Reconfigurable Intelligent Surfaces (RISs) are recognized as a promising solution for enhancing the energy efficiency (EE) of next-generation wireless networks, attributable to their low power consumption and signal enhancement capabilities. To address the EE maximization in RIS-aided multi-user wireless networks, we propose an innovative graph neural network (GNN)-based framework for the joint optimization of base station (BS) beamforming and RISs phase shifts. The framework models the network as a heterogeneous graph, enabling the GNN to capture complex interactions between RIS and user equipment (UE) nodes. We introduce two distinct feature initialization methods—Vertex-initialized GNN (VIGNN) and Edge-initialized GNN (EIGNN)—and develop specialized loss functions to guide the learning process, with theoretical analyses confirming their convergence. Extensive numerical simulations for both single-cell and cell-free scenarios validate the effectiveness of our approach, achieving up to a 5% improvement in EE over conventional methods and showcasing its scalability and adaptability in dynamic network environments.

In spite of promising outcomes in optimizing energy usage for Radio Access Network (RAN) Base Station (BS) hardware, deployment, and resource management, existing methods frequently lack flexibility for scenarios involving multiple frequencies and technologies of BSs. This investigation presents a comprehensive BS switching strategy based on a threshold, tailored for real-world multi-frequency and multi-technology BSs within the RAN. The proposed approach strategically deactivates BSs using a threshold parameter that determines the maximum allowable growth in transmission power for active BSs, ensuring both coverage for users affected by BS deactivation and energy saving . Simulations conducted on a realistic multi-technology 5G New Radio (NR) RAN in an urban environment validate the efficacy of the proposed strategy, achieving up to 73% of energy saving. The study assesses the influence of the frequency order of BS deactivation and examines user re-association strategies aimed at minimizing either path loss or transmission power.

Most spectrum surveys conducted worldwide demonstrate that the radio-electric spectrum in use at any given location and instant of time is below 25%. Current spectrum management policies and spectrum utilization inefficiency is becoming unsustainable for future development of radio technologies and services. In this context, dynamic spectrum access is a promising technique for improving spectrum utilization efficiency. A key scientific gap is identifying inaccurate spectrum data from hidden nodes that is not homogeneously distributed in the spatial domain and dynamically vary in time and frequency. For bridging this gap, our paper presents the research results of a spectrum feature extraction algorithm based on multi-correlation and Random Forest. Our algorithm is capable of estimating the spectrum utilization pattern in the spatial and frequency domain with a minimum reliability of 92% for a real heterogeneous networking scenario.

in Ultra Wideband (UWB), large ranging errors occur under Non-Line-of-Sight (NLoS) conditions, which significantly degrades positioning accuracy. Human body shadowing (HBS) is a specific case of NLoS, which is a prominent error source for on-body UWB positioning. This work presents a tracking algorithm based on a Particle Filter (PF), designed to mitigate HBS-induced positioning errors by using an orientation-adaptive measurement model, consisting of a bank of Gaussian Mixture Models. The relative orientation is derived from Inertial Measurement Unit (IMU) data, and predicted positions from the tracking algorithm itself. We propose a second tracking algorithm in order to train the adaptive measurement model in a semi-unsupervised way, eliminating the need for accurate ground truth. The proposed algorithm outperforms a state of the art algorithm by an average of 11% (unsupervised) to 39% (supervised) in an experimental evaluation.

Wireless-based human activity recognition has become an essential technology that enables contact-free human–machine and human–environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets’ locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5$\text{m}\times 10$m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.

Facing the climate change and the energy crisis, plenty of challenges remain to achieving the carbon neutrality in the energy intensive industry. This work investigates a flow-shop production scheduling problem minimizing the energy cost without delaying any jobs. The research problem comes from a Belgium printing company with solar panels as its own energy source. Two sub-problems, the job sequence determination and the job-machine allocation, are tackled using a memetic algorithm. An energy-efficient local search heuristic is also designed to make best use of the self-generated electricity. Validated using realistic production data of 12 jobs in a 2 day planning horizon, the proposed method outperforms a standard binary-encoded genetic algorithm in both solution quality (having 27.17% lower energy cost) and the calculation time (saving 69.89%).

A Visible Light Positioning (VLP) network planner holds tremendous economic potential in that it permits designing a roll-out within given cost, illuminance and accuracy bounds. In this manuscript, the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm is applied to simultaneously optimise a roll-out's maintained illuminance and positioning error, by varying the placement of the VLP-enabled LED transmitters. With simulations that differ in positioning and/or environment parameters, the important illuminance-positioning trade-off is revealed. The corresponding Pareto fronts and LED arrangements are studied. Guidelines regarding where to place the LEDs and which LEDs to select for positioning are provided.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve decimeter-level accuracy. However, the performance can degrade significantly under Non-Line-of-Sight (NLoS) conditions. Detection and mitigation of NLoS conditions is a complex problem, and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is an important cause of NLoS. In this paper, we propose an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors by attaching an inertial measurement unit to the UWB tag. Two algorithms are designed and implemented, of which the second algorithm is designed for robustness against errors in the IMU's estimated heading. The proposed algorithms are validated by UWB Two Way Ranging (TWR) measurements, performed in two environments. Two more algorithms are implemented as a benchmark, of which one is based on the estimated first path power, and the other is based on range residuals. The proposed algorithm outperforms the other algorithms in the higher error statistics, achieving a 49% reduction of the p90 error depending on the environment.

