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In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS. [ABSTRACT FROM AUTHOR]

In the soft-input soft-output Viterbi algorithm (SOVA), the log-likelihood ratio (LLR) of each bit is determined by the minimum metric difference between the ML path and its competitive paths. This paper proposes to trim large metric differences in order to reduce the complexity of SOVA. By trimming the metric differences, only a small number of backtracking operations are carried out, while many LLRs may be omitted as the result of the lack of metric differences. By revealing the relationship among neighboring LLRs, the omitted LLRs are estimated from its neighoring LLRs as well as intrinsic information. The extrinsic information transfer chart analysis demonstrates that the proposed algorithm has similar convergence behavior as the Log-MAP algorithm, if the trimming factor $M$ is moderate. Other analyses verify that our approach provides good LLR quality with only at most $1/M$ backtracking operations of SOVA. Simulation results show that it outperforms SOVA and performs as well as its variants and the Log-MAP algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate parameter estimation for generalized frequency division multiplexing (GFDM) signals over frequency-selective fading channels. Based on the characteristics of the second-order cyclostationary statistics of GFDM signals, we develop algorithms to blindly estimate pure block duration, overall block duration, symbol duration, and number of subcarriers. The proposed algorithms are robust to timing, phase, and carrier frequency uncertainties and do not require any a priori knowledge of the received signal. Simulation results verify the feasibility of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms. [ABSTRACT FROM PUBLISHER]

The wide recognition that emerging nano-devices will be inherently unreliable motivates the evaluation of information processing algorithms running on noisy hardware as well as the design of robust schemes for reliable performance against hardware errors of varied characteristics. In this paper, we investigate the performance of a popular statistical inference algorithm, belief propagation (BP) on probabilistic graphical models, implemented on noisy hardware, and we propose two robust implementations of the BP algorithm targeting different computation noise distributions. We assume that the BP messages are subject to zero-mean transient additive computation noise. We focus on graphical models satisfying the contraction mapping condition that guarantees the convergence of the noise-free BP. We first upper bound the distances between the noisy BP messages and the fixed point of (noise-free) BP as a function of the iteration number. Next, we propose two implementations of BP, namely, censoring BP and averaging BP, that are robust to computation noise. Censoring BP rejects incorrect computations to keep the algorithm on the right track to convergence, while averaging BP takes the average of the messages in all iterations up to date to mitigate the effects of computation noise. Censoring BP works effectively when, with high probability, the computation noise is exactly zero, and averaging BP, although having a slightly larger overhead, works effectively for general zero-mean computation noise distributions. Sufficient conditions on the convergence of censoring BP and averaging BP are derived. Simulations on the Ising model demonstrate that the two proposed implementations successfully converge to the fixed point achieved by noise-free BP. Additionally, we apply averaging BP to a BP-based image denoising algorithm and as a BP decoder for LDPC codes. In the image denoising application, averaging BP successfully denoises an image even when nominal BP fails to do so in the presence of computation noise. In the BP LDPC decoder application, the power of averaging BP is manifested by the reduction in the residual error rates compared with the nominal BP decoder. [ABSTRACT FROM PUBLISHER]

In this paper, closed-form expressions for the uncoded but error probability of closed-loop transmit diversity (CLTD) algorithms with two transmit and one receive antennas and noisy channel state information (CSI) in time-varying Rayleigh fading channels are derived. Two CLTD algorithms considered are the phase-amplitude CLTD (PA-CLTD), where the transmit antennas may transmit with different signal energy, and the phase-only CLTD (PO-CLTD), where the transmit antennas must transmit with the same signal energy. In addition to the uncoded bit error probability, this paper also derives the pairwise error probability when finite-depth interleaved convolutional codes are used with CLTD algorithms. However, due to the complexity of the coded system, the pairwise error probabilities are not in the closed-form expressions. The results have shown that PA-CLTD performs slightly better than PO-CLTD although PA-CLTD requires significantly more feedback information and that, when the Doppler spread is large, the performance of CLTD algorithms may degrade significantly. Moreover, a comparison between PA-CLTD, the Alamouti space-time code and the SISO system indicates that PA-CLTD outperforms the other two systems when the Doppler spread is small and the pilot SNR is large. Finally, the analytical results are compared with results from Monte Carlo simulation and the comparison shows that the analytical results match well with the simulation results. [ABSTRACT FROM AUTHOR]

