The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly within the Radio Access Network (RAN). This growing energy demand raises operational and sustainability challenges for mobile network operators, requiring novel solutions to enhance energy efficiency while maintaining Quality of Service (QoS). 5G networks are evolving towards disaggregated, programmable, and intelligent architectures, with Open Radio Access Network (O-RAN) spearheaded by the O-RAN Alliance, enabling greater flexibility, interoperability, and cost-effectiveness. However, this disaggregated approach introduces new complexities, especially in terms of power consumption across different network components, including Open Radio Units (RUs), Open Distributed Units (DUs) and Open Central Units (CUs). Understanding the power efficiency of different O-RAN functional splits is crucial for optimising energy consumption and network sustainability. In this paper, we present a comprehensive measurement study of power consumption in RUs, DUs and CUs under varying network loads, specifically analysing the impact of Physical resource block (PRB) utilisation in Split 8 and Split 7.2b. The measurements were conducted on both software-defined radio (SDR)-based RUs and commercial indoor and outdoor RU, as well as their corresponding DU and CU. By evaluating real-world hardware deployments under different operational conditions, this study provides empirical insights into the power efficiency of various O-RAN configurations. The results highlight that power consumption does not scale significantly with network load, suggesting that a large portion of energy consumption remains constant regardless of traffic demand.

This paper explores the application of uplink fronthaul compression techniques within Open RAN (ORAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) systems. With the ever-increasing demand for high data rates and system scalability, the fronthaul load becomes a critical bottleneck. Our method uses ORAN compression techniques to efficiently compress the fronthaul signals. The goal is to greatly lower the fronthaul load while having little effect on the overall system performance, as shown by Block Error Rate (BLER) curves. Through rigorous link-level simulations, we compare our quantization strategies against a benchmark scenario with no quantization, providing insights into the trade-offs between fronthaul data rate reduction and link performance integrity. The results demonstrate that our proposed quantization techniques not only lower the fronthaul load but also maintain a competitive link quality, making them a viable solution for enhancing the efficiency of next-generation wireless networks. This study underscores the potential of quantization in O-RAN contexts to achieve optimal balance between system capacity and performance, paving the way for more scalable and robust DD-MIMO deployments.

Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcement learning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.

The advent of 6G technology demands flexible, scalable wireless architectures to support ultra-low latency, high connectivity, and high device density. The Open Radio Access Network (O-RAN) framework, with its open interfaces and virtualized functions, provides a promising foundation for such architectures. However, traditional fixed base stations alone are not sufficient to fully capitalize on the benefits of O-RAN due to their limited flexibility in responding to dynamic network demands. The integration of Unmanned Aerial Vehicles (UAVs) as mobile RUs within the O-RAN architecture offers a solution by leveraging the flexibility of drones to dynamically extend coverage. However, UAV operating in diverse environments requires frequent retraining, leading to significant energy waste. We proposed transfer learning based on Dueling Double Deep Q network (DDQN) with multi-step learning, which significantly reduces the training time and energy consumption required UAVs to adapt to new environments. We designed simulation environments and conducted ray tracing experiments using Wireless InSite with real-world map data. In the two simulated environments, training energy consumption was reduced by 30.52% and 58.51%, respectively. Furthermore, tests on real-world maps of Ottawa and Rosslyn showed energy reductions of 44.85% and 36.97%, respectively.

Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, FL deployments in wireless networks face significant challenges, including communication overhead, unreliable connectivity, and high energy consumption, particularly in dynamic environments. This paper proposes EcoFL, an integrated FL framework that leverages the Open Radio Access Network (ORAN) architecture with multiple Radio Access Technologies (RATs) to enhance communication efficiency and ensure robust FL operations. EcoFL implements a two-stage optimisation approach: an RLbased rApp for dynamic RAT selection that balances energy efficiency with network performance, and a CNN-based xApp for near real-time resource allocation with adaptive policies. This coordinated approach significantly enhances communication resilience under fluctuating network conditions. Experimental results demonstrate competitive FL model performance with 19 % lower power consumption compared to baseline approaches, highlighting substantial potential for scalable, energy-efficient collaborative learning applications.

