Recent developments in cyber-physical systems have increased the importance of maximizing the freshness of the information about the physical environment. However, optimizing the access policies of Internet of Things devices to maximize the data freshness, measured as a function of the Age-of-Information (AoI) metric, is a challenging task. This work introduces two algorithms to optimize the information update process in cyber-physical systems operating under thegenerate-at-will model, by finding an online policy without knowing the characteristics of the transmission delay or the age cost function. The optimization seeks to minimize the time-average cost, which integrates the AoI at the receiver and the data transmission cost, making the approach suitable for a broad range of scenarios. Both algorithms employ policy gradient methods within the framework of model-free reinforcement learning (RL) and are specifically designed to handle continuous state and action spaces. Each algorithm minimizes the cost using a distinct strategy for deciding when to send an information update. Moreover, we demonstrate that it is feasible to apply the two strategies simultaneously, leading to an additional reduction in cost. The results demonstrate that the proposed algorithms exhibit good convergence properties and achieve a time-average cost within 3% of the optimal value, when the latter is computable. A comparison with other state-of-the-art methods shows that the proposed algorithms outperform them in one or more of the following aspects: being applicable to a broader range of scenarios, achieving a lower time-average cost, and requiring a computational cost at least one order of magnitude lower.

In Beyond5G and 6G networks, a common theme is that sensing will play a more significant role than ever before. Over this trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing functionalities and the communications ones and to pursue direct tradeoffs between them as well as mutual performance gains. We frame the resource tradeoff between the SAC functionalities within an economic setting. We model a service provision by one operator to the users, the utility of which is derived from both SAC functionalities. The tradeoff between the resources that the operator assigns to the SAC functionalities is analyzed from the point of view of the service prices, quantities and profits. We demonstrate that equilibrium quantities and prices exist. And we provide relevant recommendations for enforcing regulatory limits of both power and bandwidth.

Network slicing is an architectural enabling technology that logically decouples the current cellular networks into infrastructure providers (InPs) and Network Slice Tenants (NSTs). The network resources (e.g., radio access resources at each cell) are owned by the InP, and are shared by the NSTs to provide a service to their mobile users. In this context, we proposed a business model that includes resource allocation and user subscription to NSTs in a competitive setting, and provides, among other things, closed-form expressions for the subscription indicators in equilibrium of each NST at each cell. This model relies on the widely adopted logit model to characterize user subscriptions. However, as a consequence of user mobility and radio propagation, some of the underlying assumptions in the logit model do not hold. Therefore, further research is needed to assess the accuracy of the results provided by the logit model in a mobile radio scenario. We carry out a thorough evaluation of the validity of the model by comparing its results against those obtained through computer simulation. Our simulation model includes complete and realistic characterizations of user mobility and radio propagation. From the results, we conclude that in most cases the logit model provides valid results in a mobile radio scenario.

In wake-up radio (WuR) enabled Internet of things (IoT) networks, a data communication occurs in a synchronous or asynchronous manner initiated either by a transmitter or receiver. A synchronous transmission is triggered when multiple devices report an event simultaneously, or by a common wake-up call. In this paper, we focus on synchronous transmissions and propose a multicast triggered${s}$ynchronous${t}$ransmission protocol, abbreviated as MURIST, which enables contention based and coordinated data transmissions among distributed devices in order to reduce transmissions latency and energy consumption. Furthermore, we develop a novel analytical model based on an absorbing Markov chain to evaluate the performance of MURIST in a network cluster. Unlike existing models that are merely targeted at the behavior of a single device, the novelty of our model resides in a generic framework to assess the behavior of a cluster of devices for synchronous data transmissions. Based on the analytical model, we obtain closed-form expressions for the distributions of successful and discarded transmissions, number of collisions, and delay, as well as for energy consumption. Extensive simulations are performed to validate the accuracy of the analytical model and evaluate the performance of our scheme versus that of two other schemes.

