Simplified per-survivor Kalman processing in fastfrequency-selective fading channels

Per-survivor processing (PSP) is now seen as an attractive approach to performing maximum-likelihood sequence estimation (MLSE) over mobile radio channels that are rapidly time varying. An optimal PSP strategy incorporates statistical channel modeling and Kalman filtering. For severely time-dispersive channels, this approach becomes prohibitively complex. A novel filtering algorithm is presented to approximate Kalman PSP. MLSE with the new scheme offers a large reduction in computational complexity, and achieves performance close to the optimal Kalman approach and superior to existing PSP schemes in rapidly fading channels. The exact expressions presented for the pairwise error probability of MLSE with Kalman PSP may be used to predict the detector performance without resorting to lengthly simulations     

Simplified Soft-Output Detection of CPM Signals Over Coherent and Phase Noise Channels

We consider continuous phase modulations (CPMs) in iteratively decoded serially concatenated schemes. Although the overall receiver complexity mainly depends on that of the CPM detector, almost all papers in the literature consider the optimal maximum a posteriori (MAP) symbol detection algorithm and only a few attempts have been made to design low-complexity suboptimal schemes. This problem is faced in this paper by first considering the case of an ideal coherent detection, then extending it to the more interesting case of a transmission over a typical satellite channel affected by phase noise. In both cases, we adopt a simplified representation of an M-ary CPM signal based on the principal pulses of its Laurent decomposition. Since it is not possible to derive the exact detection rule by means of a probabilistic reasoning, the framework of factor graphs (FGs) and the sum-product algorithm (SPA) is used. In the case of channels affected by phase noise, continuous random variables representing the phase samples are explicitly introduced in the FG. By pursuing the principal approach to manage continuous random variables in a FG, i.e., the canonical distribution approach, two algorithms are derived which do not require the presence of known (pilot) symbols, thanks to the intrinsic differential encoder embedded in the CPM modulator.     

On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

We consider the problem of carrier synchronization in future 2nd-generation satellite digital video broadcasting (DVB-S2) receivers. In this scenario, this task is made harder by the complexity constraints, related to the use of consumer-grade equipment. Making use of the distributed pilot symbols of the DVB-S2 standard, low-complexity techniques for fine frequency estimation and for detection in the presence of a strong phase noise, typical of consumer-grade equipment, will be proposed. The performance of the described algorithms will be analysed in detail through computer simulations.     

Resource Allocation and Adaptive Modulation in Uplink SC-FDMA Systems

In this paper, we study joint power and sub-channel allocation, and adaptive modulation in Single Carrier Frequency Division Multiple Access (SC-FDMA) which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. A sum-utility maximization problem is considered. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel optimization framework for the solution of this problem which is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary-integer programming problem, and then transform this binary-integer programming problems into a continuous space canonical dual problem that is a concave maximization problem. Based on the solution of the continuous space dual problem, we derive joint power and sub-channel allocation algorithm whose computational complexity is polynomial. We provide conditions under which the proposed algorithms are optimal. We also propose an adaptive modulation scheme which selects an appropriate modulation strategy for each user. We compare the proposed algorithm with the existing algorithms in the literature to assess their performance. The results show a tremendous performance gain.     

Adaptive Iterative Detectors for Phase-Uncertain Channels via Variational Bounding

The problem of iterative detection/decoding of data symbols transmitted over an additive white Gaussian noise (AWGN) channel in the presence of phase uncertainty is addressed in this paper. By modelling the phase uncertainty either as an unknown deterministic variable/process or random variable/ process with a known a priori probability density function, a number of non-Bayesian and Bayesian detection algorithms with various amount of suboptimality have been proposed in the literature to solve the problem. In this paper, a new set of suboptimal iterative detection algorithms is obtained by utilizing the variational bounding technique. Especially, applying the generic variational Bayesian (VB) framework, efficient iterative joint estimation and detection/decoding schemes are derived for the constant phase model as well as for the dynamic phase model. In addition, the relation of the VB-based approach to the optimal noncoherent receiver as well as to the classical approach via the expectation-maximization (EM) algorithm is provided. Performance of the proposed detectors in the presence of a strong dynamic phase noise is compared to the performance of the existing detectors. Furthermore, an incremental scheduling of the VB (or EM) algorithm is shown to reduce the overall complexity of the receiver.     

