We investigate the design of an efficient rerouting scheme for dynamic bursty high granularity traffic as a part of an agile network management and control scheme in cognitive optical networks. An efficient rerouting scheme allows more efficient use of wavelengths resources. The design includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers. Rerouting is necessary to maintain users' quality of service when the network load exceeds the load limit of the primary path. Our design is to trigger reconfiguration when a load threshold is crossed, and the threshold is determined by requirements on both the delay and the blocking (i.e. no available lightpath) probability. The delay is dependent on the probability of exhausting all wavelengths on the primary route, the availability of secondary routes, the holder (buffer) size, and the topology of the network. A dynamic rerouting scheme implemented with a shortest-path algorithm with immediate wavelength availability is proposed to find the alternate paths. It is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths. In addition, a good candidate network topology should have high edge-connectivity. Wavelength reservation for rerouted traffic does not maximize wavelength utilization. Traffic with different sizes should be broken up into multiclasses with dedicated partitioned resources and will have different maximum load thresholds for triggering rerouting to meet the performance requirements.

This paper considers a quantum media-access-control (MAC) algorithm. We show for low light levels in a broadcast channel where quantum effects become significant, the classical ALOHA scheme can be modified to a Quantum ALOHA system with physics-induced diminished throughput due to quantum measurement errors. The quantum optimum throughput can be achieved using a modified Pseudo-Bayesian Rivest Algorithm. Simulations and analytic results agree well. This is one of the first instances of a quantum network algorithm at the Media-Access-Control (MAC) Layer.

Emerging network traffic requires a more agile network management and control system to deal with the dynamic network environments than today's networks. We propose the use of cognitive techniques for the fast and adaptive management of future optical networks. As a first approximation, we model our expected traffic arrivals as a multi-state Markov process and categorize different network traffic environments by the length of the network coherence time. For the traffic with moderate and short coherence times, the stopping-trial estimator still responses to the traffic changes with a short detection time as long as the inter-arrival times of traffic transactions are independent. The algorithm provides no prejudice on the exact network traffic distribution avoiding having to sense and estimate detailed arrival traffic statistics. To further deal with the fast-changing traffic, we model the transient convergent behaviors of network traffic drift as a result of traffic transition rate changes and validate the feasibility and utility of the traffic prediction. When the network traffic rate changes quickly, our sequential maximum likelihood estimator will capture the traffic trend with a small number of arrivals and provide fast reconfiguration, which is very important for maintaining quality of service during large traffic shifts.

Increasingly serious traffic congestion requires an accurate and timely traffic speed prediction, which will significantly benefit both individual drivers and decision makers in travel planning and traffic management. However, traffic speed prediction is a long-standing and challenging topic. Due to the availability of traffic datasets and powerful computation resources, deep learning becomes a promising solution to this problem. In this paper, based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, we propose a model named CLM, which is the first to make use of CNN to extract the features of daily and weekly periodicity of traffic speed at the target area and also extract the spatiotemporal features together with the output of CNN by LSTM layers. We conduct comprehensive simulations to assess the performance of our proposed method based on the real-world dataset of Hong Kong. The results indicate that our proposed CLM model can better predict traffic speed in different forecast time periods than the other five competing methods, including SVR, MLP, Lasso, Random forest, and LSTM.

Significant sampling is an adaptive monitoring technique proposed for highly dynamic networks with centralized network management and control systems. The essential spirit of significant sampling is to collect and disseminate network state information when it is of significant value to the optimal operation of the network, and in particular when it helps identify the shortest routes. Discovering the optimal sampling policy that specifies the optimal sampling frequency is referred to as the significant sampling problem. Modeling the problem as a Markov Decision process, this paper puts forth a deep reinforcement learning (DRL) approach to tackle the significant sampling problem. This approach is more flexible and general than prior approaches as it can accommodate a diverse set of network environments. Experimental results show that, 1) by following the objectives set in the prior work, our DRL approach can achieve performance comparable to their analytically derived policy φ' - unlike the prior approach, our approach is model-free and unaware of the underlying traffic model; 2) by appropriately modifying the objective functions, we obtain a new policy which addresses the never-sample problem of policy φ', consequently reducing the overall cost; 3) our DRL approach works well under different stochastic variations of the network environment - it can provide good solutions under complex network environments where analytically tractable solutions are not feasible.

We face a growing ecosystem of applications that produce and consume data at unprecedented rates and with strict latency requirements. Meanwhile, the bursty and unpredictable nature of their traffic can induce highly dynamic environments within networks which endanger their own viability. Unencumbered operation of these applications requires rapid (re)actions by Network Management and Control (NMC) systems which themselves depends on timely collection of network state information. Given the size of today's networks, collection of detailed network states is prohibitively costly for the network transport and computational resources. Thus, judicious sampling of network states is necessary for a cost-effective NMC system. This paper proposes a deep reinforcement learning (DRL) solution that learns the principle of significant sampling and effectively balances the need for accurate state information against the cost of sampling. Modeling the problem as a Markov Decision Process, we treat the NMC system as an agent that samples the state of various network elements to make optimal routing decisions. The agent will periodically receive a reward commensurate with the quality of its routing decisions. The decision on when to sample will progressively improve as the agent learns the relationship between the sampling frequency and the reward function. We show that our solution has a comparable performance to the recently published analytical optimal without the need for an explicit knowledge of the traffic model. Furthermore, we show that our solution can adapt to new environments, a feature that has been largely absent in the analytical considerations of the problem.

We propose a contention-based priority wireless reservation MAC protocol that guarantees latency and throughput for users with time-critical applications. The protocol is designed to accommodate two anticipated requirements of Smart City networks: first, that critical traffic be served within a specified delay without excessive throttling of non-critical traffic; and second, that surges in critical traffic from a geographically-concentrated region be handled gracefully without expansive overprovisioning.

In this paper we propose a novel approach to deliver better delay-jitter performance in dynamic networks. Dynamic networks experience rapid and unpredictable fluctuations and hence, a certain amount of uncertainty about the delay-performance of various network elements is unavoidable. This uncertainty makes it difficult for network operators to guarantee a certain quality of service (in terms of delay and jitter) to users. The uncertainty about the state of the network is often overlooked to simplify problem formulation, but we capture it by modeling the delay on various links as general and potentially correlated random processes. Within this framework, a user will request a certain delay-jitter performance guarantee from the network. After verifying the feasibility of the request, the network will respond to the user by specifying a set of routes as well as the proportion of traffic which should be sent through each one to achieve the desired quality of service. We propose to use mean-variance analysis as the basis for traffic distribution and route selection, and show that this technique can significantly reduce the end-to-end jitter because it accounts for the correlated nature of delay across different paths. The resulting traffic distribution is often non-uniform and the fractional flow on each path is the solution to a simple convex optimization problem. We conclude the paper by commenting on the potential application of this method to general transportation networks.

We address the design of fast wavelength assignment and reconfiguration using cognitive management and control that can quickly and accurately adapt to the operating conditions for optical networks. The traffic detection performances of two Bayesian estimators and a stopping-trial (sequential) estimator are examined based on the transient behaviors of networks. The stopping-trial estimator has the fastest response time to the changes of traffic arrival statistics. We propose a wavelength reconfiguration algorithm with continuous assessment where the system reconfigures whenever it deems necessary. The reconfiguration can involve addition or subtraction of multiple wavelengths. Using the fastest detection and reconfiguration algorithm can reduce queueing delays during traffic surges without over provisioning and thus can reduce network capital expenditure and prevent waste of resources upon erroneous decision on occurrence of surges.