For coherent detection, block-coded modulation is a bandwidth efficient scheme. In this paper, we propose theorems about the error performance of block-coded modulation using Mary phase-shift keying (MPSK) for noncoherent detection. Based on these theorems, we propose a novel block-coded modulation scheme for noncoherent detection called noncoherent block-coded MPSK. The proposed scheme provides flexible designs of noncoherent block codes with different code rate, block length and error performance. Good noncoherent block codes can be easily obtained by properly choosing binary linear block codes as the component codes. Moreover, noncoherent block codes of this new scheme can be decoded by multistage decoding, which has the advantage of low complexity and satisfactory error performance. In this paper, two algorithms of multistage decoding for noncoherent detection are proposed as welt. The error performance of some designed codes and decoding algorithms is verified by computer simulation. [ABSTRACT FROM AUTHOR]

We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a “divide and conquer” approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the “smooth streaming regime,” i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times. [ABSTRACT FROM PUBLISHER]

Reading efficiency is one of the key factors to evaluate Radio Frequency Identification (RFID) systems. For the system using multi-branch protocols, the performance would be better if the tags are properly divided into multiple groups. This paper firstly gives the closed-form of system efficiency for binary tree algorithm. Based on the theoretical analysis, the optimal branches number is derived. An efficient multi-branch tree (EMBT) algorithm is proposed subsequently, along with a tag number estimation algorithm and performance evaluation. Both theoretical analysis and simulation results indicate that multi-branch tree algorithm has better performance than the conventional binary tree algorithm. System identification efficiency of the proposed method can achieve above 45%, while that of the binary tree algorithm is only 34.8%. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced permeable layer mechanism useful for highly robust packetized multimedia transmission. Packet header recovery at various protocol layers using MAP estimation is the cornerstone of the proposed solution. The inherently available intra-layer and inter-layer header correlation proves to be very effective in selecting a reduced set of possible header configurations for further processing. The best candidate is then obtained through soft decoding of CRC protected data and CRC redundancy information itself. Simulation results for WiFi transmission using DBPSK modulated signals over AWGN channels show a substantial (4 to 12 dB) link budget improvement over classical hard decision procedures. We also introduce a sub-optimal and hardware realizable version of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Adaptive Belief Propagation (ABP) algorithm was recently proposed by Jiang and Narayanan for the soft decoding of Reed-Solomon (RS) codes. In this paper, simplified versions of this algorithm are investigated for the turbo decoding of product codes. The complexity of the Turbo-oriented Adaptive Belief propagation (TAB) algorithm is significantly reduced by moving the matrix adaptation step outside of the belief propagation iteration loop. A reduced-complexity version of the TAB algorithm that offers a trade-off between performance and complexity is also proposed. Simulation results for the turbo decoding of product codes show that belief propagation based on adaptive parity check matrices is a practical alternative to the currently very popular Chase-Pyndiah algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the performance of bit-interleaved coded multiple beamforming (BICMB) [1], [2] with imperfect knowledge of beamforming vectors. Most studies for limited-rate channel state information at the transmitter (CSIT) assume that the precoding matrix has an invariance property under an arbitrary unitary transform. In BICMB, this property does not hold. On the other hand, the optimum precoder and detector for BICMB are invariant under a diagonal unitary transform. In order to design a limited-rate CSIT system for BICMB, we propose a new distortion measure optimum under this invariance. Based on this new distortion measure, we introduce a new set of centroids and employ the generalized Lloyd algorithm for codebook design. We provide simulation results demonstrating the performance improvement achieved with the proposed distortion measure and the codebook design for various receivers with linear detectors. We show that although these receivers have the same performance for perfect CSIT, their performance varies under imperfect CSIT. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of multipath traffic engineering in optical burst switching (OBS) networks. The main goal of this work is to minimize burst loss rate in the network by adaptively balancing the burst traffic among multiple paths based on the measurement and analysis of path congestion. We develop three distributed traffic splitting algorithms by considering the special features of OBS networks. Among these three algorithms, one algorithm considers burst level traffic distribution, and the other two algorithms consider flow level traffic distribution to avoid packet reordering problem. The proposed multipath traffic engineering algorithms achieve significant performance improvement in reducing burst loss probability in a distributed manner in OBS networks. Further, through theoretical analysis and simulations, we show that the proposed algorithms converge quickly on a sudden traffic increase in the network. [ABSTRACT FROM AUTHOR]