Smartphone-based WiFi ranging positioning based on fine time measurement (FTM) always collapses in real-life scenarios. In this work, a novel map-aided particle filter (PF)-based WiFi FTM fingerprinting approach is proposed to address the poor performance of the WiFi FTM ranging positioning. Different from manually collecting fingerprints, this approach utilizes the theoretical received signal strength and geometric distances between the access points and reference points as the fingerprints, which means less labour-intensive work. For accurate WiFi position estimation, a map-aided PF is designed to find the optimal position. Extensive experiments are carried out in the non-line-of-sight (NLoS) and mixed line-of-sight/non-line-of-sight (LoS/NLoS) environments, and the testing results show that the accuracy and stability of FTM fingerprinting are improved by using the mixed RSS and ranging data fingerprints. The minimal mean location errors (MEs) of the PF-based WiFi FTM fingerprinting in NLoS and mixed LoSINLoS conditions are 1.70 m and 1.85 m, respectively. Compared to the classic weighted least square method, the MEs are reduced by 54.91 % and 45.43 %, respectively. The testing results demonstrate that the PF-based FTM fingerprinting is an effective approach that provides satisfactory localization results in real-life indoor environments.

Within the context of the Internet of Things (IoT), many applications require high-quality positioning services. As opposed to traditional technologies, the two most recent positioning solutions: 1) ultra-wideband (UWB) and 2) (unmodulated) visible light positioning [(u)VLP] are well suited to economically supply centimeter-to-decimeter level accuracy. This manuscript benchmarks the 2-D positioning performance of an 8-anchor asymmetric double-sided two-way ranging (aSDS-TWR) UWB system and a 15-LED frequency-division multiple access (FDMA) received signal strength (RSS) (u)VLP system in terms of feasibility and accuracy. With extensive experimental data, collected at two heights in a 8 m by 6 m open zone equipped with a precise ground-truth system, it is demonstrated that both visible light positioning (VLP) and UWB already attain median and 90thpercentile positioning errors in the order of 5 and 10 cm in line-of-sight (LOS) conditions. An approximately 20-cm median accuracy can be obtained with uVLP, whose main benefit is it being infrastructureless and thus very inexpensive. The accuracy degradation effects of non-LOS (NLOS) on UWB/(u)VLP are highlighted with four scenarios, each consisting of a different configuration of metallic closets. For the considered setup, in 2-D and with minimal tilt of the object to be tracked, VLP outscores UWB in NLOS conditions, while for LOS scenarios similar results are obtained.

This paper presents a fast, memory-efficient, and worldwide map matching algorithm based on raw geographic coordinates and enriched open map data with support for trajectories on foot, by bike, and motorized vehicles. The proposed algorithm combines the Markovian behavior and the shortest path aspect while taking into account the type and direction of all road segments, information about one-way traffic, maximum allowed speed per road segment, and driving behavior. Furthermore, a self-adapting lane detection algorithm based solely on accelerometer readings is added on top of the map matching algorithm. An experimental validation consisting of 30 trajectories on foot, by bike, and by car, showed the efficiency and accuracy of the proposed algorithms, with an average F1-score and median error of 99.5% and 1.89 m for the map matching algorithm and an average F1-score of 86.7% for the lane detection algorithm, which resulted in the correctly estimated lane 93.0% of the time. Moreover, the proposed technique outperforms existing state of the art techniques with accuracy improvements up to 45.2%.

The integration of advanced sensing, signal processing, deep learning, and edge computing into vehicles is enabling intelligent automated vehicles that can navigate autonomously in various environments. There are several exciting developments in new technologies that may contribute to the improvement of the robustness of autonomous vehicles and thus making them safer on the road. However, the development of suitable context prediction methodologies in order to provide proactive behavior for intelligent transportations remains a challenge. The reason is that future context information, hidden in the raw context traces left by users in the real world, is not immediately accessible to applications. Therefore, sophisticated context prediction approaches are required that could discover and mine patterns (e.g., of a driver’s behavior) from observed context history. The major challenge of a context prediction approach is in the prediction accuracy and prediction expressiveness. Neural networks along with deep-learning methods have shown noticeably better performance in comparison with previous methods regarding the accuracy of the outcomes. However, deep learning also issues more complexity and interpretability problems and, hence, arises serious challenges regarding the verifiability of these approaches. This Special Issue aims to provide the scientific community with a comprehensive overview of innovative technologies, advanced architectures, and potential challenges for context prediction of autonomous vehicles.

This paper proposes a genetic algorithm (GA) combined with ray tracer to generate a cell-free topology of massive MIMO (mMIMO) for the optimal focusing performance serving multiple users. The realistic hardware impairment, for instance the non-ideal power amplifier, is taken into account of the system modeling and topology optimization. To the best of our knowledge, this is the first attempt to apply GA in optimizing the hardware-impaired multi-user cell-free mMIMO. Although the demonstrated numerical analysis is for indoor scenario, the proposed approach is transferable for generic scenarios. In GA, the base station (BS) antennas’ placement is encoded with an adjusted binary matrix representation, which is straightforward for the subsequent genetic operations. The explored candidates by GA can evolve beyond the parents, where the fitness of individuals is evaluated dynamically via a ray tracer radio channel simulator. Compared to the traditional GA, our proposed GA can find better solutions with a faster convergence speed. The algorithm provides near-optimal results in experiments, applicable to generic environment with multiple mobile users and different signal-to-interference-plus-noise ratios.

With the ongoing push towards an automated Industry 4.0, data-driven intelligent algorithms are getting more attention. Warehouse operators have traditionally required human labor to identify and register their assets. Autonomous flying drones will help alleviate this task by flying through the warehouse and detecting assets. This can be done based on vision, requiring expensive and energy consuming hardware, limiting drone flight time. In contrast, we propose a solution using radio-frequency identification (RFID) tags and machine learned algorithms to localize assets, which does not require a well-lit environment and can be processed in an energy efficient way. Our machine learning model achieves a 92–93 % accuracy, even when the drone is flying at different heights than the assets. Additionally, the model is easily implementable on off-the-shelf and low-energy consuming embedded hardware. This data-driven solution can easily be retrained for different environments and allows cheap RFID-based horizontal localization of assets in warehouses of the future.

