Practical network coding for two way relay channels in LTE networks

In this paper, the implementation aspects and constraints of the simplest network coding (NC) schemes for a two-way relay channel (TWRC) composed of a user equipment (mobile terminal), an LTE relay station (RS) and an LTE base station (eNB) are considered in order to assess the usefulness of the NC in more realistic scenarios. The information exchange rate gain (IERG), the energy reduction gain (ERG) and the resource utilization gain (RUG) of the NC schemes with and without subcarrier division duplexing (SDD) are obtained by computer simulations. The usefulness of the NC schemes are evaluated for varying traffic load levels, the geographical distances between the nodes, the RS transmit powers, and the maximum numbers of retransmissions. Simulation results show that the NC schemes with and without SDD, have the throughput gains 0.5% and 25%, the ERGs 7 - 12% and 16 - 25%, and the RUGs 0.5 - 3.2%, respectively. It is found that the NC can provide performance gains also for the users at the cell edge. Furthermore, the ERGs of the NC increase with the transmit power of the relay while the ERGs of the NC remain the same even when the maximum number of retransmissions is reduced.

Improved multi-beam neural network scatterometer forward models

Current methods for retrieving near surface winds from scatterometer observations over the ocean surface require a foward sensor model which maps the wind vector to the measured backscatter. This paper develops a hybrid neural network forward model, which retains the physical understanding embodied in ¸mod, but incorporates greater flexibility, allowing a better fit to the observations. By introducing a separate model for the mid-beam and using a common model for the fore- and aft-beams, we show a significant improvement in local wind vector retrieval. The hybrid model also fits the scatterometer observations more closely. The model is trained in a Bayesian framework, accounting for the noise on the wind vector inputs. We show that adding more high wind speed observations in the training set improves wind vector retrieval at high wind speeds without compromising performance at medium or low wind speeds.

Protection schemes for meshed VSC-HVDC transmission systems for large-scale offshore wind farms

This chapter discusses network protection of high-voltage direct current (HVDC) transmission systems for large-scale offshore wind farms where the HVDC system utilizes voltage-source converters. The multi-terminal HVDC network topology and protection allocation and configuration are discussed with DC circuit breaker and protection relay configurations studied for different fault conditions. A detailed protection scheme is designed with a solution that does not require relay communication. Advanced understanding of protection system design and operation is necessary for reliable and safe operation of the meshed HVDC system under fault conditions. Meshed-HVDC systems are important as they will be used to interconnect large-scale offshore wind generation projects. Offshore wind generation is growing rapidly and offers a means of securing energy supply and addressing emissions targets whilst minimising community impacts. There are ambitious plans concerning such projects in Europe and in the Asia-Pacific region which will all require a reliable yet economic system to generate, collect, and transmit electrical power from renewable resources. Collective offshore wind farms are efficient and have potential as a significant low-carbon energy source. However, this requires a reliable collection and transmission system. Offshore wind power generation is a relatively new area and lacks systematic analysis of faults and associated operational experience to enhance further development. Appropriate fault protection schemes are required and this chapter highlights the process of developing and assessing such schemes. The chapter illustrates the basic meshed topology, identifies the need for distance evaluation, and appropriate cable models, then details the design and operation of the protection scheme with simulation results used to illustrate operation. © Springer Science+Business Media Singapore 2014.

A structural method for the design of fault tolerant distributed control systems

Distributed digital control systems provide alternatives to conventional, centralised digital control systems. Typically, a modern distributed control system will comprise a multi-processor or network of processors, a communications network, an associated set of sensors and actuators, and the systems and applications software. This thesis addresses the problem of how to design robust decentralised control systems, such as those used to control event-driven, real-time processes in time-critical environments. Emphasis is placed on studying the dynamical behaviour of a system and identifying ways of partitioning the system so that it may be controlled in a distributed manner. A structural partitioning technique is adopted which makes use of natural physical sub-processes in the system, which are then mapped into the software processes to control the system. However, communications are required between the processes because of the disjoint nature of the distributed (i.e. partitioned) state of the physical system. The structural partitioning technique, and recent developments in the theory of potential controllability and observability of a system, are the basis for the design of controllers. In particular, the method is used to derive a decentralised estimate of the state vector for a continuous-time system. The work is also extended to derive a distributed estimate for a discrete-time system. Emphasis is also given to the role of communications in the distributed control of processes and to the partitioning technique necessary to design distributed and decentralised systems with resilient structures. A method is presented for the systematic identification of necessary communications for distributed control. It is also shwon that the structural partitions can be used directly in the design of software fault tolerant concurrent controllers. In particular, the structural partition can be used to identify the boundary of the conversation which can be used to protect a specific part of the system. In addition, for certain classes of system, the partitions can be used to identify processes which may be dynamically reconfigured in the event of a fault. These methods should be of use in the design of robust distributed systems.

