In this paper, we present a method of constructing PAM constellations with non-uniform spacing by determining the symbol amplitudes based on warping the uniform distancing by applying the inverse error function. The construction method can adapt to symbol energy (SNR) constraints by adjusting one parameter. This parameter determines the degree of separation of the outermost pair of constellation symbols from the inner symbols and the degree of spacing uniformity. Optimization of this parameter is discussed for maximizing the equiprobable mutual information of the channel having the constellation. The equiprobable mutual information of the constellation constructed by the suggested method is compared against the uniform spacing and the Gaussian approximated spacing, which asymptotically approaches the AWGN capacity.

In this paper, we study the issue of selecting the transmission power (energy per symbol) to minimize the expected energy required to successfully deliver a link-layer packet to the destination node in a wireless link performing the automatic repeat request. We consider the system that has a slow-fading channel and employs a minimum mean square error-based channel estimation scheme for a pilot symbol-assisted modulation. We derive a closed-form expression for the packet error probability (PkEP) in the presence of channel estimation error, and we use this expression for minimizing the PkEP in allocating the transmission energy among the pilot and data symbols. We also derive the expected energy required to successfully deliver a link-layer packet as a function of the transmission power and present an optimization method based on the properties of this function.

In this work, we consider a cognitive radio network comprising a pair of sources and multiple relays exercising the amplify-and-forward cooperation in a shared frequency band. For such a network, we consider the problem of determining transmit powers of the sources and relays so that the lesser of the achievable error-free data rates from the two sources is maximized. The formulated problem is a non-convex optimization problem and does not have a structure to guarantee the quality of a solution. We present the transformation of this problem to an equivalent optimization problem for which the monotonic optimization methods can be applied. We establish that the polyblock outer approximation algorithm for this monotonic optimization terminates with an ε-optimal solution after a finite number of iterations.

In this paper, we study the issue of optimizing the data transmission power (energy per symbol) for minimizing the expected energy required to successfully deliver a packet to the destination in a wireless point-to-point link through automatic repeat request (ARQ). We focus on the system that has a slow fading wireless channel and employs a pilot symbol-assisted modulation (PSAM) scheme along with a channel estimation scheme based on the minimum mean square error (MMSE) criterion. We derive the expected energy as a function of the data transmission power. Then, we derive a closed-form expression of the data transmission power that minimizes that function.

Low power wireless sensors, personal wireless hub (PWH) and receivers can reduce the workload of the paramedic staff in a hospital. In this paper, we use multiple PWHs to transfer sensor data to the main central controller, which helps the wireless sensor devices. A well designed multiple PWH assignment and power control scheme can reduce the electromagnetic and in-band interference induced to the other medical devices in the hospital. We propose a framework and low complexity algorithm for interference aware joint power control and multiple PWH assignment (IAJPCPA) in a hospital building with cognitive radio capability. The proposed IAJPCPA is a non-convex mixed integer non-linear optimization problem (NC-MINLP) which is generally NP-hard. We present an efficient PWH assignment and power control scheme for IAJPCPA. We also propose an upper bound on the IAJPCPA that converts the non-convex problem into a convex optimization problem. We examine the effect of different system parameters.

In this paper, we study the issue of optimally detecting symbols for a Selective-Decode-and-Forward (SDF) relay-assisted communication system in the presence of channel estimation error for flat Rayleigh fading channels. In the system studied, a pilot symbol-assisted modulation (PSAM) scheme is used along with a channel estimation scheme based on minimum mean square error (MMSE) criterion. In particular, we present a novel spectrum detection in presence of channel estimation error for the SDF protocol. Numerical simulation results are presented to show that the presented spectrum detection scheme performs well in terms of bit error probability (BEP).