Deploying new optimised routing policies on routers in the event of link failure is difficult due to the strong coupling between the data and control planes and the absence of topology information about the network. Because of the distributed architecture of traditional Internet protocol networks, policies and routing rules are spread in a decentralised way, resulting in looping and congestion problems. Software-defined networking (SDN) enables centralised network programmability. As a result, data plane devices just focus on packet forwarding, leaving the control plane's complexities to be managed by the controller. Thus, the controller centrally installs the policies and rules. Considering the controller's knowledge of the global network architecture, central control enhances the flexibility of link failure identification and restoration. Therefore, this paper uses SDN architecture to enhance network resilience against link failures by introducing the Hybrid Intelligent Fast Failure Recovery (HIFFR) framework, which aims to improve the speed and effectiveness of network failure recovery.

The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (BSs), which account for the majority of power usage in mobile networks. The O-RAN architecture, with its emphasis on open and intelligent design, offers a promising framework to address the Energy Efficiency (EE) demands of modern telecommunication systems. This paper introduces two xApps designed for the O-RAN architecture to optimize power savings without compromising the Quality of Service (QoS). Utilizing a commercial RAN Intelligent Controller (RIC) simulator, we demonstrate the effectiveness of our proposed xApps through extensive simulations that reflect real-world operational conditions. Our results show a significant reduction in power consumption, achieving up to 50% power savings with a minimal number of User Equipments (UEs), by intelligently managing the operational state of Radio Cards (RCs), particularly through switching between active and sleep modes based on network resource block usage conditions.

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires significant retraining when the UAV is confronted with a new environment, resulting in wasted resources and time. Therefore, it is essential to develop techniques that can reduce the overhead of retraining DRL models, enabling them to adapt to constantly changing environments. This paper presents a novel method to reduce the need for extensive retraining using a double deep Q network (DDQN) model as a pre-trained base, which is subsequently adapted to different urban environments through Continuous Transfer Learning (CTL). Our method involves transferring the learned model weights and adapting the learning parameters, including the learning and exploration rates, to suit each new environment's specific characteristics. The effectiveness of our approach is validated in three scenarios, each with different levels of similarity. CTL significantly improves learning speed and success rates compared to DDQN models initiated from scratch. For similar environments, Transfer Learning (TL) improved stability, accelerated convergence by 65%, and facilitated 35% faster adaptation in dissimilar settings.

Integrating unmanned aerial vehicles (UAVs) into wireless communication as aerial platforms to mount small cell base stations has grown rapidly in recent years. One of the main objectives of UAV integration into wireless networks is to optimize UAV deployment while meeting user expectations with the fewest UAVs. To ensure that users receive the requested data rate, management of UAV placement and user association is necessary due to the limited capacity of aerial base stations. Besides the user-base station distance, environmental conditions and propagation mode affect the data rate received by the users. When accounting for uncertain conditions, network management decisions become more realistic and productive. This article considers a random propagation mode for each link depending on the environmental conditions of the desired area. We exploit the stochastic programming framework to reflect propagation mode uncertainty in the optimization problem, which impacts the received data rate and path loss. The suggested mathematical formulation determines the minimum number of required UAVs, their 3-D positions, and the best user association strategy. The proposed model also includes interference-aware constraints for optimal radio resource allocation to base stations. The nonlinear path loss and line-of-sight (LoS) probability distribution functions in terms of the base station positions lead to a nonlinear formulation. We obtain a mixed-binary linear formulation by replacing nonlinear functions with their piecewise linear approximations and solve the model accurately using the CPLEX solver. The implementation results show that stochastic approaches provide more accurate diagnoses of the environment, as well as superior performance to deterministic optimization.

The Network Function Virtualization (NFV)-Resource Allocation (RA) problem is NP-Hard. Traditional deployment methods revealed the existence of a starvation problem, which the researchers failed to recognize. Basically, starvation here, means the longer waiting times and eventual rejection of low-priority services due to a ‘time out’. The contribution of this work is threefold: a) explain the existence of the starvation problem in the existing methods and their drawbacks, b) introduce ‘Adaptive Scheduling’ (AdSch) which is an ‘intelligent scheduling’ scheme using a three-factor approach (priority, threshold waiting time, and reliability), which proves to be more reasonable than traditional methods solely based on priority, and c) a ‘Dynamic Prioritization’ (DyPr), allocation method is also proposed for unseen services and the importance of macro- and micro-level priority. We presented a zero-touch solution using Deep Deterministic Policy Gradient (DDPG) for adaptive scheduling and an online-Ridge Regression (RR) model for dynamic prioritization. The DDPG successfully identified the ‘Beneficial and Starving’ services, efficiently deploying twice as many low-priority services as others, reducing the starvation problem. Our online-RR model learns the pattern in less than 100 transitions, and the prediction model has an accuracy rate of more than 80%.