Flexible fault-tolerant topology for switched reluctance motor drives

Switched reluctance motor (SRM) drives are one competitive technology for traction motor drives. This paper proposes a novel and flexible SRM fault-tolerant topology with fault diagnosis, fault tolerance, and advanced control functions. The converter is composed of a single-phase bridge and a relay network, based on the traditional asymmetrical half-bridge driving topology. When the SRM-driving system is subjected to fault conditions including open-circuit and short-circuit faults, the proposed converter starts its fault-diagnosis procedure to locate the fault. Based on the relay network, the faulty part can be bypassed by the single-phase bridge arm, while the single-phase bridge arm and the healthy part of the converter can form a fault-tolerant topology to sustain the driving operation. A fault-tolerant control strategy is developed to decrease the influence of the fault. Furthermore, the proposed fault-tolerant strategy can be applied to three-phase 12/8 SRM and four-phase 8/6 SRM. Simulation results in MATLAB/Simulink and experiments on a three-phase 12/8 SRM and a four-phase 8/6 SRM validate the effectiveness of the proposed strategy, which may have significant economic implications in traction drive systems.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Multi-valued control problems and mixture density network

We have proposed a novel robust inversion-based neurocontroller that searches for the optimal control law by sampling from the estimated Gaussian distribution of the inverse plant model. However, for problems involving the prediction of continuous variables, a Gaussian model approximation provides only a very limited description of the properties of the inverse model. This is usually the case for problems in which the mapping to be learned is multi-valued or involves hysteritic transfer characteristics. This often arises in the solution of inverse plant models. In order to obtain a complete description of the inverse model, a more general multicomponent distributions must be modeled. In this paper we test whether our proposed sampling approach can be used when considering an arbitrary conditional probability distributions. These arbitrary distributions will be modeled by a mixture density network. Importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The effectiveness of the importance sampling from an arbitrary conditional probability distribution will be demonstrated using a simple single input single output static nonlinear system with hysteretic characteristics in the inverse plant model.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Minimum description length, regularisation and multi-modal data

Conventional feed forward Neural Networks have used the sum-of-squares cost function for training. A new cost function is presented here with a description length interpretation based on Rissanen's Minimum Description Length principle. It is a heuristic that has a rough interpretation as the number of data points fit by the model. Not concerned with finding optimal descriptions, the cost function prefers to form minimum descriptions in a naive way for computational convenience. The cost function is called the Naive Description Length cost function. Finding minimum description models will be shown to be closely related to the identification of clusters in the data. As a consequence the minimum of this cost function approximates the most probable mode of the data rather than the sum-of-squares cost function that approximates the mean. The new cost function is shown to provide information about the structure of the data. This is done by inspecting the dependence of the error to the amount of regularisation. This structure provides a method of selecting regularisation parameters as an alternative or supplement to Bayesian methods. The new cost function is tested on a number of multi-valued problems such as a simple inverse kinematics problem. It is also tested on a number of classification and regression problems. The mode-seeking property of this cost function is shown to improve prediction in time series problems. Description length principles are used in a similar fashion to derive a regulariser to control network complexity.

Multi-criteria logistics distribution network design using SAS/OR

This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualitative factors. In the integrated approach, AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, a GP model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. To facilitate the use of integrated multiple criteria decision making approach by SAS users, an ORMCDM code was implemented in the SAS programming language. The SAS macro developed in this paper selects the chosen variables from a SAS data file and constructs sets of linear programming models based on the selected GP model. An example is given to illustrate how one could use the code to design the logistics distribution network.

