Input Symbols Research Articles

Parallel Automata Processor

Finite State Machines (FSM) are widely used computation models for many application domains. These embarrassingly sequential applications with irregular memory access patterns perform poorly on conventional von-Neumann architectures. The Micron Automata Processor (AP) is an in-situ memory-based computational architecture that accelerates non-deterministic finite automata (NFA) processing in hardware. However, each FSM on the AP is processed sequentially, limiting potential speedups. In this paper, we explore the FSM parallelization problem in the context of the AP. Extending classical parallelization techniques to NFAs executing on AP is non-trivial because of high state-transition tracking overheads and exponential computation complexity. We present the associated challenges and propose solutions that leverage both the unique properties of the NFAs (connected components, input symbol ranges, convergence, common parent states) and unique features in the AP (support for simultaneous transitions, low-overhead flow switching, state vector cache) to realize parallel NFA execution on the AP. We evaluate our techniques against several important benchmarks including NFAs used for network intrusion detection, malware detection, text processing, protein motif searching, DNA sequencing, and data analytics. Our proposed parallelization scheme demonstrates significant speedup (25.5x on average) compared to sequential execution on AP. Prior work has already shown that sequential execution on AP is at least an order of magnitude better than GPUs, multi-core processors and Xeon Phi accelerator.

Energy Minimization With Network Coding via Latin Hypercubes

Network coding is mostly used to achieve the capacity of communication networks. In this paper, motivated by the nanoscale communications where the energy cost for the channel symbols is asymmetric due to the widely employed on–off keying modulation, we design energy-minimizing network codes. We develop the best mapping between the input and output symbols at the network coding node that minimizes the average codeword energy using Latin squares, which we call the minimum energy network code (MENC). We define the class of networks composed of coding nodes with N incoming and 1 outgoing symbols as in-N networks. First, we derive the condition on the network code to minimize the average energy in in-two networks and propose two linear MENCs. Later, we investigate the MENCs for in-N networks using the Latin hypercubes and propose a low-energy network code (LENC) to reduce the average energy with network coding. We compare MENC with the classical XOR and random network codes for in-two networks. The performance comparison between LENC and random network codes for in-N networks shows that the proposed network codes provide significant energy gains.

Improved online fountain codes based on shaping for left degree distribution

In this paper, an improved encoding scheme for online fountain codes is proposed with the joint optimization of variable node degree and check node degree is proposed. The coding scheme can be divided into the build-up phase and the completion phase. In the build-up phase, left degree distribution is exploited to guarantee optimal performance phase by modifying the traditional coding scheme of choosing input symbols uniformly at random. A memory-based selecting of the source symbols is employed to decrease the number of connected components, which can thus produce the dimension increasement of the linear subspace of a decoding graph constructed in the build-up phase. The upper bound on coding overhead is also derived from the analysis of random graph theory. Compared with conventional online fountain codes, it can be seen from the simulation results that the proposed scheme can provide significant performance improvement with respect to both coding overhead and feedback cost. Moreover, the lower encoding/decoding complexities may make the proposed scheme more practical in energy-constrained applications such as distributed storage.

Multihead Multitrack Detection for Next Generation Magnetic Recording, Part II: Complexity Reduction — Algorithms and Performance Analysis

To achieve large storage capacity on magnetic hard disk drives, very high track density is required, causing severe intertrack interference (ITI). Multihead multitrack (MHMT) detection has been proposed to better combat the effects of ITI. Such detection, however, has prohibitive implementation complexity. Reduced-state sequence estimation (RSSE) is a promising technique for significantly reducing the complexity, while retaining good performance. In this paper, several different MHMT models are considered, including symmetric and asymmetric 2H2T systems, and a symmetric 3H3T system. By carefully evaluating the effective distance between two input symbols, we propose optimized set partition trees for each channel model. Different trellis configurations for RSSE are constructed based on the desired performance/complexity tradeoff. Simulation results show that the reduced MHMT detector can achieve near maximum-likelihood (ML) performance with a small fraction of the original number of trellis states. We also use error event analysis to explain the behavior of RSSE. The proposed algorithm could be potentially applied to next generation magnetic recording systems, especially when the ML detector is infeasible due to the high computational complexity.

Generalized error-locating codes with component codes over the same alphabet

We consider generalized error locating (GEL) codes over the same alphabet for both component codes. We propose an algorithm for computing an upper bound on the decoding error probability under known input symbol error rate and code parameters. Is is used to construct an algorithm for selecting code parameters to maximize the code rate for a given construction and given input and output error probabilities. A lower bound on the decoding error probability is given. Examples of plots of decoding error probability versus input symbol error rate are given, and their behavior is explained.

