Maintaining robust physical-to-virtual twin connectivity remains a critical challenge in the deployment of highfidelity digital twin networks (DTNs). To ensure that each virtual twin (VT) in such DTNs is a true replica of its counterpart physical twin (PT), a reliable and cost-effective connectivity framework is essential, especially considering the potential mobility of PTs in the physical environment. This paper thus presents a new multi-modal PT trajectory prediction-enabled multi-connectivity framework where the counterpart VT model is proactively duplicated along multiple predicted paths to compensate for the effects of prediction errors, thereby avoiding communication loss between such PT-VT pair. While such a proposed scheme can improve the overall reliability of the network, the resulting connectivity solution may suffer from network congestion issues, complicating the overall cost. To address this, we analyze the trade-offs between overall reliability and cost and formulate an optimal VT placement problem aimed at minimizing delay, longterm connectivity cost, and prediction errors. We then propose a deep reinforcement learning-based adaptive VT placement strategy for cost-effective, mobility-aware PT-VT connectivity. Simulation results show the solution effectively meets the reliability and cost requirements of DTNs.

Traditional power grids have been predominantly one-way delivery systems. Recently, a two-way power delivery and communication system has been proposed to be incorporated into the current power grid system, thereby establishing a Smart Grid, to alleviate the challenges associated with one-way systems. The advanced system includes a large number of sensors deployed at multiple locations and consumer sites (e.g. Smart Meters) for monitoring usage and faults. The two-way communication infrastructure provides the utility control centers with the means to collect data on power usage from the smart meters, along with the ability to communicate back operation-critical information and control actions to their client's smart meters, thus establishing an infrastructure for both monitoring and controlling different services on the grid. In this paper, we suggest a framework of the network model for analyzing the performance of bidirectional data communication in the advanced metering infrastructure, along with our future directions.

Data timeliness, privacy, and security are key enablers for data-sharing systems to support time-sensitive and mission-critical systems and applications. While blockchain-enabled data sharing frameworks can offer reliable security and privacy when properly implemented, the timeliness of data and the related latency are important issues that can limit the adoption of blockchain in large-scale mission-critical applications. This paper thus carried out a performance analysis of the blockchain-enabled data-sharing framework from latency and data age perspectives to investigate the suitability of blockchain technology in data sharing systems. To achieve this, the uniqueness of such systems such as transactions validation latency, transaction generation rate, waiting time, blockchain-appending rate, and overall communication latency were jointly studied. The communication latency was characterized following the spatiotemporal modeling approach. We further adopted the practical Byzantine fault tolerance (PBFT) consensus protocol due to its well discussed suitability in large-scale data sharing applications and captured the validation stages of such a PBFT scheme using the Erlang distribution of order$k$. Simulations results show that various influential system parameters must be carefully considered when adopting blockchain technology in time-sensitive data sharing applications. This will guide the adoption of blockchain technology in various data sharing applications and systems.

Blockchain and cloud-edge computing techniques are promising technologies for next-generation, secure, and privacy-preserving data-sharing systems. By integrating these technologies, the data demands of many data users, such as research institutes, hospitals, manufacturers, etc., can be met. Despite this promising integration, it is yet to be understood how the vulnerability and uncertainties of wireless communication links between the data producers, blockchain systems, cloud-edge computing-based platforms, and data users, as well as unstable validation parameters can affect the overall performance of such blockchain-enabled data-sharing systems. In this article, we considered a collaborative data-sharing scheme, where multiple data providers and data users collaborate to accomplish data-sharing tasks through the proposed blockchain and cloud-edge computing schemes. We considered the spatial distribution of data providers and data users to follow the independent homogeneous Poisson point process, while the transactions generation rate at each node was also modeled using an independent Bernoulli process. We then obtained analyses for some selected performance metrics of interest and evaluate the performance of the system. The obtained results showed that the proposed analyses can be useful in investigating the performance of any blockchain-enabled data-sharing system. This will aid successful deployments of effective data-sharing systems.

Interference modeling in cognitive radio network is important to ensure adequate coverage in the network. A reliable interference model, however, depends on accurately characterizing the distribution of users. In this paper, the dependence between primary and secondary networks is examined in order to capture more system parameters related to system characterization. Hence, two cases are considered - primary user (PU) interference control and PU with secondary user (SU) interference control mechanisms. Under PU interference control, distributions of PUs follow the Matern hard core process while the distribution of SUs follow the Poisson hole process (PHP). However, under PU with SU interference control, the distribution of active SUs follow a modified PHP. Bound and approximate expressions were derived for coverage probability at both primary and secondary networks, while simple yet accurate expressions were obtained to depict the number of simultaneous active users supported for the two cases. The tight closeness between the bound and the approximate expressions shows the reliability of the presented theoretical analysis. Furthermore, the bipolar network model assumption was relaxed while the case of independence assumption among users was also considered. Numerical result showed close tightness when the bipolar network model assumption was relaxed while the independence assumption was shown to overestimate users' coverage probability.

Integration of mobility into system characterization can complicate system analysis, as characterizing interference in mobile wireless networks is more difficult, especially when the assumption of independence of users' distributions is relaxed. However, in some practical systems, quite a number of users may be mobile, making the integration of mobility important. In this paper, mobile users' coverage in cognitive radio networks is investigated under the assumption that primary users are distributed following the Matern hard core process, while secondary users are distributed following the extended Poisson hole process. A relaying based mobility technique was proposed to sustain coverage at any tagged receiver when such receiver is located outside the coverage of its corresponding tagged transmitter. Analyses of outage probability and average throughput were derived in both primary and secondary networks and verified through simulations. The outcomes of numerical results show that the proposed mobility scheme is capable of improving coverage of mobile users in cognitive networks.

Malicious users (MUs) have the tendency to disrupt the activities of honest users in the network if not properly controlled. In a massive cognitive radio network (CRN) with priority queues, malicious secondary users (SUs) can manipulate their priority queue requirements and mislead legitimate SUs to vacate the channels. In this paper, a game theoretic based signal detection approach is proposed to control the presence of MUs in CRN. If the received signal strength is less than the predefined threshold for primary transmissions in the presence of interference and noise, such a user is marked to be malicious and its payoff table is updated. Through the mixed strategy Nash equilibrium method, the payoff table of each user can be updated to aid removal of MUs from the network. The outcome of the simulation results shows that such an approach can reduce the impact of malicious activities in the massive CRN where SUs are expected to be low-power energy-efficient devices.

Cognitive Wireless Sensor Networks have become an integral part of the Internet of Things and are envisioned to be utilized by a wide range of services. While equipping the sensor nodes with cognitive radios would allow for the different spectrum bands to be utilized efficiently, most of the developed channel allocation schemes assume the data transmissions to be restricted to a single spectrum band at any given time. In an effort to further enhance the network's performance, we propose an allocation scheme that allow for the nodes to simultaneously utilize the multiple spectrum bands. In this paper, we extend our previous results and present a model for analyzing the performance of the nodes that employ our proposed multiple spectrum band allocation scheme. This model can also be applied for determining the optimal scheduling of transmissions across the various spectrum bands.

A novel empirical analysis of a decentralised network under a probabilistic Byzantine attack was performed. A decentralised cognitive radio network was simulated using the Neyman-Pearson Belief Propagation (NP-BP) algorithm for cooperative spectrum sensing (CSS). The network was exposed to malicious users (MUs) that attempted to increase the false alarm or missed detection rates in the sensing of the primary user (PU). The NP-BP algorithm was seen to reduce the occurrence of spectrum sensing (SS) errors (in comparison to likelihood detection) when no MUs were present. The performance of the data fusion algorithm was very sensitive to MUs that increased the false alarm rate of PU detection. Conversely, the network performed much better when missed detection attacks were conducted by the MUs. The NP-BP algorithm tends to prefer moderate network connectivity for optimal SS results.