In this paper, we derive closed form upper bounds on the error probability of low-density parity-check (LDPC) coded modulation schemes operating on quasi-static fading channels. The bounds are obtained from the so-called Fano-Gallager's tight bounding techniques, and can be readily calculated when the distance spectrum of the code is available. In deriving the bounds for multiple-input multiple-output (MIMO) systems, we assume the LDPC code is concatenated with the orthogonal space-time block code as an inner code. We obtain an equivalent single-input single-output (SISO) channel model for this concatenated coded-modulation system. The upper bounds derived here indicate good matches with simulation results of a complete transceiver system over Rayleigh and Rician MIMO fading channels in which the iterative detection and decoding algorithm is employed at the receiver. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel carrier frequency offset (CEO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CEO outlier. Therefore, the proposed tracking algorithni is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis. [ABSTRACT FROM AUTHOR]

Importance sampling is used in this paper to address the classical yet important problem of performance estimation of block codes. Simulation distributions that comprise discrete- and continuous-mixture probability densities are motivated and used for this application. These mixtures are employed in concert with the so-called g-method, which is a conditional importance sampling technique that more effectively exploits knowledge of underlying input distributions. For performance estimation, the emphasis is on bit by bit maximum a-posteriori probability decoding, but message passing algorithms for certain codes have also been investigated. Considered here are single parity check codes, multidimensional product codes, and briefly, low-density parity-check codes. Several error rate results are presented for these various codes, together with performances of the simulation techniques. [ABSTRACT FROM AUTHOR]

This paper considers multiple-symbol differential detection (MSD) of differential unitary space-time modulation (DUSTM) over multiple-antenna systems. We derive a novel exact maximum-likelihood (ML) detector, called the bound-inter-section detector (BID), using the extended Euclidean algorithm for single-symbol detection of diagonal constellations. While the ML search complexity is exponential in the number of transmit antennas and the data rate, our algorithm, particularly in high signal-to-noise ratio, achieves significant computational savings over the naive ML algorithm and the previous detector based on lattice reduction. We also develop four BID variants for MSD. The first two are ML and use branch-and-bound, the third one is suboptimal, which first uses BID to generate a candidate subset and then exhaustively searches over the reduced space, and the last one generalizes decision-feedback differential detection. Simulation results show that the BID and its MSD variants perform nearly ML, but do so with significantly reduced complexity. [ABSTRACT FROM AUTHOR]

In this paper, a fair scheme to allocate subcarrier, rate, and power for multiuser orthogonal frequency-division multiple-access systems is proposed. The problem is to maximize the overall system rate, under each user's maximal power and minimal rate constraints, while considering the fairness among users. The approach considers a new fairness criterion, which is a generalized proportional fairness based on Nash bargaining solutions and coalitions. First, a two-user algorithm is developed to bargain subcarrier usage between two users. Then a multiuser bargaining algorithm is developed based on optimal coalition pairs among users. The simulation results show that the proposed algorithms not only provide fair resource allocation among users, but also have a comparable overall system rate with the scheme maximizing the total rate without considering fairness. They also have much higher rates than that of the scheme with max-min fairness. Moreover, the proposed iterative fast implementation has the complexity for each iteration of only O (K² N log2 N + K4), where N is the number of subcarriers and K is the number of users. [ABSTRACT FROM AUTHOR]