The next-generation automated inventory management solution revolves around unmanned aerial drones that operate without any human intervention, including during (wireless) charging. As the inductive power transfer system’s efficiency depends on its coil alignment, a precision landing system is required that is lightweight, accurate, and of low-cost. This manuscript demonstrates the potential of received signal strength (RSS) Visible Light Positioning (VLP) with a single photodiode (PD), which is inherently lightweight. A landing system is proposed, in which a PD-equipped drone self-localises with respect to light-emitting diodes (LEDs) that are integrated into the landing zone. The LEDs are furthermore sequentially strung together to cut costs, thereby forming a LED strip. Automated measurements characterise the particular (propagation) challenges and the positioning performance of 3D VLP on a small scale, when mimicking drone landing on a flat surface or in a commercial funnel. For the flat surface, a 50 Hz update rate and a vertical range up to 1 meter, the target of a third quartile VLP-only positioning error bounded by 3.5 cm is attained with multilateration and a 6-element LED strip. The presence of the reflections-inducing drone funnel degrades the positioning performance significantly. However, a sufficient accuracy can still be reached. Both configurations exhibit larger errors close to the landing zone.

We present a new floor number detection algorithm for use in smartphone-based indoor localisation systems. It is designed to complement any pedestrian dead reckoning (PDR) algorithm able to detect steps and estimate a 2D trajectory from data of the smartphone’s inertial measurement unit.Our proposed method is based on the Viterbi algorithm, fusing data from an off-the-shelf smartphone’s accelerometer, barometer and wifi received signal strength (RSS) measurements. The accelerometer is used to detect accelerating elevators, while the barometer is used to detect stair climbing. This is combined with model-based wifi RSS fingerprinting, enabling accurate floor number detection. Our system is tested in an office environment with 7 41 m x 27 m floors, each of which has 2 pre-existing wifi access points. The algorithm is evaluated with a total of 116 minutes of recorded data, in which the floor number changed 76 times and a distance of 4.8 km was travelled. Since the Viterbi algorithm allows to easily correct past states (i.e. floor numbers) based on new information, it is evaluated in real-time and batch mode. Our proposed algorithm achieves a floor number detection accuracy of 99.1% (real-time) and 99.7% (batch), while using only RSS measurements resulted in 91% accuracy.

This work assesses the applicability of the well-known SAGE algorithm for time-of-arrival estimation on ultra-wideband (UWB) measurements taken with cheap COTS hardware. Performance is comparable with a simple leading-edge detection (LDE) algorithm, establishing a general precision of approximately 30 cm/60 cm. SAGE performance is slightly worse in general (33 cm/71 cm), but is more stable in non-line-of-sight (NLOS) caused by human body presence. A more detailed breakdown of the effect of incidence angle on one-dimensional ranging accuracy is studied in relationship to human body shadowing effects. Within a cone of 135 degrees in front of the UWB device (pointing away from the body), the azimuthal incidence angle has no influence on the ranging performance of either algorithm.

Fifth generation networks (5G) will be associated with a partial shift to higher carrier frequencies, including wavelengths comparable in size to insects. This may lead to higher absorption of radio frequency (RF) electromagnetic fields (EMF) by insects and could cause dielectric heating. The yellow fever mosquito (Aedes aegypti), a vector for diseases such as yellow and dengue fever, favors warm climates. Being exposed to higher frequency RF EMFs causing possible dielectric heating, could have an influence on behavior, physiology and morphology, and could be a possible factor for introduction of the species in regions where the yellow fever mosquito normally does not appear. In this study, the influence of far field RF exposure on A. aegypti was examined between 2 and 240 GHz. Using Finite Difference Time Domain (FDTD) simulations, the distribution of the electric field in and around the insect and the absorbed RF power were found for six different mosquito models (three male, three female). The 3D models were created from micro-CT scans of real mosquitoes. The dielectric properties used in the simulation were measured from a mixture of homogenized A. aegypti. For a given incident RF power, the absorption increases with increasing frequency between 2 and 90 GHz with a maximum between 90 and 240 GHz. The absorption was maximal in the region where the wavelength matches the size of the mosquito. For a same incident field strength, the power absorption by the mosquito is 16 times higher at 60 GHz than at 6 GHz. The higher absorption of RF power by future technologies can result in dielectric heating and potentially influence the biology of this mosquito.

In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.

The fading process in vehicular communications is inherently non-stationary. In this paper, vehicle-to-infrastructure (V2I) radio channel measurements are performed inside a tunnel for low and medium traffic conditions to estimate the stationarity time, in addition to the time-varying RMS delay and Doppler spreads. Furthermore, we show the good fit of the spreads to a lognormal distribution, as well as for the Rician K-factor of the fading amplitude. From our analysis we conclude that the traffic density has an impact on the large-scale parameters as it increases delay and Doppler spreads, while reducing the correlation between them as well as the average K-factor. Larger traffic densities may be required to impact the stationarity time.

The article describes a numerical approach for Massive MIMO channel modeling that accounts for the effects of electromagnetic coupling between a user and the receiving device. The modeling is performed by a combination of the Finite-Difference Time-Domain and the Ray-Tracing methods, supplemented with a stochastic geometry model of the propagation environment. The influence of user-coupling on the channel properties was studied statistically using the singular value spread and matrix power ratio metrics of the channel correlation matrix. The time-averaged Poynting vector distribution in the near-field of the receiver was evaluated using a realistic human phantom model and the Maximum Ratio Transmission precoding scheme in the downlink. The average enhancement of the time-average Poynting vector magnitude at the receiver location, compared to the surrounding area, was found to be around 10 dB when using 36 antenna elements at the base station. The electromagnetic field exposure of the phantom was assessed in terms of the 10g-average peak-spatial Specific Absorption Rate and compared with the existing public guidelines. Comparison of the EMF and exposure results provides a new perspective on the future regulatory procedures.