Error restricted fast MAP decoding of VLC

Joint source channel techniques based on Variable-Length Coding (VLC) have been widely used. One of the most famous VLC decoders is optimal Maximum A Posteriori (MAP) decoder based on directed graph search and soft-input theory. Due to the high complexity of directed graph search, many reduced complexity methods have been proposed. In this paper, we propose two error restricted algorithms for fast MAP decoding of VLC and compare them with three existing methods. Simulation results show that our methods outperform existing methods in terms of decoding complexity with nearly the same performance on Symbol Error Rate (SER) of optimal decoding. When used in a larger codeword set, the superiority in decoding complexity of our methods is more remarkable.     

Adaptive Sensor Fault Detection and Identification Using Particle Filter Algorithms

Sensor fault detection and identification (FDI) is a process of detecting and validating sensor's fault status. Because FDI guarantees system reliable performance, it has received much attention recently. In this paper, we address the problem of online sensor fault identification and validation. For a physical sensor validation system, it contains transitions between sensor normal and faulty states, change of system parameters, and a fusion of noisy readings. A common dynamic state-space model with continuous state variables and observations cannot handle this problem. To circumvent this limitation, we adopt a Markov switch dynamic state-space model to simulate the system: we use discrete-state variables to model sensor states and continuous variables to track the change of the system parameters. Problems in Markov switch dynamic state-space model can be well solved by particle filters, which are popularly used in solving problems in digital communications. Among them, mixture Kalman filter (MKF) and stochastic $M$-algorithm (SMA) have very good performance, both in accuracy and efficiency. In this paper, we plan to incorporate these two algorithms into the sensor validation problem, and compare the effectiveness and complexity of MKF and SMA methods under different situations in the simulation with an existing algorithm---interactive multiple models.      

An efficient pursuit automata approach for estimating stable all‐pairs shortest paths in stochastic network environments

This paper presents a new solution to the dynamic all‐pairs shortest‐path routing problem using a fast‐converging pursuit automata learning approach. The particular instance of the problem that we have investigated concerns finding the all‐pairs shortest paths in a stochastic graph, where there are continuous probabilistically based updates in edge‐weights. We present the details of the algorithm with an illustrative example. The algorithm can be used to find the all‐pairs shortest paths for the ‘statistical’ average graph, and the solution converges irrespective of whether there are new changes in edge‐weights or not. On the other hand, the existing popular algorithms will fail to exhibit such a behavior and would recalculate the affected all‐pairs shortest paths after each edge‐weight update. There are two important contributions of the proposed algorithm. The first contribution is that not all the edges in a stochastic graph are probed and, even if they are, they are not all probed equally often. Indeed, the algorithm attempts to almost always probe only those edges that will be included in the final list involving all pairs of nodes in the graph, while probing the other edges minimally. This increases the performance of the proposed algorithm. The second contribution is designing a data structure, the elements of which represent the probability that a particular edge in the graph lies in the shortest path between a pair of nodes in the graph. All the algorithms were tested in environments where edge‐weights change stochastically, and where the graph topologies undergo multiple simultaneous edge‐weight updates. Its superiority in terms of the average number of processed nodes, scanned edges and the time per update operation, when compared with the existing algorithms, was experimentally established. Copyright © 2008 John Wiley & Sons, Ltd.     

Performance of received power and traffic-driven handover algorithms in urban cellular networks

This article presents an investigation of the handover algorithms suitable for implementation in urban mobile cellular networks based on TDMA. More precisely, the class of received power and traffic-driven handover algorithms, which are based on absolute and relative measured values of received power and on actual traffic load, is analyzed. The algorithms usually investigated in the literature (and often implemented in actual mobile networks) belong to this class. We show the performance of traditional algorithms, and also propose some new ones, in an effort to reduce the probability of dropout due to sudden changes of received power and to distribute the traffic load over several cells; they show performance improvements with respect to those previously known from the literature. The performance of the different handover algorithms is evaluated by means of a simulation tool that allows the consideration of complex scenarios, suitable mobility and propagation models, power control, cell sectorization, nonuniform user distribution, and so on. The metrics used for performance comparison are the outage, blocking, dropout, and satisfaction probabilities, whose different definitions are discussed in the article, and the average number of handovers per call. Several urban scenarios, characterized by uniform and nonuniform traffic distributions, are taken into account. Our results show that one of the proposed algorithms, based on traffic estimates, yields the best performance of all the considered scenarios.     

