Coordinated service offloading is essential to meet Quality-of-Service (QoS) targets under non-stationary edge traffic. Yet conventional schedulers lack dynamic prioritization, causing deadline violations for delay-sensitive, lower-priority flows. We present PRONTO, a multi-agent framework with centralized training and decentralized execution (CTDE) that jointly optimizes SLA-aware offloading and adaptive service orchestration. PRONTO builds on Twin Delayed Deep Deterministic Policy Gradient (TD3) and incorporates spatiotemporal, topology-aware graph attention with top-K masking and temperature scaling to encode neighborhood influence at linear coordination cost. Gated Recurrent Units (GRUs) filter temporal features, while a hybrid reward couples task urgency, SLA satisfaction, and utilization costs. A priority-aware slicing policy divides bandwidth and compute between latency-critical and throughput-oriented flows. To improve robustness, we employ stability regularizers (temporal smoothing and confidence-weighted neighbor alignment), mitigating action jitter under bursts. Extensive evaluations show superior QoS and channel utilization, with up to 27.4% lower service delay and over 18% higher SLA Satisfaction Rate (SSR) compared with strong baselines.

This paper examines the application of federated reinforcement learning (FRL) to enable resource-constrained vehicular edge nodes to learn their communication parameters from a central parameter server (PS). In cellular vehicle-to-everything communication (C-V2X), non independently-and-identically-distributed (non-i.i.d.) data samples impose additional communication requirements and increase the training time for model convergence. By exploring correlations between local model updates and the global model aggregation distributions, we accelerate this convergence using FRL. In the proposed method, Q-values undergo weight adaptation at each training round to update the global model. Local gradient vectors at vehicles and global gradient vectors at the PS measure the contribution of vehicle local models. Furthermore, the Q-values are quantified via nonlinear mapping that reinforces positive rewards, leading to dynamic measurements of local model contributions. Using FRL, policy-based and value-based learning methods reduce the number of communication rounds by upto 40%.

The sixth generation (6G) mobile communication system is expected to utilize millimeter wave and THz frequency bands, in addition to RF bands. The propagation characteristics and ranges of these bands vary vastly while the multi-band users supposedly experience seamless coverage, high throughput and consistent Quality of Service. Heterogeneous base stations (BSs) equipped with these multiple technologies shall be fairly loaded for this. SINR based approaches tend to assign more users to RF channels while starving other bandwidth rich mediums. In this paper, we propose an algorithm to improve the performance of multi-band 6G networks by optimizing the user association to heterogeneous BS to maximize the cumulative data rate while ensuring an acceptable transmission power and fair load balancing among the BSs. The optimization problem is solved using the Lagrangian method. Simulation results show an improved cumulative throughput and fairness.

This paper studies automatic modulation classification (AMC) for cognitive radio systems. We propose a deep learning neural network approach enhanced with an intelligent attention mechanism to correctly classify, detect, and segment spatially distributed modulation data. AMC is achieved by training the neural network to focus only on specific significant regions learnt using the attention mechanism. The proposed approach is tested for detection efficiency and accuracy to distinguish different modulation data using the publicly available RML2016.10a dataset. The outcome shows the accuracy of the proposed scheme is comparable to other state-of-the-art deep learning algorithms with a reduced complexity. The real-time assessment of the temporal states is achieved based on the spectral characteristics of modulation constellation images at various signal-to-noise ratio (SNR) values. The model performance is evaluated using mean average precision (mAP), F1 score, and speed-accuracy trade-off.

This paper addresses object detection and scene perception for connected and autonomous vehicles. Road object detection at high accuracy and fast inference speed is a challenging task for safe autonomous driving as false positives arising from false localization can lead to fatal outcomes. The paper proposes a convolutional neural network (CNN) to recognize images to enhance intelligent adaptive behavior in autonomous vehicles by correctly classifying, detecting, and segmenting spatially distributed objects in the driving environment. By focusing on specific regions of an image, the most significant region of the image is learned by appending a CNN with probabilistic attention mechanism aided with transformers. The proposed approach is analyzed for detection efficiency and accuracy to distinguish different objects to make appropriate driving decisions. The proposed method is validated on the publicly available Berkeley deep drive (BDD) dataset and shows an accuracy comparable to other state-of-the-art deep learning algorithms to make driving decisions based on real-time assessment of the temporal states encountered while navigating the driving environment. The proposed model performance is evaluated using mean average precision (mAP) and speed-accuracy trade-off.

