes it). It provides insights to the network controller to either enable more reliability enhancement features (e.g., repetition), if the metrics are below requirements, or to enable features increasing network utilization, otherwise. We evaluate our theoretical results by realistic and detailed simulations of a factory automation scenario. Our simulation results confirm the viability of the theoretical framework under various burst error tolerance and load conditions.

In this paper, we develop an analytical model for the performance analysis of dynamic power and channel allocation (DPCA) in downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems. To make the performance analysis tractable, we first relax the condition in the optimization problem of DPCA by assuming an ideal MC-NOMA system in which an arbitrary number of users can be multiplexed per subchannel. We use the basic decomposition method to solve the optimal DPCA for this problem and design a low-complexity algorithm for inter-channel power allocation. Then, based on the derivation of this optimal DPCA solution, we analyze its data rate performance and prove that it is an upper bound for practical MC-NOMA systems in which the number of multiplexed users per subchannel is limited. The simulation results verify our analysis of the upper bound performance and confirm that using its analytical results for data rate estimation in practical MC-NOMA systems has high accuracy under different system configurations. In addition, we study the influence of multi-user and multi-channel diversities on the performance of MC-NOMA, serving as a guideline for its deployment and optimization in practice.

In this paper, we investigate the potential benefits and challenges of using millimeter wave (mm-Wave) cellular network to carry out Beyond Visual Line of Sight (BVLOS) operations for remote piloting of Unmanned Aerial Vehicles (UAVs). We evaluate the reliability and latency of the wireless link between a UAV and a serving mm-Wave Base Station (BS) in the finite blocklength regime with the ultra reliable low latency communications (URLLC) requirements of 5G. To perform a comprehensive evaluation, we integrate several realistic models including mm-Wave antenna arrays, beamforming, mm-Wave propagation, and LOS probabilities. Our results show that cooperation and coordination among BSs are critical for UAV piloting using mm-Wave technology. We analyze the performance of mm-Wave links under three scenarios representing different levels of interference mitigation. We show that there is a certain range of message size and block length where it is possible to achieve URLLC requirements with packet error probability of 10−5and latency below 1 ms when assuming cooperation.

We design a lightweight beam-searching algorithm for mobile millimeter-wave systems. We construct and maintain a set of path skeletons, i.e., potential paths between a user and the serving base station to substantially expedite the beam-searching process. To exploit the spatial correlations of the channels, we propose an efficient algorithm that measures the similarity of the skeletons and re-executes the beam-searching procedure only when the old one becomes obsolete. We identify and optimize several tradeoffs between: i) the beam-searching overhead and the instantaneous rate of the users, and ii) the number of users and the update overhead of the path skeletons. Simulation results in an outdoor environment with real building map data show that the proposed method can significantly improve the performance of beam-searching in terms of latency, energy consumption and achievable throughout.

QUIC is a new transport layer protocol proposed by Google that is rapidly increasing its share from Internet traffic. It is designed to improve performance for HTTPS connections and partly replace TCP, the dominant standard of Internet for decades, in application scenarios where new requirements such as packet encryption, stream multiplexing and connection migration are emerging and which have proven to be challenging for the TCP service model. QUIC has been massively deployed to serve some of the most popular Internet services, including YouTube. To enable easy deployment and rapid evolution to the protocol, the current deployment of QUIC runs in user-space, usually as part of the Chrome/Chromium browser. This potentially reduces the achievable performance of the protocol, as each message, including control messages, triggers a context switch between kernel and user spaces. To investigate the potential performance of QUIC in kernel mode and to achieve a fair comparison between QUIC and TCP, we implement QUIC in the Linux kernel where TCP and other transport layer protocols are running. We have conducted extensive measurements in both virtual machines and in a custom-built WIFI testbed to compare the two protocols. The empirical results indicate that QUIC outperforms TCP in major application scenarios such as network with low latency and high packet loss rate, while QUIC also shows a TCP-friendly rate control when the two protocols are running concurrently.

Non-orthogonal multiple access (NOMA) has been investigated as a candidate radio access technology for the fifth generation mobile networks. In this paper, we focus on the dynamic power and channel allocation (DPCA) schemes for downlink multi-channel NOMA (MC-NOMA) systems. First, we assume an ideal MC-NOMA system with an arbitrary number of multiplexed users per subchannel and develop the optimal DPCA scheme for it based on a low-complexity inter-channel power allocation algorithm. Its performance serves as an upper bound for practical MC-NOMA systems where the number of multiplexed users per subchannel is limited. We utilize this optimal scheme and propose a user preselection (UP)-based DPCA scheme for practical systems. The performance of the UP-based DPCA scheme is evaluated by simulations and compared with the optimal scheme in the literature and the upper bound obtained in the ideal system. It achieves close-to-optimal performance with extremely low computational complexity. Moreover, we analyze the impacts of multi-user and multi-channel diversities on the system performance, serving as a guideline for practical deployments and configurations of MC-NOMA systems.

Emerging Wi-Fi technologies are expected to cope with large amounts of traffic in dense networks. Consequently, proposals for the future IEEE 802.11ax Wi-Fi amendment include sensing threshold and transmit power adaptation, in order to improve spatial reuse. However, it is not yet understood to which extent such adaptive approaches - and which variant - would achieve a better balance between spatial reuse and the level of interference, in order to improve the network performance. Moreover, it is not clear how legacy Wi-Fi devices would be affected by new-generation Wi-Fi implementing these adaptive design parameters. In this paper we present a thorough comparative study in ns-3 for four major proposed adaptation algorithms and we compare their performance against legacy non-adaptive Wi-Fi. Additionally, we consider mixed populations where both legacy non-adaptive and new-generation adaptive populations coexist. We assume a dense indoor residential deployment and different numbers of available channels in the 5 GHz band, relevant for future IEEE 802.11ax. Our results show that for the dense scenarios considered, the algorithms do not significantly improve the overall network performance compared to the legacy baseline, as they increase the throughput of some nodes, while decreasing the throughput of others. For mixed populations in dense deployments, adaptation algorithms that improve the performance of new-generation nodes degrade the performance of legacy nodes and vice versa. This suggests that to support Wi-Fi evolution for dense deployments and consistently increase the throughput throughout the network, more sophisticated algorithms are needed, e.g. considering combinations of input parameters in current variants.