List-sequence (LS) decoding has the potential to yield significant coding gain additional to that of conventional single-sequence decoding, and it can be implemented with full backward compatibility in systems where an error-detecting code is concatenated with an error-correcting code. LS maximum-likelihood (ML) decoding provides a list of estimated sequences in likelihood order. For convolutional codes, this list can be obtained with the serial list Viterbi algorithm (SLVA). Through modification of the metric increments of the SLVA, an LS maximum a posteriori (MAP) probability decoding algorithm is obtained that takes into account bitwise a priori probabilities and produces an ordered list of sequence MAP estimates. The performance of the resulting LS-MAP decoding algorithm is studied in this paper. Computer simulations and approximate analytical expressions, based on geometrical considerations of the decision domains of LS decoders, are presented. We focus on the frame-error performance of LS-MAP decoding, with genie-assisted error detection, on the additive white Gaussian noise channel. It is concluded that LS-MAP decoding exploits a priori information more efficiently, in order to achieve performance improvements, than does conventional single-sequence MAP decoding. Interestingly, LS-MAP decoding can provide significant improvements at low signal-to-noise ratios, compared with LS-ML decoding. In this environment, it is furthermore observed that feedback convolutional codes offer performance improvements over their feedforward counterparts. Since LS-MAP decoding can be implemented in existing systems at a modest complexity increase, it should have a wide area of applications, such as joint source-channel decoding and other kinds of iterative decoding. [ABSTRACT FROM AUTHOR]

In this paper, a derandomized algorithm for sampling decoding is proposed to achieve near-optimal performance in lattice decoding. By setting a probability threshold to sample candidates, the whole sampling procedure becomes deterministic, which brings considerable performance improvement and complexity reduction over to the randomized sampling. Moreover, the upper bound on the sample size K, which corresponds to near-maximum likelihood (ML) performance, is derived. We also find that the proposed algorithm can be used as an efficient tool to implement soft-output decoding in multiple-input multiple-output (MIMO) systems. An upper bound of the sphere radius R in list sphere decoding (LSD) is derived. Based on it, we demonstrate that the derandomized sampling algorithm is capable of achieving near-maximum a posteriori (MAP) performance. Simulation results show that near-optimum performance can be achieved by a moderate size K in both lattice decoding and soft-output decoding. [ABSTRACT FROM AUTHOR]

Quantized Congestion Notification (QCN) has been developed for IEEE 802.1Qau to provide congestion control at the Ethernet Layer or Layer 2 in data center networks (DCNs) by the IEEE Data Center Bridging Task Group. One drawback of QCN is the rate unfairness of different flows when sharing one bottleneck link. In this paper, we propose an enhanced QCN congestion notification algorithm, called fair QCN (FQCN), to improve rate allocation fairness of multiple flows sharing one bottleneck link in DCNs. FQCN identifies congestion culprits through joint queue and per flow monitoring, feedbacks individual congestion information to each culprit through multi-casting, and ensures convergence to statistical fairness. We analyze the stability and fairness of FQCN via Lyapunov functions and evaluate the performance of FQCN through simulations in terms of the queue length stability, link throughput and rate allocations to traffic flows with different traffic dynamics under three network topologies. Simulation results confirm the rate allocation unfairness of QCN, and validate that FQCN maintains the queue length stability, successfully allocates the fair share rate to each traffic source sharing the link capacity, and enhances TCP throughput performance in the TCP Incast setting. [ABSTRACT FROM AUTHOR]

The construction of finite-length irregular LDPC codes with low error floors is currently an attractive research problem. In particular, for the binary erasure channel (BEC), the problem is to find the elements of selected irregular LDPC code ensembles with the size of their minimum stopping set being maximized. Due to the lack of analytical solutions to this problem, a simple but powerful heuristic design algorithm, the approximate cycle extrinsic message degree (ACE) constrained design algorithm, has recently been proposed. Building upon the ACE metric associated with a cycle in a code graph, we introduce the ACE spectrum of LDPC codes as a useful tool for evaluation of codes from selected irregular LDPC code ensembles. Using the ACE spectrum, we generalize the ACE constrained design algorithm, making it more flexible and efficient. We justify the ACE spectrum approach through examples and simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate noncoherent detection, i.e. detection assuming the absence of channel state information at the receiver, of on-off keying in an intensity modulation and direct detection (IMIDD) free-space optical (FSO) system. To partially recover the performance loss associated with symbol- by-symbol noncoherent detection, we consider the application of multiple-symbol detection (MSD), in which block-wise decisions are made using an observation window of several bit intervals. Specifically, we develop a fast search algorithm for optimal MSD and propose a reduced-complexity decision metric suitable for suboptimal MSD; performance results confirm that the optimal and suboptimal metrics perform comparably well. Significantly, the complexity of our receiver, on a per bit-decision basis, is only logarithmically dependent on the observation window size. We also present the framework for a decision-feedback receiver and obtain performance expressions for the ideal case of error-free feedback; these expressions serve as an upper bound to the performance of MSD. Analytical and simulation results indicate that as the observation window size increases, the performance of the MSD receiver approaches that of detection with channel state information. The conclusion is reached that the proposed implementation provides an attractive low-complexity mechanism for performing noncoherent MSD in FSO systems. [ABSTRACT FROM AUTHOR]

