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A precoding technique based on polar codes and probabilistic shaping is introduced for dirty paper coding. Two variants of the precoding use multi-level shaping and sign-bit shaping in one dimension. The decoder uses multi-stage successive-cancellation list decoding with list-passing across the bit levels. The approach achieves approximately the same frame error rates as polar codes with multi-level shaping over standard additive white Gaussian noise channels at a block length of 256 symbols and with different amplitude shift keying (ASK) constellations.

Dirty paper coding is an interference pre-cancellation method for known interference at the transmitter and serves as a basic building block in the digital watermarking system. In this letter, we investigate the dirty paper model in the simplest digital communication system, where both the codeword and the interference are binary. For watermark embedment, we derive the relevant coding, the constant coding, and the symmetric relevant coding when the encoder focuses on the binary codeword and interference. The channel capacity is analyzed and the optimal parameter is discussed in the case.

Traction transformer is vital equipment in high-speed railway. Insulation status decides its safety and reliability, and the frequency-domain dielectric spectrum (FDS) test is one of the most effective methods reflecting the changing of insulation status. For field applications, the following problems should be addressed: 1) how to obtain the result of paper insulation from the combined result of the oil–paper insulation system and 2) how to discriminate the defects of insulation paper between aging and damp. In this article, the first problem was transferred to a nonlinear equation set, and a cuckoo search algorithm optimized by the differential evolution algorithm and the quadratic interpolation (QI) method (CS-DQ algorithm) was proposed to solve it. Then, the insulation states were discriminated by establishing a multiclass least-squares support vector machine (LS-SVM) model, in which the CS-DQ algorithm was also used. Finally, a diagnostic approach for the insulation paper in the traction transformer was proposed. The results in the laboratory show that the pure result of insulation paper can be obtained, and the insulation states can be discriminated effectively by using the proposed approach. Meanwhile, the proposed CS-DQ algorithm has a better performance than the conventional CS algorithm. The results of field testing also verify the proposed approach.

Dirty paper coding (DPC) offers an information-theoretic result for pre-cancellation of known interference at the transmitter. In this letter, we propose practical DPC schemes that use only one error correction code. Our designs focus on practical use from the viewpoint of complexity. For fair comparison with previous schemes, we compute the complexity of proposed schemes by the number of operations used. Simulation results show that compared to previous DPC schemes, the proposed schemes require lower transmission power to maintain the bit error rate to be within $10^{-5}$ .

We investigate the performance of a watermarking system in which the encoder is forced to use a binning strategy based on antipodal binary-valued sequences. The use of antipodal binary random binning has several advantages, including the possibility of relying on simple and effective binary code constructions and the ease with which this kind of schemes can cope with amplitude scaling. By relying on a novel binning strategy, we derive a lower bound of the Gelfand-Pinsker capacity of the watermark channel when the encoder is forced to use an antipodal binary auxiliary random variable, showing that for low to moderate bit-rates, the bound coincides with Costa’s capacity. We exploit the properties of the new binning strategy, to develop a practical watermarking system and show that the new scheme outperforms previous constructions, exhibiting very good performance also in the presence of gain attack. Preliminary results on audio signals show that the new scheme retains its good performance also when used for the watermarking of real multimedia data.

This paper investigates the user selection problem of successive zero-forcing preceded multiuser multiple-input multiple- output (MU-MIMO) downlink systems, in which the base station and mobile receivers are equipped with multiple antennas. Assuming full knowledge of the channel state information at the transmitter, dirty paper coding (DPC) is an optimal preceding strategy, but practical implementation is difficult because of its excessive complexity. As a suboptimal DPC solution, successive zero-forcing DPC (SZF-DPC) was recently proposed; it employs partial interference cancellation at the transmitter with dirty paper encoding. Because of a dimensionality constraint, the base station may select a sub- set of users to serve in order to maximize the total throughput. The exhaustive search algorithm is optimal; however, its computational complexity is prohibitive. In this paper, we develop two low-complexity user scheduling algorithms to maximize the sum rate capacity of MU-MIMO systems with SZF-DPC. Both algorithms add one user at a time. The first algorithm selects the user with the maximum product of the maximum column norm and maximum eigenvalue. The second algorithm selects the user with the maximum product of the minimum column norm and minimum eigenvalue. Simulation results demonstrate that the second algorithm achieves a performance similar to that of a previously proposed capacity-based selection algorithm at a high signal-to-noise (SNR), and the first algorithm achieves performance very similar to that of a capacity-based algorithm at a low SNR, but both do so with much lower complexity.