It is well known that life style and dietary habits correlate to certain medical conditions such as Heart Disease, Stroke, and Diabetes. A type of big data that reflects people’s life style and dietary habits is their purchasing history. People who are prone to certain medical conditions might show preference towards certain food types. Purchasing history of a simulated population of 100,000 individuals is used to demonstrate that buying people’s purchasing history records from big companies could be a worthwhile investment by the public health agencies. A Neural Network that uses the Kohonen’s Self Organizing Feature Map (SOFM) is used on the purchasing data to group people into categories based on their buying habits. Individuals who have already been diagnosed with a medical condition can be identified by their purchase of certain prescribed medications. The segment of the population that clusters into a group that includes these individuals is predicted to be at risk for the same medical condition. Numerical results supporting the Big Data analytic design and validation are also presented.

Reinforcement learning is a machine learning technique that focuses on exploring an uncharted territory exploiting of current knowledge. This paper proposes a Mobility Aware Channel Allocation (MACA) algorithm for 5G Vehicular Networks using a combination of Multi-Agent Reinforcement Learning (MARL) and Semi-Markov Decision Process (SMDP). In this work, we use multiple autonomous agents operating in a common environment to address the sequential decision-making problem to optimize the long-term rewards. In MACA, first we predict the mobility of vehicles using Teammate-Learning model as it allows the vehicles to cooperate and collaborate with each other without prior coordination. Secondly, during SMDP resource allocation phase, MARL inputs are applied to the Action Selection model for each vehicle based on their priorities. This is done at Road-Side Units (RSUs). Through numerical results and evaluations, we verify that the proposed method demonstrates efficient channel allocation and high packet delivery ratio as compared in the scenario of vehicles with multiple (high, medium, and low) priorities to existing conventional SMDP and Greedy algorithms.

This article presents a comprehensive survey of visible light communication (VLC) between devices in 6G internet of things (IoT) architecture. For effective stationary and mobile device-to-device communication in both indoors and outdoors, VLC is envisaged as a technique that can enable a robust and inexpensive, interference and radiation-free IoT communications. Whereas the demands on the growth in IoT network traffic and expanded verticals are met through 5G, 5G+ and beyond 5G (B5G); communication between two IoT devices in close vicinity without resorting to radio frequency (RF) spectrum usage is still a challenging problem and lies at a crucial research stage. One potential solution is to resort to optical wireless communication (OWC), especially VLC to venture into alternatives to radio frequency (RF) communication. In this article, we aim to bridge the gap between VLC and its applications in IoT through a comprehensive survey of VLC and its applications in IoT. We begin with an introduction to IoT and emerging verticals such as internet-of-metasurfaces, internet-of- reflecting-surfaces, internet-of -nanothings, internet-of-bionanomaterials, and internet-of-space-things. Based on the current survey, several recommendations for further research are discussed at the end of this article.

This paper investigates the reliability assessment and availability prediction techniques used in modeling of advanced (next generation) wireless communication networks. The 5G, 5G+, beyond 5G (B5G) and 6G communication technologies are leading to emerging applications of wireless communication that use cloud computing, edge computing, and fog computing. In the last decade, various user-centric and service-oriented networks such as internet of things (IoT), smart cities, smart homes, smart grids, drones, and unmanned aerial vehicles (UAV) have been deployed that use technologies such as network function virtualization (NVF), software defined networking (SDN), and 5G. This has led to proliferation of IoT devices and IoT applications in various critical usage systems such as intelligent transportation systems, smart healthcare, and e-commerce. The availability and reliability of wireless connectivity in IoT devices and nodes is of significant importance as unavailability of nodes or end-user devices even for a millisecond could cause failure of healthcare systems or lead to malfunction of connected and autonomous vehicles, or compromise the smart grids power generation and distribution, leading to fatal outcomes.