Spatial-spectral flexible optical networking:enabling switching solutions for a simplified and efficient SDM network platform

The traffic carried by core optical networks grows at a steady but remarkable pace of 30-40% year-over-year. Optical transmissions and networking advancements continue to satisfy the traffic requirements by delivering the content over the network infrastructure in a cost and energy efficient manner. Such core optical networks serve the information traffic demands in a dynamic way, in response to requirements for shifting of traffics demands, both temporally (day/night) and spatially (business district/residential). However as we are approaching fundamental spectral efficiency limits of singlemode fibers, the scientific community is pursuing recently the development of an innovative, all-optical network architecture introducing the spatial degree of freedom when designing/operating future transport networks. Spacedivision- multiplexing through the use of bundled single mode fibers, and/or multi-core fibers and/or few-mode fibers can offer up to 100-fold capacity increase in future optical networks. The EU INSPACE project is working on the development of a complete spatial-spectral flexible optical networking solution, offering the network ultra-high capacity, flexibility and energy efficiency required to meet the challenges of delivering exponentially growing traffic demands in the internet over the next twenty years. In this paper we will present the motivation and main research activities of the INSPACE consortium towards the realization of the overall project solution. © 2014 Copyright SPIE.

Performance analysis of an experimental wireless relay sensor network

Communication through relay channels in wireless sensor networks can create diversity and consequently improve the robustness of data transmission for ubiquitous computing and networking applications. In this paper, we investigate the performances of relay channels in terms of diversity gain and throughput via both experimental research and theoretical analysis. Two relaying algorithms, dynamic relaying and fixed relaying, are utilised and tested to find out what the relay channels can contribute to system performances. The tests are based on a wireless relay sensor network comprising a source node, a destination node and a couple of relay nodes, and carried out in an indoor environment with rare movement of objects nearby. The tests confirm, in line with the analytical results, that more relay nodes lead to higher diversity gain in the network. The test results also show that the data throughput between the source node and the destination node is enhanced by the presence of the relay nodes. Energy consumption in association with the relaying strategy is also analysed. Copyright © 2009 John Wiley & Sons, Ltd.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Mixture density networks

Minimization of a sum-of-squares or cross-entropy error function leads to network outputs which approximate the conditional averages of the target data, conditioned on the input vector. For classifications problems, with a suitably chosen target coding scheme, these averages represent the posterior probabilities of class membership, and so can be regarded as optimal. For problems involving the prediction of continuous variables, however, the conditional averages provide only a very limited description of the properties of the target variables. This is particularly true for problems in which the mapping to be learned is multi-valued, as often arises in the solution of inverse problems, since the average of several correct target values is not necessarily itself a correct value. In order to obtain a complete description of the data, for the purposes of predicting the outputs corresponding to new input vectors, we must model the conditional probability distribution of the target data, again conditioned on the input vector. In this paper we introduce a new class of network models obtained by combining a conventional neural network with a mixture density model. The complete system is called a Mixture Density Network, and can in principle represent arbitrary conditional probability distributions in the same way that a conventional neural network can represent arbitrary functions. We demonstrate the effectiveness of Mixture Density Networks using both a toy problem and a problem involving robot inverse kinematics.

Neural network-based wind vector retrieval from satellite scatterometer data

Obtaining wind vectors over the ocean is important for weather forecasting and ocean modelling. Several satellite systems used operationally by meteorological agencies utilise scatterometers to infer wind vectors over the oceans. In this paper we present the results of using novel neural network based techniques to estimate wind vectors from such data. The problem is partitioned into estimating wind speed and wind direction. Wind speed is modelled using a multi-layer perceptron (MLP) and a sum of squares error function. Wind direction is a periodic variable and a multi-valued function for a given set of inputs; a conventional MLP fails at this task, and so we model the full periodic probability density of direction conditioned on the satellite derived inputs using a Mixture Density Network (MDN) with periodic kernel functions. A committee of the resulting MDNs is shown to improve the results.