In this paper, we propose a quasi-decentralized resource allocation scheme with network assistance that allocates resources to device-to-device (D2D) user pairs. In this scheme the D2D user pairs are able to select between the full-duplex (FD) and the half-duplex (HD) modes to maximize the throughput while causing acceptable interference to cellular users. Here, we formulate an optimization problem to jointly assign channels and allocate power for a given set of D2D pairs. This problem was then decomposed such that each D2D pair could run the optimization only knowing the interference threshold at the base station and the CSI between the pair and the cellular base station. Then, this non-linear mixed integer program was decomposed into two sub-problems, namely channel assignment problem and power allocation problem. Thereafter, we used the monotonic optimization framework to solve the power allocation sub-problem and used integer linear programming to solve the channel assignment problem. Then we solved the same problem after making the power allocation problem convex using a substitution. The scheme is evaluated numerically.

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.

In an overlay/underlay cognitive radio network the channel can primarily be in two modes; occupied or unoccupied by a primary user, thus being in an overlay or underlay mode for the secondary user. However, during the underlay mode, the channel can present different capacities for the secondary user due to the varying primary user power levels. In this paper we introduce a method for modeling the multi-mode overlay/underlay levels for the secondary users and beyond the traditional 3-mode levels applied in the previous work. We essentially consider the case where the service levels of the channel can switch between the different modes, hence the channel availability and effective capacity to a secondary user are dynamic. We further present a discrete time queueing model that captures this scenario through the use of a discrete time Markov chain for analyzing the behavior of the system.

In this paper, we present examples of a class of Markov chains that occur frequently, but whose associated matrices are a challenge to construct efficiently. These are Markov chains that arise as a result of several identical Markov chains running in parallel. Specifically for the cases considered, both the infinitesimal generator matrix for the continuous case, and more so the transition probability matrix for the discrete equivalent, are complex to construct effectively and efficiently. We summarize the algorithms for constructing the associated matrices and present examples of applications, ranging from special queueing problems to reliability issues and order statistics. MATLAB subroutines are provided in an online supplement for the implementation of the algorithms. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0759 .

Cognitive Wireless Sensor Networks are envisioned to be utilized by a large number of wireless devices and for a variety of applications. This has prompted the need to research optimal data transmission schemes between the nodes to address these high demand expectations and for achieving higher throughput. Various data transmission schemes have been proposed but were restricted to having those transmissions made across a single spectrum band at any given time. In this paper, we propose having the nodes transmit their data across multiple spectrum bands and simultaneously. We further propose a Markov Decision Process approach for the optimal scheduling of transmissions on different channels in an environment where multiple spectrum bands are available. For ease of presentation and clarity, the analysis in this paper considers the simple case where each node has access to only two spectrum bands. The same method can be applied for the extended case of multiple spectrum bands.

A channel share scheme is proposed as a solution to the resource allocation problem in cognitive radio networks whereby two secondary users can occupy the same channel simultaneously with the primary user. The solution will increase utilisation thus freeing up much needed spectrum to accommodate many more users. Processor sharing techniques have been developed but have not been extensively applied in cognitive radio networks, specifically, the weighted head of line processor sharing scheme has the potential of improving spectrum usage in networks. In this paper, we propose using transmission power as the basis for the weighting parameter. We then develop the state transition matrices for the model using continuous time Markov chains. Queues are imposed on both secondary users. The model is evaluated through simulations done on various network conditions. The results show that the proposed model offers a significant improvement over an ordinary M/M/1 two priority system. The applications can lead to improved network efficiency by allowing more users to transmit within a network.

In this paper, we study dynamic load-balancing spectrum decision for a cognitive radio network (CRN) that dynamically distributes packets from the secondary user (SU) to different available primary channels. We consider two different classes of services at the SU, i.e., delay sensitive (DS) and best effort (BE) services, and assign a higher priority to the DS services. We apply priority queuing model to address this priority issue in the CRN. Based on the queuing model, two Markov decision processes (MDPs) are formulated with objectives to minimize the average delay of both services while guaranteeing the priority of the DS services. Reinforcement learning is applied to find the optimal solutions when the traffic and channel characteristics are unknown. To address the computational complexity issue in the MDP solutions, we propose a myopic method based on the estimated packet sojourn time, which is derived by formulating a phase type distribution. Simulation results demonstrate the effectiveness of all proposed algorithms for load-balancing spectrum decision. It also shows that the proposed myopic scheme can achieve significant reduction on computational complexity with a cost on the delay performance of low priority BE services.

Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA’s productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

With the recent advances in radio modules capable of carrying out full duplex (FD) communication, the spectrum efficiency promised by the concept of Cognitive radio (CR) can be stretched even further by incorporating full duplex transceivers in CRs. This incorporation brings about certain challenges in the resource allocation. In this work we try to address some of the challenges in joint channel and power allocation. We proposed a decentralized scheme which is capable of selecting which channels to be used as half duplex and which to be used as FD between a pair of CRs. Then we decomposed the problem into two sub-problems one dealing with channel assignment and the other dealing with power allocation. In the power allocation we did not assume the signal to interference plus noise ratio to be high like most work in the literature. The monotonic optimization framework was used to solve the power allocation problem. Then the channel assignment problem was approximated by a linear objective function and was solved as a linear integer program.

Cognitive wireless sensor networks exploit the spectrum heterogeneity to access the different spectrum bands for data transmission. The various spectrum access schemes proposed in the previous work assume that each node can access and transmit their data using a single spectrum band at any given time, with the access to the different spectrum bands being dynamic and relative to the wireless medium conditions. However, this approach may not be enough to address the networks with a large number of nodes that are exchanging high volumes of data. In this paper, we propose a transmission scheduling policy for allowing the sensor nodes to transmit its data across a multiple number of spectrum bands, and simultaneously. We also present a mathematical model to analyze the performance of the sensor nodes that employ our proposed scheduling policy. The results of the analysis can further be used to determine the optimal policy parameters.

Most research on the mathematical modelling of wireless sensor networks (WSNs) have focussed mainly on the optimization aspects, such as those relating to sensor placements, data routing, reliability, etc. Surprisingly the issue relating to performance analysis of data processing and transmission at the nodes, have not received as much attention. A considerable amount of delay to data actually happens at the nodes as a result of queue build up. Hence, understanding the role of queueing in WSN modelling is very important. In this paper we study the literature of queueing as applied to WSNs and provide insight to the current state of the art and directions for the future. The utilization of queueing theory in WSNs is broadly classified into four categories, namely, congestion control methods, power allocation schemes, network performance evaluation techniques and scheduling schemes.

In a cognitive radio network (CRN) of multiple secondary users (SUs) and multiple primary user channels, the protocol of allocating channel resources to SUs and the evaluation of the effectiveness of the protocol are still open issues.In this paper, we developed a novel protocol to allocate channels to SUs and an analytical criterion to evaluate the performance of this protocol.The system settings and environmental parameters, including spectrum sensing errors of SUs, adaptive modulation and coding schemes, and finite SU buffer size, are taken into consideration in our framework.By modeling the behavior of each SU as a discrete-time queue, we obtained a performance evaluation function (PEF) considering the rejected packets, collision packets and error packets.Simulation and numerical results showed that the protocol is better than random allocation in terms of PEF. The impact of different system settings on the optimal protocol settings and the optimal PEF was also investigated.

In this paper, an incentive-compatible mechanism for transmission scheduling in electronic health (e-health) networks with delay-sensitive medical packets is studied. Unlike existing works in the literature, we focus on the beyond wireless body area network (beyond-WBAN) communications. In the considered system, medical packets arrive randomly at each gateway (which ordinarily stands for one patient), and their transmission requests are reported to the network regulator (i.e., the base station) with specific delay sensitivities that reflect their medical signal severities. The base station then determines the order of transmission by formulating a priority queue. With the construction of the packets' utility and the base station's profit functions, we analyze the characteristics of the service system and design an incentive-compatible mechanism such that all gateways will be forced to report the actual delay sensitivities of their medical packets. Theoretical analyses show that our proposed mechanism can maximize the profit of the base station (i.e., minimize the total waiting cost from all medical packet transmissions) while guaranteeing higher service priorities to more emergent medical packets. Numerical results examine the properties of the proposed mechanism, and demonstrate its feasibility in providing economic incentives for all individuals.