This paper describes a new tool aimed for rapid development and modeling of MAC protocols that is based on hierarchical finite state machines with concurrency models. The developed tool is named Decomposite MAC Description Language (DMDL) and we are going to provide it open source in order to support MAC protocol design, implementation, and exchange of ideas among researchers. Following the modular design philosophy, the atomic elements of DMDL are the elementary MAC function components selected from a large-scale survey on different types of MAC that we have conducted. In DMDL, a MAC protocols is modeled as a directed graph with the MAC function components at the vertices. A token-like controlling unit is defined to pass control signals and data elements among MAC function components via the directed connections. The high decomposability and modularity enables DMDL to be used as a pure graphical language, and the concurrency nature of the synchronous data flow inherently ensures concurrency as a fundamental feature of DMDL. However, the tool supports also low level direct programmability and mixing of different programming languages through a common GNU Radio approach. In order to expands its usability, DMDL is developed on GNU Radio with a number of classic MAC protocols. The performance of the protocols implemented with DMDL is in accordance with the theoretical expectations, which demonstrates that DMDL is an efficient MAC designing and implementing tool with high flexibility and reusability.

In this paper, we present an analytical model for performance analysis of dynamic proportional fair scheduling (PFS) in downlink power-domain non-orthogonal multiple access (PD-NOMA) networks. In order to develop a tractable model of analytical performance, we relax the condition in the PFS optimization problem and assume an ideal NOMA system with an arbitrary number of multiplexed users per frame. We derive a closed-form solution of the optimal power allocation for the relaxed problem and design a low-complexity algorithm for joint power allocation and user set selection. With this optimal solution, the transmission performance in the ideal NOMA system is proved to be an upper bound. Based on our derivation, we develop an analytical model of the upper bound throughput performance. The analytical performance is used to estimate user data rates and overall throughput in practical NOMA systems. We conduct system-level simulations to evaluate the accuracy of our data rate estimation. The simulation results verify our analysis of the upper bound performance of PFS in NOMA systems and confirm that using its analytical results for data rate estimation guarantees high accuracy. The impact of partial and imperfect channel state information on the estimation performance is investigated as well.

The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for future wireless networks. This paper focuses on the proportional fair scheduling (PFS) for downlink single-carrier NOMA systems. Assuming an ideal NOMA system with an arbitrary number of multiplexed users, we derive a closed- form solution of the optimal power allocation for PFS. Based on this solution, we design a low- complexity algorithm for joint power allocation and user set selection (USS). Its performance is an upper bound for various PFS schemes in practical NOMA systems where the number of multiplexed users is limited. We utilize this algorithm to pre-select the candidate users and propose a pre-selection-based USS (PSU) scheme. The performance of the PSU scheme and various existing PFS schemes in the literature is evaluated by simulations and compared with the upper bound obtained in the ideal NOMA system. The proposed PSU scheme achieves the optimal performance that is close to the upper bound. Moreover, its computational complexity is significantly reduced and is nearly linear in the number of users.

Object localization is at the core of several context-aware applications envisioned for the Internet of Things. However, the present localization approaches are often too expensive, or are limited by indoor layouts and noise. In recent years, radio tomographic imaging (RTI) has generated great interest as a device-free localization approach. While several RTI algorithms have been proposed in the literature, their robustness and comparative performance in indoor environments, with real-world impairments, has not yet been experimentally studied. In this paper, we compare the performance of three state- of-the-art RTI algorithms and analyze the impact of different environmental conditions and algorithm parameters on the localization accuracy. Our experimental results show that multipath propagation is the main limiting factor for indoor localization using RTI: our measurements over a diverse set of indoor locations exhibit a 90th percentile localization error of between 0.8 m and 2.85 m. Additionally, our experiments reveal that co-channel interference and external human mobility further degrade the accuracy by 5%-20%. Furthermore, we show that while some improvements are achieved through modifications in network configuration and the fundamental RTI algorithm, these changes (such as increased node density and multi-channel RTI) are not feasible for cost- effective deployments.

In its evolution to provide ever higher data rates, the Wi-Fi standard has incorporated sophisticated PHY-layer techniques, which has in turn increased the complexity of network-wide interference relationships. Proper modelling of the resulting inter-device interactions is crucial for accurately estimating Wi-Fi network performance, especially in the contemporary context of traffic and network densification. Event-driven simulators like the open-source ns-3 are in principle able to capture these interactions, however it is imperative to validate, against experimental results, whether their underlying models reflect the network behaviour in practice. In this paper we first perform experiments in a large-scale indoor testbed to validate the IEEE 802.11ac Wi-Fi model in ns-3, for various channel width and allocation configurations. Our results show that ns-3 captures Wi-Fi co-channel interactions with reasonable precision, but fails to model adjacent channel interference (ACI), which our experiments show to be critical in dense networks. We therefore propose and implement an ACI model in ns-3. Importantly, our model successfully captures the qualitative behaviour of the CSMA/CA mechanism when transmissions on adjacent channels occur. Further, our ACI implementation significantly improves the accuracy of both the network and per-device throughput estimates for the considered dense IEEE 802.11ac network compared to the basic ns-3 Wi-Fi model without ACI. For example, without ACI modelling, ns-3 overestimates the aggregate network throughput by up to 230%, whereas with our ACI implementation the aggregate throughput estimate is no more than 65% higher than the experimental results.

As a device-free approach, radio tomographic imaging (RTI) is ideally suited for low-cost indoor localization in context-aware Internet-of-Things applications. However, the fundamental RTI algorithm relies on shadowing of the line of sight (LOS) links and therefore, conventional RTI implementations using 2.4 GHz sensing networks (microRTI) fail to accurately localize users in multipath-rich indoor environments. The localization accuracy is further degraded by external human movement that affects the signal propagation. In this paper, we propose mmRTI, a novel RTI approach based on a highly-directional, millimeter-wave sensing network, that aims to improve indoor localization by utilizing the LOS-dominant nature of millimeter-wave signal propagation. We experimentally evaluate mmRTI, operating at 60 GHz, with and without human movement around the sensing network, in two indoor environments, and compare its performance against the conventional microRTI approach. We observe that mmRTI achieves a 90%-ile localization error of 0.07m-0.25m, an improvement of 2.41 m-2.60 m compared to microRTI, while remaining unaffected by external human movement, which degrades the microRTI localization accuracy by up to 1.2 m.