On the Design of a WiFi Direct 802.11ac WLAN under a TGn MIMO Multipath Fading Channel

WiFi Direct (WD) is a state of the art technology for a Device-to-Device (D2D) communication in 802.11 networks. The performance of the WD system can be significantly affected by some key factors such as the type of application, specifications of MAC and PHY layer parameters, and surrounding environment etc. It is, therefore, important to develop a system model that takes these factors into account. In this paper, we focus on investigating the design parameters of the PHY layer that could maximize the efficiency of the WD 802.11 system. For this purpose, a basic theoretical model is formulated for a WD network under a 2x2 Multiple In Multiple Out (MIMO) TGn channel B model. The design level parameters such as input symbol rate and antenna spacing, as well as the effects of the environment, are thoroughly examined in terms of path gain, spectral density, outage probability and Packet Error Rate (PER). Thereafter, a novel adaptive algorithm is proposed to choose optimal parameters in accordance with the Quality of Experience (QoE) for a targeted application. The simulation results show that the proposed method outperforms the standard method thereby achieving an optimal performance in an adaptive manner.

Device-independent tests of quantum channels.

We develop a device-independent framework for testing quantum channels. That is, we falsify a hypothesis about a quantum channel based only on an observed set of input-output correlations. Formally, the problem consists of characterizing the set of input-output correlations compatible with any arbitrary given quantum channel. For binary (i.e. two input symbols, two output symbols) correlations, we show that extremal correlations are always achieved by orthogonal encodings and measurements, irrespective of whether or not the channel preserves commutativity. We further provide a full, closed-form characterization of the sets of binary correlations in the case of: (i) any dihedrally covariant qubit channel (such as any Pauli and amplitude-damping channels) and (ii) any universally-covariant commutativity-preserving channel in an arbitrary dimension (such as any erasure, depolarizing, universal cloning and universal transposition channels).

Online recognition of handwritten music symbols

In this paper, we propose an effective online method to recognize handwritten music symbols. Based on the fact that most music symbols can be regarded as combinations of several basic strokes, the proposed method first classifies all the strokes comprising an input symbol and then recognizes the symbol based on the results of stroke classification. For stroke classification, we propose to use three types of features, which are the size information, the histogram of directional movement angles, and the histogram of undirected movement angles. When combining classified strokes into a music symbol, we utilize their sizes and spatial relation together with their combination. The proposed method is evaluated using two datasets including HOMUS, one of the largest music symbol datasets. As a result, it achieves a significant improvements of about 10% in recognition rates compared to the state-of-the-art method for the datasets. This shows the superiority of the proposed method in online handwritten music symbol recognition.

Reduced-complexity MPA decoder based on multi-level dynamic input thresholds

As a key technology of the fifth generation (5G) wireless communications, sparse code multiple access (SCMA) system has quite high frequency utilization, but its message passing algorithm (MPA) decoder still has high time complexity. By the aid of the proposed multi-level dynamic threshold, the complexity of the MPA decoding can be greatly reduced with little error performance loss. In order to reduce a great deal of insignificant computational amounts of message update, we compare the multi-level symbols probability products with optimized multi-level thresholds step by step before they are used for message update calculation. The dynamic threshold configuration refers to the three factors: the level of input symbol probabilities, signal noise ratio (SNR) and the number of iterations. Especially, in the joint iterative MPA-Turbo decoding procedure, since most encoded bits have good convergence, the input thresholds can avoid more unnecessary computational overhead of message update and reduce the decoding time more significantly. The simulation results show that the proposed multi-level dynamic thresholds considerably reduce the decoding delay in both additive white Gaussian noise (AWGN) channel and frequency selective fading channel.

A Polynomial Formula for Finite-Length BATS Code Performance

Batched sparse (BATS) codes have been proposed for communication through networks with packet loss. BATS codes include a matrix generalization of fountain codes as the outer code and random linear network coding at the intermediate network nodes as the inner code. BATS codes, however, do not possess a universal degree distribution that achieves the optimal rate for any distribution of the transfer matrix ranks, so that fast performance evaluation of finite-length BATS codes is important for optimizing the degree distribution. The state-of-the-art finite-length performance evaluation method has a computational complexity of $\mathcal {O}(K^{2}n^{2}M)$ , where $K$ , $n$ , and $M$ are the number of input symbols, the number of batches, and the batch size, respectively. We propose a polynomial-form formula for finite-length BATS codes performance evaluation with the computational complexity of $\mathcal {O}(K^{2}n\ln n)$ . Numerical results demonstrate that the polynomial-form formula can be significantly faster than the previous methods.