Abstract In this paper, a game theoretic relay load balancing and power allocation scheme is proposed for downlink transmission in a decode‐and‐forward orthogonal frequency division multiple access‐based cellular relay network. A system with a base station communicating with multiple users via multiple relays is considered. The relays have limited power, which must be divided among the users they support. In traditional scheme, each relay simply divides its transmit power equally among all its users. Moreover, each user selects the relay with the highest channel gain. In this work, we do not apply the traditional relay scheme. It is because the users are distributed randomly, and by applying the traditional relay selection scheme, it may happen that some relays have more users connected to them than other relays, which results in having unbalanced load among the relays. In order to avoid performance degradation, achieve relay load balancing, and maximize the total data rate of the network, a game theoretic approach is proposed, which efficiently assigns the users to relays. The power of each relay is wisely distributed among users by the efficient power allocation scheme. Simulation results indicate that the proposed game‐based scheme can considerably improve the average sum‐spectral efficiency. Moreover, it shows that by applying the game, users who can connect to uncongested relays join them as opposed to connecting to congested relays. Copyright © 2015 John Wiley & Sons, Ltd.

A novel physical-layer based security method that utilizes jammers to generate additional interference for devices that are eavesdropping on wireless network communication is proposed. The scheme involves the intelligent placement of continuous jammers in order to achieve maximum protection and data confidentiality for wireless mesh networks with multiple eavesdroppers, sources and destinations. Furthermore, the scheme is optimized in terms of the transmitting power associated with each jammer so that the energy expended by the jammers is kept at a minimum. The protection scheme precludes the use of any cryptographic techniques and is only physical layer based. The security method is modeled as a minimization mixed integer non-linear problem and is approximated as the sequential solution of two linear optimization sub-problems relating to the placement and power allocation of the wireless jammers. The placement of the jammers takes the form of a multiple demand multidimensional knapsack problem with a minimization objective. The power allocation problem is modeled as a linear real-valued minimization optimization problem. The performance of the proposed security method is evaluated through appropriate simulations conducted on random network instances.

Cognitive radio (CR) network is currently an emerging technology for future wireless communications. In this paper, the problem of resource allocation for heterogeneous users in an underlay CR network is addressed. The objective is to maximize the weighted sum of the data rate for all the categories of secondary users within the network. The problem is first formulated as a non-linear optimization problem which is non-deterministic polynomial-time (NP) hard. The problem is then cleverly re-formulated as an integer linear programming problem and solved through branch-and-bound method. The impact of the weights on the different categories of users is also investigated. The results obtained show that through the approach developed by the authors, optimal resource allocation is achievable for heterogeneous CR networks.

We investigate optimal call admission control (CAC) policy for multicast streaming services (MSS) in 3rd generation (3G) and beyond wireless mobile networks. Several MSS sessions are supported simultaneously in a bandwidth-limited network. Active sessions are those that are currently serving some users, and inactive sessions are those that are currently not serving any users. An admission decision in MSS is required only when an inactive session is requested, unlike in unicasting. For this reason, if a user request for an inactive MSS session arrives, we should make an admission decision in anticipation of (i) the possible reward earned based on users served during a session active time generated by accepting it, and (ii) the influence of the session active time upon the future status of network bandwidth and admission decisions. Our objective is to determine when to admit or block a user asking an inactive MSS session to achieve the optimality in rewards. We formulate this problem as a semi-Markov decision process (SMDP), and a value iteration algorithm is used to obtain an optimal stationary deterministic policy. We also derive the user blocking probability of the optimal policy by analyzing an embedded Markov chain induced by it.

In this paper, we study radio resource allocation for multicasting in OFDMA based high altitude platforms (HAPs). We formulate and solve an optimization problem that finds the best allocation of HAP resources such as radio power, sub-channels, and time slots. The problem also finds the best possible frequency reuse across the cells that constitute the service area of the HAP. The objective is to maximize the number of user terminals that receive the requested multicast streams in the HAP service area in a given OFDMA frame. A bounding subroutine in a branch and bound algorithm can be obtained by decomposing it into two easier subproblems, due to its high complexity, and solving them iteratively. Subproblem 1 turns out to be a binary integer linear program of no explicitly noticeable structure and therefore Lagrangian relaxation is used to dualize some constraints to get a structure that is easy to solve. Subproblem 2 turns out to be a linear program with a continuous knapsack problem structure. Hence a greedy algorithm is proposed to solve subproblem 2 to optimality. The subgradient method is used to solve for the dual variables in the dual problem to get the tightest bounds.

Abstract In this paper, channel assignment for spectrum sensing is studied in multi‐channel cognitive radio (CR) networks to maximize the number of channels satisfying sensing performance (called available channels). Beginning with a nonlinear integer programming problem, we derive the upper bound of optimal value through many‐to‐many assignment problem and then propose three algorithms for both centralized and distributed scenarios. In centralized case, a heuristic scheme is proposed based on the signal‐to‐noise ratios (SNRs) over all primary channels (PCs). Then, a greedy scheme is proposed to reduce the reported information from the CRs. In distributed case, a novel scheme with multi‐round operation is designed following the coalitional game theory. In each round, each CR selects some PCs based on SNRs. Then, the CRs selecting the same channel play coalitional game, and thereby, multiple games are played concurrently over multiple channels. Finally, the best coalition for each channel is chosen among the formed coalitions to perform the cooperative spectrum sensing. The simulation results show that the proposed schemes can significantly increase the number of available channels. Copyright © 2013 John Wiley & Sons, Ltd.

The authors have proposed a fair solution to optimal rate adaptation. The problem has been described in terms of the objective function, decision variables and constraints. Furthermore, using this problem definition, a rate adaptation heuristic was developed and divided into two sub- problems; part one requires finding the optimal rate allocation within the network, part two continues by finding a fair solution whilst still keeping an optimal rate allocation. The heuristic relies on cooperation in the network, and information regarding selected rates and loss due to interference, is distributed between neighbouring nodes. Furthermore, the heuristic is modelled as a repeated game with infinite horizon and it is shown how cooperation can be enforced. A Stack topology has been used for analysing and comparing performance and OMNeT++ 4.2.2 has been selected as simulation platform. The authors have shown that the heuristic effectively reduces the packet loss ratio (PLR). Thereafter, it was shown that the solution is both fair and optimal in terms of data rate.

This paper considers random access protocols with multipacket reception (MPR), which include both slotted-Aloha and slotted τ-persistent CSMA protocols. For both protocols, each node makes a transmission attempt in a slot with a given probability. The goals of this paper are to derive the optimal transmission probability maximizing a system throughput for both protocols and to develop a simple random access protocol with MPR, which achieves a system throughput close to the maximum value. To this end, we first obtain the optimal transmission probability of a node in the slotted-Aloha protocol. The result provides a useful guideline to help us develop a simple distributed algorithm for estimating the number of active nodes. We then obtain the optimal transmission probability in the τ-persistent CSMA protocol. An in-depth study on the relation between the optimal transmission probabilities in both protocols shows that under certain conditions the optimal transmission probability in the slotted-Aloha protocol is a good approximation for the τ-persistent CSMA protocol. Based on this result, we propose a simple τ-persistent CSMA protocol with MPR which dynamically adjusts the transmission probability τ depending on the estimated number of active nodes, and thus can achieve a system throughput close to the maximum value.

In this paper, a framework for power allocation is presented for downlink transmissions in an OFDMA-based cellular relay network. We consider a system with a single base station communicating with multiple users assisted by multiple relays. The relays have limited power which must be divided among the users they support in order to maximize the data rate of the whole network. By finding the optimal power requirement of each user, and based on knapsack problem the optimal power allocation which maximizes the data rate is proposed as an upper bound. Another power allocation scheme, weighted-based scheme, is proposed considering the fairness issue. Finally, to utilize the power wisely and improve the data rate of the network, an efficient power reallocation scheme is proposed. Simulation results demonstrate the efficacy of the proposed schemes.