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule.

Fingerprinting indoor localization provides high positioning accuracy with low cost and easy deployment. Considering the unsatisfying precision of received signal strength (RSS)-based fingerprinting, hybrid metrics including angle-of-arrival (AoA) and time-of-flight (ToF), are incorporated to the RSS fingerprinting system. To evaluate the positioning performance of hybrid metrics, the closed-form Cramér-Rao lower bound (CRLB) is derived in this paper. The existence conditions of CRLBs, as well as the relationship of the CRLBs between single and hybrid metrics is revealed. Numerical results based on an office building scenario show that hybrid metrics greatly improve the positioning performance and the robustness to measured standard deviations compared to the single metric's case. Furthermore, hybrid schemes of the AoA/RSS/ToF metrics are also investigated, and simulations reveal that the scheme of AoA/ToF-supporting access points (AP) enhanced with single RSS-supporting APs achieves the best positioning accuracy among all hybrid schemes.

This paper assesses the accuracy of a three-dimensional Visible Light Positioning (VLP) algorithm for two different Light Emitting Diode (LED) configurations using the same four LEDs, but mounted at different locations on the ceiling. The two configurations are both simulated and measured at 22801 test points. It is observed that a classic square LED configuration results in position ambiguities, causing errors up to several meters. Alternatively, a star-shaped LED configuration is able to uniquely reconstruct the photodiode's location. For LEDs at a height of approximately 3 m above the receiver, median errors of 12.7 cm and maximal errors of 21.1 cm are experimentally obtained, showcasing the applicability of 3D VLP for drone navigation.

When a disaster occurs, the land-based cellular network could go offline for some days. Using an Unmanned Aerial Base Station (UABS) network is a promising solution to serve unconnected ground users. In this article, we propose a multifrequency backhaul architecture, which considers power and capacity constraints, to support the UABS network in a realistic 3D scenario in the city of Ghent, Belgium. Simulations results show that at the optimal flight height (80 m), up to 87% of the users could be supported using the multifrequency scenario compared with single frequency scenarios where coverage is about 70%.

Price-based demand response stimulates factories to adapt their power consumption patterns to time-sensitive electricity prices, so that a rise in energy cost is prevented without affecting production on the shop floor. This paper introduces a multiobjective optimization (MOO) model that jointly schedules job processing, machine idle modes, and human workers under real-time electricity pricing. Beyond existing models, labor is considered due to a common tradeoff between energy cost and labor cost. An adaptive multiobjective memetic algorithm (AMOMA) is proposed to fast converge toward the Pareto front without loss in diversity. It leverages feedback of cross-dominance and stagnation in a search and a prioritized grouping strategy. In this way, adaptive balance remains between exploration of the nondominated sorting genetic algorithm II and exploitation of two mutually complementary local search operators. A case study of an extrusion blow molding process in a plastic bottle manufacturer and benchmarks demonstrate the MOO effectiveness and efficiency of AMOMA. The impacts of production-prohibited periods and relative portion of energy and labor costs on MOO are further analyzed, respectively. The generalization of this method was further demonstrated in a multimachine experiment. The common tradeoff relations between the energy and labor costs as well as between the makespan and the sum of the two cost parts were quantitatively revealed.

This paper is the design of a Radio Environment Map (REM) with a real-time tool to sense the radiofrequency spectrum and optimally places with Surrogate Modelling and Sequential Experimental Design tools a total of 72 SDR sensors in the selected area, using LoRa and/or NB-IoT technologies for networking. It permits the regulatory body to check the correct use of the assigned spectrum and constitutes a communication alternative in case of a catastrophic event, such as a hurricane or an earthquake, where radio and TV broadcasting play an important role in keeping people informed after such meteorological event. The radiobroadcast services use large antennas and high towers, making them vulnerable to such events. Regardless of the chosen technology, the IoT monitoring network will be more robust, since it uses small antennas and lower towers, and often a given area is covered by multiple base stations. The tool can be used to deploy new services in the nonserved area (e.g., 4G in the 700 MHz band at a lower cost or using TVWS techniques to provide communications and internet connection) and optimal interference management.

The precise (location) tracking of automated guided vehicles will be key in enlarging the productivity, efficiency and safety in the connected warehouse or production infrastructure. Combining the modest price tag, the adequate coverage and the potential centimetre accuracy makes Visible Light Positioning (VLP) systems appealing as replacements for the current, high-cost, tracking systems. Model-fingerprinting-based received signal strength (RSS) VLP enables the required accuracy. It requires an elaborate optical channel model fingerprinted in a fine-grained, and predefined positioning grid. Depending on the grid's granularity, constructing the fingerprint database demands a significant computation and storage effort. This paper employs response adaptive or sequential experimental design to form sparse channel models, vastly reducing the storage and computation. It is shown that model-fingerprinting-based RSS only requires modelling less than 1 percent of the grid points, in an elementary positioning cell. The sparse model can be re-evaluated as a way to cope with environment changeover. Model recomputation as a way of compensating for LED ageing is also studied.

This paper experimentally compares the positioning accuracy of TDoA-based and RSS-based localization in a public outdoor LoRa network in the Netherlands. The performance of different Received Signal Strength (RSS)-based approaches (proximity, centroid, map matching,...) is compared with Time-Difference-of-Arrival (TDoA) performance. The number of RSS and TDoA location updates and the positioning accuracy per spreading factor (SF) is assessed, allowing to select the optimal SF choice for the network. A road mapping filter is applied to the raw location estimates for the best algorithms and SFs. RSS-based approaches have median and maximal errors that are limited to 1000 m and 2000 m respectively, using a road mapping filter. Using the same filter, TDoA-based approaches deliver median and maximal errors in the order of 150 m and 350 m respectively. However, the number of location updates per time unit using SF7 is around 10 times higher for RSS algorithms than for the TDoA algorithm.