The connectivity-aware path design is crucial in the effective deployment of autonomous unmanned aerial vehicles (UAVs). Recently, reinforcement learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this article, we propose a transfer learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter-wave (mmWave). The teacher path policy, previously trained at the sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov decision process (CMDP). We employ a Lyapunov-based model-free deep$Q$-network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.

In recent years, using the Unmanned Aerial Vehicle (UAV) as a Base Stations (BS) to cover users in wireless networks has increased dramatically. One of the main goals of integrating UAVs into wireless networks is to deploy UAVs in such a way that user expectations are met with the fewest number of UAVs. To achieve this aim, the coverage area of each UAV should include as many users as possible. Furthermore, the resources assigned to the backhaul links for such UAV deployments must fulfill the requirements of users served by each UAV. In this paper the goal is to position the least number of UAVs in a 3D position to cover cellular network users. To provide appropriate quality of service, we consider a maximum path loss allowed for the network. The path loss of potential links is affected by the propagation environment and might vary depending on network structure. To reflect this uncertainty, path loss is expressed as a random variable with a probability distribution based on environmental characteristics. As a result, we're dealing with an optimization problem with uncertain information. We use stochastic programming to work with uncertain information and formulate the UAV positioning and data rate assignment problem. The implementation results of our proposed mixed-binary linear mathematical model and Monte Carlo simulation in various scenarios show its optimum performance in different dimensions.

Orthogonal frequency division multiplexing (OFDM) based non-orthogonal multiple access (NOMA) systems can considerably increase the attainable data rate and spectral efficiency in novel communication systems. High peak-to-average power ratio (PAPR) is one of the main issues in OFDM-based systems. It makes high-power amplifier (HPA) work in the non-linear region and degrades system performance. Partial Transmit Sequences (PTS) based methods are one of the prominent schemes to reduce the PAPR value. However, they have high computational complexity, and they mostly degrade the system’s Bit Error Rate (BER) performance. In this paper, a low complexity PTS-based method, utilizing the dummy sequence insertion (DSI) technique and cyclic shift sequence (CSS) PTS, is proposed to overcome the previous limitations. Using numerical simulations, we will demonstrate that the proposed method outperforms similar ones in terms of PAPR reduction and BER performance with less computational complexity.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

The choice of the transmitting frequency to provide cellular-connected Unmanned Aerial Vehicle (UAV) reliable connectivity and mobility support introduce several challenges. Conventional sub-6 GHz networks are optimized for ground Users (UEs). Operating at the millimeter Wave (mmWave) band would provide high-capacity but highly intermittent links. To reach the destination while minimizing a weighted function of traveling time and number of radio failures, we propose in this paper a UAV joint trajectory and band switch approach. By leveraging Double Deep Q-Learning we develop two different approaches to learn a trajectory besides managing the band switch. A first blind approach switches the band along the trajectory anytime the UAV-UE throughput is below a predefined threshold. In addition, we propose a smart approach for simultaneous learning-based path planning of UAV and band switch. The two approaches are compared with an optimal band switch strategy in terms of radio failure and band switches for different thresholds. Results reveal that the smart approach is able in a high threshold regime to reduce the number of radio failures and band switches while reaching the desired destination.

In vehicular ad hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, big storage and high computation platform are needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of the resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with the resource allocation problem assisted by cloud computation. By integration of EC and software-defined networking (SDN), this study provides a new SDN edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multiagent RL-based approach using experience reply. The proposed algorithm stores the users' communication information and the network tracks' state in real time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study.