In this paper we study the performance of machine learning based signal classification approaches in realistic wireless environments. We focus in particular on impact of interference from modulated signals and influence of realistic wireless channel conditions on classification performance. For this we use both numerical simulations as well as software defined radio based implementations. We also propose to use additional time series statistics originating from complex systems research as features for the classifier, in addition to classical second and higher order statistics previously employed. Our results show that the extended feature set results in robust classification performance in wide variety of channel conditions, and also when significant modulated interference is present in addition to Gaussian noise.

IEEE 802.11ad millimeter-wave (mm-wave) WLAN nodes achieve high throughput at the cost of the frequent re-steering requirement of highly directional antenna beams to establish and maintain links disrupted by beam misalignment, node mobility, or link blockage. Consequently, rapid and robust beamsteering algorithms are essential to enable seamless communication in mm-wave WLANs. In this paper we propose AoASteer, a beamsteering algorithm that speeds up the link establishment process in IEEE 802.11ad mm-wave WLANs by preferentially searching over a subset of mm-wave antenna sectors predicted by 2.4 GHz angle of arrival (AoA) estimation at the access point (AP). We experimentally evaluate the performance of AoASteer through extensive measurements in several indoor and outdoor locations, using 60 GHz USRP-SiversIMA packet radio transceivers to gather real mm-wave link information, and a 2.4 GHz USRP-based receiver with an 8-element uniform linear antenna array for AoA estimation, both implemented using GNU Radio. Our evaluation results, obtained for APs with different numbers of beams per sector, show that AoASteer typically selects a near-optimal LOS or NLOS link to establish communication, while significantly reducing the link establishment latency. For example, for 4-sector mm-wave antennas, AoASteer reduces the latency by 46 μs for 73% of our measurement cases compared to the IEEE 802.11ad beamsteering algorithm, while achieving the highest data rate of 6.7 Gbps for 92% of the cases.

We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary user's (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; but not useful to evaluate the performance of CSA under realistic conditions. An in-depth analysis of independent spectrum measurement traces reveals that wireless channels have typically heavy-tailed PU OFF times. In this paper, we propose an extension to the Predictive CSA framework and its generalization for heavy tailed PU OFF time distribution, which represents realistic scenarios. In particular, we calculate the probability of channel being idle for hyper-exponential OFF times to use in CSA. We implement our proposed CSA framework in a wireless test-bed and comprehensively evaluate its performance by recreating the realistic PU channel occupancy patterns. The proposed CSA shows significant reduction in channel switches and energy consumption as compared to Predictive CSA which always assumes exponential PU ON-OFF times. Through our work, we show the impact of the PU channel occupancy pattern on the performance of CSA in multichannel CRN.

In-band full-duplex is emerging as a promising solution to enhance throughput in wireless networks. Allowing nodes to simultaneously send and receive data over the same bandwidth can potentially double the system capacity, and a good degree of maturity has been reached for physical layer design, with practical demonstrations in simple topologies. However, the true potential of full-duplex at a system level is yet to be fully understood. In this paper, we introduce an analytical framework based on stochastic geometry that captures the behavior of large full-duplex networks implementing an asynchronous random access policy based on Aloha. Via exact expressions, we discuss the key tradeoffs that characterize these systems, exploring among the rest the role of transmission duration, imperfect self-interference cancellation, and fraction of full-duplex nodes in the network. We also provide protocol design principles, and our comparison with slotted systems sheds light on the performance loss induced by the lack of synchronism.

In this article, we assess the viability of underlay sensor networks in frequencies used by an incumbent digital TV broadcasting system, that is, in the so-called TV grayspaces (TVGS). Grayspace operations are particularly interesting when other unlicensed bands are overcrowded, for example, due to high-volume WiFi operations. We simulate the operational characteristics of the recent IEEE 802.15.4m standard for low-rate wireless personal area networks to evaluate the performance degradation of an incumbent Digital Video Broadcasting - Terrestrial (DVB-T) system if a secondary network of low-power low-rate devices are co-deployed in the same frequency bands. Our results show that short sensor messages will not disrupt the DVB-T service due to the existing error-correction capabilities. Furthermore, if sufficient separation distances to primary transmitters are maintained, transmit powers are sufficient to achieve reasonable connectivity levels of the secondary network. In order to obtain realistic figures on the predicted feasibility of grayspace sensor networks, we study the deployment constraints of a hypothetical secondary network co-located with the TV broadcasting network of Germany. Our analysis shows that if we aim to support a minimum sensor-sensor distance, no universal coverage can be maintained in this country. While our quantitative results are specific to Germany, we deem them indicative for the expected results also in other potential deployments. We found that while a secondary wireless sensor network in TVGS is technically possible, the necessary constraints on operational parameters and service levels for TVGS co-existence will significantly limit its practical viability.

The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the next generation mobile networks. It realizes powerdomain user multiplexing by employing the successive interference cancellation (SIC). This paper focuses on the power allocation and user set selection problems in multi-user downlink NOMA systems with the aim of providing proportional fairness. The theoretical optimal power allocation solution for a candidate user set is derived in closed form. We propose a tree searching-based user set selection scheme in which the redundant user sets are removed by a delicately designed pruning method. The transmission performance of the proposed power allocation and user set selection schemes is evaluated by system-level simulations and verified to be close to the optimal value. The searching complexity for user set selection is significantly reduced compared to the exhaustive searching method. Finally, the performance gains brought by the multi-user NOMA are analyzed considering the practical application.

We present results from a detailed comparative study of the performance of three different frequency assignment schemes for interference management in heterogeneous LTE networks. Realistic indoor propagation models are used in order to obtain accurate results. We show that simple graph based frequency assignment schemes yield an excellent compromise between computational complexity and high performance. Static fractional frequency reuse on the other hand is shown to result in highly suboptimal performance at all network densities. We also study the robustness of the graph based approach against uncertainties in the employed radio propagation estimates, showing that even relatively high estimation errors in radio propagation models do not result in significant degradation of performance.

Designing distributed protocols with limited requirements for exchange of feedback information between the nodes is a key element in the development of cognitive and self-organizing ad hoc networks. We argue that the Minority Game (MG) and its variations can constitute useful tools towards this direction. In particular, the MG formulation is well suited for addressing a variety of resource allocation problems in self-organizing wireless networks. In this paper we illustrate the employment of MGs by presenting a paradigm of an MG-based model for dynamic channel allocation.

