Outage Capacity and Optimal Transmission for Dying Channels

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single point-to-point channel subject to a random attack, named as a \emph{dying channel}, we model it as a block-fading (BF) channel with a finite and random delay constraint. First, we define the outage capacity as the performance measure, followed by studying the optimal coding length $K$ such that the outage probability is minimized when uniform power allocation is assumed. For a given rate target and a coding length $K$, we then minimize the outage probability over the power allocation vector \mv{P}_{K}, and show that this optimization problem can be cast into a convex optimization problem under some conditions. The optimal solutions for several special cases are discussed. Furthermore, we extend the single point-to-point dying channel result to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the corresponding asymptotic behavior of the overall outage probability with two different attack models: the independent-attack case and the $m$-dependent-attack case. It can be shown that the overall outage probability diminishes to zero for both cases as the number of sub-channels increases if the \emph{rate per unit cost} is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves over the number of sub-channels.Comment: 31 pages, 9 figures, submitted to IEEE Transactions on Information Theor

Outage Capacity and Code Design for Dying Channels

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single point-to-point channel subject to a random attack, named as a dying channel, we model it as a block-fading (BF) channel with a finite and random channel length. First, we study the outage probability when the coding length K is fixed and uniform power allocation is assumed. Furthermore, we discuss the optimization over K and the power allocation vector PK to minimize the outage probability. In addition, we extend the single point to-point dying channel case to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the corresponding asymptotic behavior of the overall outage probability with two different attack models: the independent-attack case and the m-dependent-attack case. It can be shown that the overall outage probability diminishes to zero for both cases as the number of sub-channels increases if the rate per unit cost is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves over the number of sub-channels. Besides the information-theoretical results, we also study a practical coding scheme for the dying binary erasure channel (DBEC), which is a binary erasure channel (BEC) subject to a random fatal failure. We consider the rateless codes and optimize the degree distribution to maximize the average recovery probability. In particular, we first study the upper bound of the average recovery probability, based on which we define the objective function as the gap between the upper bound and the average recovery probability achieved by a particular degree distribution. We then seek the optimal degree distribution by minimizing the objective function. A simple and heuristic approach is also proposed to provide a suboptimal but good degree distribution

Cascade decode-and-forward : spatial diversity reuse in sensor networks

In this paper, we consider a wireless sensor network that involves sensory data hoping through multiple wireless relays to reach a central collection hub. In particular we improve the decode-and-forward cooperative relaying scheme. In this paper, we propose the Cascade-Decode-and-Forward, where the number of successful relays increases with each additional cooperation stage. The achieved effect is a cascade of relays that contribute towards achieving full spatial diversity at the destination. A novel relationship between the achievable bit error rate and delay is derived for the proposed scheme. The results show that a small delay constraint relaxation, the proposed scheme can achieve full diversity. As the delay constraint relaxes further, the protocol can achieve full diversity at signals levels 10–100 orders magnitude lower than the decode-and-forward protocol. The proposed protocol can dynamically trade-off transmission reliability with delay and the analysis has shown that a certain node connectivity density is required to achieve a cascading cooperation chain with an arbitrarily low data extinction probability

Channels That Die

Given the possibility of communication systems failing catastrophically, we investigate limits to communicating over channels that fail at random times. These channels are finite-state semi-Markov channels. We show that communication with arbitrarily small probability of error is not possible. Making use of results in finite blocklength channel coding, we determine sequences of blocklengths that optimize transmission volume communicated at fixed maximum message error probabilities. We provide a partial ordering of communication channels. A dynamic programming formulation is used to show the structural result that channel state feedback does not improve performance

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs) which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO). Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD). The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop), Cooperative Depth-Based Routing (CoDBR), and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth)

Stochastic Analysis of Aloha in Vehicular Ad-hoc Networks

International audienceThe aim of this paper is to study the Aloha medium access (MAC) scheme in one-dimensional, {\em linear} networks, which might be an appropriate assumption for Vehicular Ad-hoc NETworks (VANETs). We study performance metrics based on the signal-over-interference and noise ratio (SINR) assuming power-law mean path-loss and independent point-to-point fading. We derive closed formulas for the capture probability. We consider the presence or the absence of noise and we study performance with outage or with adaptive coding. We carry out the joint optimization of the density of packet progress (in bit meters) both in the transmission probability and in the transmission range. We also compare the performance of slotted and non-slotted Aloha. We show that in contrast to planar networks the density of packet progress per unit of length does not increase with the network node density