In massive machine-type communications (mMTC), an immense number of wireless devices communicate autonomously to provide users with ubiquitous access to information and services.The current 4G LTE-A cellular system and its Internet of Things (IoT) implementation, the narrowband IoT (NB-IoT), present appealing options for the interconnection of these wireless devices.However, severe congestion may arise whenever a massive number of highly-synchronized access requests occur.Consequently, access control schemes, such as the access class barring (ACB), have become a major research topic.In the latter, the precise selection of the barring parameters in a real-time fashion is needed to maximize performance, but is hindered by numerous characteristics and limitations of the current cellular systems.In this paper, we present a novel ACB configuration (ACBC) scheme that can be directly implemented at the cellular base stations.In our ACBC scheme, we calculate the ratio of idle to total available resources, which then serves as the input to an adaptive filtering algorithm.The main objective of the latter is to enhance the selection of the barring parameters by reducing the effect of the inherent randomness of the system.Results show that our ACBC scheme greatly enhances the performance of the system during periods of high congestion.In addition, the increase in the access delay during periods of light traffic load is minimal.

Sensing is a fundamental aspect in cognitive radio networks and one of the most complex issues. In the design of sensing strategies, a number of tradeoffs arise between throughput, interference to primary users, and energy consumption. This paper provides several Markovian models that enable the analysis and evaluation of sensing strategies under a broad range of conditions. The occupation of a channel by primary users is modeled as alternating idle and busy intervals, which are represented by a Markov phase renewal process. The behavior of secondary users is represented mainly through the duration of transmissions, sensing periods, and idle intervals between consecutive sensing periods. These durations are modeled by phase-type distributions, which endow the model with a high degree of generality. Unlike our previous work, here the source of secondary users is nonsaturated, which is a more practical assumption. The arrival of secondary users is modeled by the versatile Markovian arrival process, and models for both finite and infinite queues are built. Furthermore, the proposed models also incorporate a quite general representation of the resumption policy of an SU transmission after being interrupted by PUs activity. A comprehensive analysis of the proposed models is carried out to derive several key performance indicators in cognitive radio networks. Finally, some numerical results are presented to show that, despite the generality and versatility of the proposed models, their numerical evaluation is perfectly feasible.

The monitoring of cultural heritage is becoming common in cities to provide heritage preservation and prevent vandalism. Using sensors and video cameras for this task implies the need to transmit information. In this paper, the teletraffic that cameras and sensors generate is characterized and the transmissions’ influence on the municipal communications network is evaluated. Then, we propose models for telecommunication traffic sources in an intelligent municipal heritage management service inside a smart sustainable city. The sources were simulated in a smart city scenario to find the proper quality of service (QoS) parameters for the communication network, using Valencia City as background. Specific sensors for intelligent municipal heritage management were selected and four telecommunication traffic sources were modelled according to real-life requirements and sensors datasheet. Different simulations were performed to find the proper CIR (Committed Information Rate) and PIR (Peak Information Rate) values and to study the effects of limited bandwidth networks. Packet loss, throughput, delay, and jitter were used to evaluate the network’s performance. Consequently, the result was the selection of the minimum values for PIR and CIR that ensured QoS and thus optimized the traffic telecommunication costs associated with an intelligent municipal heritage management service.

Wake-up Radios (WuRs) are becoming more popular in Internet of Things (IoT) networks owing to their overwhelming advantages such as low latency, high energy saving, and on-demand communication. In a multi-hop WuR-IoT network, route establishment between source and destination prior to actual data communications consumes a significant portion of energy. To reduce energy consumption and protocol overhead for route establishment, we propose a lightweight relay (LR) selection scheme, referred to as LR-WuR, where a lookup table and acknowledgment are used for next hop relay selection. We develop an analytical model to evaluate the performance of LR-WuR. Extensive simulations are performed to validate the accuracy of the analytical model. Furthermore, we present a framework for deriving optimal network parameters.

Massive content delivery in cellular networks is in the spotlight of the research community as data traffic is increasing at an incredibly fast pace. The existing LTE-A implementation for content broadcast presents several issues such as indoor coverage, along with low energy and spectral efficiency. Therefore, novel systems that provide efficient massive content delivery and reduced energy consumption are needed. In this paper we present a massive content distribution protocol that combines the benefits of cooperative mobile clouds (CMCs) with Random Linear Network Coding (RLNC) through multicast WiFi links. Our main goal is to offload data traffic from the LTE-A link and to reduce the energy consumption at the cooperating UEs. We solve the problem of excessive signaling that oftentimes arises in cooperative approaches by eliminating feedback messages within the CMCs. Instead, we provide a simple but accurate analytic model to correctly configure the number of coded transmissions to be performed within the CMCs. Results show that energy savings of more than 37 percent can be achieved with our protocol when compared to direct content download from the cellular base station. Furthermore, bandwidth utilization at the LTE-A link is sharply reduced.