Recently, high levels of energy consumption in datacenters has become a concern not only due to operational costs, but also due to adverse effects on the environment (i.e., carbon emission, climate change, etc.) Virtualization technology can provide better management of physical servers/machines (PM) and may help reduce power consumption. The purpose of this study is to minimize the total energy consumption through good virtual machine (VM) placement. The VM placement problem has a large search space. Finding an optimal solution of such problems using an exhaustive search is impractical. Heuristic algorithms can provide high-quality solutions with limited computing resources in acceptable time. Evolutionary Algorithms (EAs) can be considered as heuristic tools that can provide high-quality solutions to this type of problems. We propose a Biogeography Based Optimization (BBO) Algorithm for energy-efficient VM placement. We compare the BBO results with the Genetic Algorithm (GA). Overall, simulation results show that BBO outperforms GA.

MAX-SAT is a classic NP-hard optimization problem. Many real problems can be easily represented in, or reduced to MAX-SAT, and thus it has many applications. Finding optimum solutions of NP-hard optimization problems using limited computational resources seems infeasible in general. In particular, all known exact algorithms for MAX-SAT require worst-case exponential time, so evolutionary algorithms can be useful for finding good quality solutions in moderate time. We present the results of an experimental comparison of the performance of a number of recently proposed evolutionary algorithms for MAX-SAT. The algorithms include the Artificial Bee Colony (ABC) algorithm, Quantum Inspired Evolutionary Algorithm (QEA), Immune Quantum Evolutionary Algorithm (IQEA), Estimation of Distribution Algorithm (EDA), and randomized Monte Carlo (MC). Our experiments demonstrate that the ABC algorithm has better performance than the others. For problems with Boolean domain, such as MAX-SAT, the ABC algorithm requires specification of a suitable similarity measure. We experimentally evaluate the performance of the ABC algorithm with five different similarity measures to indicate the better choice for MAX-SAT problems.

In this paper, we present a novel method to derive a tight upper bound on the symbol error probability (SEP) of a rotation coded scheme in the case of Rayleigh fading channels. This upper bound has a closed-form expression and provides results closer to the exact expression than the union-Chernoff bound. We present numerical simulations that show the tightness of the proposed analytical upper bound. Based on the tight upper bound, we also find the optimal rotation angle, which minimizes the derived tight upper bound expression of the SEP.

In this paper, we study the issue of optimally allocating the transmission energy between a pilot symbol and data symbols for a point-to-point communication system over the slow fading channel. In the system studied, our performance criterion for the optimal energy allocation is the frame error probability (FEP). A pilot symbol-assisted modulation (PSAM) scheme is used along with a channel estimation scheme based on the minimum mean square error (MMSE) criterion. We derive an approximate expression for the FEP in the presence of channel estimation error and use this expression for minimizing the FEP in allocating the transmission energy. Numerical simulation results are presented to show that the derived approximate FEP expression is close to the actual FEP.

In this paper, we present a simultaneous planning of Base Stations (BSs) and Relay Stations (RSs) with link flow for a broadband wireless network. Infrastructure costs (BS cost, RS cost and their operational costs) of a wireless network is a key factor for network service providers while planning a network. The objective of this problem is to determine a set of BSs and RSs that can serve all users and fulfill their demands at the lowest cost. This problem settings is equally important for planning networks from scratch or enhancements in existing networks. This combinatorial optimization problem is NP-hard in nature. Evolutionary Algorithms (EAs) are intelligent tools that can provide high quality solution to this type of problems. Usually, efficiency of EAs depends on the problem. The aim is to find effective EAs with minimum resources such as low computational complexity, processing time and number of fitness functions evaluations. We formulate this problem as a non-linear discrete optimization and introduce four recent EAs that are motivated by natural intelligent behaviors. The objective function of this planning problem is computationally costly, and there exist a tradeoff between resources and quality of solution. These algorithms include Biogeography-based Optimization (BBO) that is inspired by the natural migration phenomenon of species between different islands, Artificial Bee Colony (ABC) based on the intelligent behavior of honey bee swarms, Quantum-inspired Evolutionary Algorithm (QEA) from the idea of quantum computing, and Immune Quantum Evolutionary Algorithm (IQEA) motivated by both the immune theory and quantum computing. Simulation results demonstrate insights of EAs' and present tradeoff between resources and quality of solutions.