This paper investigates how much the rate region of the two-user Gaussian interference channel can be enlarged by allowing the two source nodes to cooperate. Two cooperative transmission schemes are proposed, based on dirty-paper coding and the assumption that the radio bandwidth is partitioned into two parts, and each part is utilized by one source node. The achievable rate regions and the outage performance of these two schemes are compared with the simplified Han-Kobayashi scheme, which is an efficient coding scheme for the interference channel. Simulation results show that in some channel realizations, the rate region of the Han-Kobayashi scheme is a subset of the rate regions of our two proposed cooperative transmission schemes. Furthermore, a significant gain in outage performance can be obtained, as the cooperative schemes have twice the diversity order of the simplified Han-Kobayashi scheme. While both cooperative schemes are able to yield large diversity gain, one of them can be implemented by simple decoder. Besides, it has an efficient algorithm for maximizing its weighted sum rate, and can be extended easily to the multi-channel case.

Recently, the vector dirty paper coding (DPC) achievable rate region has been shown to be the capacity region of a multiple-input multiple-output Gaussian broadcast channel (MIMO GBC). With DPC, the multiuser interference noncausally known at the transmitter can be completely removed. In this paper, we present a vector DPC structure for MIMO GBC. It is a generalization of the single antenna superposition dirty paper coding for the scalar Gaussian dirty paper problem proposed by Bennatan et al. In a theoretical random code setting, this construction is shown to be able to achieve the promised rate performance of the MIMO GBC. We also implement it with existing vector quantizer and capacity-achieving channel coding. Combined with iterative decoding, a design example validates the effectiveness of our methods.

A generalization of the problem of writing on dirty paper is considered in which one transmitter sends a common message to multiple receivers. Each receiver experiences on its link an additive interference (in addition to the additive noise), which is known noncausally to the transmitter but not to any of the receivers. Applications range from wireless multi-antenna multicasting to robust dirty paper coding. We develop results for memoryless channels in Gaussian and binary special cases. In most cases, we observe that the availability of side information at the transmitter increases capacity relative to systems without such side information, and that the lack of side information at the receivers decreases capacity relative to systems with such side information. For the noiseless binary case, we establish the capacity when there are two receivers. When there are many receivers, we show that the transmitter side information provides a vanishingly small benefit. When the interference is large and independent across the users, we show that time sharing is optimal. For the Gaussian case we present a coding scheme and establish its optimality in the high signal-to-interference-plus-noise limit when there are two receivers. When the interference is large and independent across users we show that time-sharing is again optimal. Connections to the problem of robust dirty paper coding are also discussed., Comment: To appear, IT. Trans. on Information Theory. Modified to incorporate reviewer's comments. Also includes some additional results for the Gaussian case

An equation in the Appendix of a previous paper (IEEE Trans. Veh. Technol ., vol. 58, no. 7, pp. 3247-3262, Sep. 2009) contains an error, which propagates to a few more equations in the Appendix. This error does not affect the main body of the text or results presented in that paper, apart from a change to one equation. In this correspondence, we correct the error and provide a revised Appendix.

The objective of this paper was to provide an automatic engine to classify and locate information using natural language. The proposal integrates a set of two algorithms to extract information from different repositories using their own open APIs and creates a knowledge database using a natural language approach using a Bayesian algorithm to classify and a second algorithm to clean the paper. Putting said techniques together derived in a strong alternative which reach common gaps in classification and location of information including avoid the use of the whole paper to get information and not only the information introduced at the moment of upload the paper in the digital library. The proposal was oriented to classify and locate research papers in order to better describe this contribution, however, findings could be applicable to a vast range of scenarios. An adaptation of the popular methodology Crisp-DM was used to evaluate the performance of the algorithm obtaining good results in classifying, searching, and feeding the knowledge base.

This special section of the IEEE/ACM Transactions on Computational Biology and Bioinformatics contains extended versions of the best papers presented at the First International Conference on Algorithms for Computational Biology (AlCoB 2014). Out of 39 submissions to the conference, only four papers representing the current state-of-the-art in their respective domains were accepted to this special section.