In this paper, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general statistical model that can handle bursty primary user (PU) channel usage. The proposed model describes idle periods with a discrete platoon arrival process (PAP) and describes busy periods with a discrete phase type (PH) distribution. This channel model is referred to as a PAP-PH process. We further introduce a proactive access scheme as the potential application of the proposed channel model and use it to compare the performance of the proposed model, in terms of spectrum utilization and interference probability, with two traditionally encountered channel usage models, i.e., the geometrically distributed idle-busy period model and the phase type distributed idle-busy period model, under both bursty and non-bursty channel scenarios. Numerical results show that with the proposed model, the proactive access scheme can guarantee the interference threshold to the PU and can be used for both bursty and non-bursty spectrum use patterns.

In this paper, a game theoretic power allocation and relay load balancing scheme for downlink transmission in an OFDMA-based cellular relay network is proposed. In the cell, a BS is located at cell center communicating with multiple users assisted by multiple relays. The relays adopt the decode-and-forward protocol and can cooperatively assist the transmission from BS to users. In order to assign the users to relays, achieve the load balancing among relays and distribute the power of relays among users while considering the total data rate of the network, a game theoretic scheme is introduced to solve the problem. Simulation results indicate that, the proposed game-based power allocation scheme can improve the average sum- spectral efficiency approximately 20% compared to the traditional scheme.

In this paper, the performance of a multichannel cognitive radio network (CRN) where each secondary user (SU) can assemble a constant number of primary channels is investigated. The effects of imperfect spectrum sensing (with false alarms and mis-detections) are taken into account and it is assumed that spectrum handover is enabled in the secondary network. A buffering mechanism is introduced to save blocked SU requests and interrupted SU services. An analytical model based on Continuous-time Markov chains (CTMC) is developed, and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability. Numerical results show that the buffer is able to significantly reduce the SU blocking probability while increasing its throughput.

ABSTRACT In this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley & Sons, Ltd.

This paper investigates the performance of a wideband cognitive radio network where each cognitive user can assemble multiple primary channels. Two channel assembling schemes are considered: a constant channel assembling (CCA) and a variable channel assembling (VCA). In the variable channel assembling scheme, cognitive users assemble their channels on the basis of the number of detected residual channels that are unoccupied by primary users or cognitive users. The effects of imperfect spectrum sensing (with false alarms and misdetections) are taken into account and it is assumed that spectrum handover is implemented in the secondary network. These channel assembling schemes are analyzed by using Continuous-time Markov chains (CTMC), and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability. Numerical results show that channel assembling achieves lower forced termination probability, but does not increase achieved system throughput and leads to higher blocking probability. They also show that VCA outperforms CCA in terms of throughput and forced termination probability.

To analyze scenarios like spectrum handoff in overlay cognitive radio networks we need to capture the status change of the target channels in a more realistic manner. Most of the existing cognitive radio channel models assume target channels to be busy only when occupied by primary users. However, in reality the channel could be occupied by another secondary user in absence of a primary user and hence seen as busy by a secondary user that senses the channel. Actual busy channel consists of either a primary or other secondary user activity as observed by a potential incoming secondary user to the channel. Here, we present a channel model that captures a more realistic behavior of channel occupancy.

The concept of CR enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users. In this paper we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree (PST) algorithm.

In this paper, a distributed cooperative attack on multi-channel cooperative spectrum sensing is proposed. In the proposed attack, attackers play two coalitional games to maximize the number of invaded channels in a distributed manner. In the first game, attackers play with their fellow ones to allocate an optimal number of attackers for each channel, while in the second game, they play in the coalitions with other honest cognitive radios so as to conquer as many channels as possible. Simulation results show that the proposed attack can considerably decrease the number of available channels with a low attack cost.

In this paper, channel assignment in cooperative spectrum sensing is studied for multi-channel cognitive radio networks. Based on the information from each secondary user, e.g., primary signal-to-noise ratios over all channels, a centralized scheme is proposed, which assigns channels to different secondary users for sensing so that the number of available channels, which meet the sensing performance requirements in terms of miss detection and false alarm probabilities, can be greatly increased. By further taking the communication overhead into account, a greedy scheme is proposed to reduce the reporting information from the secondary users to the base station. The simulation results demonstrate that both schemes can significantly increase the number of available channels, while the latter also shows advantages in reducing the signaling overhead.

A reliability-based iterative majority-logic decoding algorithm for regular low-density parity-check (LDPC) codes was recently proposed by Huang et al. In this paper we present an improved version of that algorithm by introducing a different reliability measure for each check-sum of the parity-check matrix, and taking it into account in the computation of the extrinsic information that is used to update the reliability measure of each received bit in each iteration. Some simulations results are given, which show that the new algorithm, while requiring very little additional computational complexity, not only achieves a considerable error performance gain over the standard one, but also, importantly, outperforms the iterative decoding based on belief propagation (IDBP), especially for short and medium block length finite-geometry (FG) LDPC codes.

This paper considers a cognitive radio network where the cognitive user has a constant bandwidth which is K times that of a primary user. The primary system consists of constant M primary channels and the cognitive system consists of N = [M/K] channels, where [x] is the largest integer not greater than x. The effects of imperfect spectrum sensing (with false alarms and misdetections) are analyzed using a Markov chain. Explicit expressions for state dependent transition rates are derived for the case M = 6 and K = 2, and the system performance is evaluated in terms of throughput, blocking probability, and forced termination probability.

This paper presents a hybrid decoding algorithm for low-density parity-check (LDPC) codes based on the interior point method with barrier function for linear programming (LP) decoding introduced by Wadayama . First, an efficient implementation of Wadayama's algorithm is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. A two-stage hybrid decoding which combines the interior point decoding and a low-complexity decoding algorithm for LDPC codes is then proposed. Simulation results show that the approximations introduced in the proposed algorithms do not result in any performance degradation, while considerably reducing the decoding complexity and latency.

A new channel model for binary additive noise communication channel with memory, called weighted queue-based channel (WQBC), is introduced. The proposed WQBC generalizes the conventional queue-based channel (QBC) such that each queue cell has a different contribution to the noise process, i.e. the queue cells are selected with different probabilities. Suitably selecting the modeling function, the generalization introduced by the WQBC does not increase the number of modelling parameters required compared to the QBC. The statistical and information-theoretical properties of the new model are derived. The WQBC and the QBC are compared in terms of capacity and the accuracy in modeling a family of hard decision frequency-shift keying demodulated correlated Rayleigh and Rician fading channels. It is observed that the WQBC requires a much smaller Markovian memory than the QBC to achieve the same capacity, and provides a very good approximation of the fading channels as the QBC for a wide range of channel conditions.

This paper presents an improvement of the reliability-based iterative majority-logic decoding algorithms for regular low-density parity-check (LDPC) codes proposed by Huang et al. We improve the computation of the extrinsic information that is used to update the reliability measure of each received bit in each iteration with some kind of reliability measures of the check-sums that are orthogonal on the considered bit. The improved algorithm achieves a significant gain over the standard one with only a modest increase in computational complexity.

In this paper, a distributed cooperative multi-channel spectrum sensing scheme is proposed for the non-infrastructure based cognitive radio networks. The proposed scheme has iterative property and is carried out round-by-round. In each round, each secondary user selects a few primary channels as the candidates for sensing based on primary signal-to-noise ratio. Then, the users with the same selected channel collaboratively form coalitions through coalitional game and thereby multiple games are played concurrently over multiple channels. After generating stable coalitional structure, the best coalition on each channel is chosen to perform the cooperative spectrum sensing. The simulation results show that the proposed scheme can significantly increase the number of available channels, which can be sensed with predefined miss detection and false alarm probabilities.