Inactivation Decoding of LT and Raptor Codes: Analysis and Code Design

In this paper we analyze LT and Raptor codes under inactivation decoding. A first order analysis is introduced, which provides the expected number of inactivations for an LT code, as a function of the output distribution, the number of input symbols and the decoding overhead. The analysis is then extended to the calculation of the distribution of the number of inactivations. In both cases, random inactivation is assumed. The developed analytical tools are then exploited to design LT and Raptor codes, enabling a tight control on the decoding complexity vs. failure probability trade-off. The accuracy of the approach is confirmed by numerical simulations.

Algebraic state space approach to model and control combined automata

A new modeling tool, algebraic state space approach to logical dynamic systems, which is developed recently based on the theory of semi-tensor product of matrices (STP), is applied to the automata field. Using the STP, this paper investigates the modeling and controlling problems of combined automata constructed in the ways of parallel, serial and feedback. By representing the states, input and output symbols in vector forms, the transition and output functions are expressed as algebraic equations of the states and inputs. Based on such algebraic descriptions, the control problems of combined automata, including output control and state control, are considered, and two necessary and sufficient conditions are presented for the controllability, by which two algorithms are established to find out all the control strings that make a combined automaton go to a target state or produce a desired output. The results are quite different from existing methods and provide a new angle and means to understand and analyze the dynamics of combined automata.

Functional encryption for cascade automata

We introduce a Functional Encryption (FE) scheme for the class of languages accepted by extended automata. In an extended automaton, n Deterministic Finite Automata (DFA), each with at most q states, are linked in a cascade such that in each transition the i-th DFA performs its own transition and outputs an input symbol for DFA number i+1modn.Our scheme encrypts a message m with a word w and the resulting ciphertext can be decrypted only by a key that is associated with an automaton that accepts w. Our scheme has key size O(nq2), while the ciphertext length and encryption and decryption times are O(n|w|). Our scheme is significantly more efficient than previous proposals, e.g. Waters (Crypto'12), for interesting applications of FE for regular languages such as accepting a word in a regular language only if it is accompanied by a standard public key signature on that word.

Nondeterministic fuzzy automata with membership values in complete residuated lattices

Automata theory based on complete residuated lattice-valued logic has been initiated by Qiu, and further systematically established by Qiu et al. In this paper, we propose the notion of nondeterministic fuzzy automata with membership values in a complete residuated lattice L, called lattice-valued nondeterministic fuzzy automata (L-NFAs). In our setting, a state of an L-NFA may have more than one transition labeled by the same input symbol, which reflects nondeterminism. To compare the behaviors of L-NFAs, we introduce two language equivalence relations which have different discriminating power. Furthermore, we extend the two relations to the complete residuated lattice-valued setting and investigate their properties such as robustness and compositionality. The theory developed here is applicable to the quantitative modeling and verification of fuzzy systems.

Lossy Compression for Compute-and-Forward in Limited Backhaul Uplink Multicell Processing

We study the transmission over a cloud radio access network in which multiple base stations, acting as relay nodes, are connected to a central processor (CP) via error-free rate-limited backhaul links. We propose two lattice-based coding schemes. In the first scheme, each relay node decodes linear combinations of the users’ messages in the spirit of compute-and-forward (CoF), but departs from it essentially in that the decoded equations are remapped to equations on the users’ input symbols, sent compressed in a lossy manner to the CP, and are not required to be linearly independent. The compression accounts for the correlation between equations at the relay nodes through Wyner–Ziv coding. Also, by opposition to the standard CoF, an appropriate multi-user decoder is utilized to recover the sent messages. The second scheme generalizes the first one by also allowing, at each relay node, a joint compression of the decoded equation on the users’ input symbols and the received signal. Both schemes apply in general, but are especially suited for situations in which there are more users that relays. We show that both schemes can outperform standard CoF and successive Wyner–Ziv schemes in certain regimes, and illustrate the gains through some numerical examples.

Spatio-Temporal Waveform Design for Multiuser Massive MIMO Downlink With 1-bit Receivers

Internet-of-Things (IoT) refers to a high-density network of low-cost low-bitrate terminals and sensors where also low energy consumption is one central feature. As the power-budget of classical receiver chains is dominated by the high-resolution analog-to-digital converters (ADCs), there is a growing interest towards deploying receiver architectures with reduced-bit or even 1-bit ADCs. In this paper, we study waveform design, optimization and detection aspects of multi-user massive MIMO downlink where user terminals adopt very simple 1-bit ADCs with oversampling. In order to achieve spectral efficiency higher than 1 bit/s/Hz per real-dimension, we propose a two-stage precoding, namely a novel quantization precoder followed by maximum-ratio transmission (MRT) or zero-forcing (ZF) type spatial channel precoder which jointly form the multi-user-multiantenna transmit waveform. The quantization precoder outputs are optimized, under appropriate transmitter and receiver filter bandwidth constraints, to provide controlled inter-symbol-interference (ISI) enabling the input symbols to be uniquely detected from 1-bit quantized observations with a low-complexity symbol detector in the absence of noise. An additional optimization constraint is also imposed in the quantization precoder design to increase the robustness against noise and residual inter-user-interference (IUI). The purpose of the spatial channel precoder, in turn, is to suppress the IUI and provide high beamforming gains such that good symbol-error rates (SERs) can be achieved in the presence of noise and interference. Extensive numerical evaluations illustrate that the proposed spatio-temporal precoder based multiantenna waveform design can facilitate good multi-user link performance, despite the extremely simple 1-bit ADCs in the receivers, hence being one possible enabling technology for the future low-complexity IoT networks.