A paper sliceform or lattice-style pop-up is a form of papercraft that uses two sets of parallel paper patches slotted together to make a foldable structure. The structure can be folded flat, as well as fully opened (popped-up) to make the two sets of patches orthogonal to each other. Automatic design of paper sliceforms is still not supported by existing computational models and remains a challenge. We propose novel geometric formulations of valid paper sliceform designs that consider the stability, flat-foldability and physical realizability of the designs. Based on a set of sufficient construction conditions, we also present an automatic algorithm for generating valid sliceform designs that closely depict the given 3D solid models. By approximating the input models using a set of generalized cylinders, our method significantly reduces the search space for stable and flat-foldable sliceforms. To ensure the physical realizability of the designs, the algorithm automatically generates slots or slits on the patches such that no two cycles embedded in two different patches are interlocking each other. This guarantees local pairwise assembility between patches, which is empirically shown to lead to global assembility. Our method has been demonstrated on a number of example models, and the output designs have been successfully made into real paper sliceforms.

In this paper, we are concerned with the design and analysis of joint source-channel coding schemes for block fading channels with relay-assisted distributed spatial diversity. Assuming a progressive image coder with a constraint on the transmission bandwidth, we formulate a joint source-channel rate allocation scheme that maximizes the expected source throughput. Specifically, using Gaussian as well as BPSK inputs on flat Rayleigh fading channels, we lower bound the average packet error rate by the corresponding mutual information outage probability, and derive the average throughput expression as a function of channel code rates as well as channel SNR for both a frequency-division multiplexing-based baseline system without relaying, and a half-duplex relay system with a decode-and- forward protocol. At high signal-to-noise ratio (SNR), for the systems considered in this paper, we show that our rate optimization problem is a convex function of the channel code rates, and we show that a known recursive algorithm can be used to predict the performance of both systems.

We consider a fading version of the dirty-paper problem, as proposed by Grover and Sahai. In this formulation, the various signals involved are complex-valued, and the interference (known only to the transmitter) is multiplied by a random complex-valued coefficient, whose phase is known only to the receiver. We focus on a compound channel formulation, and seek to maximize the worst-case performance. We present an achievable strategy modeled on the lattice-based approach of Erez, Shamai and Zamir and propose heuristic methods to optimize its parameters. We also derive an upper bound on the maximum achievable transmission rates. Our bounds are shown to be tight in some settings, yielding a complete characterization of capacity. We also provide simulation results, indicating the practical effectiveness of our approaches.

In this paper, the Two-Way Channel (TWC) with noncausal Channel State Information (CSI) is investigated. For the discrete memoryless channel an achievable rate region is derived. Then by extending the result to the Gaussian TWC with additive interference noise, it is shown that the capacity region of the latter channel is the same as the capacity when there is no interference, i.e., a two-way version of Costa's writing on dirty paper problem is established.

We present a new class of irregular low-density parity-check (LDPC) codes for moderate block lengths (up to a few thousand bits) that are well-suited for rate-compatible puncturing. The proposed codes show good performance under puncturing over a wide range of rates and are suitable for usage in incremental redundancy hybrid-automatic repeat request (ARQ) systems. In addition, these codes are linear-time encodable with simple shift-register circuits. For a block length of 1200 bits the codes outperform optimized irregular LDPC codes and extended irregular repeat-accumulate (eIRA) codes for all puncturing rates 0.6~0.9 (base code performance is almost the same) and are particularly good at high puncturing rates where good puncturing performance has been previously difficult to achieve.

Nowadays volume images are frequently used in many applications. Volume images of paper can be analyzed to increase the understanding of the complexity of the fiber network in paper and its effect on the optical and mechanical properties of paper. We show how curve and surface representations of the fiber, the fiber wall, and the fiber lumen can be computed using distance transform based algorithms. These representations are used for easy computation of wall thickness, degree of collapse, fiber length, slenderness ratio, fiber curl, and torsional resistance for the fibers. Free-fiber segments are identified, again using a distance transform based algorithm. Finally, we show tools for qualitative visual inspection of the fibers. The methods are evaluated and illustrated using both sets of synthetic and of real data. In addition to the analysis of the fiber network in paper, they can be used in many other applications where shape analysis of elongated objects is to be performed.

The authors of the above titled paper (ibid., vol. 9, no. 8, pp. 2664-2675, Aug. 2010), have become aware that several of the equations in that paper were presented with errors. In this brief, we provide correct equations. The implementation of the model in the original paper was in accordance with the correct equations, so the implementation results presented in Section V. Performance Analysis are still valid.