We develop and benchmark four RSS localisation algorithms where different a priori knowledge is required. The selection of the best algorithm depends on the availability of additional information on path loss exponent and/or transmit power. We compare our algorithms with centroid localization and show that the algorithms provide better results for shadowing on the values not exceeding 6dB. We perform experiments and simulations with Bluetooth Low Energy and LoRaWAN technologies and select the best technology and algorithm for localisation in large open industrial environments.

Current (theoretical) Visible Light Positioning (VLP) systems assume the transmission of ideal waveforms. Existing VLP LED transmitters, however, transmit far from perfect signals, causing a plunge in the positioning accuracy. The impact of transmitter nonidealities, such as overshoot and rise time, remains unclear. The lack of quality (industry-ready) LED drivers presents a major hurdle in large-scale VLP adoption. The extent to which transmitter nonidealities are tolerable, is valuable information for LED driver designers. This paper provides specifications for the latter. It charts the potentially detrimental impact of transmitter nonidealities on the positioning performance. The influence of both overshoot and rise/fall time is studied.

Tissue-mimicking phantoms are widely used in literature for testing devices. Current studies are focused on achieving wideband phantoms for technologies covering wide bandwidths. Many of these phantoms have been reported for frequencies that are currently in use for mobile communications services, but not for upcoming 5G systems. This work aims at developing new phantoms with a working frequency band within 24.25 and 67 GHz for these new communication systems. Taking into account that the penetration depth of the body tissues is really low, the skin was the target tissue. Two issues were addressed, the meeting of the dielectric target values with new compounds and the gelling of these mixtures to get firm materials. For this purpose, different simple alcohols were analyzed as candidates for mimicking the skin. Furthermore, it is intended to model these data with different mixing rules in order to estimate the final relative permittivity of the solutions. Additionally, the solutions were gelled with k-carrageenan, which has promising mechanical properties without altering the dielectric ones.

In the next generation of wireless communication networks, Massive MIMO remains the appropriate technology to provide higher throughput to the users with incredible gains in terms of power consumption and energy efficiency. However, current research rarely considers the electromagnetic field exposure during the network design phase. In this paper, we propose a simulation-based method that enables an optimal design of the massive MIMO 5G networks with respect to both power consumption and electromagnetic field exposure. The results of the simulations show that the massive MIMO network achieves the same performance in terms of users coverage with 10 times less power consumption and an electric field strength 17 times weaker in comparison with the traditional 4G network.

Beamforming is one of the key features enabled in the fifth generation (5G) of wireless communications networks to accommodate the higher throughput demanded by the users for their data-intensive applications. This paper simulates energy-efficient 5G networks with beamforming capabilities deployed on a realistic area in Ghent, Belgium to respond to the instantaneous bit rate needed by the users. Various beamforming architectures have been investigated and the results are compared with the 4G reference network. When beamforming is enabled, the results of the simulations show that under the same coverage performance, 5G networks require 15% more base stations to provide more capacity to the users and are 3 times more energy-efficient than the 4G reference network. Moreover, the hybrid beamforming architecture provides good trade-off between the higher capacity and the low-power consumption requirements and needs to be considered when designing 5G cellular networks.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.

Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time‐frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

The performance of LoRa geolocation for outdoor tracking purposes has been investigated on a public LoRaWAN network. Time Difference of Arrival (TDoA) localization accuracy, update probability, and update frequency were evaluated for different trajectories (walking, cycling, and driving) and LoRa spreading factors. A median accuracy of 200 m was obtained for the raw TDoA output data. In 90% of the cases, the error was less than 480 m. Taking into account the road map and movement speed significantly improves accuracy to a median of 75 m and a 90th percentile error of less than 180 m.

For the first time, a wearable personal distributed exposimeter (WPDE) is designed and calibrated for the Long-Term Evolution (LTE) 800 and 2600 MHz downlink bands. The proposed WPDE has a 68% confidence interval of 4.8–5.6 dB for different number of antennas and polarizations. Measurements of the WPDE are compared and validated with a commercial exposimeter in a real environment.

LoRa is a long range, low power, low bit rate, single hop wireless communication technology. It is intended to be used for Internet of Things (IoT) networks, where devices are battery powered and limited bandwidth is needed. In combination with its scalability and the low end device price, LoRa is a candidate technology for low bandwidth industrial applications with a high number of communication devices spread across large areas. The use case for this paper is taken from the flower industry, where a large number of trolleys need to communicate with a server during their movement across the auction floor area. Once trolleys are outside of the auction floor they can use the public LoRaWAN network to communicate with the server, without switching communication technology. The LoRaWAN network consists of multiple end nodes and a single gateway per cell, acting as a transparent bridge between the end nodes and the network server. The measurements show that with a single LoRa gateway we can cover an indoor area of around 34000m2 only with spreading factor 7, while for spreading factor 12 the total covered area will be even higher. Also, the area outside the factory is covered when switching to spreading factor 12. We also show that the number of nodes (trolleys) that can be served by a gateway in such a case can be as high as 6000.

Due to the fast emergence of location-based services and the absence of a widely adopted localization technology for indoor environments, Indoor Localization Systems have become a central topic of research in the last decade. Although there is a significant amount of research targeting indoor localization technologies and their performance, most of these efforts only focus on theory, system design or evaluation in non-industrial environments, usually offices or healthcare spaces. In this work, a detailed performance evaluation of two commercially available accurate localization technologies, based on Bluetooth Low Energy (LE) and Ultra-wideband (UWB), in an industrial environment is presented to create an experimental understanding of their behaviour in similar conditions and to investigate their potential to be used in industrial applications with concrete localization requirements. For this purpose, these localization technologies are examined with respect to various performance criteria in several scenarios in a real industrial site.