The information on the volume of traffic flowing between all possible origin and destination pairs in an Internet Protocol (IP) network during a given period of time is generally referred to as traffic matrix (TM). This information, which is very important for various traffic engineering tasks, is very costly and difficult to obtain on large operational IP network, consequently, it is often inferred from readily available link load measurements. Several techniques have been proposed for estimation of traffic matrix on operational IP network from measured link load data and routing information. However, because the problem is a linear ill-posed and has no unique or direct solution, mathematically speaking, many of these techniques rely on some assumptions about the distribution of origin-destination (OD) flows. The validity of these assumptions and resulting prior estimates affect the performance and accuracy of the techniques. In this paper, we demonstrated the result of two hybrid techniques formed by combining iterative proportional fitting (IPF) and fanout estimation with well-known techniques such as tomogravity (TG), entropy maximization (EM) and Neural Network (NN) in producing improved estimation of the traffic matrix from link load data and sampled flow measurement. The low overhead of these hybrid techniques, as well as the significant reduction in error achieved, compared to using the gravity or similar prior estimates, makes them worthwhile approaches that can be adopted by Internet service providers (ISPs) for large-scale IP traffic matrix estimation.

In this paper, a hybrid decoding algorithm for finite-geometry low-density parity-check (FG-LDPC) codes is proposed. The algorithm is based on the interior point method with barrier function introduced by Wadayama. First, an efficient implementation of Wadayama's algorithm is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. A two-stage hybrid decoding which combines the interior point decoding (IPD) and a low-complexity decoding algorithm for FG-LDPC codes is then proposed. In the first stage, the interior point decoding is used to generate a search point. If the first stage decoding fails, the decoding is continued by the low-complexity algorithm that is initialized by the result of the IPD. Compared with a conventional iterative message-passing (IMP) decoder, the proposed hybrid algorithm achieves better bit-error rate (BER) and frame-error rate (FER) for small block lengths at medium to high signal-to-noise ratio (SNR).

In this paper, a cognitive radio (CR) network composed of K secondary users who cooperatively sense a channel using the k-out-of-K fusion rule to determine the presence of the primary user is studied. The sensing-throughput tradeoff problem is investigated in a realistic environment where both the sensing channels and reporting channels are characterized by fading channels. It is observed that taking into consideration the probability of reporting error in the CR network increases the sensing time and reduces the maximum average throughput of the secondary users. To mitigate the effect of the probability of reporting error, a transmit diversity based cooperative spectrum sensing method using randomized space-time block coding (RSTBC) is proposed. Simulations results show that the spatial diversity gain induced by RSTBC significantly decreases the sensing time and improves the throughput of the secondary users.

We develop an analytical model to investigate the performance of a decentralized MAC protocol based on IEEE 802.11 DCF for cognitive radio networks with single spectrum band. Our analysis provides the throughput obtained by secondary users and the interference ratio perceived by primary users (SUs). The theoretical approach is verified by comparing its numerical results to simulation results. Numerical and simulation results depict the fundamental properties on the performance under various network parameters such as the number of SUs, the packet size of SUs and the minimum contention window size.

Providing desired quality of service (QoS) in cellular wireless networks is a challenging problem which is further magnified when considering more than one class of calls. In this paper, a novel adaptive call admission control (CAC) scheme is prposed for two classes of calls for a cellular wireless network consisting of two cells. The proposed scheme accepts a new call if it can guarantee, with a certain probability, that a user's call will be maintained irrespective of its (his/her) movement in the system. The adaptive scheme for two-cell wireless network is then extended for multiple-cell network. The adaptive scheme is found to outperform the nonpriority and dual threshold based reservation (DTR) schemes in controlling the handoff failure and new call blocking probabilities in a cellular wireless network.

In this paper, a general framework, called probability-based solution (PBS), is proposed to balance the trade-off between the accessibility (in terms of the success probability to find a route reaching the destination) and the performance (in terms of spectral efficiency, outage probability or energy consumption) of distributed routing schemes in multi-hop wireless networks. In the PBS, the candidate nodes (receivers) in each hop are first separated into two groups based on a direction either diverging from, or converging to, the direct line between the source and destination. Then, one of the best nodes from the two groups is selected according to a carefully-defined probability by considering the uncertainty in the subsequent hops. The simulation results demonstrate that integrating the proposed PBS with conventional distributed routing schemes can significantly improve the accessibility and effectively guarantee other system performance. Moreover, the PBS can also be combined with facing routing to solve the connectivity hole problem.

In this paper, an efficient implementation of the interior point algorithm recently proposed by Wadayama for linear programming (LP) decoding of low-density parity-check (LDPC) codes is presented. The main idea behind the modification is to approximate the barrier function for the fundamental polytope defining the code so that it contains only one linear constraint for each of the parity-check constraints. Simulation results show that the approximations introduced do not result in any performance degradation, while considerably reducing the decoding complexity and latency.

In this paper, two novel distributed routing schemes, named adaptive-information-moving-rate routing scheme (AIMR) and adaptive-information-moving-distance-and-link-rate routing scheme (AIMDLR), are proposed for multi-hop wireless networks by jointly considering the number of hops and the link states. With one-hop information only, both schemes aim at improving the network spectral efficiency under two different bandwidth sharing methods (i.e., throughput-maximization bandwidth sharing and equal-time bandwidth sharing), respectively. In addition, a general scheme, called probability-based scheme (PBS), is proposed to improve the accessibility of distributed routing schemes, which is denoted by the success probability of finding a route reaching the destination. In the PBS, the node selection in each hop is based on a well-defined probability, which takes into account the locations of the transmitter and the receiver at each hop and the uncertainty in the subsequent hops. By combining the PBS with the AIMR and the AIMDLR, the proposed probability-based AIMR (PAIMR) and probability-based AIMDLR (PAIMDLR) can not only improve the accessibility significantly but also achieve higher effective spectral efficiency compared to the counterparts. Simulation results are finally presented to demonstrate the advantages of the proposed routing schemes.

In this paper, a greedy sub-channel redistribution routing scheme (GSRRS) is proposed for multi-hop wireless OFDMA networks. In GSRRS, between any two neighboring nodes, different sub-channels are routed over different paths including the direct one-hop path and the two-hop path with one intermediate node. Compared to the traditional routing scheme where all the sub-channels route over the same path, GSRRS can improve not only the link rate between two neighboring nodes but also the capacity of the shortest path derived by the Dijkstra's algorithm between any source-destination pair.

In this paper, designing channel sensing policies for cognitive radio networks is discussed. A discrete-time semi- Markov channel model is first introduced, which facilitates the analysis on more general channel occupancy behaviors and possible asynchronism among primary and secondary users. Based on the characteristics of the channels at the stationary state, multiple channel sensing policies have been proposed for different network scenarios with homogeneous channels, heterogeneous channels, and sequential channel sensing. Both analytical and simulation results are given to demonstrate the effectiveness of the proposed channel sensing policies.

Packet discarding policies have been shown to significantly enhance the goodput of the system in high speed networks. In this paper, we develop a discrete time queueing model that combines two major discarding policies - the Partial Message Discard (PMD) and the Early Message Discard (EMD) policy. The PMD policy discards any subsequent packets that belong to a message that has lost a packet due to buffer overflow. The EMD policy protects against PMD by allowing the packets of a new message into the system only when the queue length is below a particular threshold. Packets can also be corrupted by the wireless medium which causes them to be discarded from the system. We represent packet data stream generation in high speed data networks by using the discrete Platoon Arrival Process (PAP), for capturing the correlation of intervals between packet arrivals. Using this model, we are able to obtain the probabilities of a successful message transmission and the Goodput of the system. The details of our model will be given for the case of a single source arrival. We briefly describe and present our results for the case of two arrival sources. The model has applications in streaming multimedia.

In this paper, two distributed routing schemes, named adaptive information moving rate (AIMR) and adaptive information moving distance and link rate (AIMDLR), are proposed for multi-hop fixed relay networks. Both routing schemes aim to maximizing spectral efficiency in terms of throughput-oriented spectral efficiency (TOSE) and fairness-oriented spectral efficiency (FOSE), respectively, while remaining distributed properties without involving network-wide information. Moreover, the route convergence issue which is inherent to some distributed routing schemes is also discussed by proposing a new routing convergency (RC) scheme. Simulation results demonstrate that the proposed routing schemes can significantly improve the spectral efficiency with high convergency probability.