Shot noise due to direct sunlight is the major cause of SNR degradation in vehicle to vehicle visible light communications (V2V-VLC) outdoors. This shot noise can be simply reduced by the receiver ‘looking away’ from the sun rays. However, this has to be done without reducing the received optical signal power, which is a 3-D tracking problem, where both transmitter and receiver are moving. This paper analyzes the resulting optimization problem and finds the optimal angle at which the instantaneous SNR is maximized. This depends on the incident angle of the optical signal as well as the incident angle of the sunlight. Note, the mobility adds random additive noise in addition to rapid angle variations. The paper uses an extended- Kalman filter to minimize the error and possible drift in the optimal angle in the presence of noisy measurements. This results in an adaptive SNR optimization system for real-time vehicle to vehicle visible light communications. We have also presented the analytical solution to the SNR optimization problem. Simulation results, on a curved freeway on a bright sunny day, demonstrate close matching between the actual optimal SNR and that achieved by the extended-Kalman filter.

Cognitive Wireless Sensor Networks (CWSN) are known to be energy constrained because they are required to perform cognitive functions in addition to sensing, which reduces the lifetime of CWSN. RF Wireless Energy Harvesting (RFWEH) is known to improve network lifetime by compensating energy losses through harvesting gain without any additional hardware. However, the allocation of time slots for energy harvesting results in degradation of network parameters such as latency and throughput. In this paper, we propose an energy harvesting MAC protocol (R-MAC) for CWSN that balances network energy losses with harvested energy gains, thereby resulting in a self sustainable CWSN. This is done such that both latency and throughput remain close to predefined target values. R-MAC employs TDMA and relies on clustering for creation and dissemination of the schedule. We also propose an algorithm to add harvesting slots based on a recursive regression technique to achieve the energy balance. Using simulation we compared its performance with the well known cognitive KoNMAC protocol, after incorporating energy harvesting in it. The simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, resulting in an energy self sufficient network while maintaining throughput and latency within acceptable limits.

Accurate traffic flow prediction plays a crucial role in designing Ad hoc vehicular mobile networks in modern Internet of things (IoT) based intelligent systems. Several deep learning techniques have been deployed to predict traffic conditions to make vehicular communication more reliable. However, not all these approaches deal with complex road networks and spatial temporal dependencies of traffic data. In this paper, we analyze this problem using long short-term memory (LSTM), gated recurrent unit (GRU) and hybrid CNN-LSTM models. We trained our models using actual traffic flow data provided by the California Department of Transportation (Caltrans) over a 6 month duration and showed that our deep learning models outperform the traditional linear regression method. Moreover, an architectural study of deep learning models is carried out for the traffic flow prediction problem. The performance of these models is evaluated using MSE and MAE metrics. It is observed that the GRU model is the best to handle the complex vehicular traffic mechanisms. Also, that a complex hybrid model like CNN-LSTM does not always outperform the much simpler architectures such as LSTM and GRU.

The coordination of modern transportation system depends heavily on intelligent techniques, information assortment, and its analysis. Sensors play a crucial role in information assortment in charging scheduling of electric vehicles (EVs). EVs are destined to become inevitable due to their innate economic contribution, climate improvement, and social attributes as per United Nation's sustainable development goals. Innovation in EV has gained the interest of many researchers since it is one of the novel green transportation sectors. Moreover, EVs are essential to preserve conventional fuels and to maximize the utilization of renewable sources. Nevertheless, EVs have short driving ranges due to their battery limitation, which hinders the reliability. The charging stations (CS) for EVs are also unevenly distributed. This paper presents a novel strategy to schedule the charging points in EV CSs. The goal is to determine the convenient CS for EVs through Vehicular Ad-hoc Network (VANET) model. In this model, the CSs are determined and prioritized using four phases, such as driving, charge planning, charging scheduling, and battery charging. Charging scheduling was designed using a newly developed optimization strategy, exponential Harris Hawks optimization (Exponential HHO) algorithm, which combines two algorithms, Harris Hawks optimization (HHO) and exponential weighted moving average (EWMA). Furthermore, the fitness function was also newly devised by considering parameters such as average waiting time, remaining energy, number of EVs, and distance. The proposed Exponential HHO was validated using VANET simulation and the performance was improved with maximum remaining energy of 52.709 Whr, minimal distance of 27.256 km, and a maximum average waiting time of 0.352 min in comparison with existing methods. To be specific, the proposed Exponential HHO yielded better improvement, especially when considering a large number of vehicles.