In this paper, we present simultaneous planning technique for Base Stations (BSs) and Relay Stations (RSs) for a broadband wireless network while taking data flow also known as link flow into consideration. Infrastructure cost (BS cost, RS cost and their operational costs) of a wireless network proves to be a key factor for network service providers while planning a network. The objective of this study is to help determine the set of BSs and RSs that can serve all the subscribed users and fulfill their demands at the lowest cost to the utility firm. This problem setup can be used for laying new networks as well as enhancing the already existing ones. The combinatorial optimization problem at hand is NP-hard in nature. We formulate this problem as a non-linear discrete optimization problem and compare two recent Evolutionary Algorithms (EAs) in providing approximate solution to this problem. The Quantum Inspired Evolutionary Algorithm (QEA) is a probabilistic algorithm based on quantum computing with the concept of qubits and superposition of states. The Immune theory based Immune Quantum Evolutionary Algorithm (IQEA) adopts immune operator to raise the fitness and prevent deterioration during the evolutionary process. Simulation results show better performance of IQEA as compared to QEA.

In this paper we present a low-complexity Artificial Bee Colony (ABC) based interference aware relay assignment scheme with power control for a cognitive radio network comprises of one source, multiple relays and multiple destination nodes. The Exhaustive Search Algorithm (ESA) returns the optimal solution to the problem; yet it has a high computational complexity that grows exponentially with the number of users and relays. Our contribution includes formulating the jointly relay assignment with source and relays' power allocation as a mixed integer non-linear programming problem. This problem is further reduced to an integer programming problem. In order to demonstrate the performance of the discrete ABC, we compare it with other contemporary Evolutionary Algorithms (EAs) like ACO, EDA and BBO, as well as the optimal ESA. Our Binary ABC relay assignment results outperform other EAs, while its performance is close to the optimal ESA.

The authors presented low-complexity schemes for jointly deciding subcarrier assignment, power allocation and relay assignment (JSPARA) for multiuser non-regenerative relaying in a multigroup, multicast cognitive radio system (MMCRS). The authors considered two different optimisation problems. In one optimisation problem, the authors studied the problem of maximising the sum-rate of an MMCRS. In another optimisation problem, the authors considered the problem of maximising the rate of the worst multicast group of users. And also the authors proposed low-complexity iterative algorithms for JSPARA. The proposed algorithms have low-computational complexity, and their effectiveness is verified through simulation results.

The latest increase in mobile data usage and emergence of new applications such as Multimedia Online Gaming (MMOG), mobile TV and streaming contents have motivated advances in wireless broadband systems. Recently, the Long-Term Evolution (LTE) technology, which is based on the Universal Mobile Telecommunications System (UMTS) specifications, joins WiMAX as a competitor to achieve increasing demands of the broadband wireless access. Careful deployment of such a network is required to fulfill the high data rate demands with minimal cost of infrastructure and comprehensive coverage of the subscribers. In this paper, a multi-objective network planning problem is defined as utilizing the minimum number of infrastructure sites (i.e. Base Stations or eNode B in UMTS systems) while maximum number of users in service. We proposed a Quantum Inspired Evolutionary Algorithm (QIEA) in order to achieve optimized solution for this problem. The QIEA can be viewed as a probabilistic evolutionary algorithm and thus it is plausible to expect a reasonably good performance in solving combinatorial optimization problems. In this algorithm, each individual is represented by a string of Q-bits, where a Q-bit is the probabilistic representation inspired by the qubit concept in the quantum computing. Computational experiments show that our algorithm is fairly efficient to different scenarios of the network planning problem and performs better than the Genetic Algorithm (GA).

In this paper, we present a binary-based low-complexity interference-aware relay selection scheme for a cognitive radio system with one source and multiple relays, using Biogeography-Based Optimization (BBO) algorithm as one of the novel high-performance Evolutionary Algorithms (EAs). We formulate the joint source and relay power allocation as a mixed integer non-linear programming problem, further reduced to an integer programming problem. Optimally relay assignment using the Exhaustive Search Algorithm (ESA) has a high computational complexity that grows exponentially with the number of relays. We applied the BBO-based relay selection scheme with discrete power control at source and relays for the integer programming problem. We present a low-complex migration model for partial immigration-based BBO, and simulation results show that its performance is close to the optimal ESA.