This paper presents a low-complexity iterative joint detection and decoding algorithm for finite geometry-low density parity check (FG-LDPC) coded multiple-input multiple-output (MIMO) systems, in which the MIMO channel detector and the FG-LDPC decoder iteratively exchange soft information. The key to the simplicity of the algorithm is the use of a bit flipping-based decoder for FG-LDPC code. An important issue addressed here is the generation of soft information from the binary outputs of the bit-flipping decoder to be fed back to the MIMO detector. Simulation results show that the proposed joint detection and decoding algorithm achieves a substantial reduction in decoding error probability compared to a cascaded detector and a decoder. We also compare the performance with belief-propagation (BP) based detector-decoders which are significantly more complex. The new algorithm provides a practical approach to joint detection and decoding of popular FG-LDPC codes in a MIMO system, a task which is computationally unmanageable with a BP algorithm.

The European Computer Manufacturers Association (ECMA) International recently defined the ECMA-368 standard, which specifies the physical and media access control (MAC) layers for Ultra Wideband (UWB) based wireless personal area networks (WPANs). The MAC protocol in ECMA-368 has a superframe structure. Each superframe is divided into three different time periods. One of them is the prioritized contention access (PCA) period which supports contention-based access between different traffic classes. In this paper, we propose an analytical model to evaluate the performance of PCA in ECMA- 368 MAC protocol. We assume that packets follow the Markovian Arrival Process (MAP) and various service times can be modeled by different phase type distributions (PHs). We apply the Matrix Geometric Method (MGM) technique and model the system as a MAP/PH/1 queueing system. We derive the probability mass function for the number of the packets in the queue, and the cumulative distribution function for the packets' waiting time. The correctness of our proposed analytical model is validated via OPNET simulations.

In this paper, a finite-state phase-type semi-Markov channel (FSPHMC) model is proposed for wireless channels with correlated fading. By introducing a phase type distributed sojourn time between channel state transitions, the proposed model generalizes the traditional Finite-State Markov Channel (FSMC) model where the sojourn time in each channel state is assumed to satisfy geometric distribution. Thanks to the flexibility of the phase type distribution, the resultant FSPHMC model is applicable to much wider range of practical fading channels. For facilitating the implementation of FSPHMC model in practical scenarios, a special case, FSPHMC model with negative binomial sojourn time (FSPHMC-NB), is also presented under constraint computational complexity. Compared with the traditional FSMC model, the FSPHMC-NB demonstrates considerable improvement in that it matches the true state duration distribution without a significant increase in complexity. Simulation results are given to validate the flexibility and versatility of the proposed FSPHMC model.

An understanding of the network traffic behavior is essential in the evolution of today's wireless networks, and thus leads to a more efficient planning and management of the network's scarce bandwidth resources. Prior reservation of radio resources at the future locations of a user's mobile trajectory can help with optimizing the allocation of the network's limited resources, as well as help with sustaining a desirable level of QoS. The objective of this study is to propose a framework for a mobility prediction model using Markov renewal processes, for computing the likelihoods of the next-cell transition, along with anticipating the duration between the transitions, for an arbitrary user in a wireless network. The proposed technique can also be used to estimate the expected traffic load and activity at each location in a network's coverage area.

The recent ECMA-368 standard specifies the use of ultra wideband (UWB) technology for high rate communications in wireless personal area networks (WPANs). This paper proposes an analytical model for the performance analysis of the medium access control (MAC) protocol standardized in ECMA-368. The MAC protocol uses a superframe structure. Each superframe has a beacon period, a distributed reservation protocol (DRP) period, and a prioritized contention access (PCA) period. By using the Markovian arrival process (MAP) and phase type distribution (PH), we model this MAC layer as a MAP/PH/1 queueing system, and focus our study on the performance of the DRP period in this paper. The probability mass function of the number of DRP packets in the system, as well the cumulative distribution of the DRP packet's waiting time are derived and compared with the simulation results in OPNET.

Video-on-demand (VoD) is the entertainment source that, in the future, will likely overtake regular television in many aspects. Although many companies have deployed working VoD services, some aspects of the VoD should still undergo further improvement in order for it to reach to the foreseen potentials. An important aspect of a VoD system is the underlying network in which it operates. According to the huge number of customers in this network, it should be carefully designed to fulfill certain performance criteria. This process should be capable of finding optimal locations for the nodes of the network as well as determining the content that should be cached in each one. While this problem is categorized in the general group of network optimization problems, its specific characteristics demand a new solution to be sought for it. In this paper, which is inspired by the successful use of fuzzy optimization in similar problems in other fields, a fuzzy objective function that is heuristically shown to minimize the communication cost in a VoD network is derived while also controlling the storage cost. Then, an iterative algorithm is proposed to find a locally optimal solution to the proposed objective function. Capitalizing on the unrepeatable tendency of the proposed algorithm, a heuristic method for picking a good solution from a bundle of solutions produced by the proposed algorithm is also suggested. This paper includes a formal statement of the problem and its mathematical analysis. In addition, different scenarios in which the proposed algorithm can be utilized are discussed.

In this paper, we work on maximizing the capacity of the reverse link of a CDMA wireless network. Oh and Soong (2006) presented an information-theoretic capacity model for analyzing this system. That formulation produces solutions that often result in unfairness allocation of power to most users. Here, we propose the addition of a maximum capacity constraint into the problem, thereby creating a more fair power allocation scheme. First, we improve on the algorithm presented in (Oh and Soong, 2006) by proposing a closed form solution and an algorithm which works nine times faster. Then, we propose a closed form algorithm for solving the new enhanced problem.

The impact of user relaying on the behavior of a relay node, which acts as the source node at the same time, is analyzed in a wireless relay network at the packet level. The analysis process models the behavior of the relay node as a queueing system and represents the service for its own packet transmission as an M/G/1-type Markov chain. By considering the fact that the maximum number of packet arrivals is ordinarily limited in a practical system, the M/G/1-type Markov chain is further reformatted into a quasi-birth-death (QBD) process through re-blocking so as to simplify the analysis and obtain the associated performance, such as average packet transmission delay. As an application of the results arising from the analysis, a new relay node selection scheme, based on a utility function approach that jointly considers the channel and the queue conditions at the relay node, is proposed. Numerical results show that the proposed analysis model is quite accurate and the proposed relay node selection scheme is effective in balancing cooperative diversity gain and packet transmission delay.

In this paper, designing an effective user relaying algorithm, in terms of relay node selection and power allocation, is discussed for amplify-and-forward wireless relay networks. The objective is to simplify the application of user relaying in practical wireless communication networks so that the system capacity can be improved with low computational complexity and system overhead. Beginning with the derivation of a tight threshold-based sufficient condition on the feasibility of a relay node, i.e., ensuring that user relaying via the node can achieve a larger channel capacity than direct transmission, a semi-distributed user relaying algorithm is proposed. In the proposed algorithm, each relay node can make decision on its feasibility individually, and the ultimate decision on the relay node selection among multiple feasible ones is made in a centralized manner. Since there is no need on exchanging channel state information among different network nodes, the proposed algorithm is simple for implementation and suitable for practical applications, which have stringent constraints on system overhead. By comparing with the centralized user relaying algorithm, which requires global channel state information of the whole network, the proposed semi-distributed algorithm can provide comparable system capacity, but has significantly reduced computational complexity.

We discuss the information-theoretic approach to finding the pattern of transmission powers of the stations in a CDMA system which maximizes the aggregate capacity of the reverse link. This optimization problem is solved subject to a set of constraints. Previous research has suggested a minimum guaranteed quality of service plus bounds on individual transmission powers and the aggregate transmitted power as the constraints. Solving this problem, it is found out that the solution is very prone to including one station which transmits at a rate a hundred times as much as the others. Thus, it is concluded that the above constraints are not enough to produce a solution which can be realized in an actual system. It is suggested that lack of any constraint which explicitly controls either the maximum capacity of each station or the unfairness of the whole system is responsible for this shortcoming. We include a maximum capacity constraint into the formulation and propose an algorithm for solving the resulting optimization problem. Then, empirical evidence are analyzed to show that the system actually becomes more balanced and practical after the new constraint is added to it.