Distributed Detection and Estimation in Wireless Sensor Networks

Wireless sensor networks (WSNs) are typically formed by a large number of densely deployed, spatially distributed sensors with limited sensing, computing, and communication capabilities that cooperate with each other to achieve a common goal. In this dissertation, we investigate the problem of distributed detection, classification, estimation, and localization in WSNs. In this context, the sensors observe the conditions of their surrounding environment, locally process their noisy observations, and send the processed data to a central entity, known as the fusion center (FC), through parallel communication channels corrupted by fading and additive noise. The FC will then combine the received information from the sensors to make a global inference about the underlying phenomenon, which can be either the detection or classification of a discrete variable or the estimation of a continuous one.;In the domain of distributed detection and classification, we propose a novel scheme that enables the FC to make a multi-hypothesis classification of an underlying hypothesis using only binary detections of spatially distributed sensors. This goal is achieved by exploiting the relationship between the influence fields characterizing different hypotheses and the accumulated noisy versions of local binary decisions as received by the FC, where the influence field of a hypothesis is defined as the spatial region in its surrounding in which it can be sensed using some sensing modality. In the realm of distributed estimation and localization, we make four main contributions: (a) We first formulate a general framework that estimates a vector of parameters associated with a deterministic function using spatially distributed noisy samples of the function for both analog and digital local processing schemes. ( b) We consider the estimation of a scalar, random signal at the FC and derive an optimal power-allocation scheme that assigns the optimal local amplification gains to the sensors performing analog local processing. The objective of this optimized power allocation is to minimize the L 2-norm of the vector of local transmission powers, given a maximum estimation distortion at the FC. We also propose a variant of this scheme that uses a limited-feedback strategy to eliminate the requirement of perfect feedback of the instantaneous channel fading coefficients from the FC to local sensors through infinite-rate, error-free links. ( c) We propose a linear spatial collaboration scheme in which sensors collaborate with each other by sharing their local noisy observations. We derive the optimal set of coefficients used to form linear combinations of the shared noisy observations at local sensors to minimize the total estimation distortion at the FC, given a constraint on the maximum average cumulative transmission power in the entire network. (d) Using a novel performance measure called the estimation outage, we analyze the effects of the spatial randomness of the location of the sensors on the quality and performance of localization algorithms by considering an energy-based source-localization scheme under the assumption that the sensors are positioned according to a uniform clustering process

Resource Allocation in Relay Networks

Demand for high data rates is increasing rapidly, due to the rapid rise of mobile data traffic volume. In order to meet the demands, the future generation of wireless communication systems has to support higher data rates and quality of service. The inherent unreliable and unpredictable nature of wireless medium provides a challenge for increasing the data rate. Cooperative communications, is a prominent technique to combat the detrimental fading effect in wireless communications. Adding relay nodes to the network, and creating s virtual multiple-input multiple-output (MIMO) antenna array is proven to be an efficient method to mitigate the multipath fading and expand the network coverage. Therefore, cooperative relaying is considered as a fundamental element in the Long Term Evolution (LTE)-Advanced standard. In this thesis, we address the problem of resource allocation in cooperative networks. We provide a detailed review on the resource allocation problem. We look at the joint subcarrier-relay assignment and power allocation. The objective of this optimization problem is to allocate the resources fairly, so even the cell-edge users with weakest communication links receive a fair share of resources. We propose a simple and practical algorithm to find the optimal solution. We assess the performance of the proposed algorithm by providing simulations. Furthermore, we investigate the optimality and complexity of the proposed algorithm. Due to the layered architecture of the wireless networks, to achieve the optimal performance it is necessary that the design of the algorithms be based on the underlying physical and link layers. For a cooperative network with correlated channels, we propose a cross-layer algorithm for relay selection, based on both the physical and link-layer characteristics, in order to maximize the linklayer throughput. The performance of the proposed algorithm is studied in different network models. Furthermore, we investigate the optimum number of relays required for cooperation in order to achieve maximum throughput. Buffering has proven to improve the performance of the cooperative network. In light of this, we study the performance of buffer-aided relay selection. In order to move one step closer to the practical applications, we consider a system with coded transmissions. We study three different coding schemes: convolutional code, Turbo code, and distributed Turbo code (DTC). For each scheme, the performance of the system is simulated and assessed analytically. We derive a closed form expression of the average throughput. Using the analysis results, we investigate the diversity gain of the system in asymptotic conditions. Further, we investigate the average transmission delay for different schemes