Using cellular networks for providing machine-to- machine (M2M) connectivity offers numerous advantages regarding coverage, deployment costs, security and management, among others. Nevertheless, having a large number of M2M devices activated simultaneously is difficult to tackle at the evolved Node B, and it causes complications in the connection establishment. The random access channel (RACH) in LTE-A is adequate for handling human-to-human (H2H) communications. However, for the efficient provision of simultaneous H2H/M2M communications, it is necessary to optimize the available access control mechanisms so that network overload is avoided and a better QoS can be offered. Access Class Barring (ACB) has shown to be effective in reducing the number of simultaneous users contending for access. However, it is still not clear how to dynamically adapt its parameters, especially in highly changing scenarios with bursty traffic as it can occur when M2M communications are involved. We propose a dynamic algorithm based on reinforcement learning to adapt the barring rate parameter of ACB. This algorithm can adapt it to different traffic conditions, reducing congestion and hence the number of collisions in the RACH. The results show that our proposed mechanism increases the access success probability for all the users while barely impacting H2H users and other key performance indicators.

In wake-up radio (WuR) enabled wireless sensor networks (WSNs), a node triggers a data communication at any time instant by sending a wake-up call (WuC) in an on-demand manner. Such wake-up operations eliminate idle listening and overhearing burden for energy consumption in duty-cycled WSNs. Although WuR exhibits its superiority for light traffic, it is inefficient to handle high traffic load in a network. This paper makes an effort towards improving the performance of WuR under diverse load conditions with a twofold contribution. We first propose three protocols that support variable traffic loads by enabling respectively clear channel assessment (CCA), backoff plus CCA, and adaptive WuC transmissions. These protocols provide various options for achieving reliable data transmission, low latency, and energy efficiency for ultralow power consumption applications. Then, we develop an analytical framework based on an M/G/1/2 queue to evaluate the performance of these WuR protocols. Discrete-event simulations validate the accuracy of the analytical models.

Providing channel access opportunities for new service requests and guaranteeing continuous connections for ongoing flows until service completion are two challenges for service provisioning in wireless networks. Channel failures, which are typically caused by hardware and software failures or/and by intrinsic instability in radio transmissions, can easily result in network performance degradation. In cognitive radio networks (CRNs), secondary transmissions are inherently vulnerable to connection breaks due to licensed users' arrivals as well as channel failures. To explore the advantages of channel reservation on performance improvement in error-prone channels, we propose and analyze a dynamic channel reservation (DCR) algorithm and a dynamic spectrum access (DSA) scheme with three access privilege variations. The key idea of the DCR algorithm is to reserve a dynamically adjustable number of channels for the interrupted services to maintain service retainability for ongoing users or to enhance channel availability for new users. Furthermore, the DCR algorithm is embedded in the DSA scheme enabling spectrum access of primary and secondary users with different access privileges based on access flexibility for licensed shared access. The performance of such a CRN in the presence of homogeneous and heterogeneous channel failures is investigated considering different channel failure and repair rates.

5G networks will make network sharing agreements between mobile operators technically possible. However, depending on the agreed and implemented quality‐of‐service isolation, the provision of services may lead to unsustainable business cases. In this paper, the economic feasibility of such arrangements is analyzed for the case of two operators. Concretely, while one network operator owns the spectrum, one virtual operator does not, and each one provides service to its subscriber base. Two sharing alternatives, namely, pooling and priority sharing, are studied regarding the profits that each operator gets. We conclude that the network operator is worse off under any circumstances under a pooling agreement, while a lump sum payment may leave the network operator better off under a priority sharing agreement.