Using an Unmanned Aerial Base Station (UABS) i.e., a base station carried by a UAV (Unmanned Aerial Vehicle) or drone, is a promising approach to offer coverage and capacity to those users that are not being served by the base stations of the terrestrial network. In this paper, we propose an approach to the design of the drone's trajectory to account for the quickly varying user traffic and pattern. This approach is based on the identification of clusters made of nearby users to be served. The decision on which cluster to visit next by the UABS depends on a cost-function considering the distance to the next cluster, the user density and spread in the cluster, and the direction compared to the previously visited cluster. Furthermore, we propose a radio resource assignment algorithm to minimize the interference from the UABS to the terrestrial network when both are operating in the same frequency band. The potential improvements in terms of network capacity (sum throughput) and user satisfaction are estimated in this study.

Wireless network deployments in industry often grow organically with new technologies added over time, among which many use the non-licensed spectrum to avoid licensing costs. As a result, technologies competing for the same spectrum end up deployed in the same area, causing coexistence problems to manifest themselves at a later stage. To avoid unexpected performance degradation, there is a need to evaluate the impact of additional wireless technologies on an existing network before the actual deployment. This paper proposes to simplify the impact assessment by emulating the signals of the potential wireless network with a single software-defined radio. To evaluate the emulator's performance, the impact of Bluetooth on Wi-Fi technology is considered as the reference scenario. A series of real-life experiments with configurable traffic load and network scale are conducted to estimate the impact of Bluetooth network on a Wi-Fi link, and the corresponding measurements are repeated with the emulated Bluetooth signals. To the best of the authors' knowledge, we are the first to propose such a solution, and it is shown that the use of our emulator gives a reliable indication of the expected impact at the location of the Wi-Fi link. As such, this paper provides an important step toward a simple, cost efficient, and reliable solution, to assess the impact of a wireless network prior to its deployment.

This paper presents a physical-statistical radio channel power delay profiles model for room-to-room communication systems combining the room electromagnetic theory for modeling deterministic channel components with a geometry-based stochastic channel model with time-variant statistics for modeling stochastic components. The deterministic channel component, i.e., mean power delay spectrum, is comprised of specularly reflected paths plus diffuse components due to scattering and diffraction. The specular components are modeled with a set Dirac function, whereas the diffuse components modeling approach is a room electromagnetic theory-based model. Dynamic indoor communication channels are characterized by a non-stationary time- and delay-fading process due to changes in the environment. We analyze and model the time-delay variability of channels using K-factor for small-scale variations and the t-location scale distribution parameters for large-scale variations. It turns out that these parameters cannot be assumed to be constant in time and delay. After modeling of time-delay variations of the first order statistics, we generate channel realizations with appropriate second order statistics. As the result, the presented model enables to describe the evolution of the power delay profile in the time domain.

Nowadays, the energy price is rising. The consciousness of environmental sustainability of governments and customers has been ever increasing. Consequently, manufacturing enterprises are increasingly motivated to reduce the energy cost involved in their production activities. This paper proposes a novel production scheduling method to minimize the energy cost involved in the production at the unit process level. Compared to the emerging energy-conscious production scheduling methods, this method builds the finite state machine based energy model from power data that are measured from the shop floor. By following the formulated mixed integer linear programming model, the power states and changeovers of a unit process can be additionally scheduled, and the potential multiple process idle modes can be optimally selected between two jobs. In addition, the process power consumption behavior can be predicted along with the optimal schedule. This method was demonstrated in an extrusion blow molding process in a Belgian plastic bottle manufacturer. Compared to two conventional schedules, i.e., “as-early-as-possible” and “as-late-as-possible”, the schedule given by the proposed method is able to reduce 21% and 11% of electricity cost for completing the same production task before a due date.

Wireless communication is an enabling technology for industrial automation. For mobile industrial devices operating in large areas, the performance of the wireless handover process is crucial. For the welfare of industrial processes short time communication outage must be ensured, especially for time-critical traffic. This paper assesses the handover performance for three industrial real-life use cases with different requirements. It covers handover performance under heavy interference, its impact on time-critical traffic and on broadcast traffic latency, followed by lessons learned and opportunities for further research.

In this study, the potential of applying different strategies to reduce the energy consumption of a wireless access network, powered by a photovoltaic panel system, during energy shortages is investigated. The goal is to reduce the amount of energy that should be bought from the traditional energy grid during a renewable energy shortage. Three different strategies are compared and the results are looking very promising. Depending on the strategy, up to 72% less energy should be bought compared to the fully operational network for a worst case scenario and a time period of 1 week. However, applying such a strategy has also its influence on the network performance. The influence on the user coverage is limited, with a reduction of 3% at maximum, but the capacity offered by the network decreases significantly with 51% up to even 71%.

A mobile application is presented for real-time testing and optimization of path-loss models and network planning, based on the execution of validation measurements in the considered environment. The application is tested in three indoor scenarios and for three path-loss models. Without optimization, average absolute prediction errors of about 5 dB are obtained, with a simple free-space model performing best. Executing a limited set of ten additional measurements suffices to improve predictions by up to more than 40%. The application is particularly useful for very quick path-loss model tests in a certain environment or for easily obtaining more accurate network deployments, as a single measurement only takes a few seconds and optimization of the path-loss model is fully automated.

In case of a large scale disaster, the wireless access network can become quickly saturated. This is of course undesirable because for this kind of situations we actually need a reliable wireless connectivity. In this study, the potential of mounting LTE femtocell base stations on drones to offer an alternative for the saturated existing wireless infrastructure is investigated. Our preliminary results show that this a very promising approach although a high amount of drones are needed to cover all users in the city center of Ghent, Belgium during a 1h intervention. The number of drones can be significantly reduced (up to 64%) by using a more advanced type of drone, by decreasing the user coverage requirement (11% less drones when requiring 80% instead of 90%) or by increasing the fly height of the drones (about 10% less drones needed when increasing the fly height by 10 m). This study shows that it is interesting to further investigate the use of drones to provide an emergency wireless access network.