Future networks have to accommodate an increase of 3-4 orders of magnitude in data rates with very heterogeneous session sizes and sometimes with strict time deadline requirements. The dynamic nature of scheduling of large transactions and the need for rapid actions by the Network Management and Control (NMC) system, require timely collection of network state information. Rough estimates of the size of detailed network states suggest large volumes of data with refresh rates commensurate with the coherence time of the states (can be as fast as 100 ms), resulting in huge burden and cost for the network transport (~300 Gbps/link) and computation resources. Thus, judicious sampling of network states is necessary for a cost-effective network management system. In this paper, we consider a construct of an NMC system where sensing and routing decisions are made with cognitive understanding of the network states and short-term behavior of exogenous offered traffic. We have studied a small but realistic example of adaptive monitoring based on significant sampling techniques. This technique balances the need for accurate and updated state information against the updating cost and provides an algorithm that yields near optimum performance with significantly reduced burden of sampling, transport and computation. We show that our adaptive monitoring system can reduce the NMC overhead by a factor of 100 in one example. The essential spirit of the cognitive NMC is that it collects network states ONLY when they matter to the network performance.

Future optical networks will have 103-4 increase in rates and highly granular traffic due to large transactions. Cognitive techniques will provide agile automated fast scheduling of resources, topology changes and agile adaptations for congestion control, load balancing and reconfiguration involving all layers.

We propose an optical broadcast architecture for intra-data center communication to address the growth of traffic volume in data center networks. The bandwidth of current wavelength-division multiplexed (WDM) systems is limited by the low loss region of an optical fiber and the bandwidth of optical amplifiers. However, the bandwidth of an optical fiber greatly exceeds the bandwidth of optical amplifiers. We investigate the scalability and power requirements of an out of amplifier band optical broadcast architecture for intra-data center communication. We show that the broadcast architecture is limited by splitting loss and not fiber loss for short distance communication. We also analyze the delay performance of the broadcast architecture as a function of wavelength loading and the size of data flows. We show that the broadcast architecture requires fewer fiber lengths and optical switches than traditional switched network architectures.

The advanced multibeam satellite equipped with phased array antenna and solid state power amplifiers can generate flexible beams, by managing interbeam interference to serve a very large number of users effectively over its coverage area. Onboard processing (OBP) functionality can enhance the flexibility of a large-scale antenna by speeding up computational processes and saving precious radio link spectrum. In this paper, we derive a cross-layer OBP design of switching/routing, beamforming, and user scheduling as taking advantage of fine spatial resolution capability of phased array antenna satellites. We evaluate tradeoff between OBP computational complexity and throughput performance, showing that the additional complexity of an increased number of switch ports and phased array antenna gain patterning is compensated by high throughput gain achieved by mitigating interference. Our analysis shows that throughput gain for the next generation satellite system can be as high as 40, compared with the conventional multiple beam antenna with travelling wave tube amplifiers, and that beamforming is critical for achieving high spectral efficiency in the crowded service area. We then investigate the impacts of onboard switching and phased array antenna beamforming to practical routing protocols, such as open short path first and routing information protocol.

We highlight the advantages of dynamic wavelength allocation and path rerouting in hybrid optical flow-switched data center networks compared with static electronic packet-switched data center networks. Current electronic packet-switched networks perform routing based on lookup tables and shortest path routing over high speed optical connections. However, load balancing and path rerouting are not designed in coordination with the optical data plane. In this work, we design and analyze the performance of load balancing and resource allocation in hybrid flow data center architectures. In the hybrid flow architecture, the transport layer and control plane are responsible for load balancing, congestion control and reliable data delivery in wavelength division multiplexed networks. The transport layer abstracts the underlying network and data link layers from user applications. Applications are unaware of whether flows are transmitted via the electronic data plane, optical data plane, or both, where the relevant metric for applications is transaction delay. We show that the data center architecture that minimizes delay in the presence of elephant flows and dynamic loads is the hybrid flow architecture with dynamic resource allocation.

We design and analyze optical data link layer protocols for data center networks. Specifically, we compare the delay performance of an Ethernet only architecture and a hybrid flow architecture. The need of a new architecture becomes apparent as data centers scale to support increasing data volume. The bursty and large volume of data introduced by “elephant” flows, flows composed of 100s to 1000s of frames or more, can cause extreme congestion in the network. Scaling networking components in line rate is not sufficient. We show that data center traffic is more efficiently serviced with a hybrid flow architecture. We find the breakpoint when the traffic mix is such that the hybrid architecture delay is less than the Ethernet only architecture delay. As the ratio of elephant flows increase or as the size of elephant flows increase, the performance gains of the hybrid architecture compared to an Ethernet only architecture increases.

We explore a hybrid approach as means to eliminate burst errors at transport layer for reliable and high performance communication in Optical Flow Switching (OFS) networks. Previously, there have been several suggestions at different network layers to minimize the damage caused by burst errors, but there has not been an effort to bring them together. We show that the hybrid approach of adiabatic switching at the physical layer and File Segmentation / Burst error correction at the transport layer is able to eliminate the majority of burst errors. This remaining errors will be corrected by an ARQ. First, we develop a stochastic model to characterize transients that produce burst errors. Next, we develop a simulation framework to validate the model. Finally, we show that the hybrid approach shows superior performance compared to the approaches used individually. This hybrid approach is promising as it is cost-effective and easily implementable.

Switching-induced physical layer impairments in EDFA-amplified optical links pose significant challenges to the applicability of optical flow switching (OFS). Based on measured data we have developed a model to predict lightpath quality and a scheduling algorithm that guarantees quality of lightpaths. Our algorithm greatly reduces control complexity and control traffic and can set up new lightpaths within ~5ms plus round-trip time and eliminate the need for an iterative turn-on process.

In this work, we provide design concepts of efficient physical and lightpath topology architectures for a LAN/MAN all-optical network that support optical flow switching. We found good architectures are coupled intimately with media access control protocol designs and must be optimized jointly. Two architectures that represent extreme cases were examined: Quasi-Static Architecture and Dynamic Per Flow Routing Architecture. The performance and costs are compared to provide an economical architecture building strategy.

In Optical Flow Switched networks, the control plane manages the data plane, performs scheduling, reconfigures the network, etc. For large wide area networks the scale of the data plane and the stochastic nature of the requests pose big challenges in achieving both data plane responsiveness/efficiency and control plane scalability which are coupled. In this work, we give an overview of the control functions and a scheduling algorithm for an OFS network. We consider two possible WAN lightpath topology architectures: meshed or tunneled. We use the blocking probability and throughput of the data plane to provide a common platform for the performance comparison of the designs of the control plane and algorithm complexity. With high traffic demands, the tunneled architecture provides comparable data plane performance as the meshed architecture but requires much less network management and control traffic and processing complexity and is thus much more scalable to large networks.