The authors investigate the interference-aware joint secondary user (SU) selection/scheduling and quantised power control (JSUS-QPC) schemes for the uplink communication in the cognitive multiple-input multiple-output (MIMO) system. The main objective of JSUS-QPC is to maximise the sum-rate capacity of the cognitive MIMO uplink communication system under the constraint that the interference to the primary user (PU) is below a specified level. The computational complexity of finding an optimal JSUS-QPC scheme by exhaustive search grows exponentially with the number of users and power levels. The authors also show that the JSUS-QPC is a non-deterministic polynomial-time hard problem and present two low-complexity algorithms for JSUS-QPC problem. Also, the effect of different system parameters (e.g. interference threshold level, the number of PUs, the number of SUs, the number of quantised power levels, etc.) on the performance of the proposed algorithms is examined. The proposed algorithms have low computational complexity, and their effectiveness is verified through simulation results.

This paper quantitatively analyzes the gain in error-constrained data throughput due to the extremely rapid (symbol-by-symbol) adaptation of symbol durations. The analysis includes systems equipped with maximal ratio combining. For uncoded communication systems, we provide numerical results obtained from the analysis of theoretical throughput gains. For some coded communication systems with commonly used error correction codes, we provide numerical throughput gain results obtained from simulations.

In this paper, we investigate the effect of imperfect channel estimation on the performance of inter-vehicular communication systems. In particular, we derive the bit error rate (BER) expression in the presence of channel estimation errors for a space-time block coded (STBC) inter-vehicular communication system based on cascaded Rayleigh fading. Our diversity analysis reveals that the underlying scheme is able to extract the full spatial diversity. Based on the derived BER expression, we formulate a power-allocation problem to optimally allocate power to both training and data transmission phases.

In this paper, we present two low-complexity interference aware multiple relay assignment schemes for cognitive radio systems. The main objective in assigning multiple relays is to maximize the sum capacity of the cognitive radio system under the constraint of acceptable interference to the primary users (PU). The computational complexity of finding an optimal assignment by exhaustive search grows exponentially with the number of relays and users. The proposed schemes have low computational complexity, and their effectiveness is verified through simulation results.

In this paper, we present a low-complexity power allocation scheme for non-regenerative (amplify and forward) relaying in cognitive radio systems. The main objective of the power allocation is to maximize the signal to noise ratio (SNR) at the destination under the constraint of acceptable interference to the primary users (PU). In this paper, we propose an iterative power allocation using SNR upper Bound (IPAUB) for non-regenerative relaying. The proposed algorithm has low computational complexity, and we verify its effectiveness through simulation results.

In this paper, we present a binary particle swarm optimization (BPSO)-based low-complexity interference aware relay assignment scheme for multiple-user cognitive radio networks with discrete power control. We consider a network of cognitive radio nodes comprising single source, multiple relays and multiple destinations. For this system, we formulate an optimization problem to allocate power to source and relays and assign the relays to the destinations. The optimization problem is formulated as a mixed integer nonlinear program. Then, we show that the formulation can be reduced into a simpler integer programming problem. Then, we propose a BPSO-based relay assignment scheme to attain a good suboptimal solution to this integer programming problem. The proposed scheme has low computational complexity, and simulation results show that its performance is close to the optimal exhaustive search algorithm.

We present a spreading gain adaptation protocol between a transmitter and a receiver communicating with each other through a direct-sequence spread spectrum system. This protocol does not require the transfer of an explicit control message indicating the change of spreading gain from the transmitter to the receiver. Also, according to this protocol, the transmitter can change the spreading gain "symbol by symbol" (SBS) as opposed to "frame by frame" (FBF); this enables extremely fast adaptation to the time-varying wireless channel and/or to the rate variation of the traffic. The proposed scheme uses the orthogonal variable spreading factor (OVSF) codes to enable the receiver to detect the spreading gain used by the transmitter. Our analysis indicates that fast adaptation can significantly improve the average throughput over a slow or no adaptation.