Price and the quality of service are two key factors taken into account by wireless network users when they choose their network provider. The recent advances in wireless technology and massive infrastructure deployments have led to better coverage, and currently at each given wirelessly covered area there are a few network providers and each have different pricing strategies. These providers can potentially set unfair expensive prices for their services. In this article, we propose a novel crowdsourcing-based approach for fair wireless service pricing in the Internet of Things (IoT). In our considered oligopoly, the regulatory sets a dynamic maximum allowed price of service to prevent anti-trust behavior and unfair service pricing. We propose a three-tire pricing model, where the regulator, wireless network providers, and clients are the players of our game. Our method takes client preferences into account in pricing and discovers the fair service pricing just above the marginal costs of each network provider. Our results show that our model is not prone to collusion and will converge only if one network announces the fair price.

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot.

Common spatial patterns (CSP) is one of the most popular feature extraction algorithms for brain-computer interfaces (BCI). However, CSP is known to be very sensitive to artifacts and prone to overfitting. This paper proposes a novel dynamic time warping (DTW)-based approach to improve CSP covariance matrix estimation and hence improve feature extraction. Dynamic time warping is widely used for finding an optimal alignment between two time-dependent signals under predefined conditions. The proposed approach reduces within class temporal variations and non-stationarity by aligning the training trials to the average of the trials from the same class. The proposed DTW-based CSP approach is applied to the support vector machines (SVM) classifier and evaluated using one of the publicly available motor imagery datasets. The results showed that the proposed approach, when compared to the classical CSP, improved the classification accuracy from 78% to 83% on average. Importantly, for some subjects, the improvement was around 10%.

Networked flying platforms can be used to provide cellular coverage and capacity. Given that 5G and beyond networks are expected to be always available and highly reliable, resilience and reliability of these networks must be investigated. This paper introduces the specific features of airborne networks that influence their resilience. We then discuss how machine learning and blockchain technologies can enhance the resilience of networked flying platforms.

In this paper, we investigate traits and trade-offs of a system combining Unmanned Aerial Vehicle (UAV)s with Base Station (BS) or Cloud Radio Access Networks (C-RAN) for extending the terrestrial wireless coverage over the sea in emergency situations. Results for an over the sea deployment link budget show the trade-off between power consumption and throughput to meet the Search and Rescue targets.

As the capacity of the optical access networks increases, the case for sharing this capacity amongst multiple operators becomes stronger. In addition to the capital and operating expenditure savings that infrastructure sharing can provide for the operators, providing a higher degree of infrastructure customization will be a strong motivator for operators to participate in the sharing ecosystem. Thanks to the network virtualization technologies, the higher degree of control over the infrastructure can be a motivator for the new virtual operators to join. Given this control, each operator will make decisions for their share of the resources according to their policies. However, when it comes to the infrastructure provider to aggregate all these decisions, ensuring trust becomes vital. It is essential to study the incentives of all the operators and design a sharing mechanism that incentivizes truthfulness. In this paper, we propose such an auction mechanism to monetize the exchange of excess capacity between network operators to increase resource efficiency. The proposed market design is based on a sealed-bid VCG auction for homogeneous multi-item goods with a reserve price. Through market simulations, we show that our proposed market design can achieve all the fundamental economic properties of a market including, truthful value announcing, individual rationality and weak budget balance.

Network Flying Platforms (NFPs) such as unmanned aerial vehicles, unmanned balloons or drones flying at low/medium/high altitude can be employed to enhance network coverage and capacity by deploying a swarm of flying platforms that implement novel radio resource management techniques. In this paper, we propose a novel layered architecture where NFPs, of various types and flying at low/medium/high layers in a swarm of flying platforms, are considered as an integrated part of the future cellular networks to inject additional capacity and expand the coverage for exceptional scenarios (sports events, concerts, etc.) and hard-to-reach areas (rural or sparsely populated areas). Successful roll-out of the proposed architecture depends on several factors including, but are not limited to: network optimisation for NFP placement and association, safety operations of NFP for network/equipment security, and reliability for NFP transport and control/signaling mechanisms. In this work, we formulate the optimum placement of NFP at a Lower Layer (LL) by exploiting the airborne Self-organising Network (SON) features. Our initial simulations show the NFP- LL can serve more User Equipment (UE)s using this placement technique.