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users’ channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

A memoryless bivariate Gaussian source is transmitted to a pair of receivers over an average-power limited bandwidth-matched Gaussian broadcast channel. Based on their observations, Receiver 1 reconstructs the first source component while Receiver 2 reconstructs the second source component both seeking to minimize the expected squared-error distortions. In addition to the source transmission digital information at a specified rate should be conveyed reliably to Receiver 1–the “stronger” receiver. Given the message rate we characterize the achievable distortions region. Specifically, there is an ${\sf SNR}$ -threshold below which Dirty Paper coding of the digital information against a linear combination of the source components is optimal. The threshold is a function of the digital information rate, the source correlation and the distortion at the “stronger” receiver. Above this threshold a Dirty Paper coding extension of the Tian-Diggavi-Shamai hybrid scheme is shown to be optimal.

Using an operator approach this letter shows that Metzger's Algorithm for pdf computation (see ibid., vol. 35, pp. 396-402, Apr 1987) has an error in formula (15), which defines the procedure of generating the successive series of pdf approximation. A correction is given.

A six-port reflectometer calibration method using five standards is developed, and gives explicit unambiguous expressions for the calibration constants. The standards are restricted only in that their impedances may neither all have the same magnitude nor all have the same argument. The method permits exact (nonidealized) descriptions of the standards to be used, and the redundancy inherent in the analysis is utilized to reduce the problems associated with measurement noise.

A technique for the optimization and sensitivity analysis of broad classes of electrical networks is illustrated. The method utilizes the exact algebraic partial derivatives of functions with respect to any desired independent variable. This completely automated technique has the obvious advantage that the derivatives of any circuit response function with respect to any desired component parameter may be obtained with no additional analytical effort on the part of the designer. Several examples are given to illustrate the procedure.

Vector perturbation (VP) is a nonlinear precoding technique that achieves near-capacity performance in multiuser multiple-input multiple-output systems at the expense of large complexity due to the search for the optimum perturbation vector. In this paper, we present the hybrid Tomlinson–Harashima VP (TH-VP) algorithm, a novel zero-forcing precoding scheme, which combines TH precoding to remove interuser interference, and VP precoding to equalize each user's multiple spatial streams. We show that the two nonlinear techniques can be integrated in a single optimization and that the proposed algorithm has lower computational requirements than any other. The performance of TH-VP is analyzed and simulation results show that TH-VP outperforms conventional zero-forcing VP and approaches the performance of dirty paper coding.

A hybrid communication network with a common analog signal and an independent digital data stream as input to each node in a multiple access network is considered. The receiver/base-station has to estimate the analog signal with a given fidelity, and decode the digital streams with a low error probability. Treating the analog signal as a common state process, we set up a joint state estimation and communication problem in a Gaussian multiple access channel (MAC) with additive state. The transmitters have non-causal knowledge of the state process, and need to communicate independent data streams in addition to facilitating state estimation at the receiver. We first provide a complete characterization of the optimal trade-off between mean squared error distortion performance in estimating the state and the data rates for the message streams from two transmitting nodes. This is then generalized to an N-sender MAC. To this end, we show a natural connection between the state-dependent MAC model and a hybrid multi-sensor network in which a common source phenomenon is observed at N transmitting nodes. Each node encodes the source observations as well as an independent message stream over a Gaussian MAC without any state process. The receiver is interested estimating the source and all the messages. Again the distortion-rate performance is characterized., 12 pages, Journal submission

The PDF version of the manuscript is added. The abstract/manuscript is accepted to ICOPS 2020. The full paper version is added after the conference.

We study communication systems over state-dependent channels in the presence of a malicious state-aware jamming adversary. The channel has a memoryless state with an underlying distribution. The adversary introduces a jamming signal into the channel. The state sequence is known non-causally to both the encoder and the adversary. Taking an Arbitrarily Varying Channel (AVC) approach, we consider two setups, namely, the discrete memoryless Gel'fand-Pinsker (GP) AVC and the additive white Gaussian Dirty Paper (DP) AVC. We determine the randomized coding capacity of both the AVCs under a maximum probability of error criterion. Similar to other randomized coding setups, we show that the capacity is the same even under the average probability of error criterion. Even with non-causal knowledge of the state, we prove that the state-aware adversary cannot affect the rate any worse than when it employs a memoryless strategy which depends only on the instantaneous state. Thus, the AVC capacity characterization is given in terms of the capacity of the worst memoryless channels with state, induced by the adversary employing such memoryless jamming strategies. For the DP-AVC, it is further shown that among memoryless jamming strategies, none impact the communication more than a memoryless Gaussian jamming strategy which completely disregards the knowledge of the state. Thus, the capacity of the DP-AVC equals that of a standard AWGN channel with two independent sources of additive white Gaussian noise, i.e., the channel noise and the jamming noise., Comment: 24 pages, 3 figures