An efficient way to reduce the peak-to-average power ratio (PAPR) in OFDM systems is clipping. After the clipping in an MIMO-OFDM system, the additive noise may not be white. In this paper, we develop fast (single-symbol) ML decoding algorithms for orthogonal space-time block codes (OSTBC) and (linearly transformed) quasi orthogonal space- time block codes (QOSTBC) in clipped MIMO-OFDM systems by using a clipping noise model with Gaussian approximation. By using the statistics of the clipping distortions, our newly developed fast ML decoding algorithms improve the performance for clipped MIMO-OFDM systems with OSTBC and QOSTBC while the decoding complexities are not increased. Simulation results are presented to illustrate the improvement. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This decoding algorithm shows better bit error rate (BER) performance than the conventional message-passing decoding algorithm, especially for the small number of iterations. Analytical results indicate that as the number of partitioned subsets of check nodes increases, the BER performance is improved. We also derive the recursive equations for mean values of messages at check and variable nodes by using density evolution with a Gaussian approximation. From these equations, the mean values are obtained at each iteration of the sequential decoding algorithm and the corresponding BER values are calculated. They show that the sequential decoding algorithm converges faster than the conventional one. Finally, the analytical results are confirmed by the simulation results. [ABSTRACT FROM AUTHOR]

It is shown that binary low-density generator matrix codes can achieve the rate-distortion bound of discrete memoryless sources with general distortion measure via multilevel quantization. A practical encoding scheme based on the survey-propagation algorithm is proposed. The effectiveness of the proposed scheme is verified through simulation. [ABSTRACT FROM AUTHOR]

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds. [ABSTRACT FROM AUTHOR]

We propose novel techniques to reduce the complexity of the well-known Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original one for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them particularly effective over one of the three different classes of channels affected by intersymbol interference (minimum-phase, maximum- phase, and mixed-phase channels). The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, chosen according to the maximum a posteriori criterion. Moreover, some optimization techniques improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and we compare them with other solutions in the literature. [ABSTRACT FROM AUTHOR]

We address the problem of the performance analysis of the stochastic fair sharing (SFS) algorithm for fair link sharing. The SFS scheme has been proposed to carry out a fair link sharing and fair sharing among virtual private networks. Depending upon the current utilization and provisioned capacities of the classes, the SFS admission control algorithm decides which sessions to accept and which to reject. In this letter, we undertake the performance evaluation of the SFS scheme analytically. We explore the tradeoff between fairness and the blocking probability by varying the trunk reservation parameter. The results show that the analytical performance measure agrees well with the simulation results. [ABSTRACT FROM AUTHOR]

The application of successive relaxation (SR) to the fixed-point problem associated with the iterative decoding of low-density parity-check (LDPC) codes was proposed by Hemati et al.. The simulation results presented by Hemati et al. for the SR version of belief propagation (BP) in the likelihood ratio (LR) domain and that of min-sum (MS) in the log-likelihood ratio (LLR) domain are based on the assumption of all-zero codeword transmission. This assumption however results in erroneous error rates when SR is applied in the LR domain. Here, we correct the simulation results reported by Hemati et al. for SR-BP in the LR domain. Furthermore, we investigate the performance of SR-BP and SR-MS in the LLR and LR domains, respectively. The results for a binary input additive white Gaussian noise (BIAWGN) channel show that for both BP and MS, the application of SR in the two domains of LR and LLR results in different error correcting performance. In particular, for the tested codes, it is shown that among the four algorithms, SR-MS-LLR has the best performance. It outperforms standard MS and BP by up to about 0.6 dB and 0.3dB, respectively, offering an attractive solution in terms of performance/complexity tradeoff. [ABSTRACT FROM AUTHOR]