A brokering platform distributing the data collected by a set of Wireless Sensor Networks (WSNs) to end customers is considered, where WSNs receive a payment from the broker proportional to the amount of data they provide and pay the network provider a flat fee, while end customers pay a flat fee to access those data. In the case where the broker can set both prices, the optimal pricing strategy, maximizing the broker's profit, is analysed as to its outcomes for all the stakeholders. The attractiveness for end customers is evaluated through the overall amount of data on offer and the service penetration rate (which is quite low for a typical context). The service penetration rate among WSNs (which is instead quite high), as well as their profit, are considered as the variables measuring the platform's success among WSNs. Additionally, the network fee is absorbed by the broker, with a negligible impact on the broker's profit and even a positive impact on the average WSN's profit. Finally, the overall sensing data rate and its impact on the platform's profit are also assessed.

This paper analyses the economic viability of serving Machine Type Communications (MTC) and Human Type Communications (HTC) on a common network infrastructure under monopolistic and duopolistic scenarios. The scenarios are compared against the case where one operator serves HTC. The service provision is modelled by a two-priority queue and the competition is analyzed as a two-stage game. In the first stage, each operator posts a price and in the second stage, users decide to subscribe or not to the service based on a utility function related with the packet delay and the price charged. The HTC-MTC coexistence is shown to be feasible in the duopolistic scenario. The entry of the second operator to serve MTC users is desirable not only from the point of view of resource usage efficiency and from the point of view of the users, but also from the point of view of the two operators. The HTC-MTC service provision is therefore feasible and economically viable, and more efficient than the baseline scenario.

A business model for the provision of wireless sensor network (WSN)-based services is proposed, where there exist three roles in the market: the WSNs, which provide sensing data; the service providers, which gather the data and compose useful services for the users; and end users. We assess an affine pricing scheme for the acquisition of the WSNs data and analyze the model in a duopoly scenario by means of game theory. The competition outcome is compared against a social optimum outcome. We show that the pricing scheme avoids that the providers use all available resources and that the prices are kept low, so that the consumer surplus improves at the expense of the providers profits and the overall social welfare.

In order that the Internet of Things (IoT) realize its full potential, it has been claimed for the IoT industry to follow the smartphone recipe of market-creating innovation. Indeed, the main mobile apps platforms derive a significant part of their brand value and pricing power from their reputation to have most innovative apps coming to them. Having witnessed that the same steady stream of developer-driven innovation is already emerging in IoT, demand for IoT-based services will be driven by this growing innovative community of IoT developers. In order to make it possible, main players should focus on building platforms that support third party applications instead of developing applications for their own devices. As a way to materialize the above idea, we propose a business model for IoT-based services where a platform creates a multisided market. The business model comprises a platform that serves as an intermediary between human users, app developers and Wireless Sensor Networks, so that the users use the apps, and the apps process the data supplied by the sensor networks. The platform, acting as a monopolist, posts a fee for each of the three sides so as to maximize its profit. We conduct an analysis of the profit maximization problem faced by the platform. We show that the relative strength of the value that advertisers attach to the users determines the platform price structure. All in all, a high valuation is beneficial for all agents (users, platform, developers, and WSNs).

The performance evaluation of the random access (RA) procedure in LTE-A has recently become a major research topic as these networks are expected to play a significant role in upcoming 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that sacrifice precision in exchange of simplicity. In this paper, we present an analytic model for the performance evaluation of the LTE-A RA procedure. Using this analytic model, each and every one of the KPIs suggested by the 3GPP can be obtained with a minimal error when compared to the results obtained by simulation. To the best of our knowledge, this is the most accurate analytic model of the LTE-A RA procedure.

With the rapid development of wireless network and communication technology, mobility in wireless network is essential issue represents challenges because of the requirement of continuous connectivity. Process switching from one base station to another is known as handover (HO). Parameters like traffic received and traffic sent, throughput, jitter, load, delay, received power, and signal to noise ratio are chosen as the decision criteria for triggering the HO process. In this paper we analyze the performance of handover in WiMAX; we also provide a detailed description of the simulation models developed using OPNET 14.5.