Free space optical systems can transmit at high data rates and can provide cheap, easily deployable solutions for both short-term and long-term communication needs. Controlling the direction of energy propagation from the optical transmitter can prevent communication between two users from interfering with another user in an optical network and can reduce the susceptibility of the system to eavesdropping. This paper discusses a method to maximize the power transmitted from an optical aperture array to the intended receiver of a free space optical link while limiting the power transmitted to a defined region within the main lobe of the transmission. We find that it is possible to suppress by over 10 dB the power to a region as close as 0.2 beamwidths away from the receiver without compromising the ability of the intended user to receive the signal.

We explore transport layer protocol design for an optical flow-switched (OFS) network. The design guarantees the reliable delivery of data files over an all-optical end-to-end flow-switched lightpath which is a burst-error channel due to gain transients in EDFAs. TCP performs poorly for OFS. TCP flow control and fair resource allocation with windowing are unnecessary in OFS networks since they are done via scheduling. In our protocol design, we optimize throughput efficiency and delay using file segmentation and reassembly, interleaving with forward error-correction and frame retransmission.

Present-day networks are being challenged by dramatic increases in data rate demands of emerging applications. A new network architecture, incorporating “optical flow switching,” will enable significant rate growth, power efficiency, and cost-effective scalability of next-generation networks. We will explore architecture concepts germinated 22 years ago, technology and testbed demonstrations performed in the last 17 years, and the architecture construct from the physical layer to the transport layer of an implementable optical flow switching network that is scalable and manageable.

Free space optical communication through the atmosphere has the potential to provide secure, low-cost, rapidly deployable, dynamic, data transmission at very high rates. Despite the ability for free space optical technology to transmit a narrow beam of light to a specific destination, it is still possible for an eavesdropper to gather light and decode information intended for another user. This paper addresses the use of spatial diversity to improve confidentiality, thereby impeding potential eavesdroppers. We find that spatial diversity with coherent detection and wavefront predistortion based on turbulence state feedback is an effectively technique to improve the confidentiality of free space optical communication.

1TCP is a popular transport layer protocol. The performance of TCP is known to be unsatisfactory in wireless network, due to high bit error rates or drop outs. Employing error correction at the transport layer can alleviate the problem to some extent. We make three contributions in this paper. First, we optimize the efficiency of repetitive codes. In particular, we propose a good estimate on throughput and obtain a simple formula to estimate the optimal number of paths that maximizes efficiency. Second, we analyze the optimization of efficiency when using Reed-Solomon Erasure Code with TCP. We show that the improvement is dramatic for stable channels but not as significant for noisy channels. Third, we find that the bottleneck on the efficiency for multipath TCP transmission with coding is the protocol TCP. Therefore, to drastically increase efficiency in wireless network, we should use another protocol instead of TCP.

TCP does not perform well in networks with stochastic channels, with links that randomly drop packets or have long outages. Diversity routing has been proposed to improve TCP's performance in these networks. In diversity routing, a sublayer between TCP and the network replicates each transmitted packet and sends the multiple copies along parallel paths. As long as at least one of the copies reaches the receiver, TCP considers the transmission successful and maintains high throughput. Previous investigations of diversity routing have analyzed TCP's performance when there is a single flow and no congestion. In this paper, we analyze the performance of multiple flows in a congested network. The contribution of this work is the discovery of three adverse effects that occur when TCP is combined with diversity routing. The effects are: link capacity overflow, rate unfairness, and lock-out of late-arriving flows. Simulation is used to analyze the root cause of these effects. Our conclusion is that naïve diversity routing fundamentally breaks the TCP congestion control mechanism and cannot be used for TCP performance improvement in networks with stochastic channels, such as many wireless and satellite networks. We propose an improvement to diversity routing that may overcome these problems, enabling use of unmodified TCP.

In recent results, a theoretical framework has been developed that shows that wavefront predistortion with transmitter channel state information can effectively mitigate turbulence induced fading for free space communication systems. The theoretical framework, however, relies on the assumption of perfect channel state information available at the transmitter, which is not physically realizable since it would require an infinite rate feedback link. This paper extends the theoretical framework to physically realizable systems with finite rate feedback. Specifically, we provide the performance and optimal feedback strategy given a finite rate feedback link.

Optical Flow Switching (OFS) is a promising architecture to provide end users with large transactions with cost-effective direct access to core network bandwidth. For very dynamic sessions that are bursty and only last a short time (~1S), the network management and control effort can be substantial, even unimplementable, if fast service of the order of one round trip time is needed. In this paper, we propose a fast scheduling algorithm that enables OFS to set up end-to-end connections for users with urgent large transactions with a delay of slightly more than one round-trip time. This fast setup of connections is achieved by probing independent paths between source and destination, with information about network regions periodically updated in the form of entropy. We use a modified Bellman-Ford algorithm to select the route with the least blocking probability. By grouping details of network states into an average entropy, we can greatly reduce the amount of network state information gathered and disseminated, and thus reduce the network management and control burden to a manageable amount; we can also avoid having to make detailed assumptions about the statistical model of the traffic.

This paper focuses on the design and analysis of scheduling approaches for Optical Flow Switching (OFS) serving high performance applications with very stringent time deadline constraints. In particular, we attempt to meet setup times only slightly longer than one roundtrip time with networks at moderate to high loading. This paper proposes three possible scheduling mechanisms for OFS connection setup in a WDM network: (i) a simple algorithm, which awards pre-emptive priority to applications requiring time deadline performance; (ii) a multi-path probing mechanism using only coarse average loading information (i.e., no detailed network state information) but without pre-emption; and (iii) a multi-path probing mechanism using periodically updated network state information and without pre-emption. The updating scheme calls for a slow control plane, which refreshes and broadcast network states only periodically on the order of seconds or longer. Our results show that for a low blocking probability, the update interval must be a fraction of the mean service time of transactions. We conclude that this algorithm, a combination of both slow centralized and fast distributed processes, delivers an efficient and scalable control design for a high-speed transport network of the future.

Optical communication through the atmosphere has the potential to provide data transmission over a distance of 1 to 100 km at very high rates. The deleterious effects of turbulence can severely limit the utility of such a system, however, causing outages of up to 100 ms. In this paper, we investigate the use of spatial diversity with wavefront predistortion and coherent detection to overcome these turbulence-induced outages. This system can be realized, especially if the turbulence or the receiver is in the nearfleld of the transmitter. New results include closed-form expressions for average bit error rate, outage probability, and power efficiency. Additionally, system performance gains in the presence of a worst-case interferer are presented. These significant results are used to develop design intuition for future system implementation.

In our previous work, we presented optical flow switching (OFS) as a key enabler of scalable future optical networks. In the present work, we propose a practical scheduling algorithm and conduct an approximate throughput-delay analysis for OFS networks.

In satellite communications, narrow spotbeams can provide high power and data rates to the desired location while reducing spatial interference. Advanced transmission antenna technology is critical to generate and switch narrow beams rapidly among a large number of users under quality of service (QoS) constraints such as average delay. In this paper, we jointly optimize resource allocation and congestion control, and compare the performances of two types of satellite transmit antennas: a multiple beam antenna and a phased array antenna. For a multiple beam antenna with traveling wave tube amplifiers (TWTA), throughput is decided by either the most demanding user or the average of all user parameters. For a phased array antenna, joint antenna gain patterning and beam scheduling is given as a function of channel conditions, interference (depending on users' geographical distribution), and average delay requirements. We then develop a low-complexity on-line algorithm of choosing either interference suppression or sequential service for the active users who are closely located within the width of a spotbeam. Due to flexible power allocation, the phased array antenna can provide better performance than the multiple beam antenna when a small number of users are very demanding or many users are densely crowded in a small area.