LoRa is a promising wireless technology for various sensing and positioning applications in Smart Cities. LoRa uses Chirp Spread Spectrum (CSS) with different Spreading Factors (SF) to handle varying intensities of multipath reflections and interference. However, the standard LoRaWAN uses the pure ALOHA algorithm that suffers from both Intra-SF collisions and Inter-SF collisions which limit it to mostly low density environment. In this work, we optimize the transmission parameters of a LoRaWAN system in high density Smart City traffic environment using golden section search and parabolic interpolation. Our approach of optimum distribution of spreading factors not only significantly improves the success rate, but also enable more nodes to use lower spreading which results in lower delay.

Device‐to‐device (D2D) communication can effectively use cognitive radio network approach to coexist with cellular users. For such a cognitive D2D system, two approaches (underlay and overlay) are considered to manage the spectrum sharing among the cellular (primary) users and the D2D (secondary) users. Energy efficiency (EE) is crucial in both these cases due to limited battery capacity and quality of service requirements of the D2D users. This study effectively models the power allocation problem of such a cognitive D2D system by maximising the EE of the D2D users subject to a minimum rate requirement for both the D2D users and the cellular users. This leads to a non‐linear fractional optimisation problem which is more complicated and computationally intractable. Alternatively, geometric water‐filling approach have been utilised for power allocation to solve this optimisation problem which results in an ‘ exact ’ and ‘ low complexity ’ solution. Simulation results reveal the benefits of the proposed algorithm.

Heterogeneous networks (HetNets), with low power nodes and distributed antennas will be an integral part of 5G wireless networks. Finding optimum Micro/Pico base station (BS) locations covering all tiers is a challenging issue. Poisson point process (PPP) stochastic model has been adopted to decide the BS location of in cellular networks, where cells have irregular shapes and coverage areas. In this paper, we develop an algorithm to find the optimum location of low power Micro BSs in high demand dense networks to improve signal to interference and noise ratio (SINR). Also, we discuss a novel pilot sequence length optimization technique in multi-tier networks for better performance under real channels to improve sum rate capacity and mean squared error. We have used an analytical model to study the performance of a HetNet massive MIMO system in a rich scattering multipath Rayleigh fading channel using Clarke model and compared it with simulation results.

Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.

Automation of modern industrial plants require real-time tracking of object locations and sensing of local and ambient parameters for variety of applications such as counting and tracking of objects in assembly line, detection and positioning of failures of machines etc. Mostly, discrete Real Time Location System (RTLS) performs object tracking in existing industrial automation without its integration with the sensing and control network, which constrains application's responsiveness. In this paper, we propose an integrated solution of Wireless Positioning Sensor Network (WPSN) that is designed for accuracy, reliability, scalability and optimal network operation. The proposed WPSN is applicable for harsh indoor industrial environments for not only monitoring and control of plant operations but also for identification, localization and tracking of assets and inventory in industrial warehouses. The indoor industrial environment poses challenging conditions for radio signal propagation that adversely affects reliability of communication of sensing and location data. We approach reliability by incorporating redundancy and making our network reconfigurable through adaptive intelligent learning process. We employ adaptive clustering technique to address the need of scalable deployment for varied industrial scenarios. We include hybrid localization scheme to provide high precision positioning but with fallback reduced precision operation to deal with long-term channel impairment. The WPSN is connected with backend cloud infrastructure for low cost monitoring and control.