Clock synchronization has become an indispensable requirement in wireless sensor networks due to its central importance in vital network operations such as data fusion and duty cycling, and has attracted considerable research interest recently. Assuming exponentially distributed random delays in a two-way message exchange mechanism, this work proposes a network-wide clock synchronization algorithm using a factor graph representation of the network. Message passing using the max-product algorithm is adopted to derive the update rules for the proposed iterative procedure. A closed form solution is obtained for each node's belief about its clock offset at each iteration. Simulation results show that the application of the proposed message passing-based network-wide clock synchronization algorithm provides convergent estimates for both regular cycle-free and random topologies. Moreover, the mean square error (MSE) performance of the proposed algorithm is also compared with the Cramer-Rao bound (CRB) for small example networks, which further highlights the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We introduce analytical characterization of the nonlinear interference that results when passing more than one high-order modulation carrier through the same nonlinear transponder high-power amplifier (HPA). A Volterra filter is proposed which is novel in its implementation of this analytical characterization and modeling of intersymbol interference (ISI) and adjacent channel interference (ACI). Our focus is on adaptive algorithms with pilot-based training so that the solutions are completely blind to unknown transponder HPA characteristics, and can rapidly respond to varying operating back-off level. Furthermore, two families of adaptive solutions are provided to compensate for nonlinear ISI and ACI. The first set performs adaptive channel inversion and then applies equalization. The second set of solutions performs adaptive channel identification and then applies cancellation. The effectiveness of the proposed analysis and techniques is demonstrated via extensive simulations for high-order QAM and APSK modulations. Also included is the coded performance with selected LDPC codes designed for the DVB-S2 standard. Finally, computational complexity is assessed and performance impact is quantified when complexity is reduced by decreasing the number of Volterra coefficients. [ABSTRACT FROM AUTHOR]

An efficient algorithm for bit-loading in multicarrier systems is proposed based upon an allocation rhythm/order underlying the classical optimal greedy bit-allocation procedure. An accurate complexity comparison in terms of the exact number of execution cycles over a processor is made with the classical Greedy [1] solution along with another recently proposed optimal bit-loading algorithm [2]. Simulation results verify the significant complexity advantage of our algorithm with no loss in performance. [ABSTRACT FROM AUTHOR]

For binary linear block codes, this letter deals with a class of decoding algorithms which utilize bounded-distance and encoding-based decodings with input sequences that are calculated from a received sequence and given test patterns. We propose a new method for selecting the test patterns by simulation. The effectiveness of the decoding algorithm whose test patterns are selected by the proposed method is also shown by simulation. [ABSTRACT FROM AUTHOR]

It has recently been recognized by many researchers that adaptive modulation is most effective when the channel diversity order is small. In this letter, we propose a simple adaptive modulation scheme for orthogonal frequency division multiplexing (OFDM) systems on channels that provide a small order of diversity. The proposed adaptation algorithm is based on a novel and very simple analytical formula we derive for the performance of BICM on block fading channels. Simulation results show that the derived analytical formula is very tight when the targeted bit error probability is small. OFDM systems on indoor channels provide small orders of diversity and form an ideal scenario for adaptation. In order to keep system complexity and feedback requirements at a minimum, we will consider symbol-level adaptive modulation wherein all the subcarriers in an OFDM symbol use the same modulation. [ABSTRACT FROM AUTHOR]

A class of low-density parity-check (LDPC) codes with a simple 2-state trellis structure is presented. For LDPC decoding, the conventional belief propagation (BP) algorithm consists of numerous sub-decoders of single-parity check codes and exchanges information between sub-decoders in an iterative manner. If the single-parity check codes can be constructed and grouped in a proper way, the decoder can be decomposed into few identical 2-state trellis decoders. Therefore, instead of numerous sub-decoders of single-parity check codes, an iterative decoding algorithm based on few sub-decoders over 2-state trellises is proposed. The proposed decoding algorithm improves the efficiency of message passing between sub-decoders and hence provides a fast convergent rate as compared to the standard BP algorithm. Simulation results show that the proposed scheme provides a better performance and a fast convergent rate as compared to those of standard BP algorithm. The result also shows that the proposed algorithm has a similar performance as that of asynchronous replica shuffled BP algorithm and has a slightly inferior performance than that of synchronous replica shuffled BP algorithm. However, complexity analysis shows that our proposed algorithm has complexity that is lower than that of the replica shuffled BP algorithm. [ABSTRACT FROM AUTHOR]