The millimeter-wave (mm-wave) bands are currently being explored for multi-Gbps wireless local area networks (WLANs). Directional antennas are required to overcome the high attenuation inherent at the mm-wave frequencies. However, directionality makes link maintenance and establishment tasks complex, especially under node mobility, as slight misalignment of antenna beams between nodes leads to link disruption. Consequently, low latency beamsteering algorithms are needed for fast link re-establishment to support seamless data provisioning. Solutions based on exhaustive sequential scanning induce high latency, thereby disrupting communication. On the other hand, existing low latency proposals typically consider only static links, depend on additional hardware, or require a priori information about the network environment. In this paper, we propose a generic, fast mm-wave beamsteering algorithm that utilizes the previous valid link information to initiate the feasible antenna sector pair search and adaptively increases the sector search space around it to re-establish a link. Additionally, we experimentally evaluate the performance of our algorithm through measurements conducted in a real indoor environment using 60 GHz packet-radio transceivers. The results show that, compared to exhaustive sequential scanning, our algorithm reduces the required sector search space, and thereby the link re-establishment latency, by 89% on average compared to exhaustive sequential scanning.

Spectrum sharing mechanisms need to be carefully designed to enable inter-technology coexistence in the unlicensed bands, as these bands are an instance of a spectrum commons where highly heterogeneous technologies and deployments must coexist. Unlike in licensed bands, where multiple technologies could coexist only in a primary-secondary dynamic spectrum access mode, a spectrum commons offers competition opportunities between multiple dominant technologies, such as Wi-Fi and the recently proposed LTE in the 5 GHz unlicensed band. In this paper, we systematically study the performance of different spectrum sharing schemes for inter-technology coexistence in a spectrum commons. Our contributions are threefold. First, we propose a general framework for transparent comparative analysis of spectrum sharing mechanisms in time and frequency, by studying the effect of key constituent parameters. Second, we propose a novel throughput and interference model for inter-technology coexistence, integrating per-device specifics of different distributed MAC sharing mechanisms in a unified network-level perspective. Finally, we present a case study of IEEE 802.11n Wi-Fi and LTE in the 5 GHz unlicensed band, in order to obtain generalizable insight into coexistence in a spectrum commons. Our extensive Monte Carlo simulation results show that LTE/Wi-Fi coexistence in the 5 GHz band can be ensured simply through channel selection schemes, such that time-sharing MAC mechanisms are irrelevant. We also show that, in the general co-channel case, the coexistence performance of MAC sharing mechanisms strongly depends on the interference coupling in the network, predominantly determined by building shielding. We thus identify two regimes: (i) low interference coupling, e.g., residential indoor scenarios, where duty cycle mechanisms outperform sensing-based listen-before-talk (LBT) mechanisms and (ii) high interference coupling, e.g., open-plan indoor or outdoor hotspot scenarios, where LBT outperforms duty cycle mechanisms.

Crosstalk between physically co-located lines is a pressing issue in VDSL2 access networks. In order to enhance the crosstalk mitigation capabilities of the latest extension to VDSL2, vectoring G.993.5, full control over all lines within the same cable binder is required. However, this is not always possible in practical deployments due to regulatory, structural, or late technology adoption constraints. In these cases a technique to minimize interference from non-controlled lines, known as binder management, aims at rearranging the line configuration within each binder. In this work, we quantify the advantages of binder management in a partially controlled setup. We initially establish a model of a commonly used 50-pair cable binder and provide its far-end crosstalk (FEXT) characterization. We then carry out an extensive simulation study for various degrees of control over the lines and realistic line length distributions to yield tangible metrics on vectoring performance for downstream transmission. Our results show that binder management is of limited use in partially controlled systems. Consequently, we provide an additional comparison study to help DSL providers to evaluate the remaining gains of upgrading to VDSL2-vectoring in such scenarios for different levels of dominance in the cable binder.

The non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology in future mobile networks. It is able to implement power-domain user multiplexing based on successive interference cancellation (SIC). This paper focuses on the user pairing and power allocation problem in the 2-user NOMA system. The optimal solution in closed-form is derived with the proportional fairness objective and is used for the design of the user pair power allocation scheme. The prerequisites for user pairing are also formulated in order to avoid unnecessary comparison of candidate user pairs. The performance of the proposed scheme is evaluated by system-level simulations and results in higher gains than the search-based transmission power allocation. On the other hand, the computational complexity is reduced by the proposed scheme effectively.

Accurate estimation of aggregate interference is crucial for understanding the potential capacity gains from densification of cells. In this paper we study the impact of three-dimensionality of femtocell deployments on both cross-tier and co-tier aggregate interference using realistic building data and accurate propagation models. Our results show that the three-dimensional character of urban femtocell deployments can result in significantly higher interference effects compared to typically used two-dimensional evaluation scenarios. Further, we also show that heterogeneity in building heights plays an important role in the amount of aggregate interference, and should be considered more carefully when specifying standard topologies for interference studies.

To meet the rapidly growing requirement for universal coverage of high-speed mobile services, wireless cellular networks have been moving towards increasing density. Inter-cell cooperation in such densely deployed networks is becoming more significant than ever before. Among others, data rate estimation is a fundamental issue for inter-cell resource management and optimization. In this paper, we focus on the data rate estimation problem based on stochastic SINR models. We derive a closed-form solution of user SINR distribution in multi-interference networks. We calculate upper and lower bounds of the SINR distribution and extend them to a weighted sum SINR model to achieve more accurate estimation of data rate. The simulation results reveal that our designed model can guarantee the accuracy of data rate estimation in diverse wireless network environments such as urban and suburban scenarios. It decreases the error of estimation and the ratio of high-error users even with very small signaling overhead fed back per user. Various factors, such as the number of reported cells, low-SINR effect, propagation environments and inaccuracy of channel measurement, which influence the estimation performance are analyzed and evaluated as well. The weighted sum model is verified to have great resistance to the influence of these factors and achieve accurate estimation.

The large contiguous bandwidths available in the mm-wave frequency bands have recently started to gain significant attention due to the growing belief that it can alleviate the spectrum shortage at lower frequencies and meet the increasing high data rate demands. While some commercially available mm-wave transceivers in the 60 GHz and 70 GHz bands have been developed, only a few studies exist on the achievable data rates in realistic deployment scenarios. Moreover, practical issues such as interference and coexistence of heterogeneous applications have not been investigated. In this paper, we present our empirical study on the bit error ratio and throughput for realistic indoor application scenarios in 60 GHz and 70GHz using orthogonal frequency division multiplexing based physical layer on various commercially available transceivers. Furthermore, we analyze the effects of interference on the performance characteristics of the mm-wave frequency links in different deployment setups.