This paper presents a future terminal antenna to enhance the performance of wireless container monitoring systems. It first introduces an optimized design that is experimentally validated for Wireless Personal Area Network (WPAN) technology taking into account the harsh environment of containers. The proposed antenna exhibits excellent performance next to the container in terms of return loss, gain, and bandwidth. Further, deterministic and stochastic path loss models for inter-container link budget calculations are used to illustrate the impact of the optimized antenna on path loss and attainable maximum ranges.

Wireless body area networks have raised considerable attention within the wireless personal area community. To optimize the network architecture and the energy consumption of on-body equipments, a good knowledge and understanding of the radio link and the propagation channel are required. This work addresses the influence of medium presence on the reflection loss of horn and textile antennas. In particular, the path loss in free space and under skin-equivalent phantom of horn-to-horn and textile-to-textile communications is presented. The obtained results show that the path loss differs considerably in the analyzed situations; with horn-to-horn links showing a path loss exponent between 1.13 and 2.35, while it is reaching 4.7 for textile-to-textile links. The obtained results provide additional input for the design of communications links in the context of future 5G and IoT eco-systems.

Wireless systems often need to optimize multiple conflicting objectives (low delay, high reliability, and low cost), which are difficult to fulfill simultaneously. In such cases, the wireless system exhibits multiple optimal operation points, referred to as the optimal Pareto front (OPF). However, due to the large number of parameter settings to be evaluated and the time-consuming nature of performing wireless experiments, it is typically not possible to identify the OPF by exhaustively evaluating all possible settings. Instead, for many use cases, an approximation is good enough. To this end, this paper applies a multi-objective surrogate-based optimization (MOSBO) toolbox to efficiently optimize wireless systems and approximate the OPF using a limited number of iterations. Moreover, a real Wi-Fi conferencing scenario is optimized that has two conflicting objectives (exposure and audio quality) and four configurable parameters (Tx-Power, Tx-Rate, Codec Bit-Rate, and Codec Frame-Length). The benefits of using the MOSBO approach for such a network problem is demonstrated by approximating the OPF using 94 iterations instead of requiring the exploration of 6528 different parameter combinations, while still dominating 96.58% of the complete design space.

The performance loss of 802.11 OFDM systems due to propagation delay spread has been analyzed as a function of OFDM parameters for a wide range of reverberation times. This analysis gives physical insight and solutions for the OFDM design to suppress the performance degradation.

Biomedical implantable sensors transmitting a variety of physiological signals have been proven very useful in the management of chronic diseases. Currently, the vast majority of these in-body wireless sensors communicate in frequencies below 1 GHz. Although the radio propagation losses through biological tissues may be lower in such frequencies, e.g., the medical implant communication services band of 402 to 405 MHz, the maximal channel bandwidths allowed therein constrain the implantable devices to low data rate transmissions. Novel and more sophisticated wireless in-body sensors and actuators may require higher data rate communication interfaces. Therefore, the radio spectrum above 1 GHz for the use of wearable medical sensing applications should be considered for in-body applications too. Wider channel bandwidths and smaller antenna sizes may be obtained in frequency bands above 1 GHz at the expense of larger propagation losses. Therefore, in this paper, we present a phantom-based radio propagation study for the frequency bands of 2360 to 2400 MHz, which has been set aside for wearable body area network nodes, and the industrial, scientific, medical band of 2400 to 2483.5 MHz. Three different channel scenarios were considered for the propagation measurements: in-body to in-body, in-body to on-body, and in-body to off-body. We provide for the first time path loss formulas for all these cases.

The performance of OFDM systems over a multipath channel can strongly degrade due to the propagation delay spread. The distortion of the received signal over the fast Fourier transform window is referred to as multipath noise. This work aims to analytically determine the performance loss due to multipath noise as a function of OFDM and channel parameters for narrowband OFDM systems. First, it is investigated whether it is possible to describe the multipath noise, varying over different OFDM packets due to the temporal variation of the channel, by an effective noise factor Fdelay, from which the loss factor is directly determined. Second, the theory of room electromagnetics is applied to develop a closed-form expression for Fdelayas a function of the OFDM and reverberation parameters. This analytical method is validated with excellent agreement. Finally, the loss factor is determined for IEEE 802.11 based on channel measurements in two large conference rooms, providing values up to 19 dB for an 800 ns cyclic prefix length.

Every year in Europe, about 1500 people die in traffic because they are not noticed by the truck driver. This problem could be solved by developing a wireless communication system where the truck driver and the cyclist are informed about each others presence. In this paper a test setup is presented in which the position of the cyclist is determined and displayed on a screen when being in the neighborhood of a truck. The cyclist gets an indication about notification by the truck. Because of the fast changing network, the cyclist must be added quickly to the network and the position must be updated very fast. For this reason a Zigbee communication system is used. The position of the cyclist is displayed in zones around the truck. The setup is experimentally tested and it is demonstrated that the proposed setup leads to a reliable and fast method to reduce the number of blind spot incidents.

The goal of this paper is to investigate the performance of a mobile WiMAX system for various settings of its physical-layer parameters and for realistic propagation channels. For this, a physical layer model of IEEE 802.16e is developed in software. Different propagation channels are implemented, such as the Rayleigh channel and the SUI channel models. Moreover, a tapped delay line channel model is developed for a specific area in Ghent (Belgium) using raytracing software. For this area, the maximum achievable range of a realistic mobile WiMAX system is found to be 2.7 km. Additionally, the performance gain of MIMO systems with diversity is investigated. The diversity gain of a 2x2 MIMO Alamouti diversity scheme is found to vary between 3 and 5.5 dB.