Formulas are presented for the direct calculation of the scattering parameters of a linear two-port, when it is measured by an imperfect network analyzer. Depending on the hardware configuration of the test set, the measurement system is represented by one of two flowgraph models. In both models presented, leakage paths are included. The error parameters, i.e., the scattering parameters of the measuring system, are six respective ten complex numbers for each frequency of interest. A calibration procedure, where measurements are made on standards, will determine the error parameters. One of many possible calibration procedures is briefly described. By using explicit formulas instead of iterative methods, the computing time for the correction of the scattering parameters of the unknown two-port is significantly reduced. The addition of leakage paths will only have a minor effect on computational complexity while measurement accuracy will increase.

The objective of this paper is to study the impact of advanced physical layer techniques on the maximum achievable throughput of wireless multihop mesh networks. We formulate a cross-layer optimization framework for the routing and scheduling problem jointly with the following physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding and their combinations. In the case when each node is enabled with superposition coding, we need to formulate a power allocation subproblem for the optimal power partition of the superimposed signals. We solve these joint problems exactly to compute the maximum achievable throughput in realistic size networks. This allows us to quantify the performance gains obtained by using these techniques (and their combinations). Specifically, we find that the use of dirty-paper coding (only at the gateway) is not justified in networks with mixed uplink and downlink flows. On the other hand, the combination of superposition coding with successive interference outperforms significantly other techniques across all transmission power range for both uplink and downlink flows. We also provide a number of interesting practical insights on throughput improvement by comparing different combinations of these techniques.

The opportunistic beamforming system (OBS) is currently receiving much attention in the field of downlink beamforming due to its simple random beamforming, low feedback complexity, and same throughput scaling obtained with perfect channel-state information using dirty paper coding at the transmitter. In this paper, we focus on its closed-form throughput evaluation over Rayleigh fading channels, based on the asymptotic theory of extreme order statistics. First, the throughput of a single-beam OBS is investigated, and an analytical solution tighter than the previously reported one is derived. Then, the asymptotic throughput bounds on a multibeam OBS are presented, and also, our analytical expression is shown to be very tight with the simulation results even with fewer users. After that, we argue that the reported conclusion that the single-beam OBS is much preferable to the multibeam OBS in the high-signal-to-noise-ratio (SNR) regime is inaccurate, but that, instead, it is satisfied only when the number of users is very small, due to its limited multiuser diversity gain. Finally, we show that four transmit beams is the most preferable in the multibeam OBS with a large number of users and moderate SNR, which arrives at the tradeoff between increasing spatial multiplexing gain and disappearing multiuser diversity gain.

In this paper, a novel relaying strategy that uses multiple-input multiple-output (MIMO) fixed relays with linear processing to support multiuser transmission in cellular networks is proposed. The fixed relay processes the received signal with linear operations and forwards the processed signal to multiple users creating a multiuser MIMO relay. This paper proposes upper and lower bounds on the achievable sum rate for this architecture assuming zero-forcing dirty paper coding at the base station, neglecting the direct links from the base station to the users, and with certain structure in the relay. These bounds are used to motivate an implementable multiuser precoding strategy that combines Tomlinson-Harashima precoding at the base station and linear signal processing at the relay, adaptive stream selection, and QAM modulation. Reduced complexity algorithms based on the sum rate lower bounds are used to select a subset of users. We compare the sum rates achieved by the proposed system architecture and algorithms with the sum rate upper bound and the sum rate achieved by the decode-and-forward relaying.