Erasure selection for errors-and-erasures decoding algorithms is investigated and analyzed, where declaring erasures provides additional gain for hard decision decoding. To derive an analytical criterion for optimal erasure selection, we first investigate erasure effects on decoding performance, by which a metric function for erasure selection is derived. From the derived metric, a suboptimal threshold is defined, which can be used for errors and erasures decoding of large code-distance channel codes. Moreover, application to M-ary (M>2) PAM constellation is also described. For verification of the proposed erasure threshold, simulation results with popular channel codes are included. [ABSTRACT FROM AUTHOR]

In joint source-channel arithmetic coding (JSCAC) schemes, additional redundancy may be introduced into an arithmetic source code in order to be more robust against transmission errors. The purpose of this work is to provide analytical tools to predict and evaluate the effectiveness of that redundancy. Integer binary Arithmetic Coding (AC) is modeled by a reduced-state automaton in order to obtain a bit-clock trellis describing the encoding process. Considering AC as a trellis code, distance spectra are then derived. In particular, an algorithm to compute the free distance of an arithmetic code is proposed. The obtained code properties allow to compute upper bounds on both bit error and symbol error probabilities and thus to provide an objective criterion to analyze the behavior of JSCAC schemes when used on noisy channels. This criterion is then exploited to design efficient error-correcting arithmetic codes. Simulation results highlight the validity of the theoretical error bounds and show that for equivalent rate and complexity, a simple optimization yields JSCACs that outperform classical tandem schemes at low to medium SNR. [ABSTRACT FROM AUTHOR]

Abstract-Most network operators have considered reducing Label Switched Routers (LSR) label spaces (i.e. the number of labels that can be used) as a means of simplifying management of underlaying Virtual Private Networks (VPNs) and, hence, reducing operational expenditure (OPEX). This letter discusses the problem of reducing the label spaces in MultiProtocol Label Switched (MPLS) networks using label merging - better known as MultiPoint-to-Point (MP2P) connections. Because of its origins in IP, MP2P connections have been considered to have tree-shapes with Label Switched Paths (LSP) as branches. Due to this fact, previous works by many authors affirm that the problem of minimizing the label space using MP2P in MPLS - the MERGING PROBLEM - cannot be solved optimally with a polynomial algorithm (NP-complete), since it involves a hard- decision problem. However, in this letter, the MERGING PROBLEM is analyzed, from the perspective of MPLS, and it is deduced that tree-shapes in MP2P connections are irrelevant. By overriding this tree-shape consideration, it is possible to perform label merging in polynomial time. Based on how MPLS signaling works, this letter proposes an algorithm to compute the minimum number of labels using label merging: the Full Label Merging algorithm. As conclusion, we reclassify the MERGING PROBLEM as Polynomial-solvable, instead of NP-complete. In addition, simulation experiments confirm that without the tree-branch selection problem, more labels can be reduced. [ABSTRACT FROM AUTHOR]

We propose an iterative soft detection algorithm based on the QR decomposition and M-algorithm (soft-QRD-M) for MIMO-OFDM incorporating error correction coding. The soft-QRD-M step generates approximate a posteriori probabilities (APPs) with significant computational savings over the optimal sum-product algorithm. Simulation results show comparable performance of the soft-QRD-M detector and SPA in a Turbo-coded iterative MIMO-OFDM receiver. [ABSTRACT FROM AUTHOR]

We consider multiple-bit differential detection (MBDD) of differentially encoded shaped-offset quadrature phase-shift keying (SOQPSK), a highly bandwidth-efficient and popular constant-envelope modulation. We propose two MBDD schemes that are based on a recent continuous phase modulation interpretation of SOQPSK. We show that the performance of these MBDD schemes approaches that of coherent detection (CD) as the multiple-bit observation window N increases. The first scheme uses a detection window that spans the full N-bit observation window (F-MBDD), and is shown to require very large values of N to approach the performance of CD. This presents a practical problem since the complexity of MBDD grows exponentially with N. The second scheme is an improved version (I-MBDD) with a detection window that is shortened to N - 2 bit intervals. Although the complexity of I-MBDD also increases exponentially with N, it represents a significant improvement since only modest values of N are needed for high performance. These performance characteristics are identified via a detailed performance analysis, which provides asymptotic formulas for the bit error probability that are confirmed with computer simulations. The analysis is also used to find the symmetric frequency pulse shapes with the best and worst error performance. Finally, we develop a simplified and practical decision feedback differential detection algorithm that achieves near-optimal performance with complexity that grows only linearly with N. [ABSTRACT FROM AUTHOR]