Directional antennas in wireless networks can provide substantial performance gain and opportunities. In the case of high-frequency systems, such as emerging 60 GHz networks, they are inherent feature of the system design. In contrast to the traditional omni-directional antennas, directional antennas are capable of reducing the interference level, increasing the communication range, and improving spatial reuse. However, new challenges need to be solved, such as the deafness problem and the asymmetric-in-gain. In this paper, we propose Cooperative-Directional MAC (Coop-DMAC) protocol to address the new challenges. The Coop-DMAC is completely distributed and relies on limited cooperation between networked nodes. In Coop-DMAC, nodes are supposed to exchange local information including the angle of arrival of their neighboring nodes and the duration of data transmission to improve their spatial reuse. In this paper, we analyze and evaluate the performance of Coop-DMAC, and pay particular attention to the performance and network topology. Simulation results validate our theoretical analysis and show that the proposed Coop-DMAC protocol outperforms the standard IEEE 802.11ad MAC protocol in terms of higher goodput in most common network topologies considered in the paper.

Recent rulings by US and UK regulators allowing access to unused portions of TV spectrum have elicited high hopes for the practical value of these TV whitespaces (TVWS) for secondary exploitation. However, this optimism has been largely fueled by rather simple early studies, which do not consider all the system aspects in sufficient detail; the few early experimental works reported are proprietary and consider only a small number of nodes, whereas the existing more sophisticated theoretical studies focus on singular aspects of secondary spectrum access in isolation from the overall system interactions. In this paper we study quantitatively the deployment of secondary networks in TVWS by considering two archetypal candidate systems: LTE-like cellular and Wi-Fi-like networks. We develop a systematic framework for the performance evaluation of secondary networks, which we then use to obtain realistic estimates of the performance of our example systems. The secondary network performance assessment methodology demonstrated in this paper can be directly applied for other regions and systems. Our work explicitly takes into account limitations arising from aggregate interference, user density, and specific secondary transceiver characteristics. We argue that a systematic analysis, jointly considering all of these aspects, is key for obtaining realistic and robust results on the estimated value of whitespace spectrum. Our detailed system-level approach reveals a much more conservative picture of the realistic benefit of TVWS deployments than what has been commonly assumed thus far. We find that cellular TVWS networks have limited capabilities, but that a macro-cellular-only network may be a viable option for traffic offloading. Our results also show that Wi-Fi-like secondary deployments in TVWS, although increasing coverage range, lead to increased congestion, which limits the system capacity.

In this paper we analyze the potential use of underutilized TV frequencies to enable high-speed hotspots and data offloading in trains. We propose a system where the hotspots connect via TV frequencies to base stations that are located at the train stations. Using data from the railway network of Germany and detailed spectrum availability information we carry out a performance analysis for various realistic scenarios and configurations of a secondary system that relies on LTE technologies. Our analysis shows that sustainable data rates beyond 20 Mbps per train can be achieved in all considered scenarios, and that a proposed modified spectrum policy for authorized shared access of the secondary system will yield near-universal coverage. The results show that it would be possible to provide cost-efficient broadband connectivity for a national high-speed train network by using unused TV frequencies.

We present results from a measurement-based study of the performance of the emerging IEEE 802.11ac Wi-Fi standard in an indoor environment. The measurements were conducted in a typical office building, and show that for small distances IEEE 802.11ac offers significantly improved performance compared to IEEE 802.11n. However, these performance improvements were also found to be quite sensitive to channel conditions, with the achieved data rates rapidly declining as the distance between the transmitter and the receiver is increased. We also studied the coexistence properties of IEEE 802.11ac through measurements, observing that adjacent channel interference from legacy Wi-Fi devices can have a severe performance impact. For co-channel interference, the medium access control mechanism of IEEE 802.11ac allows it to share the channel effectively with other Wi-Fi devices.

Traffic load balance is among the most crucial issues in heterogeneous cellular networks (HCNs), which can lead to much higher throughput, better user fairness and larger coverage for HCNs. In this paper, we consider the user association problem in HCNs and design a handover scheme for traffic load balancing among macro and femto cells. Three types of handover factors are delicately designed with different objectives focusing on throughput or fairness or trying to balance the both. Correspondingly, three typical resource schedulers are adopted to improve the desired performance. The performance of the proposed scheme is evaluated by simulations and reveals considerable improvement in comparison with the maximum biased-received-power association scheme, especially the data rates of the low-rate users. The impact of different base station and user distributions on the balancing results is also analyzed.

The traffic load in heterogeneous cellular networks (HCNs) is often unevenly distributed, which results in unbalanced resource allocation among users and underutilized access points. In this paper, we focus on the user association problem in HCNs, which refers to the problem of assigning mobile users to diverse cells. We extend our previous research on the handover scheme for traffic load balance based on user data rate estimation in realistic HCNs. We formulate an independent interference analytical model and derive the closed-form distribution of the user SINR upper bound. The analytical results are verified very close to the numerical ones. Based on the obtained solution of the distribution, we further estimate user data rates under proportional fairness scheduling for our traffic load balancing scheme. The performance is evaluated by simulations which adopt real site locations in Los Angeles. The numerical results of our balancing scheme indicate considerable improvement of data rate in comparison to the conventional maximum biased-received-power association scheme, especially the data rates of the low-rate users. The impact of user distribution in hotspots on the balancing results is also discussed.

A better understanding of the spatial structure of existing opportunistic wireless network deployments will offer new insights for future deployments, e.g. dense femtocell networks. In this paper we present a performance analysis for three different localization algorithms that support such efforts by finding the location of IEEE 802.11 Wi-Fi access points (APs) from distributed signal strength measurements. These algorithms extend prior works on omnidirectional single-point measurements and includes angular information into the estimation process. Using a custom-made measurement setup, we have collected data for typical indoor and outdoor scenarios which are likely to be encountered in large measurement campaigns. We quantify and reason on the localization error for each scenario and algorithm, and propose a weight spread estimate for determining the robustness of the localization approaches. Through variations in the used sample data set we further discuss relevant observations on the stability of the different algorithmic estimation approaches. Our results show that overall propagation-centric algorithms perform best, but that algorithms that build a stochastic model of angular signal strength distribution are more robust if only few measurements are available.