Throughput scaling and optimal hop distance of interference-limited wireless networks have been well characterized in literature. For some emerging wireless networks, throughput may be more limited by battery energy rather than by interference. In characterizing throughput scaling and optimal hop distance of such power-limited networks, prior work have invoked a zero signal processing energy assumption, which led to the belief that whispering to the nearest neighbor (WtNN, with the average number of hops per source destination pair increasing with increasing node density) achieves the optimal throughput scaling. We show that this belief must be modified for power-limited networks when signal processing energy is not an insignificant factor. In fact, for a power-limited network with nodes uniformly randomly distributed in a bounded region and in the limit of interference-free operation, taking Theta(1) (i.e. does not increase or decrease with increasing node density) number of hops is throughput, energy, and delay optimal, achieving Theta(1) pairwise throughput, energy per bit, and packet delay under uniform traffic, whereas WtNN is strictly suboptimal, achieving a pairwise throughput of O(logradic( n/ n)), which decreases with increasing node density. In addition, we show that a constant characteristic hop distance of dcharsimultaneously achieves the pairwise throughput scaling and minimum network energy consumption for random networks.

Enterprise networks form the backbone of corporate communication. Ethernet is the default layer-2 transport protocol and has undergone decades of constant innovation and improvisation. We propose a solution called CAMPUS for enterprise networks primarily based on end-to-end Ethernet. This all-Ethernet campus networking paradigm is designed for large enterprises or even metro networks relegating IP functions to only large core routers, while making good use of carrier-class Ethernet switches in the access and the metro area. Implementation of the CAMPUS solution is discussed from a theoretical as well as hardware perspective. Service provisioning, scalability and performance features are evaluated through extensive simulations model.

A scalable fault management system, including fault detection and localization capability, is crucial for future all-optical networks. In our previous work [5-7], we have proposed adaptive fault diagnosis schemes that deploy proactive lightpath probes to identify network failures, and have developed an asymptotically optimal run-length probing scheme to minimize the diagnostic effort (i.e., the number of lightpath probes). In this research, we aim to investigate the diagnostic hardware cost, i.e., the cost resulted from transmitter/receiver (Tx/Rx) pairs for probe transmission and detection. As a benchmark, we first show that, in order to identify all possible network failures, all the network nodes have to be equipped with diagnostic Tx/Rx pairs. We then develop a probabilistic analysis framework to characterize the trade-off between hardware cost (i.e., the number of nodes equipped with Tx/Rx pairs) and diagnosis capability (i.e., the probability of successful failure detection and localization). Our results suggest that, for practical situations, the hardware cost can be reduced significantly by accepting a small amount of uncertainty about the failure status.

In this work, we consider access network design for the Optical Flow Switching (OFS) transport architecture [1]-[3]. Our work addresses the all-optical physical layer of the data plane in the context of tree-based networks. We consider the advantages and disadvantages of passive and active components - including optical amplifiers - and ultimately integrate these building blocks in the most economically attractive fashion for the required number of users.

Optical communication through the atmosphere possibly enables high data rate communication. However, the optical field undergoes fades or outages due to air turbulence. Spatial diversity is an attractive technique to mitigate these fades. The outage probability of a system that uses diversity has been analyzed for diversity incoherent detection. This paper investigates the benefit of coherent detection with spatial diversity for fade mitigation. We also compare the performance improvement of diversity coherent detection over diversity incoherent detection. We find that coherent detection provides power gain of at least a few more dB for typical background noise limited applications (e.g. 2.86 dB at an error probability threshold of 0.1 and average received background noise photons per bit of 1).

Wireless networks that do not require pre-deployed infrastructures are ideal for applications such as disaster relieve, search and rescue, and small tactical unit operations. However, since the type of information exchange in these applications typically have stringent delay requirements, existing ad hoc wireless networks that provide best-effort service cannot satisfy the service demands. We propose a proactive mobile wireless network paradigm - one that proactively maintains network connections in order to provide assured delivery for applications with high quality of service (QoS) demands. The proactive techniques include node localization, trajectory prediction, judicious insertion of helper nodes, and helper node trajectory control In addition to presenting an architectural framework for proactive mobile wireless networks, we describe two helper node trajectory control schemes. Through simulations, we show that with proper trajectory control, even a few helper nodes can significantly improve network connectivity under typical operating scenarios.

We consider the problem of detecting failures for all-optical networks, with the objective of keeping the diagnosis cost low. Compared to the passive paradigm based on parity check in SONET, optical probing signals are sent proactively along lightpaths to probe their state of health and failure pattern is identified through the set of test results (i.e., probe syndromes). As an alternative to our previous adaptive approach where all the probes are sent sequentially, we consider in this work a non-adaptive approach where all the probes are sent in parallel. The design objective is to minimize the number of parallel probes, so as to keep network cost low. The non-adaptive fault diagnosis approach motivates a new technical framework that we introduce: combinatorial group testing with graph-based constraints. Using this framework, we develop several new probing schemes to detect network faults for all-optical networks with different topologies. The efficiency of our schemes often depends on the network topology; in many cases we can show that our schemes are optimal in minimizing the number of probes.

Traditional ad hoc wireless networks designed for best effort performance lack provisions to maintain network connectivity, particularly if nodes are mobile. For applications that require continual and timely service, current commercial grade ad hoc wireless networks cannot satisfy these demands. We study several architecture options that proactively deploy additional fixed or mobile infrastructure nodes (we call helper nodes) to help maintain user network connectivity. Our goal is to answer the questions of when and how helper nodes should be used. Using random line networks as a point of departure, we compare random and strategic helper node deployment schemes and show that at a nominal normalized user node density of N ~ 1, the number of helper nodes needed under random helper deployment can be more than 10 times the number needed under strategic deployment. At lower TV, this factor increases significantly without bound. Based on the expected number of helper nodes needed for different values of N, we delineate scenarios where each scheme is a sensible architecture choice.

We compare three optical transport network architectures - optical packet switching (OPS), optical flow switching (OFS), and optical burst switching (OBS) - based on a notion of network capacity as the set of exogenous traffic rates that can be stably supported by a network under its operational constraints. We characterize the capacity regions of the transport architectures, and show that the capacity region of OPS dominates that of OFS, and that the capacity region of OFS dominates that of OBS. We then apply these results to two important network topologies - bidirectional rings and Moore graphs - under uniform all-to-all traffic. Motivated by the incommensurate complexity/cost of comparable transport architectures, we also investigate the dependence of the relative capacity performance of the switching architectures on the number of switch ports per fiber at core nodes.

In this work, we evaluate an attractive candidate for optical network data transport: optical flow switching (OFS). We describe the operation and implementation of the architecture, characterize its capacity region and capacity-cost tradeoff, and compare it to other prominent optical network architectures.