A multiuser bit-loading problem is investigated in multicarrier communication systems. Assuming knowledge of all the channel gains, we propose a multiuser bit-loading algorithm that attempts to minimize the total power to transmit a target rate-sum of all users. It is shown that this algorithm is optimal when the interference among users is nonexistent or strong. The simulation results show that the proposed algorithm finds a near-optimal solution for very-high-speed digital subscriber line systems. [ABSTRACT FROM AUTHOR]

Resource allocation is an important means to increase system performance in wireless networks. In this letter, a game-theory approach for distributed resource allocation is proposed. Observing the bilinear matrix inequality nature of resource allocation, we construct two interrelated games: a power-control game at the user level, and a throughput game at the system level, respectively, to avoid local optima. An optimal complex centralized algorithm is developed as a performance bound. The simulations show that the proposed games have near-optimal system performance. [ABSTRACT FROM AUTHOR]

Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives. [ABSTRACT FROM AUTHOR]

We start by identifying a relatively efficient version of sphere decoding algorithm (SDA) that performs exact maximum-likelihood (ML) decoding. We develop novel algorithms based on an improved increasing radius search (IIRS), which offer error performance and decoding complexity between two extremes: the ML receiver and the nulling-canceling (NC) receiver with detection ordering. With appropriate choices of parameters, our LIRS offers the flexibility to trade error performance for coma plexity. We provide design intuitions and guidelines, analytical parameter specifications, and a semianalytical error-performance analysis. Simulations illustrate that IIRS achieves considerable complexity reduction, while maintaining performance close to ML. [ABSTRACT FROM AUTHOR]

The problem of adaptive detection of signals contaminated with Middleton's class-A impulsive noise and transmitted over a fast time-varying frequency-selective fading channel is addressed. Adaptive algorithms are derived to update the estimate of the channel parameters to the detector. A theoretical performance evaluation of the detector is provided. Computer simulations are performed to validate the theoretical developments. [ABSTRACT FROM AUTHOR]

Bursts consist of a varying number of asynchronous transfer mode cells corresponding to a datagram. Here, we generalized weighted fair queueing to a burst-based algorithm with preemption. The new algorithm enhances the performance of the switch service for real-time applications, and it preserves the quality of service guarantees. We study this algorithm theoretically and via simulations. [ABSTRACT FROM AUTHOR]

Fast estimation methods for small outage probabilities of signaling in fading multiple-Input multiple-output (MIMO) channels are developed. Communication over such channels is of much current interest, and quick and accurate methods for estimating outage capacities are needed. The methods described herein use adaptive importance sampling (IS) techniques as developed in a series of recent publications. Fast algorithms are provided for evaluating "nonergodic" capicities of Rayleigh fading MIMO channels. The methodology can be extended to more general models. Numerical results on outage capacity are provided, and these extend and complement known results in the literature. [ABSTRACT FROM AUTHOR]

Two important topics in equalizer design are its complexity and its training. We present a family of blind equalizers which, by incorporating a decomposition finite-impulse response filtering technique, can reduce the complexity of the convolution operation therein by about one half. The prototype algorithm in this equalizer family employs the prevalent Godard cost function. Several simplified algorithms are proposed, including a sign algorithm which eliminates multiplications in coefficient adaptation and a few delayed versions. We also study the convergence properties of the algorithms. For the prototype algorithm, we show that, in the limit of an infinitely long equalizer and under mild conditions on signal constellations and channel characteristics, there are only two sets of local minima on the performance surface. One of the sets is undesirable and is characterized by a null equalized channel response. The other corresponds to perfect equalization, which can be reached with proper equalizer initialization. For the simplified algorithms, corresponding cost functions may not exist. Some understanding of their convergence behaviors are obtained via examination of their adaptation equations. Simulation results are presented to demonstrate the performance of the algorithms. [ABSTRACT FROM AUTHOR]