This paper formulates abstract problems of assigning subtasks to agents (processors) in a distributed system with a goal that they can perform its global task efficiently. The paper models the distributed system with a graph that describes the communication capabilities of the constituting agents. This graph is referred to as the "organizational graph.” In addition, the desired task-performing activity is modeled with another graph describing the required communications. Then, a few variants of the task assignment problem are formulated with potentially conflicting objectives (or constraints) of load balancing and communication costs. For some of these variants this paper provides efficient algorithms that solve the assignment problem. Some problems are proven NP-complete, and some others are left open.

Relaying can replace one transmission over a long distance with several transmissions over shorter distances, and the total transmission power of mobile stations thus can be lower in the case of relaying. As such, the relaying concept and protocols in a cellular environment have been much discussed in the literature recently. We mathematically analyze the idea of uplink relaying schemes, using a simple model that represents their essential functions. In order to simplify our performance analysis, we assume that the originator of a packet sets the maximum number of hops to the base station to be 2. Our results indicate that the performance benefits of relaying are significant

We design a forest with nodes that represent orthogonal variable spreading factor (OVSF) sequences of different lengths (spreading codes of different lengths that can be used for multi-rate DS-CDMA). In addition to the non-descendant OVSF property exhibited in well-known tree-structured generation of sequences [(F. Adachi, et al., 1997) and (E.H. Dinan and B. Jabbari, 1998)], the sequences represented by the nodes of our forest have useful properties that can be used to achieve multi-channel communication (with a sequence providing a channel) with a lower total peak-to-mean envelope power ratio (PMEPR). These OSVF sequences grouped by our forest structure also have properties that facilitate symbol-by-symbol adaptation of the symbol duration in multi-rate CDMA systems. For example, certain lineages in the forest have the property that any pair of code sequences in the same lineage are shift orthogonal to each other, with the unit shift length being that of the shorter sequence. We present the forest-structured generation of the sequences (spreading codes) and their properties.

The tradeoff between the throughput and the blocking probability of various code assignment schemes for OVSF-CDMA (orthogonal variable spreading factor CDMA) systems is investigated, and the optimal fixed and dynamic code assignment schemes that maximize the average data throughput under a blocking probability constraint are proposed. The optimal schemes are obtained using constrained optimization through the Markov decision process and linear programming. The performance of each scheme is numerically evaluated. Numerical results demonstrate that the proposed new schemes can achieve a satisfactory balance between the average throughput and the blocking probability.

We present a closed-loop architecture and protocols for rapid dynamic spreading gain adaptation and fast feedback between a transmitter and a receiver communicating with each other in CDMA networks. These protocols and architecture do not require the transfer of an explicit control message indicating the change of CDMA spreading gain from transmitter to receiver. Also, with these protocols, the transmitter can change the spreading gain symbol-by-symbol as opposed to frame-by-frame, and feedback information (e.g., the fast-varying channel condition) can be exchanged almost as frequently as the symbol rate; thus, faster adaptation to the time-varying channel conditions of wireless networks and/or the rate variation of traffic is possible than with the existing frame-by-frame approach.

We analyze an automatic repeat-request (ARQ) scheme of the Consultative Committee for Space Data Systems file-delivery protocol for the single-hop file-transfer operation. With regard to performance measures, this paper is mainly concerned with the time taken to transfer a file (file-delivery time) and throughput efficiency. We discuss the ARQ timer-setting rule that minimizes the expected file-delivery time under the constraint that the throughput efficiency is maximized. Then, for that timer-setting rule, we derive the expected file-delivery time.

We analyze the symbol error rate of a multiple-branch predetection maximal ratio combiner for correlated Nakagami-m-fading channels. Focusing on the correlations between diversity branches, we provide a simple analytic proof, applicable to different modulation schemes, that uncorrelated branches result in the lowest symbol error probability. First, we present a proof assuming that all branches have the same fading severity m, where m can be a noninteger. We then present a proof for the case where the branches have different but, integer-valued fading severities (ms).