It is widely believed that "whispering to the nearest neighbor" (ie. taking a large number of short hops) is the throughput optimal routing scheme for all ad-hoc wireless networks. We show that for power-limited networks where processing energy is not an insignificant factor, instead of taking short hops, there exists a characteristic hop distance that, along with load balancing, can attain high throughput, even for networks with a small number of nodes. In fact, the "whisper to the nearest neighbor" routing scheme leads to strictly suboptimal uniform throughput on the order of O(radic(inN/N)) when processing energy is considered. We show that taking thetas(1) number of hops is throughput, energy, and delay optimal, achieving thetas(1) uniform capacity under uniform traffic. These results are contrary to previous results on the capacity of power-limited wireless networks under a zero-processing energy assumption, which showed that capacity increases with increasing number of nodes. Hence, failing to account for processing energy not only leads to suboptimal routing schemes, but also leads to inflated optimism about the capacity scaling behavior of power-limited wireless networks.

In this work, we provide analytical insights into physical architectures that can serve as benchmarks for designing cost-efficient WDM metropolitan area networks (MANs). For analytical simplicity, we restrict our attention to uniform all-to-all traffic and regular topologies with nodal symmetry. By formulating a first-order cost model and characterizing the tradeoff between fiber and switching resources, we identify a class of regular graphs - generalized Moore graphs - that have attractive properties for MAN designs. Our previous and new results show that, in conjunction with minimum hop routing (which is shown to be optimal), Moore Graphs (special cases of Generalized Moore Graphs) achieve the minimum cost and simultaneously use the least number of wavelengths. We also broaden the scope of our work by addressing irregular network topologies, which represent most existing networks. We show that our results on networks that are generalized Moore networks can be used to provide useful estimates of the cost of irregular networks. These properties indicate that generalized Moore graphs and their close relatives are good reference architectures for the designs of practical networks.

In this work, we conduct a throughput-cost study of several optical network architectures: optical flow switching (OFS), tell-and-go (TaG), electronic packet switching (EPS), and generalized multiprotocol label switching (GMPLS). The simple, multi-tiered optical network that we consider comprises two groups of users, each in a distinct metropolitan-area network (MAN), which wish to communicate over a wide-area network (WAN). Our network cost model focuses on initial capital expenditure: transceiver, switching, routing, and amplification costs. Our results indicate that: OFS is the most scalable architecture of all, in that it is most cost-efficient when the average user data rate is high and the number of users in the network is large; EPS is most sensible when the product of the number of users and the average user data rate is low; the GMPLS architecture, which is conceptually intermediate to EPS and OFS, is optimal when the product of the number of users and the average user data rate is moderate; and, finally, there does not exist an optimal regime for TaG.

We investigate the fault diagnosis problem for all- optical networks with probabilistic link and node failures in this paper. Our major contribution is the development of diagnosis algorithms that minimize the operating effort to identify failures. We achieve this by employing the fault diagnosis approach based on proactive probing with perfect feedback: knowledge of the network state is progressively refined through a sequence of optical probe signals, each of which is determined upon the results of previous probe signals (i.e. probe syndromes). To detect and localize failures in all-optical networks with probabilistic node and link failures, we introduce a network transformation that maps both link and node failures in an undirected graph into arc failures in a directed graph and apply our previously developed run-length probing scheme [1] to the directed graph. Our analytical and numerical investigation verifies our previously established guideline for efficient fault diagnosis algorithms: each probe should provide approximately 1-bit of state information, and thus the total number of probes required is approximately equal to the entropy of the network state. Hence the complexity of optical network fault management functionality is fundamentally related to the information entropy of the network state.

We compare three optical transport network architectures—optical packet switching (OPS), optical flow switching (OFS), and optical burst switching (OBS)—based on a notion of network capacity as the set of exogenous traffic rates that can be stably supported by a network under its operational constraints. We characterize the capacity regions of the transport architectures, and show that the capacity region of OPS dominates that of OFS, and that the capacity region of OFS dominates that of OBS. We then apply these results to two important network topologies—bidirectional rings and Moore graphs—under uniform all-to-all traffic. Motivated by the incommensurate complexity/cost of comparable transport architectures, we also investigate the dependence of the relative capacity performance of the switching architectures on the number of switch ports per fiber at core nodes.

Network management and control contribute to at least half of the operating cost of current optical networks. All-optical networks with end-to-end transparent lightpaths promise significant cost savings using optical switching at network nodes. However, this cost saving cannot be realized unless the cost of network management is also reduced. In this paper we explore a promising technique towards that goal. The fault diagnosis problem for all-optical networks is investigated via an information theoretic approach, with the objective to minimize the operating 'cost' of failure detection and localization in the optical layer. Under a probabilistic link failure model, we first interpret the run-length probing scheme previously developed for Eulerian graphs as a constrained source-coding algorithm, and characterize its performance via the code rate of its corresponding run-length code. We then extend the run-length probing scheme to non-Eulerian graphs via two alternative approaches: the disjoint-trail decomposition approach and the path-augmentation approach, and obtain their performance analytically. The analytical and numerical results indicate that the run-length probing scheme is asymptotically optimum for both Eulerian and non-Eulerian graphs of large size. The property of the run-length probing scheme also suggests that each probe in an efficient probing scheme should provide approximately one bit of network state information and thus the total number of probes (or equivalently, the operating cost of failure identification) is lower-bounded and approximated by the entropy of the network states. We believe that our approach using information theory in an inter-disciplinary effort can provide new insights on network management, and substantial cost-reduction for all-optical networks can be realized

Many wireless network studies pertain to the derivation of throughput bounds and cross-layer optimization for energy conservation. From a practical perspective, often a network must meet the competing objective of attaining high throughput while conserving energy for prolonged operation. The tradeoff between these two important network metrics has not been adequately addressed in the literature. A further deficiency in existing research lies in the suppression of non-distance-dependent processing energy in energy models, which can lead erroneously to routing on paths with large number of hops. We model processing energy explicitly, and show that for a particular implementation of wireless network, the average path power is a monotonically increasing, piecewise linear function of throughput. Furthermore, we quantify a network region size threshold such that in a small region relative to the threshold, direct transmission routing is both energy conserving and throughput achieving. For larger regions, path power optimal routing may not achieve the maximum throughput. Higher throughput may be achieved with suboptimal routing schemes that lead to higher marginal power increase as the throughput increases

We present a framework to help the network designer to dimension optical metropolitan area networks (MAN) incorporating uncertainties in demands. In this framework the interplay among topology design, resource provisioning, and routing are analyzed jointly, based on two stochastic optimization models that use probability distributions of demands as inputs. In one model we minimize the weighted sum of network installation cost and expected penalty cost for unsatisfied traffic; in another model we minimize the network installation cost subject to certain service level requirements. The optimization results enable us: (1) to identify the physical architectures that are most robust (in cost) to demand uncertainties among rich classes of regular topologies, assuming the (random) uniform all-to-all traffic; and (2) to provide analytical references on how the optimum dimensioning, network connectivity, and network cost change as functions of the designer's level of risk aversion, service level requirements, and probability distributions of the demand.