In this paper, we consider two different models of partial channel state information at the base station transmitter (CSIT) for multiple antenna broadcast channels: 1) the shape feedback model where the normalized channel vector of each user is available at the base station and 2) the limited feedback model where each user quantizes its channel vector according to a rotated codebook that is optimal in the sense of mean squared error and feeds back the codeword index. This paper is focused on characterizing the sum rate performance of both zero-forcing dirty paper coding (ZFDPC) systems and channel inversion (CI) systems under the given two partial CSIT models. Intuitively speaking, a system with shape feedback loses the sum rate gain of adaptive power allocation. However, shape feedback still provides enough channel knowledge for ZFDPC and CI to approach their own optimal throughput in the high signal-to-noise ratio (SNR) regime. As for limited feedback, we derive sum rate bounds for both signaling schemes and link their throughput performance to some basic properties of the quantization codebook. Interestingly, we find that limited feedback employing a fixed codebook leads to a sum rate ceiling for both schemes for asymptotically high SNR.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. Our scheme is based on joint optimization of a modulator and demodulator, subject to a constraint on the average transmit power. The demodulator picks the information symbol (as a function of the received symbol) that minimizes the average error probability. We emphasize that our focus is on transmission in a single (complex) dimension, and hence the proposed technique is a “modulation” rather than a “coding” scheme. We illustrate that the new scheme outperforms Tomlinson–Harashima precoding, which is a classical but suboptimal solution to the one-dimensional known-interference precoding problem. In our simulations, the new approach is able to perform close to the no-interference bound. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Mikael Skoglund and Erik G. Larsson, Optimal Modulation for Known Interference, 2008, IEEE Transactions on Communications, (56), 11, 1892-1899.http://dx.doi.org/10.1109/TCOMM.2008.060382

Recently, the capacity region of a multiple-input multiple-output (MIMO) Gaussian broadcast channel, with Gaussian codebooks and known-interference cancellation through dirty paper coding, was shown to equal the union of the capacity regions of a collection of MIMO multiple-access channels. We use this duality result to evaluate the system capacity achievable in a cellular wireless network with multiple antennas at the base station and multiple antennas at each terminal. Some fundamental properties of the rate region are exhibited and algorithms for determining the optimal weighted rate sum and the optimal covariance matrices for achieving a given rate vector on the boundary of the rate region are presented. These algorithms are then used in a simulation study to determine potential capacity enhancements to a cellular system through known-interference cancellation. We study both the circuit data scenario in which each user requires a constant data rate in every frame and the packet data scenario in which users can be assigned a variable rate in each frame so as to maximize the long-term average throughput. In the case of circuit data, the outage probability as a function of the number of active users served at a given rate is determined through simulations. For the packet data case, long-term average throughputs that can be achieved using the proportionally fair scheduling algorithm are determined. We generalize the zero-forcing beamforming technique to the multiple receive antennas case and use this as the baseline for the packet data throughput evaluation.

It has been shown recently that dirty paper coding (DPC) achieves optimum sum-rate capacity in a multi-antenna broadcast channel with full channel state (CSI) information at the transmitter. With only partial feedback, random beamforming (RBF) is able to match the sumrate of DPC for large number of users. However, in the presence of spatial correlation, RBF incurs an SNR hit as compared to DPC. In this letter, we explore precoding techniques to reduce the effect of correlation on RBF. We thus derive the optimum precoding matrix that minimizes the rate gap between DPC and RBF. Given the numerical complexity involved in calculating the optimum precoder, we derive approximate precoding matrices that are simple to calculate and close in performance to the optimum precoder.

The problem of broadcasting a parallel Gaussian source over an additive white Gaussian noise broadcast channel under the mean-squared error distortion criterion is studied. A hybrid digital-analog coding strategy which combines source coding with side information, channel coding with side information, layered source coding, and superposition broadcast channel coding is presented. When specialized to the open problem of broadcasting a white Gaussian source over an additive white Gaussian noise broadcast channel with bandwidth mismatch which has been the subject of several previous investigations, this coding scheme strictly improves on the state-of-the-art., 24 pages, 9 figures

A two-user interference channel is considered where each transmitter has access to a part of the information intended to the other destination. A primary objective is to maximize the information rates, by exploring the cooperation between the transmitters for interference mitigation, based on the partial side information. It is clear that full cooperation between the transmitters is not possible since each transmitter has only a part of the side information. With this insight, several “layered coding” schemes, consisting of binning and superposition at different stages, are developed. These schemes are are carefully built on coding strategies for the classical interference channel and node cooperation mechanisms. In particular, two layered coding schemes, which are based on a combination of MIMO broadcast coding and the Han-Kobayashi (HK) coding, are thoroughly studied : The first one, namely layered coding with binning, makes heavy use of the Gelfand-Pinsker binning and the HK coding and the second one, namely layered superposition coding, involves superposition coding over different tiers. Rate regions corresponding to the proposed schemes are derived. Then the application of these coding schemes are illustrated for the Gaussian case and numerical results corroborate that the proposed layered coding schemes yield substantial gains at high SNR.