A methodology is presented for integrating effective-bandwidth-based routing for QoS-sensitive traffic and datagram routing of the best-effort traffic. To prevent excessive delays of best-effort traffic in a network domain, we develop (1) a constraint, stated in the form of a residual link bandwidth, and (2) a cost function for application to routing of QoS connections. Link-based and path-based problem formulations and algorithms are presented. For the case that a cost quantization condition holds, we develop an efficient implementation of a link-based routing strategy that first minimizes a QoS cost, then secondarily minimizes a best-effort cost. The performance of this approach is further enhanced by explicitly accounting for the difference between the effective bandwidth and the average bandwidth of traffic. Simulation results illustrate the application of our BE-friendly method to an algorithm for path routing with restoration.

An argument is provided to establish the equality in ergodic polling systems between the average number of packets seen by an arriving packet and the average number of packets an arriving packet must wait for before its service.

In this paper, we analyze the capacity of the forward link for CDMA systems with adaptive antenna arrays, considering soft handoffs. We take it into account that a mobile near a cell boundary can receive from two or more base stations and use MRC. For the capacity analysis we divide a cell into two regions: the region in which the soft handoff occurs and the one in which the handoff does not occur. Then, we compute the maximum number of users a base station can support under the constraint of acceptable outage probability. We assume that there is no limitation on the base station's transmission power, so a cell's capacity is limited by the interference between signals intended for different users in the same cell and in different cells.

Abstract We describe the Component Architecture for Simulating Network Objects (CASiNO) useful for the implementation of communication protocol stacks and network simulators. This framework implements a rich, modular coarse‐grained dataflow architecture, with an interface to a reactor kernel that manages the application's handlers for asynchronous I/O, real timers and custom interrupts. These features enable developers to write applications that are driven by both data flow and asynchronous event delivery, while allowing them to keep these two functionalities distinct. We provide an example program and expository comments on the program to illustrate the use of the CASiNO framework. Published in 2002 by John Wiley & Sons, Ltd.

We explore a methodology to divide an application's data into several categories and send them through different IP differentiated services vices (DiffServ) grades. The issues are motivated by a futuristic infrastructure wherein the Internet service provider charges different prices to packets going through different service grades. This paper discusses how a subscriber of the DiffServ can assign different grades to real-time video packets on the basis of the packets' relative importance under the constraint of communication expenses. For relative DiffServ, we present an adaptive packet forwarding mechanism to preserve the performance ratio among service grades persistently over different time scales. Through experiments we observe how this packet forwarding mechanism improves the the quality of the application that maps the packets into the different service grades. In the experiment the end-to-end performance is evaluated by the error resilient packet video transmission using ITU-T H.263+ codec over a simulated Diffserv network.

By use of the complementary slackness condition for linear programming, we prove analytically that for a statistical multiplexer allocating link bandwidth to connections of a single traffic class as based on the number of connections in progress, the connection admission control policy which maximizes the probability of admission subject to a packet loss quality of service requirement is of "soft-threshold" type.

We introduce a forest whose nodes represent orthogonal variable spreading factor (OVSF) code sequences of different lengths used for multi-rate CDMA. These code sequences have properties that facilitate symbol rate adaptation and blind rate detection, which can be used in a fast time-varying environment such as mobile communication systems. The properties of these codes also makes flexible the assignment of code sequences of different lengths to different users who communicates at different constant symbol rates. In particular, certain lineages in the forest have a property that the set of code sequences represented by such a lineage are cyclic orthogonal to each other with unit length being that of the shortest sequence in the set. We present the forest-structured generation of the code sequences and their properties. Then, we present a symbol rate adaptation protocol and a blind rate detection algorithm that use the code properties.

We further simplify the numerically friendly bit-error rate expression presented by Simon and Alouini (see ibid., vol.46, p.1625-38, 1998) for noncoherent and differentially coherent systems with multichannel reception over additive white Gaussian noise and generalized fading channels.