Analysis of the U.S. Electric Power Industry

In the U.S., the power industry is a primary energy consumption sector. Accurate knowledge on production efficiency in the industry has vital welfare implication from both economic and environmental perspectives. The first two essays investigate the causal impact of the vertical separation of the electricity transmission sector from the generation sector on production efficiency. In the first essay, I ask whether the specific market restructuring is sufficient to enhance how efficiently production is allocated among producers. Based on a difference-in-difference comparison on cost-sensitivity of utilization between coal-fired generators in the treatment region (Southwest Power Pool) and that in a control region, I fail to find any significant private cost savings by reallocating production across firms. My second essay takes a further step and looks into one potential explanation of the results: enabled market power under restructuring. Following a common method to measure competition, I simulate the prices that would have occurred had the wholesale market been competitive. Then I compare the simulated prices with the best estimates available for actual wholesale prices to measure the market price-cost margins. Empirical results demonstrate that the vertical separation of the electricity transmission sector actually led to an increase in the markup in the wholesale market, indicating evidence of market power exercised. In the last essay, we propose to investigate whether there is stickiness in the pass-through from fossil fuel spot prices to the fossil-fuel procurement costs for the U.S. electric power producers, and if there is, to what extent the sluggishness is, and how it varies across different types of fossil fuels

Wireless sensor networks using network coding for structural health monitoring

Wireless Sensor Networks (WSNs) have been deployed for the purpose of structural health monitoring (SHM) of civil engineering structures, e.g. bridges. SHM applications can potentially produce a high volume of sensing data, which consumes much transmission power and thus decreases the lifetime of the battery-run networks. We employ the network coding technique to improve the network efficiency and prolong its lifetime. By increasing the transmission power, we change the node connectivity and control the number of nodes that can overhear transmitted messages so as to hopefully realize the capacity gain by use of network coding. In Chapter 1, we present the background, to enable the reader to understand the need for SHM, advantages and drawbacks of WSNs and potential the application of network coding techniques has. In Chapter 2 we provide a review of related research explaining how it relates to our work, and why it is not fully applicable in our case. In Chapter 3, we propose to control transmission power as a means to adjust the number of nodes that can overhear a message transmission by a neighbouring node. However, too much of the overhearing by high power transmission consumes aggressively limited battery energy. We investigate the interplay between transmission power and network coding operations in Chapter 4. We show that our solution reduces the overall volume of data transfer, thus leading to significant energy savings and prolonged network lifetime. We present the mathematical analysis of our proposed algorithm. By simulation, we also study the trade-offs between overhearing and power consumption for the network coding scheme. In Chapter 5, we propose a methodology for the optimal placement of sensor nodes in linear network topologies (e.g., along the length of a bridge), that aims to minimise the link connectivity problems and maximise the lifetime of the network. Both simple packet relay and network coding are considered for the routing of the collected data packets towards two sink nodes positioned at both ends of the bridge. Our mathematical analysis, verified by simulation results, shows that the proposed methodology can lead to significant energy saving and prolong the lifetime of the underlying wireless sensor network. Chapter 6 is dedicated to the delay analysis. We analytically calculate the gains in terms of packet delay obtained by the use of network coding in linear multi-hop wireless sensor network topologies. Moreover, we calculate the exact packet delay (from the packet generation time to the time it is delivered to the sink nodes) as a function of the location of the source sensor node within the linear network. The derived packet delay distribution formulas have been verified by simulations and can provide a benchmark for the delay performance of linear sensor networks. In the Chapter 7, we propose an adaptive version of network coding based algorithm. In the case of packet loss, nodes do not necessary retransmit messages as they are able to internally decide how to cope with the situation. The goal of this algorithm is to reduce the power consumption, and decrease delays whenever it can. This algorithm achieves the delay similar to that of three-hop direct-connectivity version of the deterministic algorithm, and consumes power almost like one-hop direct-connectivity version of deterministic algorithm. In very poor channel conditions, this protocol outperforms the deterministic algorithm both in terms of delay and power consumption. In Chapter 8, we explain the direction of our future work. Particularly, we are interested in the application of combined TDMA/FDMA technique to our algorithm.Open Acces