Методи синтезу легкотестованих цифрових пристроїв і систем

The analysis of previously published works in the field of experiments with automates shows that the methods of constructing of diagnostic experiments with automates, having a distinctive sequence are studied in majority of cases. The known experiments with initial automates are based on the assumption of correctness of automate setting to the initial state. Therefore, methods of construction of complete checking sequences using the distinctive, characteristic and synchronizing sequences, which also verify the correctness of the initial setting are proposed in this article for the class of initial automates. The purpose of creating of method of transformation of automate diagram of discrete devices by introduction of additional input symbol and automate state coding, which provides for this symbol transition function of automate diagram of shift register is set in the article. As well creating of method of finding of Hamiltonian cycles in transition graph shift register with given number of states, which create counter binary sequences that formed by shift register with nonlinear feedback is the purpose of the research.

A low complexity high efficiency hybrid multiplicative-additive crest factor reduction for OFDM systems

In this paper, we propose a novel technique to reduce the crest factor (CF) in orthogonal frequency division multiplexing systems. It consists of two inverse fast Fourier transform (IFFT) blocks, the input symbols of the first IFFT are the mapped symbols, whereas the input symbols of the second IFFT are the summations of the absolute value of the real part of the outer signal constellation points and zero symbols. First, the output of the two IFFT blocks is partitioned into four subblocks, which are subsequently used to rearrange the subblocks with padding zeros in a specific manner. Then, a new optimization scheme is introduced, in which only a single two-phase sequence and four iterations need to be applied. Numerical analysis shows that the proposed hybrid technique achieves better CF reduction performance with significantly lower complexity and better bit error rate performance than the existing scrambling (multiplicative) and additive CF techniques.

Massive But Few Active MIMO

In this paper, an emerging wireless communication concept, which is termed as spatial modulation (SM) for large-scale multiple-input–multiple-output (MIMO), is considered. The results show that from the information-theoretic perspective, SM achieves capacity comparable to the open-loop MIMO capacity, although a subset of transmit antennas is activated in every channel use because both the channel coefficients and the input symbols carry information in SM. As a result, SM compensates the loss of information capacity due to a subset of antennas being activated by modulating information in the antenna index; therefore, the total information rate remains high. In particular, an upper bound for the capacity of SM is derived in closed form, and it is shown analytically that this upper bound is almost certainly achievable in the massive MIMO regime. Moreover, it is shown that the upper bound is achievable with no channel state information at the transmitter (CSIT) but with channel distribution information (CDI) at the transmitter (CDIT). The optimum transmission strategy should adapt the channel input distribution to fading using CDI such as the $K$ factor in Rician fading or the shape parameter $m$ in Nakagami- $m$ fading.

Novel joint encoding/decoding algorithms of fountain codes for underwater acoustic communication

Fountain codes are considered to be a promising coding technique in underwater acoustic communication (UAC) which is challenged with the unique propagation features of the underwater acoustic channel and the harsh marine environment. And Luby transform (LT) codes are the first codes fully realizing the digital fountain concept. However, in conventional LT encoding/decoding algorithms, due to the imperfect coverage (IC) of input symbols and short cycles in the generator matrix, stopping sets would occur and terminate the decoding. Thus, the recovery probability is reduced, high coding overhead is required and decoding delay is increased. These issues would be disadvantages while applying LT codes in underwater acoustic communication. Aimed at solving those issues, novel encoding/decoding algorithms are proposed. First, a doping and non-uniform selecting (DNS) encoding algorithm is proposed to solve the IC and the generation of short cycles problems. And this can reduce the probability of stopping sets occur during decoding. Second, a hybrid on the fly Gaussian elimination and belief propagation (OFG-BP) decoding algorithm is designed to reduce the decoding delay and efficiently utilize the information of stopping sets. Comparisons via Monte Carlo simulation confirm that the proposed schemes could achieve better overall decoding performances in comparison with conventional schemes.