The advent of the Internet of Things (IoT) is expected to bring major benefits to a wide range of areas. However, the successful deployment of the IoT calls for the existence of sustainable and well-understood business models. In this paper, we propose and analyze a business model for a likely scenario in the IoT, which is made up of WSNs, service providers and users. The service providers compete against each other in the intermediation between the virtualized WSNs and the users that benefit from enhanced services built on the sensed data. The service providers pay to the WSNs for the data and charge the users for the service. The model is analyzed by applying oligopoly theory and game theory, the conditions for the existence and uniqueness of the Nash equilibrium are established, and the equilibrium and the social optimum are obtained. Our results show that the business model is sustainable, provided that the users' sensitivity to the value-to-price ratio is not negligible and, in this situation, the number of active service providers is upper bounded by a value that depends on the sensitivity and the market size. Furthermore, the operation of such a market is shown to efficiently use the information provided by the WSNs, and, when compared to the social optimum, to produce an increase in users' and service providers' surpluses, but a reduction in WSNs' surplus.

The performance evaluation of the random access (RA) in LTE-A has recently become a major research topic as these networks are expected to play a major role in future 5G networks. Up to now, the key performance indicators (KPIs) of the RA in LTE-A have been obtained either by performing a large number of simulations or by means of analytic models that, oftentimes, sacrifice precision in exchange for simplicity. In this paper, we present an analytical model for the performance evaluation of the LTE-A RA procedure that incorporates the access class barring (ACB) scheme. By means of this model, each and every one of the KPIs suggested by the 3GPP can be obtained with minimal error when compared with results obtained by simulation. To the best of our knowledge, this paper presents the most accurate analytical model, which can be easily adapted to incorporate modifications of network parameters and/or extensions to the LTE-A system. In addition, our model of the ACB scheme can be easily incorporated to other analytic models of similar nature without further modifications.

It is well understood that in wireless networks, channel failures, which are typically caused by equipment or power failures as well as intrinsic features in radio transmissions, such as fading and shadowing, can easily result in network performance degradation. Therefore, fast recovery from channel failures is an important measure that should be incorporated with those networks. Consequently, in a cognitive radio network (CRN), channel failures can cause significant performance degradation in both primary and secondary networks. Instead, retainability, i.e., the capability of providing continuous connection for users must be guaranteed even if a significant network element is disrupted. In this paper, we develop an analytical model to analyze the system performance of CRNs with a focus on random channel failures. In addition to performance analysis under channel failures, a channel reservation scheme is proposed. The scheme can be adopted in dynamic channel access strategies in order to prioritize ongoing sessions over new user requests. Furthermore, optimal channel reservations subject to different performance requirements are also studied.

The number of devices that communicate through the cellular system is expected to rise significantly over the coming years. But cellular systems, such as LTE-A, were designed to handle human-to-human traffic. Hence, they are not suitable for managing massive machine-to-machine communications. Therefore, additional congestion control methods must be developed and evaluated. Up to date, access class barring (ACB) and extended access barring (EAB) methods are the preferred solutions for reducing congestion in the access channels of the evolved NodeB. These methods are based on restricting the access of certain classes of UEs, so the system capacity is not exceeded. Due to the high complexity of the LTE-A system, evaluating its performance is not straightforward. Specifically, a large number of variables, coexistent mechanisms, and test scenarios make it difficult to identify the network parameters that enhance performance. In this paper, we analyze the ACB method in highly congested environments. For this, we evaluate the effect of ACB parameters (barring rates and barring times) by means of several key performance indicators (KPI) such as delay, energy consumption (preamble transmission attempts required) and success probability. We observed that ACB is appropriate for handling sporadic congestion intervals in LTE-A networks.

A brokering platform is considered, acting as an intermediary to deliver the data collected by a set of wireless sensor networks as a bundle to end customers. The wireless sensor networks provide data at sensing rates following a Zipf law and are rewarded by a linear pricing scheme, while end customers pay a flat fee to access the whole bundle of data. The optimal pricing policy to be adopted by the broker is derived. Even sensor networks with a very small sensing rate find it profitable to join the platform, unlike the case of uniform distribution of sensing rates. The optimal price paid to WSNs grows as the sensing rate grows, acting as an incentive to deploy better technology. Instead, the penetration rate among end customers is equal to the case of uniform distribution, being quite low unless a pronounced sensitivity for prices forces the broker to lower prices so as to make the service attractive to end customers.