Free space optical transmission suffers from the line of sight requirement and object induced shadowing. We investigate free-space optical beam steering by means of an optical phased array (OPA) in order to increase signal strength in the desired direction and allow greater mobility. To overcome beam squint and jitter, a novel heterodyne feedback is introduced as a method to stabilize the output beam. This may alleviate the effects of shadowing and enable optical space division multiple access and tracking.

With the ever-increasing population and change in lifestyle, transport and communication is becoming more demanding. Every year, roads see significant increase in the number of vehicles. Urbanization and convergence of population makes it more challenging to handle communication in a more secure and efficient way. Several accidents and mishaps happen mainly because of drivers' error. Due to this, it becomes very important to monitor driver behavior in real-time. With several sensors doing this job, in addition to sensors for the vehicle and environment, data aggregation and communication becomes yet another challenge. To cope with time-sensitivity of the data communication, fog computing paradigm is to be involved. In this paper, we present a fog-based architecture for driver behavior monitoring and assisting intelligent transportation system (ITS). We provide evaluation on how much fog can assist in this regard, by making a comparison of cloud-only and fog-cloud scenarios.

There has been a lot of work on emergency reporting in smart transportation systems, but we find less information about road side asset reporting and management. Currently available mechanisms do not efficiently handle asset management and associated emergency reporting. Asset management is based on either reactive maintenance (reported by people) or preventative scheduled maintenance (scheduled). In this article, we present a reporting architecture for emergency situations and, management through Foglets and visible light communication (VLC). We propose the use of Foglets near road sides to improve information processing and Quality of Service (QoS) and to reduce communication delay. We use the VLC link between Foglets and smart vehicles and discuss its safety and suitability for the road assets infrastructure, as compared to conventional RF links. We model this VLC link and discuss its luminosity and BER characteristics.

In recent years, Internet of Things (IoT) has attained considerable attention by the research community. IoT is an interconnected network of smart devices capable of sensing and communicating ubiquitously. Cognitive radio networks (CRN's) are recommended to increase capacity for next generation wireless networks for IoT applications. To prolong battery life of these unattended smart devices, energy harvesting is an obvious solution. RF Wireless energy harvesting (RFWEH), in which freely available ambient energy from RF sources is utilized to increase battery life, has gained much attention recently. In this paper, we propose a hybrid source switch-based RFWEH technique which adaptively switches between high-power RF sources, in contrast to previous works, in which low power sources are preferred. We show that high-power RF sources, such as TV and radio, are preferred over low power sources such as cell phones, Wi-Fi etc. Our simulation results show that RFWEH from radio antennas outperform RFWEH from TV antennas even though the former operates at a much lower transmit power. Furthermore, switch-based RFWEH gives better performance compared with single source RFWEH.

In cognitive radio networks, multichannel spectrum sensing yield accurate results compared to single channel sensing schemes. However, often realistic correlation among multiple sensing channels is ignored. In this paper, we investigate the performance of a cognitive radio spectrum sensing systems considering more realistic exponential correlation among multiple sensing channels. Closed-form expressions for average detection probabilities are derived considering dual and triple correlated branches in a Selection Combining (SC) scheme. Nakagami-m fading channels are considered with different fading severities. Numerical results show the inter-branch correlation impacts the detector performance significantly, especially in deep fading scenarios.

Emergency situations in a mining environment have the potential for the loss of human life. Tracking mobile assets and personnel can significantly improve the safety of all miners. Existing distributive localization algorithms are not well suited for a mobile asynchronous mine environment. We propose the Mobile Cooperative Localization Algorithm (MCLA) to increase the accuracy and number of nodes which are localized. The proposed algorithm has been validated through simulation using ns-3 and demonstrates an increase in accuracy for a mobile distributed environment.

Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.

Directional antennas are known for spatial reuse, extended range, less interference, and less energy consumption as compared to omnidirectional antennas. In this paper, we propose an energy-efficient scheduled directional MAC (DTRAMA) algorithm for sensor networks, which is based on TRAMA with some modifications to exploit spatial reuse of directional antennas. In TRAMA, nodes achieve energy efficiency by following traffic-adaptive communication and sleep schedules. DTRAMA introduces spatial reuse checks in TRAMA to allow an otherwise sleep node to schedule transmission. Our simulation results show that DTRAMA is effective in exploiting spatial reuse of directional antennas by improving packet delivery ratio and packet delay of TRAMA. It, however, compromises energy efficiency by reducing average sleep time of a node.