In this paper, we investigate, for the first time, the performance of a full-duplex (FD) relaying protocol, where a single-RF spatial modulation (SM) multiple-input multiple-output (MIMO) system is employed at the relay node. We refer to this protocol as SM-aided FD relaying (SM-FDR). At the destination, a demodulator that takes advantage of the direct connectivity between the source and destination is developed in order to maximize its performance. Based on this demodulator, we introduce a mathematical framework for computing the average error-probability of SM-FDR in the presence of residual self-interference (SI). Furthermore, we derive mathematical expressions for computing the achievable rate of SM-FDR. With the aid of these achievable rate expressions, we provide an estimate on the quality of SI cancellation required for the suitability of FD transmission. In addition, we develop and evaluate three relay selection policies specifically designed for the SM-FDR protocol. The mathematical analysis is substantiated with the aid of extensive Monte Carlo simulations. Finally, we also assess the performance of SM-FDR against traditional FD relaying protocols.

In this work we propose a virtualized network architecture for an infrastructure provider that shares the physical resources of a Massive MIMO cell among several virtual network operators (VNOs) using spatial multiplexing. In this architecture the infrastructure provider allocates spatial streams to the VNOs, which enables each VNO to select its own scheduling policy and user priority to differentiate its service from the other VNOs. To assign the spatial streams to the VNOs that value them the most, we propose an auction-based spatial stream allocation approach. We show that the proposed auction- based approach performs very close to the optimal (fixed) approach in the case of homogeneous static VNOs demand. In case of heterogeneous demands, the auction mechanism is able to dynamically allocate the resources according to the needs of different VNOs.

This paper analyses a scenario where a Device-To-Device (D2D) pair coexists with an Orthogonal Frequency Division Multiplexing (OFDM) based incumbent network. D2D transmitter communicates in parts of spectrum left free by cellular users, while respecting a given spectral mask. The D2D pair is misaligned in time and frequency with the cellular users. Furthermore, the D2D pair utilizes alternative waveforms to OFDM proposed for 5G. In this study, we show that it is not worth synchronising the D2D pair in time with respect to the cellular users. Indeed, the interference injected into the incumbent network has small variations with respect to time misalignment. We provide interference tables that encompass both time and frequency misalignment. We use them to analyse the maximum rate achievable by the D2D pair when it uses different waveforms. Then, we present numerical results showing what waveform should be utilized by the D2D pair according to the time-frequency resources that are not used by the incumbent network. Our results show that the delay induced by linearly convolved waveforms make them hardly applicable to short time windows, but that they dominate OFDM for long transmissions, mainly in the case where cellular users are very sensitive to interference.

In this paper, we present an eye tracking dataset of computer game players who played the side-scrolling cloud game Somi. The game was streamed in the form of video from the cloud to the player. This dataset can be used for designing and testing game-specific visual attention models. The source code of the game is also available to facilitate further modifications and adjustments. For collecting this data, male and female candidates were asked to play the game in front of a remote eye-tracking device. For each player, we recorded gaze points, video frames of the gameplay, and mouse and keyboard commands. For each video frame, a list of its game objects with their locations and sizes was also recorded. This data, synchronized with eye-tracking data, allows one to calculate the amount of attention that each object or group of objects draw from each player. As a benchmark, we also show various attention patterns could be identified among players.

Through massive deployment of additional small cell infrastructure, Dense Small cell Networks (DSNs) are expected to help meet the foreseen increase in traffic demand on cellular networks. Performance assessment of architectural and protocol solutions tailored to DSNs will require system and network level simulators that can appropriately model the complex interference environment found in those networks. This paper identifies the main features of DSN simulators, and guides the reader in the selection of an appropriate simulator for their desired investigations. We extend our discussion with a comparison of representative DSN simulators.

This paper addresses the channel selection problem for Long Term Evolution Unlicensed (LTE-U). Channel selection is a frequency-domain mechanism that facilitates the coexistence of multiple networks sharing the unlicensed band. In particular, the paper considers a fully distributed approach where each small cell autonomously selects the channel to set-up an LTE-U carrier. The problem is modeled using a non-cooperative repeated game and the Iterative Trial and Error Learning - Best Action (ITEL-BA) learning algorithm is used to drive convergence towards a Nash Equilibrium. The proposed approach is evaluated by means of simulations in different situations analyzing both the throughput performance and the convergence behavior.