Optical communications provides a means of high data rate communication over the clear atmosphere provided the communication system is designed properly. This paper examines the performance of TCP and similar protocols when operated over atmospheric optical channels. We first develop a simple stochastic channel model, and use this to derive approximate transport layer protocol throughput. We found that the throughput over short atmospheric paths is nearly optimal and the throughput over medium range atmospheric paths can be made fairly efficient with the use of diversity and/or link margin. However, its throughput at high data rates (>1 Gb/s) over long paths such as from one location on earth to a geosynchronous satellite is poor even with 10 dB of link margin and 16 diversity transmitters/receivers. This points to the necessity of a new transport layer protocol without a window closing feature.

We extend two existing narrowband beamforming techniques for ultra-wideband applications: minimum mean square error (MMSE) beamforming and MMSE beamforming subject to the constraint on complete nulling of interfering signals. Assuming that the signal at each antenna element is processed by a linear time invariant (LTI) filter, we derive the optimal filters for each beamformer. We then focus on the scenarios in which the number of antenna elements L is much larger than the number of users K in the system, e.g. multi-element antenna arrays on satellites. To improve on the expensive baseline implementation in which one electronic LTI filter is used for each antenna element, for a total of L filters, we propose the use of simple optical signal processing to reduce the cost of electronic hardware. We show that, by performing conventional beamforming in optics using fiber delay lines as a preprocessing step, the number of electronic filters required for both MMSE and constrained MMSE beamforming is reduced to K. The key observation is that, for L>K, the number of filters depends on the number of users K and not on the number of antenna elements L.

This paper attempts to find a new analytical framework to identify cost-effective topological architectures of optical cross-connect (OXC)-switched wavelength-division multiplexing (WDM) networks. To keep the analysis tractable, we first focus on regular networks and a deterministic uniform traffic model. Regular topologies with symmetries are good approximations of metropolitan area and local area networks but only a fair resemblance of wide area networks. We find that for a regular topology the minimum-hop distance, which is normally used for gauging the size of the network, is an important parameter in dimensioning switching resources. By setting up a first-order cost model and evaluating the tradeoff between network resources, we obtain closed form solutions for the optimal node degree and network cost. For network design under stochastic traffic, we study the worst case capacity dimensioning and blocking probability among the set of all possible stochastic traffic distributions with the same mean and variance. The analytical approach presented in this paper helps us gain insights of parametric dependency of an optimal network architecture on key network design parameters.

In this paper, we propose using spatial diversity via multiple node-disjointed lightpaths at the optical layer to achieve ultra-reliable communication with low delay between any source-destination pair in all-optical networks. Using a doubly stochastic point process model and a "genie-aided" receiver, we obtain an exponentially tight error probability bound for the lightpath diversity scheme under an independent lightpath failure model. Error probability of the proposed scheme can be designed to be significantly lower than that of a system without lightpath diversity, and system parameters (e.g., the number of lightpaths) can be optimized to achieve efficient utilization of a limited amount of transmitted optical energy. In particular, at the optimum operating point, each lightpath is allocated an optimum average number of signal photons per bit and is biased to have an effective error probability 2f if the decision is based on that path alone, where f is the lightpath failure probability. We also investigate the tradeoff between the error probability and the implementation complexity within the class of all "structured" receivers. We derive receiver architectures for both the optimal receiver, which has the best error performance but complicated receiver architecture, and the equal-gain-combining (EGC) receiver, which has suboptimum error performance but simpler receiver architecture. Closed-form error bounds for both receivers are obtained and compared with the "genie-aided" limit of the lightpath diversity scheme. Performance comparison shows that the simpler equal-gain-combing receiver provides similar performance as the optimal receiver in the regime of high signal-to-noise photon rate ratio (/spl Omega/=/spl lambda//sub s//M/spl lambda//sub n/, where /spl lambda//sub s//M is the signal photon rate per path, /spl lambda//sub n/ is the noise photon rate per path), and performs slightly worse than the optimal receiver in the low and medium signal-to-noise photon rate ratio regimes. It indicates that the simpler EGC receiver is preferred over the complicated optimum receiver in practical receiver design.

We investigate the necessary amount of network management information for light-path assessment to dynamically set up end-to-end light-paths across administrative domains in optical networks. Our focus is on the scalability of partial management information. We pose light-path assessment as a decision problem, and define the performance as the Bayes probability of an erroneous decision. We then characterize the scalability of management information as its growth rate with respect to the total resources of the network to achieve a desired performance. Scalability is achieved if the management information needed is only a negligible fraction of the total network resources. Specifically, we consider in this work one type of partial information that grows only logarithmically with the number of wavelengths supported per link. We derive an upper bound for the Bayes error in terms of the blocking probability when a new call is presented at the entrance of the network. We evaluate the upper bound using both independent and dependent models of wavelength usage for intra- and inter-domain calls. Our study shows that there exists a "threshold effect": The Bayes error decreases to zero exponentially with respect to the load when the load is either below or above a threshold value; and is nonnegligible when the load is in a small duration around the threshold. This suggests that the partial information considered can indeed provide the desired performance, and a small percentage of erroneous decisions can be traded off to achieve a large saving in the amount of management information.

For data services over satellite networks, the efficient management of satellite downlink communication resources is crucial for economic competitiveness of the medium. To support a broad spectrum of users with small terminals at high rates, narrow transmit spotbeams on the satellite will be used. Since satellite transmitter resources are expensive and there can be many spotbeam-coverage cells within the satellite service area, it is attractive to use some form of agile scanning beam system and to time-share these precious resources. An optimized design of the multibeam antenna pattern and scheduling can further improve the efficiency of transmission and power management. In this paper, the advantage of parallel multibeams in terms of spectral efficiency and power gain is shown, and the issue of multibeam power allocation based on traffic demands and channel conditions over satellite downlinks with power and delay constraints is addressed. The study indicates that the use of a parallel multibeam scheme with optimum power allocation can achieve a substantial power gain and reasonable proportional fairness. By coupling power allocation with multibeam scheduling when the number of active beams is smaller than the number of cells, the authors show that a modest number of active parallel beams are sufficient to cover many cells efficiently.

A data satellite network would have to support a broad spectrum of bursty unscheduled users with different delay constraints over time-varying atmospheric satellite channels. Since onboard resources are expensive and a broadband satellite will have many spotbeam-coverage cells within its service area, it is desirable to use agile scanning beam systems, adaptive to service requirements and channel conditions, and time-share a small number of active downlink beams. In addition, a congestion control mechanism of incoming traffic into the satellite service queues is required to provide a reasonable queueing delay within delay deadlines. In this paper, a jointly optimized scheme of multibeam allocation and congestion control based on incoming traffic and channel conditions over fading satellite downlinks with beam-sharing and delay constraints is developed. With the assumptions of quasistatic channel conditions and predictable incoming traffic rates (at least in the short term), an analytical solution for joint beam allocation and congestion control is considered. Numerical examples will be given to show that the joint scheme outperforms uniform allocation by serving, with fairness, more accepted traffic (e.g., a factor of 2 in the case of linearly distributed traffic across cells) and/or a smaller queueing delay.