In this work, we analyse the performance of Cognitive Radio Spectrum Sensing (CRSS) systems with multiple receiving antennas considering the effect of correlation among fading branches. Exact closed-form expressions for the average detection probabilities (P̅D) are derived employing Probability Density Functions (PDF) approach for n.i.i.d.-L number of diversity branches over Nakagami-m fading channels with Maximal Ratio Combining (MRC) diversity. Performance analysis reveals the detrimental effect of the correlation on detection performance thus decreasing detection probability. However, results also show that this effect could be compensated through employing diversity combining technique and by increasing the diversity branches.

This paper provides performance analysis of relay-based cognitive radio (CR) networks and presents a detect-amplify-and-forward (DAF) relaying strategy for cooperative spectrum sensing over non-identical Nakagami-m fading channels. An advanced statistical approach is introduced to derive new exact closed-form expressions for average false alarm probability and average detection probability. We also introduce a novel approximation to alleviate the computational complexity of the proposed models. This paper points out the inconsistency of several assumptions that are typically used for performance analysis of CR networks and reveals that channel fading on the relaying links yields similar performance degradations as on the sensing channel. The study also shows that it is not necessary to incorporate all CRs in the cooperative process and that a small number of reliable radios are enough to achieve practical detection level. Compared with the amplify-and-forward strategy, refraining the heavily faded relays in the DAF strategy improves the detection accuracy and reduces the bandwidth requirement of the relaying links. The presented analysis could lead to intuitive system design guidelines for CR networks impaired with non-identical faded channels.

In this study, the authors address the problem of bandwidth limitations of the reporting channels in cognitive radio (CR) networks. They propose a cluster-based spectrum-sensing approach that minimizes the bandwidth requirements by reducing the number of terminals reporting to the fusion centre to a minimal reporting set. The approach replaces the secondary base station by a local fusion centre and combats the destructive channel conditions by replacing the global reporting channels with local channels. They also propose a new approach to select the location of the local fusion centre using the general centre scheme in graph theory. The minimal dominating set (MDS) clustering algorithm is used to obtain the minimal set of clusters that keep the network connected. This study investigates how the sensing efficiency, the sensing accuracy, and the per-node throughput are affected by the cluster size, the number of clusters, and the reporting channels error. The results obtained reveal that the cluster-based cooperative sensing system outperforms the conventuional cooperative sensing system in terms of throughout capacity especially when the reporting channels are subjected to a high probability of error. A systematic way to find the optimal number of cooperative clusters that gives a minimum probability of false alarm is presented.

Fingerprinting is chosen as the localization approach as fingerprinting has a higher accuracy than other approaches such as time-of-arrival or angel-of arrival. This paper introduces a positioning system based on IEEE802.15.4/ZigBee-based sensor networks. The system uses fingerprinting and employs Support Vector Machines (SVMs) to estimate node position. The system is cost-effective since it works with real deployed IEEE 802.15.4/ZigBee sensors nodes. The whole system requires minimal setup time, which makes it readily available for real-world applications.

Fiber-wireless (Fi-Wi) access fronts can support 100s of Mb/s envisioned by 4G networks. However, a major issue associated with Fi-Wi links is the nonlinear distortion of the radio-over-fiber (ROF) link coupled with the multipath dispersion of the wireless channel. Estimation and subsequent equalization of the concatenated fiber-wireless channel needs to be done, especially at high bit rates. The uplink is severely affected due to large fluctuations in the radio signal. This paper proposes an estimation and subsequent equalization algorithm for the Fi-Wi CDMA uplink. The estimation employs the properties of pseudo noise (PN) sequences and the equalization uses a novel Hammerstein type decision feedback equalizer (HDFE). The estimation and equalization are performed in the presence of multiple access interference (MAI) and wireless and optical channel noise. The cumulative effects of multiuser interference, multipath dispersion, nonlinear distortion, and noise are all considered in our analysis. Correlation properties of white-noise like PN sequences enable decoupling of the linear (wireless) and nonlinear (optical) channel portions. Furthermore, we propose a unique algorithm to mitigate MAI. Numerical evaluations show a good estimation and equalization of both the linear and nonlinear channels. Bit error rate (BER) simulations show that this algorithm leaves only small residual MAI.