For Mobile WiMAX, data rate and throughput figures are often presented in literature. However, the used calculation methods and the influence of the packet size on the data rate are often unclear. Therefore, we develop a new method in order to obtain more precise data rate figures at the MAC (Medium Access Control) layer. In this paper the common physical data rate and MAC data rate calculation method are first examined. Next, the new MAC data rate calculation method is presented. They are applied to the official set of Mobile WiMAX parameters. Our method is able to differentiate for different packet sizes and provides a prediction of the MAC layer throughput.

Wireless body area networks (WBANs) offer many promising new applications in the area of remote health monitoring. An important element in the development of a WBAN is the characterization of the physical layer of the network, including an estimation of the delay spread and the path loss between two nodes on the body. This paper discusses the propagation channel between two half-wavelength dipoles at 2.45 GHz, placed near a human body and presents an application for cross-layer design in order to optimize the energy consumption of different topologies. Propagation measurements are performed on real humans in a multipath environment, considering different parts of the body separately. In addition, path loss has been numerically investigated with an anatomically correct model of the human body in free space using a 3-D electromagnetic solver. Path loss parameters and time-domain channel characteristics are extracted from the measurement and simulation data. A semi-empirical path loss model is presented for an antenna height above the body of 5 mm and antenna separations from 5 cm up to 40 cm. A time-domain analysis is performed and models are presented for the mean excess delay and the delay spread. As a cross-layer application, the proposed path loss models are used to evaluate the energy efficiency of single-hop and multihop network topologies.

This paper investigates the actual measured performance of an 802.16-based system. A measurement methodology for evaluating the performance is proposed, which is then used for studying and comparing the results of different scenarios. More specifically, the influences of changing the modem height from 2.5 m to 6 m and base station height from 15 m to 45 m are analyzed and discussed in this paper, and it will be shown that only the latter one has a significant effect on the coverage and the performance. Furthermore, the relationship between the carrier-to-interference-noise ratio (CINR) and the wireless link throughput is analyzed, and a semi-empirical model will be proposed. The model remains identical for the different scenarios, emphasizing the usefulness of the obtained model. Indoor reception is also analyzed, and it will be shown that the reception at locations close to the window is approximately 7 dB higher than deeper inside the building. A profound statistical analysis is then performed to validate both the performance measurements and the model, and excellent correspondence is obtained. Finally, as the system supports link adaptive modulation and coding, the results of its effectiveness are discussed in this paper.

Cable operators offer today triple-play services (internet, video, and telephony) to their customers over Hybrid Fiber/Coax (HFC) networks. As the bandwidth in such an HFC network is shared by a service group of 2000 homes or less, and as the demand for bandwidth for the triple-play services heavily increases, redesign of this network may be needed to enhance the bandwidth per customer. In this short paper, two heuristic methods for reducing the number of homes per service group by moving the fiber closer to the homes at minimum cost are presented. These heuristic methods are investigated for three different areas: urban, suburban, and rural environments. The preliminary results show that nearly equal results are obtained for both heuristic methods and that the highest investment costs are generally obtained for rural areas.

This paper presents the experimental results and analysis of a link variability study for an indoor, stationary 802.11g network in a small office environment. Variability over time of throughput, jitter, and signal-to-noise ratio (SNR) is investigated. The network performance at different indoor locations is analyzed and the relationships of throughput and jitter to the SNR are obtained. The throughput prediction model is compared to other empirical models, showing the similarity between these models. A statistical distribution function of the SNR in this environment is determined, resulting in an SNR higher than 29.83 dB in 90% of the locations, corresponding with a throughput of about 18.25 Mbps and a jitter lower than 1.10 ms.

Users in todaypsilas mobile ICT environment are confronted with more and more innovations and an ever increasing technical quality, which makes them more demanding and harder to please. It is often hard to measure and to predict the user experience during service consumption. This is nevertheless a very important dimension that should be taken into account while developing applications or frameworks. In this paper we demonstrate a software tool that is integrated in a wireless living lab environment in order to validate and quantify actual user experience. The methodology to assess the user experience combines both technological and social assets. User experience of a Wineguide application on a PDA is related to signal strength, monitored during usage of the applications. Higher signal strengths correspond with a better experience (e.g. speed). Finally, difference in the experience among users will be discussed.

In this paper, large-scale fading and temporal fading characteristics of the industrial radio channel at 900, 2400, and 5200 MHz are determined. In contrast to measurements performed in houses and in office buildings, few attempts have been made until now to model propagation in industrial environments. In this paper, the industrial environment is categorized into different topographies. Industrial topographies are defined separately for large-scale and temporal fading, and their definition is based upon the specific physical characteristics of the local surroundings affecting both types of fading. Large-scale fading is well expressed by a one-slope path-loss model and excellent agreement with a lognormal distribution is obtained. Temporal fading is found to be Ricean and Ricean K-factors have been determined. Ricean K-factors are found to follow a lognormal distribution.

Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.

In this paper, the performance of a system based on IEEE 802.16-2004 is analyzed for different conditions. The influence of different parameters of the system is investigated. Channel models are compared as well as the use of MIMO systems. The throughput and range are determined for different diversity schemes for a realistic scenario at 3500 MHz for business applications.

Fixed wireless access systems operating below 11 GHz have the potential to provide broadband wireless access for non line-of-sight operation. In this paper the performance of a typical broadband fixed wireless system based on the IEEE 802.16-2004 specifications is determined. A scenario for business applications with outdoor customer premises equipment is investigated in the 3.5 GHz frequency band. Different path loss models and terrain types are considered. Coverage and throughput in a sector are determined for this business scenario