Waveband Switching for Dynamic Traffic Demands in Multigranular Optical Networks

Waveband switching (WBS) in conjunction with multigranular optical cross-connect (MG-OXC) architectures can reduce the cost and complexity of OXCs. In this paper, we study the performance of different MG-OXC architectures under dynamic traffic. In the case with online incremental traffic, we compare two MG-OXC architectures in terms of the blocking probability of new lightpath requests and study the impact of port counts and traffic loads. We develop an online integer linear programming model (On-ILP), which minimizes the number of used ports and the request blocking probability, given a fixed number of wavelengths and MG-OXC architecture. The On-ILP optimizes the routing of new lightpaths so as to maximize lightpath grouping and reduce the port count given that existing traffic cannot be rearranged. We also propose a new efficient heuristic algorithm, called maximum overlap ratio (MOR) to satisfy incremental traffic and compare it with the On-ILP, first-fit, and random-fit algorithms. Our results and analysis indicate that using WBS with MG-OXCs can reduce the size (and, hence, the cost) of switching fabrics compared to using ordinary OXCs. Based on the results and observations in the incremental traffic case, we further study the performance of a particular MG-OXC architecture under fully dynamic or fluctuating traffic. Our simulations show that the proposed heuristic algorithm waveband assignment with path graph, which groups wavelengths to bands and uses wavelength converters efficiently under fluctuating traffic, significantly outperforms other heuristic algorithms.     

DVB-S2 ACM技术中的信噪比估计

为了在DVB-S2系统自适应编码调制(ACM)技术中实现对信道信噪比的精确、高效估计,提出了一种基于子空间分解的数据辅助类信噪比估计算法。计算机仿真结果表明,在较宽的信噪比范围内,该算法的性能优于数据辅助类的最大似然估计算法和经典的基于子空间分解的ED算法,估计精度高,计算复杂度低,非常适合在DVB-S2系统ACM技术中应用。     

Simulations in wireless sensor and ad hoc networks: matching and advancing models, metrics, and solutions

The objective of this article is to give advice for carrying out a proper and effective simulation activity for protocol design. It challenges some of the existing criticisms of simulation practices that emphasized validation aspects. This article advocates the use of simple models, matching assumptions and metrics in the problem statement and simulation to provide a basic ?proof of concept,? and comparison with truly competing solutions, which is possible only after a thorough and critical literature review. Then the complexity of the models can be increased (one parameter at a time), revising the algorithms themselves by adapting them to new assumptions, metrics, and the corresponding simulation environment. Selected independent variables should explain performance under a wide range of scenarios.     

DVB-S2 LDPC Decoding Using Robust Check Node Update Approximations

Broadband satellite services to fixed terminals are currently offered in the forward link by the 2nd generation (2G) digital video broadcasting satellite (DVB-S2) standard. For this standard the use of powerful low-density parity-check (LDPC) error correcting codes has been adopted performing within approximately 1 dB from the Shannon capacity limit. This paper studies and compares for the first time in a systematic manner different approximation methods used in check node update computation of DVB-S2 LDPC decoding with the aim of reducing computational complexity. Various performance evaluation results are presented for a wide range of DVB-S2 parameters, such as LDPC codeword size, coding rate, modulation format and including several decoding algorithms. It is shown that the proposed check node update approximations have a robust behavior, i.e. the resulting performance is quite independent of the DVB-S2 modulation and coding parameters. It is further shown that these approximations perform very close to the optimal sum-product algorithm (SPA) in degradation, which is less than 0.2 dB. Despite this small degradation, the reduction in computational complexity compared to the optimal SPA is significant and can be as high as 40% in computational time savings.     

Bayesian techniques for blind deconvolution

This paper introduces extended Bayesian filters (EBFs), a new family of blind deconvolution filters for digital communications. The blind deconvolution problem is formulated as a nonlinear and non-Gaussian fixed-lag minimum mean square error filtering problem, and the EBF is derived as a suboptimal recursive estimator. The model-based setting makes extensive use of the transmitted symbol and noise distributions. A key feature of the EBF is that the filter lag can be chosen to be larger than the channel length, while the complexity is exponential in a parameter which is typically chosen to be smaller than both the channel length and the filter lag. Extensive simulations characterizing the performance of EBFs in severe intersymbol interference channels are presented. The fast convergence and robust equalization of the EBFs are demonstrated for uncoded linearly modulated signals [e.g., differentially encoded quaternary phase shift keying (QPSK)] transmitted over unknown channels. Comparisons are made to other blind symbol-by-symbol demodulation algorithms. The results show that the EBF provides much better performance (at increased complexity) compared to the constant modulus algorithm and the extended Kalman filter, and achieves a better performance-complexity trade-off than other Bayesian demodulation algorithms. The simulations also show that the EBF is applicable with large constellations and shaped modulations     

Phase unwrapping via graph cuts.

Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper introduces a new energy minimization framework for phase unwrapping. The considered objective functions are first-order Markov random fields. We provide an exact energy minimization algorithm, whenever the corresponding clique potentials are convex, namely for the phase unwrapping classical Lp norm, with p > or = 1. Its complexity is KT (n, 3n), where K is the length of the absolute phase domain measured in 2pi units and T (n, m) is the complexity of a max-flow computation in a graph with n nodes and m edges. For nonconvex clique potentials, often used owing to their discontinuity preserving ability, we face an NP-hard problem for which we devise an approximate solution. Both algorithms solve integer optimization problems by computing a sequence of binary optimizations, each one solved by graph cut techniques. Accordingly, we name the two algorithms PUMA, for phase unwrappping max-flow/min-cut. A set of experimental results illustrates the effectiveness of the proposed approach and its competitiveness in comparison with state-of-the-art phase unwrapping algorithms.     

Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques

Frequent subgraph mining (FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been devel-oped with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing (PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, gSpan and Gaston. We compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38％depending on the dataset.     

A joint resource optimization and adaptive modulation framework for uplink single‐carrier frequency‐division multiple access systems

In this paper, we study joint resource allocation and adaptive modulation in single‐carrier frequency‐division multiple access systems, which is adopted as the multiple access scheme for the uplink in the 3GPP Long Term Evolution standard. We formulate an adaptive modulation and sum‐cost minimization (JAMSCmin) problem. Unlike orthogonal frequency‐division multiple access, in addition to the restriction of allocating a subchannel to one user at most, the multiple subchannels allocated to a user in single‐carrier frequency‐division multiple access systems should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for orthogonal frequency‐division multiple access, etc.) cannot help towards its optimal solution. We propose a novel optimization framework for the solution of this problem that is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary‐integer programming (BIP) problem and then transform this BIP problem into continuous space canonical dual problem that is the concave maximization problem. Based on the solution of the canonical dual problem, we derive joint resource allocation and adaptive modulation algorithm, which has polynomial time complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature. The results show a tremendous performance gain. Copyright © 2013 John Wiley & Sons, Ltd.     

A new distributed approximation algorithm for constructing minimum connected dominating set in wireless ad hoc networks

In recent years, constructing a virtual backbone by nodes in a connected dominating set (CDS) has been proposed to improve the performance of ad hoc wireless networks. In general, a dominating set satisfies that every vertex in the graph is either in the set or adjacent to a vertex in the set. A CDS is a dominating set that also induces a connected sub‐graph. However, finding the minimum connected dominating set (MCDS) is a well‐known NP‐hard problem in graph theory. Approximation algorithms for MCDS have been proposed in the literature. Most of these algorithms suffer from a poor approximation ratio, and from high time complexity and message complexity. In this paper, we present a new distributed approximation algorithm that constructs a MCDS for wireless ad hoc networks based on a maximal independent set (MIS). Our algorithm, which is fully localized, has a constant approximation ratio, and O(n) time and O(n) message complexity. In this algorithm, each node only requires the knowledge of its one‐hop neighbours and there is only one shortest path connecting two dominators that are at most three hops away. We not only give theoretical performance analysis for our algorithm, but also conduct extensive simulation to compare our algorithm with other algorithms in the literature. Simulation results and theoretical analysis show that our algorithm has better efficiency and performance than others. Copyright © 2005 John Wiley & Sons, Ltd.     

Bayesian techniques for known channel deconvolution

This paper introduces a new family of deconvolution filters for digital communications subject to severe intersymbol interference. These fixed lag smoothing filters for known channel demodulation are called Bayesian filters. Bayesian filters are derived using a new approach to suboptimal recursive minimum mean square error estimation for non-Gaussian processes. The family of Bayesian filters interpolates between the optimum fixed lag linear filter (i.e., the Kalman filter) and the optimum fixed lag symbol-by-symbol demodulator in both performance and complexity. The complexity of the Bayesian filter is exponential in a parameter, typically chosen smaller than the channel length and the filter lag. Hence, the Bayesian filter decouples the channel length and the filter lag from the exponential complexity in these parameters found in many other high performance demodulation algorithms. Simulations characterize the performance and compare the Bayesian filter to both optimal and reduced complexity demodulation algorithms     

基于短波突发信号的自适应均衡技术研究

文中根据短波突发通信中使用的波形结构,结合常用的两种均衡算法特点,提出在均衡器训练阶段采用平方根卡尔曼(SRK)算法,在跟踪阶段采用最小均方(LMS)算法的均衡方案,将该方案与两种常用的均衡算法进行对比仿真试验.仿真结果表明,该均衡方案收敛速度快,误码率特性较好且计算复杂度低,是一种适合于短波突发通信的均衡方案.