Despite its challenges, cloud gaming is growing its share in the gaming market by attracting more players. This has led to an increasing number of researches trying to overcome cloud gaming's challenges, including the required high bandwidth and low latency, to make cloud gaming more practical and profitable. To perform this research, researchers need a testbed to evaluate their ideas and find the best solutions. Currently, GamingAnywhere is the only open source platform and testbed to serve this goal. However, it cannot be used to stream all video games, since it depends on hooking APIs which might be incompatible with some video games. In this paper, we introduce a new open source cloud gaming testbed. In this testbed, the screen capturing module is fundamentally a DirectShow filter and, hence, can be tuned for any DirectShow compatible video game. The testbed also facilitates the measurement of delay and quality as the video is processed through its modules.

In this work we investigate the reassignment of User Equipments (UEs) between adjacent small cells to concurrently enable spatial multiplexing gains through Multi-User MIMO (MU-MIMO) and reductions in energy consumption though switching emptied small cells to a sleep state. We consider a case where UEs can be reassigned between adjacent small cells provided that the targeted neighbouring cell contains a UE with which the reassigned UE can perform MU-MIMO without experiencing excessive multi-user interference, and whilst achieving a minimum expected gain in spectral efficiency over the previous original cell transmissions as a result. We formulate the selection decision of which UEs to reassign as a set covering problem with the objective of maximising the number of small cell base stations to switch to a sleep state. Our results show that, for both indoor and outdoor LTE small cell scenarios, the proposed MU-MIMO-based reassignments achieve significant reductions in the required number of active small cell base stations, whilst simultaneously achieving increases in spectral efficiency.

This paper presents switched Multi-Element Antennas (MEAs) as a simple, yet effective, method of enhancing the performance of small cell heterogeneous networks and compensating for the small cell base station sub-optimal placement. The switched MEA system is a low-cost system which enables the small cell to dynamically direct its transmission power toward locations of high user density, in other words demand hotspots. Our simulation results show that small cell base stations equipped with switched MEA systems offer greater performance than base stations equipped with omni-directional antennas in terms of both the number of users that can be served (and hence offloaded from the macrocell network) and in terms of overall network capacity. We also compare the performance of the switched MEA with fixed directional antennas and show that fixed-directional antennas can only outperform the switched MEA if the misalignment between their direction of transmission and the direction to the demand hotspot is less than 22.5°.

Public Protection and Disaster Relief (PPDR) services can benefit greatly from the availability of mobile broadband communications in disaster and emergency scenarios. While undoubtedly offering full control and reliability, dedicated networks for PPDR have resulted in high operating costs and a lack of innovation in comparison to the commercial domain. Driven by the many benefits of broadband communications, PPDR operators are increasingly interested in adopting mainstream commercial technologies such as Long Term Evolution (LTE) in favour of expensive, dedicated narrow-band networks. In addition, the emergence of virtualization for wireless networks offers a new model for sharing infrastructure between several operators in a flexible and customizable manner. In this context, we propose a virtual Public Safety (PS) operator that relies on shared infrastructure of commercial PPDR networks to deliver services to its users. We compare several methods of allocating spectrum resources between virtual operators at peak times and examine how this influences differing traffic services. We show that it is possible to provide services to the PS users reliably during both normal and emergency operation, and examine the impact on the commercial operators.

This paper proposes a two-stage process for assigning fungible orthogonal channel sets to multiple cognitive radios (CRs) for opportunistic spectrum access. Assigning orthogonal channel sets to the CRs eliminates the possibility of collision among them, and allows the CRs to focus on avoiding collisions with the primary user (PU). In particular, each CR uses a learning-based dynamic channel selection (DCS) algorithm to maximize the exploitation of the assigned channels. We propose a neural network that can accurately estimate the performance of the adopted learning-based DCS algorithm on a set of channels, using the duty cycle and the complexity of the PU's behavior on the channels. Our simulations on synthetic and real measurement data sets show that the proposed channel sets allocation algorithm, together with the neural network, significantly outperforms a method that selects channels with the lowest duty cycle.