Radio-over-fibre (ROF) has received increasing attention for its ability to enable broadband wireless access. This fibre-based wireless access scheme meets the demand for broadband service by integrating the high capacity of optical networks with the flexibility of radio networks (the optical and wireless channels are concatenated with one another). There are, however, impairments that come with this appealing technology. The nonlinear distortion of the optical link and the multipath dispersion of the wireless channel are two of the major factors. In order to limit the effects of these distortions, estimation, and subsequently equalisation, of the concatenated fibre-wireless channel needs to be done. An estimation algorithm for the fibre-wireless uplink in a multiuser code division multiple access (CDMA) environment is presented using pseudonoise training sequences. It has already been shown by Fernando et al. (2001) that identification of the fibre-wireless uplink is possible in a single user CDMA environment. However, the more difficult task of identification in a multiuser spread spectrum environment, which is more realistic, is shown. In the multiuser case, the cumulative effect of multiuser interference, multipath dispersion, nonlinear distortion and noise should all be handled together which makes it more challenging. Numerical evaluations of the developed algorithm show that a good estimation of both the linear and nonlinear systems is possible in the presence of 16 independent users and an signal-to-noise ratio (SNR) of 22 dB. The estimation accuracy increases with the length of the PN sequence.

Dual mode handsets that support both cellular and wireless local area network (WLAN) interfaces have been recently introduced by manufacturers like Nokia. Wireless hotspots also better to be enhanced to support both these services. Simultaneous subcarrier multiplexed transmission of both WLAN and cellular radio signals over a single radio-over-fiber (ROF) link is possible for hotspot enhancement. However, link design in this multi-system scenario is a complex task. There are number of quality measures such as signal to noise, distortion and interference ratios involved both in the optical link and in the air interface. These are functions of several parameters such as the fiber length, system bandwidth, modulation depth, radio cell size and relative RF power. The scenario is quite challenging when two RF systems are involved. In this paper we analyze such a dual system downlink and show how to decide the cumulative optical modulation index (mu) and the RF power ratio (T) that will yield the best performance for both systems

Optical fiber based wireless access schemes receive renewed attention with the popularity of hot-spots. They increase capacity, QoS and support wideband multimedia services and have the possibility of utilizing existing fiber infrastructure. However, link design in a fiber-wireless system needs careful consideration of many factors. There are two signal to noise ratios involved, the optical SNR (OSNR) and the electrical SNR. These two form the cumulative SNR in the concatenated fiber-wireless channel. The OSNR is a function of the modulation index m, E/O, O/E conversion losses and, the fiber length. There is a 39 dB loss due to E/O and O/E conversion only in resistively matched wideband links and the OSNR rapidly decreases with fiber length. The cumulative SNR at the mobile unit decides the QoS and cell size. This SNR depends on OSNR, wireless channel path loss and the optical receiver amplifier gain. In this paper, we study the relationships between critical design parameters, such as maximum radio and optical link losses, cumulative and optical SNR and, optical amplifier gain in a fiber-based wireless system.

The biggest limitation of radio over fiber (ROF) links in a wireless network is its limited dynamic range due to 'non-linear distortions' (NLD). In this paper a higher order adaptive filter based modeling and predistortion scheme is proposed to compensate this NLD. The filter is adapted from the distortions of vector-modulated symbols, so that no in-depth knowledge of physical link parameters is needed. Experimental and simulation results show that a third order filter accurately models the ROF link while a second order filter adequately compensates for the phase nonlinearity. The power handling capability of the laser diode is the upper limit in this approach.