We develop a class of structured receivers for a lightpath diversity scheme, which was introduced to provide ultrareliable communication with low delay in vulnerable all-optical networks. We explore the trade-off between implementation complexity and error probability to achieve optimum and near-optimum performance within a class of structured receivers. Using a doubly-stochastic point process model, we develop receiver architectures for both the optimal receiver with respect to error performance, and the equal-gain-combining receiver with suboptimum error performance but simpler receiver architecture. Closed-form error bounds for both receivers are obtained and compared with the 'Genie-aided' limit of the lightpath diversity transmission scheme. The comparison shows that the error performance of both receivers approaches the 'Genie-aided' limit when the signal is strong. Numerical results also demonstrate that additional power over what is required for the optimum receiver is needed to be transmitted in order for the equal-gain-combining receiver to achieve the same target error probability, and the power penalty decreases with decreasing noise level. These results suggest that the simpler equal-gain-combing receiver provides similar performance as the optimal receiver in the high signal-to-noise ratio (SNR) regime, but the optimal receiver should be used in the low SNR regime for significantly better performance.

In this paper, we consider the design of a physical network topology that meets a high level of reliability using unreliable network elements. We are motivated by the use of networks, and in particular, all-optical networks, for high-reliability applications which involve unusual and catastrophic stresses. Our network model is one in which nodes are invulnerable and links are subject to failure, and we consider both statistically independent and dependent link failures. Our reliability metrics are the all-terminal connectedness measure and the less commonly considered two-terminal connectedness measure. We compare in the low and high stress regimes, via analytical approximations and simulations, common commercial architectures designed for all-terminal reliability when links are very reliable with alternative architectures which are mindful of both of our reliability metrics and regimes of stress. Furthermore, we show that for independent link failures network design should be optimized with respect to reliability under high stress, as reliability under low stress is less sensitive to graph structure; and that under high stress, very high node degrees are required to achieve moderate reliability performance. Finally, in our discussion of correlated failure models we show the danger in relying on an independent failure model and the need for the network architect to minimize component failure dependencies.

Satellite network architecture plays an important role in the success of a satellite business. For future commercial broadband data satellite networks integrated with the terrestrial network, satellite network topology, link capacity, and routing have major impacts on the cost of the network and the amount of revenue the network can generate. To find the most cost-effective satellite network topology, we propose a unified mathematical framework using a two-stage stochastic programming formulation. The solution to the stochastic programming formulation gives optimal link capacities and an optimal routing strategy for different network topologies, taking into account uncertainties in long-term aggregate traffic statistic estimation. Using a simple satellite network example, we show the feasible topology regions for three different satellite topologies and show that, for some parameter values, the hybrid topology is more cost effective than nonhybrid topologies. In the limit of high traffic rejection cost, stochastic dimensioning reduces to static dimensioning. We study worst case static dimensioning for a general geosynchronous earth orbit satellite network and show the feasible topology regions, as well as effective cost comparisons for different topologies. We conclude with a discussion on network cost and architectural flexibility relating to satellite network design.

Optical communication over the clear atmosphere provides a means for high data rate communication (gigabits per second) over relatively short distances (kilometers). However, turbulence in the atmosphere leads to fades of varying depths, some of which may lead to heavy loss of data. We consider spatial diversity at both the transmitter and receiver, as well as time diversity as a means to mitigate the short-term loss of signal strength. Using direct detection receivers and binary pulse position modulation as an example, we derive the outage probability of spatial diversity and time diversity systems that use equal gain combining (EGC), optimal combining, and select-max combining. The power gain of using these diversity systems is found to be substantial, and the performance of equal gain combining is found to be almost as good as optimal combining. Hence, we feel diversity should be considered as one of the system techniques to improve channel performance in clear atmospheric optical channels.

In this paper, we consider the design of a physical network topology that meets a high level of reliability using unreliable network elements. We are motivated by the use of networks and, in particular, all-optical networks, for high-reliability applications which involve unusual and catastrophic stresses. Our network model is one in which nodes are invulnerable and links are subject to failure - a good approximation for optical networks with passive nodes and vulnerable fiber under stress of disconnection - and we focus on statistically independent link failures with initial steps taken toward generalization to dependent link failures. Our reliability metrics are the all-terminal connectedness measure and the less commonly considered two-terminal connectedness measure. We compare in the low and high stress regimes, via analytical approximations and simulations, common commercial architectures designed for all-terminal reliability when links are very reliable with alternative architectures which are mindful of both of our reliability metrics and regimes of stress. We derive new results especially for one of these alternative architectures, Harary graphs, which have been shown to possess attractive reliability properties. Furthermore, we show that for independent link failures network design should be optimized with respect to reliability under high stress, as reliability under low stress is less sensitive to graph structure; and that under high stress, very high node degrees and small network diameters are required to achieve moderate reliability performance. Finally, in our discussion of correlated failure models, we show the danger in relying on an independent failure model and the need for the network architect to minimize component failure dependencies.

We propose using diversity via multiple disjoint lightpaths at the optical layer to achieve ultra-reliable communication with low delay between any source-destination pair of all-optical networks. A doubly-stochastic point process model is used to characterize the photo-events of a direct detection receiver. The error probability can be designed to be significantly lower than that of a system without lightpath diversity. System parameters, such as the number of lightpaths used, are optimized to achieve efficient utilization of the limited optical transmitter power.

Network management information for light-path assessment to dynamically set up end-to-end lightpaths across administrative domains is investigated. Our focus is on investigating what performance can be possibly achieved given partial management information, and whether a small loss in performance can trade off with a large saving in management information. The partial information we consider includes aggregated characterization of subnetworks, and local states from wavelength converters. We cast the light-path assessment as a decision problem, and define the performance as the probability of an erroneous decision. We apply the decision theory to show that the optimal performance using the partial information is the Bayes probability of error. We derive an upper bound of the Bayes error in terms of the blocking probability. We evaluate the upper bound using both independent and dependent models of wavelength usage. Our study shows that there exits a "threshold effect": the Bayes error decreases exponentially to 0 with respect to the load when the load is either below or above a threshold value; and is nonnegligible when the load is in a small duration around the threshold. This suggests that a small percentage of error decisions can trade off with a large saving in management information.

For multimedia services over satellite networks, the efficient management of satellite downlink communications will be crucial due to the high cost of satellite transmission to many users with different quality of service (QoS) requirements and limited amount of onboard resources. An optimized design of the agile antenna multibeam pattern can improve the efficiency of transmission and power management. In this paper, we show the advantage of parallel multibeams in terms of spectral efficiency and power gain, and address the issue of multibeam power allocation based on traffic demands over satellite downlinks with power restriction. The analysis indicates that the use of a parallel multibeam scheme with optimized power splits can achieve a substantial power gain and reasonable proportional fairness. We also give a downlink multibeam scheduling solution when the number of beams is smaller than the number of cells, and show that a modest number of parallel beams are sufficient to obtain large power gains without need for many transmitters.

For future commercial broadband data satellite networks, satellite network topology, link capacity, and routing have major impacts on the cost of the network and the amount of revenue the network can generate. We propose a unified mathematical framework using a two-stage stochastic programming technique to assist network designers in selecting the most cost-effective network topology for data applications. The solution to the stochastic programming formulation gives optimal link capacities and an optimal routing strategy for different network topologies, taking into account uncertainties in longterm aggregate traffic statistics estimation. In particular, we give the optimal topology selection criteria for a general GEO satellite network and identify regions where a hybrid satellite-terrestrial topology is the most cost effective.