The main contribution of this work is the proposal and assessment of the MU-MIMO across Small Cells concept. MU-MIMO is the spatial multiplexing of multiple users on a single time-frequency resource. In small cell networks, where the number of users per cell is low, finding suitable sets of users to be co-scheduled for MU-MIMO is not always possible. In these cases we propose MU-MIMO-based cell reassignments of users into adjacent cells to enable MU-MIMO operation. From system level simulations we found that, when the initial number of users per small cell is four, cell reassignment results in a 21.7% increase in the spectral efficiency gain attributed to MU-MIMO, and a higher percentage increase when the initial number of users per cell is lower. Going forward, we will extend this work to also consider energy savings through switching off small cells which are emptied by the reassignment process.

Wireless network virtualization enables multiple virtual wireless networks to coexist on shared physical infrastructure. However, one of the main challenges is the problem of assigning the physical resources to virtual networks in an efficient manner. Although some work has been done on solving the embedding problem for wireless networks, few solutions are applicable to dynamic networks with changing traffic patterns. In this paper we propose a dynamic greedy embedding algorithm for wireless virtualization. Virtual networks can be re-embedded dynamically using this algorithm, enabling increased resource usage and lower rejection rates. We compare the dynamic greedy algorithm to a static embedding algorithm and also to its dynamic version. We show that the dynamic algorithms provide increased performance to previous methods using simulated traffic. In addition we formulate the embedding problem with multiple priority levels for the static and dynamic case.

Carrier aggregation is a key feature of next generation wireless networks to deliver high-bandwidth links. This paper studies carrier aggregation for autonomous networks operating in shared spectrum. In our model, networks decide how many and which channels to aggregate in multiple frequency bands, hence extending the distributed channel allocation framework. Moreover, our model takes into the account physical layer issues, such as the out-of-channel interference in adjacent frequency channels and the cost associated with inter-band carrier aggregation. We propose learning algorithms that converge to Nash equilibria in a reasonable number of iterations under the assumption of incomplete and imperfect information.

To improve their ability to find spectrum opportunities, intelligent secondary radios (SR) can learn from their past observations and predict possible spectrum opportunities. However, because of the diverse behavior of primary users (PU) in different spectrum bands, spectrum holes exhibit diverse characteristics, which in turn affect the performance of a learning algorithm. This paper studies the effect of the PU's activity on channel predictability. In particular, we introduce a Markov process-based learning algorithm, and we investigate the dependency of its spectrum decisions on the duty cycle (DC) and on the complexity of each channel activity, for both synthetic and real data. Our findings show that the probability of finding a free channel among a group of considered channels strongly depends on the DC and the complexity of the channel activity. Moreover, it is possible to reduce the number of observed channels without compromising the probability of finding a free channel, by only considering the more informative channels.

To realize opportunistic spectrum access, spectrum sensing is applied to detect the presence of spectrum holes. If secondary radios (SRs) randomly or sequentially sense the channels until a spectrum hole is detected, significant amount of the scarce spectrum resource will be wasted, since SRs transmit only after a decision has been made. On the other hand, with the use of an intelligent predictive method, SRs can learn from the past activities of each channel to predict the next channel state. By prioritizing the order in which channels are sensed according to the channels availability likelihoods, the probability that an SR gets a channel upon its first attempt significantly increases, and thus reduces the possible waste. This paper introduces a learning-based hidden Markov model (HMM) to predict the channel activities. Simulation results show that the proposed HMM can predict the channel activities with high accuracy after sufficient training. Our algorithm predicts the availability of the channels by only making use of the current state of the spectrum. Furthermore, by incorporating the outcome of the actual channel sense, our algorithm is able to make self-regulation before next decision, so that errors will not propagate.

Evolutionary algorithms like genetic algorithms and ACO are potential candidates for solving any NP-hard problem, because of their ability to obtain acceptable suboptimum (or sometimes could be the optimum) solutions. This paper proposes an Ant Colony Optimization (ACO) based algorithm to solve the centralized resource allocation problem of a multicell multiuser OFDMA network. The proposed ACO is assisted by a water filling algorithm for power allocation. Two metrics used in ACO algorithms: the visibility and the trail intensity are defined so that they are suitable to evaluate the solution. Visibility is used to select subcarriers and power which increase the total transmitted bit of the cell while trail intensity gives solutions which decrease the inter-cell interference. Simulation results show that with these definitions, the proposed algorithm is working successfully and increases the total network transmitted bits without increasing the maximum transmit power level.