By controlling the pattern of transmission powers of the stations in a CDMA system, the aggregate capacity of the reverse link can be optimized. To make this problem practically applicable, appropriate constraints should be added to it. In previous research, bounds on transmission power, lower limit on signal to noise ratio, and maximum capacity of single stations plus a maximum bound on the aggregate transmission power of the system were considered. However, none of these constraints directly controls the capacity share of the single stations. Hence, the solution might exhibit a transmission power pattern in which, for example, one third of the whole capacity is given to a single station. This increases the probability of the base station depending massively on one station, resulting in a major loss when that station stops transmitting. In this paper, we introduce another constraint into the available set. This way, we reformulate the available problem by adding a maximum capacity share constraint to it and propose an algorithm for solving it. The paper includes mathematical analysis of the problem and experimental results.

We consider the discrete-time GI/G/1 system with discrete interarrival times and service-times distributions that have finite supports, and formulate it as a PH/PH/1 system. We then take advantage of the resulting special structure to develop efficient methods for computing its rate matrices and the decay rates of its queue length and waiting time.

In this paper, a resource allocation scheme is proposed for wireless relay networks to achieve maximum system throughput in terms of achievable average mutual information with low computational complexity. A relay network, which has single source node, multiple relay nodes and single destination node, is analyzed from the information theory point of view. Both sufficient and necessary conditions are derived for the best relay node selection, and a close-form of optimal power allocation between the source and the relay nodes is obtained. Application of the proposed resource allocation schemes in practical relay networks with incomplete channel state information is also studied. Simulation results are presented to demonstrate the effectiveness of the proposed resource allocation schemes in wireless relay networks.

Determining point to point traffic matrix is essential for Internet service providers (ISPs) in carrying out traffic engineering tasks for network management and planning purposes. However, it is very difficult and costly to measure this traffic matrix directly. Hence, traffic matrices are inferred from link measurements through estimation, using different techniques. There are different techniques for this traffic matrix estimation and there is still a need for evaluating these existing techniques. Some of those techniques have been previously compared, but with new improved techniques recently developed there is a need to revisit the comparisons. In this paper, we have carried out studies to compare three very popular methods: the tomogravity, the entropy maximization and linear programming methods. We find that the tomogravity method best estimates the traffic matrix among the methods we tested. We then incorporate some enhancements which improve this method. Specifically we established that knowing some point to point traffic may improve the estimation but not necessarily, and this is counter-intuitive. We modify the existing entropy maximization method by adding more constraints and we find that our modified method outperforms the existing entropy maximization and tomogravity methods.

The radio link-level delay statistics in a wireless network using adaptive modulation and coding (AMC), weighted round-robin (WRR) scheduling, and automatic repeat request-based error control is analyzed in this letter. WRR scheduling can be used for service differentiation similar to that achievable by using the generalized processor sharing scheduling discipline. The analytical framework presented in this letter captures physical and radio link-level aspects of a multirate multiuser wireless network (e.g., general fading model, AMC, scheduling, error control) in a unified way. It can be used for admission control and cross-layer design under statistical delay constraints. The analytical results are validated by simulations. Typical numerical results are presented, and their useful implications on the system performance are discussed.

This paper presents a novel Markov-based model for analyzing the end-to-end transmission of a batch of packets in a multihop wireless network using multirate transmission. The end-to-end reliability of this transmission (in terms of the number of packets delivered to the destination node) is controlled through different types of automatic repeat request (ARQ)-based error control mechanisms implemented at each node. For a batch of packets, we derive complete statistics (i.e., probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. Typical numerical results obtained from the model are validated by means of simulation. These results reveal the trade-off between end-to-end latency and end-to-end reliability, which would be an important issue in designing and engineering multihop wireless networks. Also, we demonstrate the usefulness of the proposed analytical model in predicting the latency and the reliability performances of TCP (transmission control protocol) in a multihop wireless scenario

In a distributed multihop mesh/relay network (e.g., wireless ad hoc/sensor network, cellular multihop network), each node acts as a relay node to forward data packets from other nodes. These nodes are often energy-limited and also have limited buffer space. Therefore, efficient power saving mechanisms (e.g., sleeping mechanisms) are required so that the lifetime of these nodes can be extended while at the same time the quality of service (QoS) requirements (e.g., packet delay and packet loss rate) for the relayed packets can be satisfied. In this paper, we present a novel queueing analytical framework to study the tradeoff between the energy saving and the QoS at a relay node. Specifically, by modeling the bursty traffic arrival process as a MAP (Markovian arrival process) and the packet service process as having a phase-type (PH) distribution, we model each node as a MAP/PH/1 nonpreemptive priority queue. The relayed packets and the node's own packets form two priority classes and the medium access control (MAC)/physical (PHY) layer protocol in the transmission protocol stack acts as the server process. Moreover, we use a phase-type vacation model for the energy-saving mechanism in a node when the MAC/PHY protocol refrains from transmitting in order to save battery power. Two different power saving mechanisms due to the standard exhaustive and the number-limited exhaustive vacation models (both in multiple vacation cases) are analyzed to study the tradeoff between the QoS performance of the relayed packets and the energy saving at a relay node. Also, an optimization formulation is presented to design an optimal wakeup strategy for the server process under QoS constraints. We use matrix-geometric method to obtain the stationary probability distribution for the system states from which the performance metrics are derived. Using phase-type distribution for both the service and the vacation processes and combining the priority queueing model with the vacation queueing model make the analysis very general and comprehensive

This letter presents an analytical framework for radio link level performance evaluation in a wireless network using adaptive modulation and coding (AMC) and automatic repeat request (ARQ)-based error control. Both the cases of finite and infinite buffer sizes at the radio link layer are considered when the packet arrival process is modeled by a batch Markovian arrival process (BMAP), which can capture correlation in the arrival process. Using the model, radio link level performance measures such as average delay, buffer overflow probability, packet loss rate, and average spectral efficiency can be obtained, and the impacts of channel parameters on the performance measures can be determined. Using the queue length distributions for finite and infinite buffer cases, the buffer size can be designed such that the packet overflow probability remains below the desired level. Such a cross-layer analytical framework would be very useful for network designers

An analytical framework for radio link level performance evaluation under scheduling and automatic repeat request (ARQ)-based error control in a multi-rate wireless network is presented. The multi-rate transmission is assumed to be achieved through adaptive modulation and coding (AMC) in a correlated fading channel. The analytical framework, which is developed based on a vacation queueing model, can be applied to any scheduling scheme as long as the evolution of the joint service/vacation and channel processes can be determined. The exact statistics of queue length and delay are obtained and the radio link level throughput is calculated under both saturated and non-saturated buffer scenarios. As an example of using the general analytical model, we analyze the performance of max-rate (MR) scheduling scheme which exploits multiuser diversity and compare its performance with the round-robin (RR) scheduling scheme. Although the MR scheduling always results in higher throughput than the RR counterpart, we observe that the RR scheduling offers better delay performance than the MR scheme under light traffic load conditions. The usefulness of the presented analysis is highlighted by illustrating its applications for cross-layer design and packet-level admission control under delay constraints. After all, this analytical framework would be very useful for comprehensive analysis of radio link level scheduling schemes and hence for design and engineering of radio link control protocols

This paper presents an analytical framework to calculate the probability mass function (pmf) of channel access delay in IEEE 802.11 distributed coordination function (DCF) medium access control (MAC) mechanism. The access delay is defined as the time between a station chooses a new backoff value and the time it is able to access the channel for data packet transmission. Using a Markov process, the access delay is modeled as having phase-type distribution. Since the back-off is frozen when the channel is sensed busy, the access delay distribution is observed to be composed of non-continuous clusters. The envelope of the pmf as well as the envelope of each cluster resemble hyper-exponential distribution. While the proposed model is flexible enough to accommodate any distribution of MAC data frame length, the numerical results presented in this paper are for the fixed-length data frames. The model would be useful in many aspects such as queueing analysis and/or designing energy-efficient MAC protocols compatible with the IEEE 802.11 DCF standard

We present an analytical model to analyze latency for reliable end-to-end batch transmission in a multi-hop wireless network using multi-rate transmission at each hop. The end-to-end reliability is achieved through hop-level error recovery based on an automatic repeat request (ARQ) protocol with unlimited persistence. The multi-rate transmission in the radio link is achieved through adaptive modulation and coding (AMC). We derive complete statistics (i.e., probability mass function) for the end-to-end latency. The analytical results are validated through simulations. The proposed model would be useful to analyze and optimize reliable end-to-end protocol (i.e., transport layer protocol) performance in multi-hop wireless networks.