We study the topology design of an important subset of mesh network-regular mesh network in the context of wavelength division multiplex (WDM) metropolitan area network (MAN). We find that minimum hop distance is an important parameter in dimensioning both the network's physical size but also the network node resources required to support the ever increasing traffic demand. By assigning realistic cost structures to these network nodes and link resources, we are able to specify the optimal node degrees analytically for interesting classes of good regular topologies as the networks scale in size.

Optical communication through clear atmosphere provides a means for high data rate communication (Gbps) over relatively short distances (/spl sim/km). However, turbulence in the atmosphere leads to fades of varying depths, some of which may lead to heavy loss of data. We consider spatial diversity at both the transmitter and receiver as a means to mitigate the short-term loss of signal strength. Using direct detection receivers and binary pulse position modulation as an example, we derive the outage probability of spatial diversity systems that use equal gain combining and optimal combining. The power gain of using these spatial diversity systems is found to be substantial. Hence, we feel diversity systems should be used as the key technique for clear atmospheric optical channels.

Adaptive power and rate control for efficient use of satellite channels in the Ka band are considered. Fading due to rain is modeled as a log-normally distributed, autoregressive process with known statistics. Adaptation policies based on MMSE channel estimation are designed taking into account the effect of channel estimation errors in an optimal manner so as to satisfy the required outage probability for given fading dynamics. Analytical results show average power savings or average bit rate increase that are available from adaptive techniques. It is demonstrated that a large improvement over a non-adaptive system is available provided an appropriately designed adaptation scheme that makes use of the channel fading statistics. Finally, practical methods based on adaptive modulation level selection in a fixed-bandwidth system are evaluated analytically in terms of average bandwidth efficiency.

The future success of next generation communication systems depend on its ability to efficiently accommodate integrated traffic and service a variety of applications and communications with different Quality of Service requirements. In this paper, we examine resource allocation techniques in the media access control layer for shared access channels. This type of medium where resources are accessible to all users are characteristic of satellite communication uplinks, multiple user broadcast wavelength division multiplexing, all-optical fiber networks and wireless uplinks to a base station. We believe in certain cases, e.g. when large bandwidth-delay products exist, resource partitioning according to user transaction sizes is a sensible scheme. We investigate efficient channel capacity partitioning among mixed traffic, ie, multiple classes of users. We develop models that describe the overall delay for sending messages and analyze their performance. We then compare the performances to classical schemes, such as first-come first-serve, non-preemptive priority and preemptive resume priority. Analytical results for cases where two very disparate mixed traffic (small and very large messages sizes) are given. Optimum resource partitioning of channel capacity is obtained by minimizing some simple and sensible metric of overall message (not individual packet) transfer delay.

Effectiveness of prediction and adaptation of fading satellite channels induced by bad weather conditions is presented in terms of power consumption and channel capacity improvement. We suggest adaptive schemes that vary transmission rate, code rate, transmission power or all of these, incorporated with prediction functions for channel states. For prediction, we develop simple linear predictors for received signal attenuation using autoregressive (AR) models. For adaptation, we introduce a continuous power control and discrete rate control strategy. The quantitative analyses of excess average power and channel capacity indicate that the adaptive schemes can achieve substantial performance gains.

We describe recent results of the Advanced Research Projects Agency (ARPA) sponsored Consortium on Wideband All-Optical Networks which is developing architectures, technology components, and applications for ultrafast 100 Gb/s time-division multiplexing (TDM) optical networks. The shared-media ultrafast networks we envision are appropriate for providing low-access-delay bandwidth on demand to both future high-burst rate (100 Gb/s) users as well aggregates of lower-rate users (i.e., a heterogeneous user population). To realize these goals we are developing ultrafast network architectures such as HLAN, described here, that operate well in high-latency environments and require only limited processing capability at the ultrafast bit rates. We also describe results on 80-Gb/s, 90-km soliton transmission, 100-Gb/s soliton compression laser source technology, picosecond short-pulse fiber ring lasers, picosecond-accuracy optical bit-phase sensing and clock recovery, all-optical injection-locked fiber figure-eight laser clock recovery, short-pulse fiber loop storage, and all-optical pulse width and wavelength conversion.

We describe some of the results of the Advanced Research Projects Agency (ARPA) sponsored Consortium on Wideband All-Optical Networks in developing architectures, technology components, and applications for the realization of scaleable, wideband, and transparent optical wavelength-division multiplexing (WDM) networks. Our architecture addresses all-optical transport over the wide, metropolitan, and local areas. It utilizes wavelength partitioning, routing, and active multiwavelength cross-connect switches to achieve a network that is scaleable in the number of users, data rates, and geographic span. The network supports two services which can be point-to-multipoint or multipoint-to-multipoint simplex or duplex connections. The A service is a transparent physically circuit-switched service and the B-service is a scheduled time-slotted circuit which is transparent within its time slots. We have developed a 20-channel local and metropolitan area WDM testbed deployed in the Boston area, now undergoing characterization and experimental applications.

This paper describes the design and performance of anMary frequency shift keyed (FSK) signaling and demodulation scheme for an optical communication system using semiconductor lasers and heterodyne detection. Frequency or phase noise in semiconductor lasers causes spectral spreading, producing a nonzero linewidth laser signal. This degrades communication performance when compared to a system using an ideal laser with zero linewidth. We present estimates of the bit error rate (BER) performance ofM-ary frequency shift keying (FSK) with noncoherent demodulation in the presence of white Gaussian frequency noise and additive channel noise. This is typical of an optical system using semiconductor lasers and heterodyne detection. Estimates use the union-Chernoff bound with a simplified channel model to predict the effects of frequency noise. Two effects of frequency noise are identified: signal attenuation or suppression, and crosstalk. These cause an offset in the BER curve from the BER in the absence of frequency noise, and an error rate floor, respectively. The error rate floor is lower than previously predicted. When performance is not crosstalk limited,M-ary FSK is found to perform better than binary FSK with the same system bandwidth constraints, as would be predicted if ideal lasers are used. Theoretical results are compared with Monte Carlo simulations of the system.

Design considerations for an optical spatial tracking system for both coherent and noncoherent heterodyne detection are addressed. Heterodyne detection systems are much less susceptible to background radiation and device noise than conventional direct detection systems. In addition, the angle discriminator profile in a heterodyne detection system can be tailored via the local oscillator spatial distribution. An optimal LO distribution is described and its performance is compared to some Suboptimal distributions. It is shown that an ideal quadrant detector can achieve near optimal performance, since the LO effectively acts like a continuum of detectors. Cramer-Rao tracking bounds and other performance criteria are given under the conservative assumption that the angular process to be tracked is a single-pole process. The method used is directly applicable to other types of angular processes. Experimental verification of some of the basic concepts is presented.

In the presence of atmospheric or aircraft boundary layer turbulence, an optical channel may need large amounts of link margin to combat fading and beam spreading. Channel coding can reduce the average transmitter power required for reliable communication. This paper analyzes the performance of a coded PPM optical communication system that suffers log-normal fading during transmission. For moderate turbulence strengths, the coding gain in link margin is shown to be substantial.