Nowadays, software product line is an approach to reduce costs of software development, decrease time to market, and increase capabilities of reuse in designing and exploiting software development processes. Moreover, other quality attributes of the project domain should be considered to enhance quality of the product. Meanwhile, taking advantage of software product line makes developers capable of estimating development costs and time to market in a more realistic way. However, old approaches to estimate cost of development and foresee time to market are not suitable enough for software product line. In this paper, some important business parameters and a way to calculate cost and time to market in a product line are presented. Changing components among time, portion of the change in a specific product and organization issues are observed in the estimation function.

In this paper, for the first time, an Ant Colony Optimization (ACO) based algorithm is used to solve the bit and subcarrier resource allocation problem of single-cell OFDMA systems. It results in a directed multigraph if the vertexes are used to represent the subcarrier and each path corresponds to a possible chosen modulation index of a specific user. Earlier studies which applied evolutionary algorithms (EAs) focused on single-class service, with no guarantee on individual QoS requirement in terms of bit rate and bit-error-rate performance. The proposed ACO guarantees the required minimum bit rate for all users while minimizes the total power consumption at the base station (BS). Simulation results show that comparing to other classes of EAs such as the Genetic algorithm, and the extended version of water-filling algorithm to support QoS traffic, ACO can obtain better solutions more often. We also observe that the performance is significantly better when the minimum demand bit rates for users are higher. Comparing to earlier developed GA, our proposed ACO algorithm also converges much faster but it needs more memory space to implement the ACO algorithm.

Evaluating architectures and choosing the correct one is a critical issue in software engineering domain, in accordance with extremely extension of architecture-driven designs. In the first years of defining architecture styles, some special quality attributes were introduced as their basic attributes. After a moment, by utilizing them in practice, some results were obtained confirming some of attributes; some others meanwhile were not witnessed. As software architecture construction process is dependent on and addressed by both usage conditions and quality attributes, in this paper a framework has been proposed to provide an environment and a platform that can cover evaluation of architecture styles with a technique that not only exploits both qualitative and quantitative information but also considering users' needs is possible precisely and with high quality. Moreover, we define a classification and notation in order to describe heterogeneous architectures. It provides us with the ability of evaluating heterogeneous architecture styles of a software system.

Subcarrier and bit allocation has been extensively investigated in the literature to improve the spectral efficiency of multiuser OFDM systems. However, most of the earlier studies using genetic algorithms (GA) focused on single-class service and users of best effort data rate. In this paper, a novel GA method which allocates subcarriers and bits to rate guaranteed users is presented. As the reproduction process generates chromosomes which do not fulfill the constraints, our algorithm integrates the invisible walls technique used in Particle Swam Optimization to retain the diversity of the chromosomes. Simulation results show that the subcarrier and bit allocation strategy computed based on our proposed GA can achieve lower total power consumption compare to an algorithm modified from one of the pervious works. This verifies that our algorithm has better convergence rate and lower risk that the solution will be trapped at local optimums, while guarantees the required bit rate for each class of service.

A global increase in PDA and cell phone ownership and a rise in the use of wireless services have caused mobile browsing to become an important means of Internet access. However, the small screen of such mobile devices limits the usability of information browsing and searching. This paper presents a novel method that automatically adapts a desktop presentation to a mobile presentation, proceeding in two steps: detecting boundaries between different information blocks and then representing the information to fit in small screens. Distinct from other approaches, our approach analyzes both the DOM structure and the visual layout to divide the original Web page into several subpages, each of which includes closely related content and is suitable for display on the small screen. Furthermore, a table of contents is automatically generated to facilitate the navigation between different subpages. An evaluation of a prototype of our approach shows that the browsing usability is significantly improved.

Due to the enlargement and complexity of software systems and the need for maintenance and update, success of systems depends strongly on their architecture. Software architecture has been a key element in software development process in two past decades. Therefore, choosing the correct architecture is a critical issue in software engineering domain, with respect to the extremely extension of architecture-driven designs. Moreover, software architecture selection is a multi-criteria decision-making problem in which different goals and objectives should be considered. In this paper, a decision support system (DSS) has been designed which provides software architects with more precise and suitable decisions in architecture styles selection. The DSS uses fuzzy inference to support decisions of software architects and exploits properties of styles in the best way while making decisions.