With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocated to real-time and elastic users and channel access opportunities are distributed between these two queues in a way that real-time services receive higher priority. Two queuing schemes are introduced based on the delay tolerance of interrupted elastic services. Furthermore, continuous time Markov chain models are developed to evaluate the performance of the proposed CA strategy with queues, and the correctness as well as the preciseness of the derived theoretical models are verified through extensive simulations. Numerical results demonstrate that the integration of queues can further increase the capacity of the secondary network and spectrum utilization while decreasing blocking probability and forced termination probability.

Flexible channel allocation may be applied to multi-channel cognitive radio networks (CRNs) through either channel assembling (CA) or channel fragmentation (CF). While CA allows one secondary user (SU) occupy multiple channels when primary users (PUs) are absent, CF provides finer granularity for channel occupancy by allocating a portion of one channel to an SU flow. In this paper, we investigate the impact of CF together with CA for SU flows by proposing a channel access strategy which activates both CF and CA and correspondingly evaluating its performance. In addition, we also consider a novel scenario where CA is enabled for PU flows. The performance evaluation is conducted based on continuous time Markov chain (CTMC) modeling and simulations. Through mathematical analyses and simulation results, we demonstrate that higher system capacity can be achieved indeed by jointly employing both CA and CF, in comparison with the CA-only strategies. However, this benefit is obtained only under certain conditions which are pointed out in this paper. Furthermore, the theoretical capacity upper bound for SU flows with both CF and CA enabled is derived when PU activities are relatively static compared with SU flows.

The constant evolution of mobile-phone traffic demands for novel networking solutions especially focused on indoor environment. In this context, the use of femtocells, i.e., cells with very limited coverage area, has been proposed. In this paper, a femtocell network with hybrid access control mode is considered. The activity profile of the Femtocell Users (FUs) is modeled to compute the maximum achievable throughput and the consumed energy per successfully transmitted data bit by the Macrocell Users (MUs), depending on which set of channels are operated in open access mode, i.e., which channels can be used by MUs. Thus, it is identified how many and which channels must be operated in open access mode, depending on the physical capacities of the channels and the amount of time these channels are not occupied by FUs. The results motivate the need for novel resource management schemes which can dynamically adapt the set of open access channels to the network conditions.

The paper deals specifically with spectrum leasing within cognitive radio networks and it covers the economic issues that arise when primary users lease spectrum to secondary users. It aims at assessing which benefit mobile users get from the entry of secondary operators in the market. A game theory-based model is built for the case of an incumbent primary operator and an entrant secondary operator.

Many studies on Internet Service Provider (ISP) interconnection make simplifying assumptions on the implementation of the service provision. Our work explicitly models the ISP service that is provided to users that run peer-to-peer applications and it analyses the behavior of competing ISPs. The ISPs have agreed to peer each other and each ISP has purchased transit service from one Internet Backbone Provider. The quality of service, the equilibrium prices and the market shares that the competition game yields are computed by means of our model. Our work assesses the strategy of an ISP which provisions its transit link against a competing ISP in terms of competitive advantage and social welfare. And it assesses the effect of the entrance of more competing ISPs.

The case for a competitive market operated by a Mobile Network Operator (MNO) and a Mobile Virtual Network Operator (MVNO) is analysed in the paper. The resource that is leased by the MNO to the MVNO is spectrum. The MNO and the MVNO compete "a la Bertrand" posting subscription prices and the mobile users may choose to subscribe to one operator. The scenario is modeled by a three-level game comprising a bargaining game, which models the spectrum leasing by the MNO; a competition game, which models the price competition between the MNO and the MVNO; and a subscription game, which models the subscription choice by the mobile users, and the outcome of which may be either not to subscribe, to subscribe to the MNO or to subscribe to the MVNO. The game is solved through backward induction, and each level has a specific solution concept: Shapley value, for the bargain; Nash equilibrium, for the competition; and Wardrop equilibrium, for the subscription. The paper assesses which conditions lead to an equilibrium where the competition does take place, which are expressed as restrictions for the spectrum leasing price agreed at the bargaining, and the spectrum efficiency improvement achieved by the MVNO. Furthermore, it argues that the amount of the leased spectrum should be fixed exogenously in order to achieve optimal user and social welfares.