Femtocell networks have emerged as an important means of offloading traffic from today's LTE cellular networks. However, deploying LTE femtocells increases the interference in the network, as they share licensed spectrum with the macro-cellular network. Consequently, a wide range of interference mitigation techniques have been proposed to enable coexistence of femtocells and macrocells. Techniques of various implementation and signaling complexity have been considered, but the majority of existing work evaluates their performance solely based on how well they mitigate interference for macrocell users. However, it is important that both macrocell and femtocell users achieve good performance. In this paper we present a comparative system-level study on the performance of several LTE femtocell frequency and power allocation schemes. We investigate whether more complex and dynamic resource allocation schemes, i.e. power control, are worthwhile from the point of view of femtocell networks compared to lower complexity static resource allocation schemes. We evaluate the performance in terms of the users' downlink throughput and achieved signal-to-interference-plus-noise ratio (SINR), and we consider different interference environments for a wide range of network densities (urban, suburban, and rural scenarios). Our results show that power control algorithms only yield a marginal increase in throughput for the LTE femtocell users. This suggests that increasing the complexity of static frequency allocation schemes by implementing power control algorithms is not justified.

With an increasing demand for monitoring energy consumption at granularity levels down to single household appliances, it is necessary to develop new means to collect sensor measurements in a robust and cost-efficient manner. The smart grid paradigm foresees using wireless links for data transfer, albeit no dedicated spectrum bands have been designated for this purpose. In this paper we study the feasibility of opportunistic spectrum access for smart grids, and focus on underlay spectrum sharing over occupied TV channels. These frequency bands, which are commonly denoted as TV gray spaces, provide superior propagation characteristics, but are locally used by high-power (mostly DTV) broadcasting transmitters. For selected reference geometries of intra-meter and meter-to-operator communications, we study the smart meter performance (in terms of achievable throughput and transmission range), and the necessary power limits. We compare our results from a small-scale measurement campaigns against existing wireless technologies for low-power communications in other adjacent bands. Our results show that wall shielding and fading in indoor to outdoor propagation channels sufficiently protects the primary system from the interference introduced by gray space meter-to-meter communications, but that the required transmit powers to send operations data from indoor meters to outdoor collection point severely limit the applicability of TV gray spaces for such network topologies.

Cooperative spectrum sensing generally consists of two consecutive stages: sensing and reporting, where sensors measure channels at the sensing stage and then use a multiple access protocol to report their local measurement results which we call the reporting MAC protocol. In this paper, we examine the impact of reporting MAC protocols on centralized cooperative sensing in multiband cognitive networks. Specifically, we study the applicability of TDMA and IEEE 802.11 CSMA/CA protocols for cooperative spectrum sensing reporting. A general mathematical model has been developed to quantify the achievable bandwidth utilization within a secondary system, which takes into account different combinations of sensor assignment strategies, reporting MAC, fusion rules, and regulatory constraints. The model allows the cross-layer optimization on the PHY and MAC layers of cooperative detection. The results show that the reporting control signaling can dominate the sensing overhead and result in significant capacity loss in some scenarios, which emphasizes the importance of using an efficient reporting MAC in cooperative sensing.

The prospect of increasing wireless capacity via secondary access to spatio-temporally underutilized chunks of spectrum, so-called whitespaces, has been proposed as a central aspect of emerging radio systems in response to the imminent spectrum scarcity problem. In this demonstration we present a novel software tool that helps researchers, industry, and regulators in assessing the feasibility and value of secondary spectrum access beyond simple whitespace availability calculation. Whereas existing software applications merely provide visualization of estimated secondary spectrum over a geographic area, our tool uniquely enables a holistic evaluation of the realistic potential of whitespace technologies, by modelling the performance of entire secondary systems in the envisioned eco-system of dynamic spectrum access policy and technology. Our tool provides a unified and flexible software framework and assessment methodology to conduct such studies, and is composed of an extensive primary spectrum usage database, a graphical interface for user interaction, and an interface to an extensible MATLAB backend for numerical calculations. We showcase the deployment scenarios of cellular and Wi-Fi-like secondary networks in TVWS (TV whitespaces). We also compare the impact of employing FCC-type of regulatory rules (with a fixed power/no-talk distance configuration) against European WG-SE43 regulatory proposals (with probabilistic access and power control). The case studies we will demonstrate are based on real network configuration data of European and US TV networks.

In this paper we analyze the prospects of deploying a cellular network in the TV whitespaces. We study the challenges of planning for a large-scale transmitter network that is constrained by an interference-to-primary regulation. For this purpose we derive a novel methodology to maximize the downlink capacity at the cell edge of base stations that makes use of a heuristic algorithm for per-channel power allocation. A discretized formulation as a multiple-choice nested knapsack problem is presented and scenario-specific algorithmic improvements are discussed. The methodology is assessed for the case of Germany which, due to the specific challenges of the geographic region, can be considered indicative for similar deployments in other countries.

The recent decisions by regulators in the USA and UK to open up unused portions of UHF spectrum for secondary use have been met with keen interest in using these TV white spaces (TVWS) for providing broadband services through Wi-Fi-like connectivity. Amid the ensuing media hype about “Wi-Fi on steroids”, there is a widespread perception that Wi-Fi operating in TVWS will provide much longer range, superior speeds, and more reliable connections than traditional Wi-Fi at 2.4 GHz. In this paper, we present a quantitative analysis of the performance of a network of Wi-Fi-like access points (APs) operating in TVWS in order to obtain a realistic estimate of the achievable range and downlink rate of such a secondary system. Unlike previous studies, we explicitly consider the effects of inter-AP interference and congestion and use real TVWS channel availability estimates from an example region of Germany. We confirm the favourable properties of the lower TVWS frequency range, of enabling better propagation through walls and a larger coverage range for the same power budget. Our results show that operating Wi-Fi hotspots in TVWS might be technologically attractive for outdoor rural areas where user demand is low. However, the extended coverage range in TVWS leads to increased congestion which rapidly limits the system capacity for an outdoor urban deployment with high user density. Therefore, a combined technological and economical analysis is essential before any final judgement can be reached about the viability of large-scale Wi-Fi deployments in TVWS.