SEAN is freely distributed, object-oriented, extensible software for research and development in host ATM signaling. SEAN includes a complete source-level release of the host native ATM protocol stack, and implements the ATM user network interface, compliant to the ITU Q.2931 specification for point-to-point calls, the ITU Q.2971 extension for point-to-multipoint calls, and the ATM Forum extension UNI-4.0 for leaf initiated join calls, SEAN provides APIs to the programmers writing application programs that require ATM signaling. Developers can easily modify and extend SEAN, using the framework library released together. This paper describes essential parts of SEAN's architecture and guides the users and protocol developers.

This paper addresses the problem of efficiently using the fibers in the physical cabling plant to connect the pairs of communication devices. This paper formulates the structure of the physical cabling plant, the communication (physical connection) requirement, and the problem of assigning the fibers to physical connections required. Then, this paper presents algorithms that solve the fiber assignment problem. The algorithms also decide if the physical plant can satisfy the connection requirement.

This paper formulates, in the abstract, a problem of assigning subtasks to distributed agents (processors) so that they can perform a certain global task efficiently. We model the group of networked agents by a graph that describes its communication capabilities, which is referred to as the organizational graph. In addition, we assume that the desired task-performing activity defines another graph describing the required communication. We then formulate a few variants of the problem with potentially conflicting objectives (or constraints) of load balancing and communication costs. For some of these variants, we provide algorithms that solve the assignment problem, while some others are left open.

We refine the exact expressions of the average symbol error rate (SER) of several coherent, L-branched maximal-ratio-combining (MRC) receivers in a set of correlated Nakagami-m (1960) fading channels with additive white Gaussian noise (AWGN). These expressions can be applied to study the performance for M-PSK or M-QAM receivers with any number of diversity branches, with an arbitrary SNRs correlation function between branches, with any constellation size, and with any fading severity parameter m rational. In addition, we analyze the channel capacity of the M-PSK system with MRC receivers for the case of balanced branches (i.e. all branches have the same average SNR per symbol) for arbitrary SNR correlation functions. The results of this analysis can guide the design in realizing the systems that dynamically adapt modulation schemes to the time-varying channel conditions in order to maintain the data link's quality of service (QoS).

This paper quantitatively analyzes the queueing delay of the constant-bit-rate (CBR) traffic in a multiplexer, where the CBR traffic is mixed with the traffic of another session (interfering traffic) that has throughput and burstiness constraints. This paper focuses as a quality-of-service (QoS) measure on the fraction of the CBR traffic that fails to meet a certain delay requirement. Results include the worst-case QoS of CBR traffic as a function of the throughput and burstiness parameters imposed on the interfering traffic. Results of this paper can be applied to traffic management and call processing of asynchronous transfer mode (ATM) networks.

A designer of reliable multi level secure (MLS) networks must consider covert channels and denial of service attacks in addition to traditional network performance measures such as throughput, fairness, and reliability. We show how to extend the NRL data Pump to a certain MLS network architecture in order to balance the requirements of congestion control, fairness, good performance, and reliability against those of minimal threats from covert channels and denial of service attacks. We back up our claims with simulation results.

A designer of reliable MLS networks must consider covert channels and denial of service attacks in addition to traditional network performance measures such as throughput, fairness, and reliability. We show how to extend the NRL data Pump to a certain MLS network architecture in order to balance the requirements of congestion control, fairness, good performance, and reliability against those of minimal threats from covert channels and denial of service attacks. We back up our claims with simulation results.>

Studies a single server queueing system with deterministic service time in which arrivals are regulated by the leaky-bucket control. The worst traffic of arrivals passing the leaky-bucket regulation is discussed. The performance measure considered is queueing delay averaged over all packets. The worst traffic is characterized as the repetition of the following three phases: bulky arrival with bulk size related to the bucket size, arrival at every token generation for a specified length of interval, and then no arrival till the token bucket is full. The average queueing delay for the worst traffic, i.e. tight performance bound, is expressed in explicit form as a function of leaky bucket parameters (bucket size and token generation rate). The authors expect that this function will provide insights into the relationship between leaky bucket parameters and the corresponding network resources allocated.>