In cognitive radio networks, channel aggregation techniques which combine several channels together as one channel have been proposed in many MAC protocols. In this paper, spectrum adaptation is proposed in channel aggregation and two strategies which dynamically adjust channel occupancy of ongoing traffic flows are further developed. The performance of these strategies is evaluated using continuous time Markov chain models. Moreover, models in the quasi-stationary regime are analyzed and the closed-form capacity expression is derived in this regime. Numerical results demonstrate that the capacity of the secondary network can be improved by using channel aggregation with spectrum adaptation.

In this paper we consider a cognitive radio network in a heterogeneous multi-channel scenario where channels are different in terms of achievable bit rates. Upon arrival licensed users (PUs) will occupy first the channels with higher achievable rates. As a result, secondary users (SUs) will see a set of channels which are different not only in their physical capacities but also in the activity profile of PUs. Consequently, from the point of view of the effective throughput that SUs can obtain in each channel there exists a trade-off between the rate they can obtain in a certain channel and the amount of time they can use it. We develop a model that allows to solve this trade-off by computing the maximum effective throughput that SUs can achieve in each channel.

Cognitive Radio (CR) networks are envisaged as the key technology to realize dynamic spectrum access and solving the scarcity of radio spectrum. Having a temporal characterization of the spectrum white spaces in the primary network is a key element for studying and designing radio resource management mechanisms in CR networks. In that sense, most of the studies in the literature rely on an ON-OFF model with exponentially distributed on and off times. The usage of that model, however, is principally based on its analytical tractability. In this paper we propose a versatile Markovian model for the duration of the spectrum white spaces. Our model builds on a simple model of the channel holding time (CHT) in the primary network and then matrix-analytic techniques are applied to derive and analyze the duration of the white spaces. Despite its simplicity, the proposed approach is proven to be able to model very accurately scenarios where the CHT distribution is of a more complex type not amenable to mathematical analysis. Our numerical results show that the duration of the white spaces exhibits a low sensitivity to the distribution of the channel holding time beyond the mean.

This paper studies the robustness under high overloads of an extension of the Virtual Partitioning (VP) admission policy in multiservice cellular networks that carry streaming and elastic traffic. The VP policy is compared with the Multiple Fractional Guard Channel (MFGC) policy. The main contributions of the paper are the study of a new design method and the integration of streaming and elastic traffic.

In recent years there has been a growing interest in Opportunistic Routing as a way to increase the capacity of wireless networks by exploiting its broadcast nature. By contrast to traditional uni-path routing, in opportunistic routing the nodes overhearing neighbor's transmissions can become candidates to forward the packets towards the destination. In this paper we address the question: What is the maximum performance that can be obtained using opportunistic routing? To answer this question we use an analytical model that allows to compute the optimal position of the nodes, such that the progress towards the destination is maximized. We use this model to compute bounds to the minimum expected number of transmissions that can be achieved in a network using opportunistic routing.

We evaluate different admission control (AC) policies in various mobile cellular scenarios with multimedia support and QoS guarantees defined by the expected blocking probabilities for new call set up and handover requests. For each of the studied policies we determine the maximum calling rate that can be offered to the system while satisfying the QoS objective, using a hill-climbing algorithm. This approach drastically reduces the computational complexity of the optimization process. Additionally we study how sensitive these policies are to the tolerance of system parameters and to overloads. In regard to the system capacity, preliminary results show that policies of the trunk reservation class outperform policies that produce a product-form solution and the improvement ranges approximately between 5 and 15%.

In cellular systems, there is usually an overlap cell coverage area (area covered by two or more base stations) which has valuable practical applications: soft handoff is an important feature of CDMA systems, and in hard handoff, the overlap area allows the queuing of handover requests as a handover prioritization scheme. While an extensive research effort has been done to model the terminal residence time in a cell and the channel holding time, the sojourn time in the overlap area is a topic that still requires further study. We propose a model of the sojourn time in the overlap area, along with a novel numerical method to derive its probability distribution. The obtained results show that the common assumption of exponential distribution for this time is not suitable, whereas it can be satisfactorily fitted by the hyper Erlang distribution.