The design of a cooperative spectrum sensing strategy has to take two aspects into consideration: from the physical layer perspective, a number of secondary users cooperatively measuring the channels to improve the detection performance, and from the MAC layer perspective, the users efficiently reporting their measurement results to a decision entity. Both operations are time critical and required for efficient spectrum opportunity exploitation by the secondary system. However, most of the existing cooperative solutions in the literature ignore the reporting process or over simplify it by assuming a perfect timeslotted multiple access. In this paper, we consider a CSMA/CA based reporting process. We show that fairness no longer plays a role in multiple access of the reporting process. Instead, fastness becomes the dominant measure of the MAC protocol. We propose a Fast Cooperative Spectrum Sensing (FCSS) scheme over multiband cognitive networks by exploiting concurrent occurrence of the spectrum sensing activities and the reporting process with an improved CSMA/CA based MAC protocol. The proposed scheme incorporates a joint static and random sensor assignment algorithm over multiple channels. A mathematical model has been developed for evaluating the performance of FCSS and serving as a basis to select appropriate parameters.

Future wireless networks will be able to exploit unused spectrum whitespaces in the currently underutilized terrestrial TV bands. In order to protect incumbent broadcasting systems from extensive interference from whitespace devices, accurate signal power estimations are needed. Reliable and robust models for predicting the radio propagation in those bands are thus a key enabler of secondary operations. Depending on their level of abstraction and the complexity of the environment, these models can perform quite well, but they require a significant amount of auxiliary information and fine-tuning. Additionally, they usually consider transmitters in one frequency band to have uncorrelated signals, an uncommon scenario in today's digital TV networks where the same geographical area is often served by multiple towers that form a single-frequency network (SFN). Using data from a measurement campaign carried out in a mid-sized Central European city we assess the accuracy of three widely used propagation models in predicting TV signal strengths for configurations of multiple high-power transmitters. We empirically show that the standard deviation of the estimation error is up to 4 dB higher in a multi-transmitter scenarios with correlated signals than in the single-transmitter case. We put the results in contrast to the prediction capabilities of a generic propagation model that estimates pathloss coefficients and individual correction factors from measurements and combines them with information on transmitter locations, power emissions and antenna patterns. Finally, we study the use of spatial statistics based techniques for directly estimating the coverage characteristics without relying on explicit transmitter information. We show that with only a small subset of measurement locations accuracy beyond standard pathloss models can be achieved.

Cognitive radios are becoming the technological foundation for efficiently managing the scarcity of wireless spectrum, fulfilling various QoS demands and allowing different networks to coexist. Cognition and spectrum agility in MAC protocols require adaptability and close PHY-MAC interaction. Classically, MAC protocols have been implemented in hardware, which gives a limited possibility for reconfiguration and customization. Recently, software based MAC implementations have emerged although a close hardware-software co-design is typically required to keep the time critical operations in ASICs or FPGAs. We introduce a MAC development framework for enabling fast composition of MAC protocols, which are best fitted to the application requirements, communication capabilities of the radio, and current spectrum regulations and policies. We decompose MAC protocols into their basic functionalities which are perceived as building blocks and are partitioned across hardware/software. Our framework allows on-the-fly realization of the envisioned MAC protocol through wiring of these fundamental components. By exposing extended metadata and hardware functionalities for the MAC implementation through our granular components, together with the support for run-time re-configuration, spectrum agile and cognitive MAC solutions can be easily realized. In this paper, we describe the design rationale and implementation details of our framework on WARP boards. We show through experimental evaluation that the framework provides flexible means for prototyping different reconfigurable cognitive and spectrum agile MAC protocols.

The use of beamforming to enable higher data rates in telecommunications is widely appreciated, but performance gains are typically calculated assuming delay-free feedback from the receiver and neglecting processing time. This paper introduces a mathematical framework based on outage probability that measures the extent to which current channel state information is accurate. Performance gains from beamforming can then be evaluated as a function of the currency of system state. Results are provided for Multiple Input Single Output (MISO) and for Multiuser Multiple Input Multiple Output (MU-MIMO) systems. Outage probabilities and effective diversity orders are calculated for widely used methods of beamforming such as Transmit Antenna Selection as a function of the speed of channel variation.

In this paper we describe an LTE based demonstrator of the Universal Link Layer API (ULLA) and Cognitive Resource Manager (CRM) modules that are developed in ARAGORN project. The demonstrated LTE system comprises one LTE TDD eNode B and one User Equipment (UE). We first introduce ULLA and CRM framework and then demonstrate their suitability to be implemented with the existing LTE equipments. We show how, through ULLA, CRM is able to obtain PHY/MAC status information of the link between the eNode B and UE, and in turn change system parameters to achieve better resource utilization and transmission efficiency. The control logic can be implemented with simple adaptation or policy-based intelligent methods. The platform clearly shows the feasibility to use ULLA/CRM architecture for radio resource management in a LTE network. It also shows the neutrality of ULLA/CRM mechanisms towards PHY/MAC characteristics of LTE technology platform; hence the platform is viable to flexibly switch between technology platforms (e.g. between LTE access and WiFi access) under the control of ULLA/CRM.

Estimation of the available capacity in a multiple hop route in cognitive radio networks is challenging. Several complex problems like channel assignment, interference estimation and routing effects needs to be addressed. In this demonstration, we exhibit a capacity estimation engine that determines the available capacity of the routes in a dynamic cognitive radio network using USRP and WARP software-defined radios. In addition to the available capacity, we show the various types of MAC interference and its effect in the network. The tool demonstrates a fundamental capacity estimation block, which provides valuable input to higher level applications like routing, network provisioning and monitoring. A scheduling aware routing algorithm that exploits the capacity estimation engine and scheduling interactions is shown as an example application.

In this paper we verify and study through implementation our earlier game theory algorithm for load balanced dynamic channel assignment. In our algorithm every cognitive radio is initially assigned a channel in a random fashion. The radios periodically evaluate a cost function related to a Quality of Service (QoS) parameter and may change the transmission channel with probability proportional to the excess of the current cost over a cost threshold set by the user application. The actions of the cognitive radios are concurrent and the decision to switch the channel is only based on the current local link quality, which means that neither network state information exchange nor history keeping is required. The earlier theoretical work has shown that the balls-and-bins game theory algorithm has attractive convergence properties and low complexity. In this paper we evaluate the convergence properties of our dynamic channel assignment protocol over real channel conditions for different number of channels and traffic distributions. Our testbed results show that the optimal channel allocations are found in most of the cases within five to eight iterations depending on the complexity of the test case. The proposed algorithm is a good candidate for resource allocation using only local information.