We present a novel model for analyzing end-to-end transmission of a batch of packets in a multi-hop wireless network with automatic repeat request (ARQ)-based error control mechanism implemented at each node. For a batch of packets, we derive complete statistics (in terms of probability mass function) for end-to-end latency and the number of packets successfully delivered to the destination node. The analytical model is validated by means of simulation. Typical numerical results obtained from the model reveal the trade-off between end-to-end latency and reliability which would be an important issue in design and engineering of multi-hop wireless networks. The presented analytical model would be useful is analyzing and optimizing flow control and congestion control protocols in multi-hop wireless networks such as sensor networks.

We analyze the radio link level queueing performance for a multi-rate wireless network using adaptive modulation and coding (AMC), scheduling, and automatic repeat request (ARQ)-based error control. The analytical framework, which is developed based on a vacation queueing model, can be applied to any scheduling scheme as long as the evolution of the joint service/vacation and channel processes can be determined. The exact statistics of queue length and delay are obtained. As an example of using the general analytical model, we analyze the performance of a max-rate (MR) scheduling scheme which exploits multiuser diversity. Based on the queueing analysis, the impacts of channel and system parameters on the radio link level performance can be determined and hence cross-layer design and engineering can be performed. Also, efficient admission control schemes can be designed for delay-constrained applications.

We propose an analytical model for call-level transient performance analysis in cellular mobile networks with time-varying traffic patterns. We consider both static and adaptive bandwidth allocation. Based on the transient analysis, we develop a threshold-based adaptive call admission control scheme in which a threshold is used to control the admission of new calls and thereby achieve the desired call-level quality of service (QoS) in the network. Results from numerical examples based on the transient analysis are presented. These results are validated by simulation results.

Provisioning quality of service (QoS) in Web servers has gained immense importance because Web servers are a major part of the Internet. To deliver the pledged QoS, Web service providers need control over the allocation of the resources in their Web servers. Control is also necessary for reaching the optimal resource allocation through proper service differentiation. In this paper, we propose and investigate an analytic approach that enables the service providers to deploy a differentiated service policy that offers this control. The proposed service policy is configurable by tunable control parameters. We devise the relationships between the performance measures and these parameters by adopting a unique queuing theoretic approach. Once these relationships are established, we describe how these parameters can be set to their most appropriate values depending on the objectives of the service providers. We illustrate the usefulness of our approach by conducting the analysis on a real Web trace.

We analyse the queuing performance of a radio link level round-robin scheduler for downlink data transmission in a multi-rate TDMA (time division multiple access) wireless network. One broadcast channel in the downlink is shared by multiple mobile users in a time multiplexing fashion and a round-robin scheduler serves each user in exactly one time slot. The finite state Markov channel (FSMC) is used to capture different states of a slow Rayleigh fading channel. Depending on the channel condition, the modulation level at the transmitter is adapted and, therefore, one or multiple packets can be transmitted in one time slot. Using the matrix geometric method (MGM), the system is modeled as a quasi-birth and death (QBD) process and then the queue length and delay distributions are derived. We present typical numerical results and discuss their useful implications on system design.

In this paper, we present a model to analyze the performance of multi-service cellular wireless networks in terms of the new call blocking and the handoff call dropping probabilities when the call arrival rates depend on the time of the day. A Markov modulated Poisson process (MMPP) is used to represent the different rates in each time period. To capture the multiple classes of services (e.g., voice, data, video) we use a multi-dimensional Markov model where each dimension represents the number of users in each class. For each class of users a fixed number of channels (guard channels) are reserved for handoff call. The model can be also used for other types of channel reservation schemes such as fractional guard channel or thinning scheme. A recursive algorithm is applied to obtain the steady state probabilities for the system states which reduces the computational complexity. Simulation results are presented to show the accuracy of the proposed model.

Abstract In this paper, we present a queueing model to evaluate the performance of CDMA reverse link in a multiple cell environment. Since CDMA capacity is interference limited, both the communication quality of ongoing calls and the admission condition of new arriving calls for each cell depend on the time‐varying signal‐to‐interference ratio (SIR). We use a quasi‐birth‐and‐death process to capture the variation of traffic loads in cells. After obtaining the stationary distribution of the system, we study some important performance indices such as the outage probability of existing calls, the blocking probability of new calls, the average carried traffic in a cell and the dropping frequency of ongoing calls. Numerical results reveal the effects of system parameters on its performance. Copyright © 2004 John Wiley & Sons, Ltd.

In this paper, the arrival of new calls and handoff calls to a personal communications services (PCS) network is modeled by a Markov arrival process (MAP) in which we allow correlation of the interarrival times among new calls, among handoff calls, as well as between these two kinds of calls. A new call will retry again if the first attempt is blocked. The PCS network consists of homogeneous cells and each cell consists of a finite number of channels. Under the general conditions that all random variables involved have general phase type (PH) distribution, we develop an explicit expression of the infinitesimal generator matrix of the Markov chain governing the network and find its complexity. This hits been a difficult matrix to obtain, judging from the works in the literature. It is very complex to develop and has not been previously obtained by other researchers. Some methods to find the stationary probability of the network are discussed. Particularly, we introduce an effective method, from which we can obtain the new call blocking probability and the handoff call failure probability. Also, the busy period of the orbit is introduced. This is an interesting measure from the viewpoint of network provider; its distribution and expectation are then obtained. The results presented in this paper can be used to provide some guidelines to performance evaluation for PCS network design.

The arrival of calls (i.e., new and handoff calls) in a personal communications services (PCS) network are modeled by a Markov arrival process (MAP) in which we allow correlation of the interarrival times among new calls, among handoff calls, as well as between these two kinds of calls. Each cell of the network consists of a finite number of channels and a buffer with finite size for handoff calls. There exist some channels among each cell which can be used by splitting the original rate into two channels with different rates if necessary when a handoff call arrives and finds all the channels busy. We obtain the stationary joint probability of number of calls in the cell and the phase of the arrival process, the blocking probability of a new call, the forced termination probability of a handoff call, and the mean dwell time of a handoff call in the buffer. Finally, we obtain the distribution and the mean of the cell's busy period, the distribution and the mean of the first time to split the cth channel, and some other interesting performance measures for the network. Some explicit results for special cases obtained by Lin et al. (see IEEE Trans. Vech. Technol., vol.45, no.1, p.122-30, 1996 and vol.43, no.3, p.704-12, 1994), Tekinary et al. (1992) and by Yoon et al. (1993) can also be directly obtained from the general conclusion. The results presented can provide guidelines for field data processing in PCS network design and performance evaluation.

The explicit distribution function of the actual holding time of a new call in a PCS network, with general cell residence time and general requested call holding time, is obtained. The effective call holding time of an incompleted and of completed calls are shown to be the conditional actual holding time of an incompleted call and of a completed call, respectively. If the holding time is of Erlang or if both the residence and requested call holding times belong to the class of new better than used (NBU), we proved that the mean effective call holding time of an incompleted call is less than the mean requested call holding time of a call. For any kind of random variables of the residence time and requested call holding time, we proved that the mean effective call holding time of a completed call is always less than the mean requested call holding time of a call.