We study the impact of the distribution of node locations on key properties of wireless networks. In particular, using Monte Carlo simulations we study in detail the behavior of average path length for different distributions and parameters thereof. We also show that distribution of client locations has significant impact on network planning, in particular on combination of base station placement and range assignment. In both cases our results indicate that the underlying point distributions of nodes have clear effects on the wireless network properties. We also discuss the implications of our results and the role of Poisson point process as an approximation of other distributions.

Most publications on the capacity and performance of wireless ad hoc networks share the underlying assumption of a uniform random distribution of nodes. In this paper, we study the effects of different node distributions on the local throughput of the slotted ALOHA MAC protocol. The throughput achieved in a network where the nodes are distributed according to a Poisson point process is used as a baseline performance measure for the comparison with other point distributions. Our simulations show that non-uniform random node distributions have a strong impact on the local throughput which is related to the network capacity and performance. This means that the node topology should be taken into account in more detailed analyses and simulations of ad hoc, sensor, and mesh networks.

We investigate the impact of network topology on connectivity of wireless mesh and ad hoc networks. In particular we study the transmission range required to establish connectivity with some fixed proportion of network nodes. Our analysis shows that in practice it can be useful to relax the requirement of a full 100 % connectivity in ad hoc networks. We show that high network connectivity can be reached with much lower power consumption and careful node distribution strategies compared to establishment of full connectivity. Unlike most previous works we focus on non-uniform distributions of nodes. We introduce and apply modified Thomas point processes and power-law based Stoyan model for generating the locations of nodes. We show also that simple one-dimensional studies, although important, should be extended also to higher dimensional cases in order to achieve reliable results for practical network optimization purposes. Furthermore, we introduce a link between spatial correlations and the number of neighbours. This information can be used in practical topology estimation. Finally, we comment on the realism of our topology claims.

The cognitive radio and cognitive wireless networks paradigms are interesting candidates for the next generation self-organizing and self-reconfigurable wireless networks. In this paper we argue that the concept of cognitive resource manager as an evolution of the classical radio resource manager in the cellular networks is a promising concept to enable dynamic and distributed cross-layer in the future cognitive wireless networks. We further elaborate on the fundamental and implementation challenges which need to be solved to make the cognitive networks reality. Especially we point out the value of perfect information (VPI) should be taken in account in the real implementations. We also describe shortly, how different APIs need to support information reliability estimation.

In this paper we discuss on the dynamic spectrum allocation policies and architectures related to cognitive radios. First, we point out that so called flash crowd effect should be considered as a possible phenomenon in the case of flexible spectrum allocations and cognitive radios. We outline some simple technological and business models approaches that could be used to solve besides of using only traditional MAC-layers. Second, we discuss also on financial and business modeling grounds, whether the dynamic spectrum should be seen in the context of auctions or larger financial markets. Our conclusion is that spectrum auction based valuation might have only limited use to allocate spectrum within short time-scales, but could have high potential in long-term allocation, if hierarchial spectrum management approach is enabled.

This paper describes the design, implementation and performance evaluation of a wireless sensor network based scalable outdoor vehicular tracking system. The system is highly flexible and configurable both from software and hardware architecture point of views and enables it to adapt to a wide range of vehicle tracking applications. We also present some intermediate results and the rationale behind our design approach. The system was tested for a network of 100 nodes and is scalable to a few thousand node setup. We believe that the vehicle localization and tracking results from our large scale deployment of sensor nodes and system design experience are useful to the community

We present the design and implementation of graph colouring -based channel assignment mechanism for infrastructure mode IEEE 802.11 networks. Several enhancements to earlier designs as well as results from a thorough performance evaluation in a WLAN testbed are given. The performance of the colouring scheme is compared with random and "fixed default" channel assignments, as these are the ones commercially available access points most often use. It is shown that the colouring approach leads to improved utilization of wireless resources, and considerably enhances the performance of the networks.

It is desirable that future networks make the use of services and other resources simple for everyone. In that sense, service discovery comes to fill a gap in the networking field, since it eliminates most of the configuration and maintenance tasks. The Universal Plug and Play (UPnP) architecture does so by dividing the communication session in six different phases, and by providing the user with a friendly interface to the services. UPnP was mainly designed with wired or fixed wireless infrastructure networks in mind, and thus is not the optimum solution for the future mobile ad hoc networks (MANETs), where devices are usually battery operated and mobile. In this paper, we present an extension for the UPnP architecture which is designed specifically for wireless ad hoc networks. We made a performance analysis, and show that it has very promising performance characteristics. These extensions were carefully designed to be minimal ones, in order to make it compatible with the existing UPnP framework. The paper shows that UPnP is a promising alternative as service discovery protocol for heterogeneous networks.

In this demonstration paper we show and describe a flexible hardware and software platform for tracking applications. The architecture presented is extendible both on hardware and software sides, allowing for easy inclusion of sensors and signal processing algorithms of different types. During the demonstrations, examples of the software and hardware blocks developed within this generic architecture will be shown, and insight to the design choices and development issues will be given. Both outdoor and table-top versions of the possible demonstrations will be described.

IEEE 802.15.4 was developed to meet the needs for simple, low-power and low-cost wireless communication. In the past couple of years it has become a popular technology for wireless sensor networks. It operates primarily in the 2.4 GHz ISM band, which makes the technology easily applicable and worldwide available. However, IEEE 802.15.4 is potentially vulnerable to interference by other wireless technologies working in this band such as IEEE 802.11 and Bluetooth. This paper gives a short overview of the IEEE 802.15.4 and carefully analyzes the properties and performance of IEEE 802.15.4 through measurement of the RSSI, PER and run lengths distribution using real off-the-shelf hardware. Furthermore we present simulation results from the evaluation of the IEEE 802.15.4 MAC protocol. Finally, we address the coexistence between IEEE 802.11 and IEEE 802.15.4 and measure the impact these two wireless technologies have on each other when operating concurrently and in range

Classical graph colouring and its generalisations have been used to model various frequency and channel allocation processes in different radio and wireless network contexts for some time now. However, most of this work has targeted cellular networks and graphs with a relatively large number of nodes. In this article we demonstrate how graph colouring can be used as a theoretical basis for a protocol to effectively assign channels to WLAN access points. We also give the outline of the protocol operation, and show its effectiveness with real-life wireless networks.