This correspondence considers communication over two Gaussian channels with additive state interference and noiseless output feedback-the multiple access channel (MAC) and the augmented broadcast channel (ABC) (a special class of the physically degraded broadcast channel). The state interference is assumed to be available noncausally at the transmitters. Deterministic coding schemes for these two channels were suggested by Ozarow when there is no state present. Recently, Merhav and Weissman derived an optimal feedback coding scheme for the single-user Gaussian channel with additive state interference. Combining ideas from these two schemes, we extend Ozarow's coding algorithms to the state-dependent case, showing that the capacity regions of the MAC and the ABC are not affected, even though not all parts of the system have knowledge of the state. The proposed schemes attain a better error performance and reduce the coding complexity with respect to the standard random binning coding technique.

A new method for diakoptics, the piecewise method for large-scale networks, is presented. While the subject of diakoptics is well known, it treats only networks described by either impedance or admittance matrix. Using the sparse-matrix approach, the new method treats networks whose constitutive relationships are not restricted to impedances or admittances. Finally, the previously known techniques for diakoptics are merely special cases of the method described here, thus this short paper provides a broad view on this subject.

Probabilistic gradient descent bit-flipping (PGDBF) is a hard-decision decoder for low-density parity-check (LDPC) codes, which offers a significant improvement in error correction, approaching the performance of soft-information decoders on the binary symmetric channel. However, this outstanding performance is known to come with an augmentation of the decoder complexity, compared to the non-probabilistic gradient descent bit flipping (GDBF), becoming a drawback of this decoder. This paper presents a new approach to implementing PGDBF decoding for quasi-cyclic LDPC (QC-LDPC) codes, based on the so-called variable-node-shift architecture (VNSA). In VNSA-based PGDBF implementations, the regularity of QC-LDPC connection networks is used to cyclically shift the memory of the decoder, leading to the fact that, a variable node (VN) is processed by different computing units during the decoding process. With this modification, the probabilistic effects in VN operations can be produced by implementing different types of processing units, without requirement of a probabilistic signal generator. The VNSA is shown to further improve the decoding performance of the PGDBF, with respect to other hardware implementations reported in the literature, while reducing the complexity below that of the GDBF. The efficiency of the VNSA is proven by ASIC synthesis results and by decoding simulations.

This paper presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multi-rail power converters. The proposed approach uses an adaptive update rate as opposed to the fixed update rate used in conventional adaptive filters. Also, the step size/forgetting factors vary at different iteration stages. As a result, a reduced computational burden and faster model update can be achieved. Besides, recursive algorithms, such as Recursive Least Square (RLS), Fast Affine Projection (FAP) and Kalman Filter (KF), contain two important updates per iteration cycle; Covariance Matrix Approximation (CMA) update and Gradient Vector (GV) update. Usually, the CMA update requires the greater computational effort than the GV update. Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the CMA, calculated from the last iteration cycle for the current update can result in computational cost savings for real-time system identification. In this paper, both iteration rate adjustment and CMA re-cycling are combined and applied to simultaneously identify the power converter models in a three-rail power conversion architecture.

This letter contains a graph paper summarizing many properties of Fourier transforms and fast Fourier transform (FFT) devices. It shows the interrelationships among data block length, frequency resolution sampling rate, FFT stages, and other related variables. The letter shows the limiting factors in a pipeline FFT and allows its performance to be effectively compared with a random access oriented FFT.

Truncation is one of the most commonly used approaches for circuit-level approximate computing. This paper proposes a scheme for error compensation of arithmetic circuits in which a so-called padding is utilized to compensate at the output for the truncated bits of the input operands. Compensation relies on adjusting the output results of an arithmetic circuit; the padding takes a value determined by utilizing statistical information based on profiling an arithmetic circuit to reduce the average signed difference between the inexact and exact values and so the mean square error. An extensive analysis and simulation-based evaluation of error metrics are performed on signed truncated adders, multipliers and dividers; an excellent agreement is found. Additional design metrics such as power consumption and circuit complexity are also assessed. Different applications of approximate arithmetic circuits with the proposed output error compensation scheme are presented. Matrix multiplication and image processing (changing detection) are investigated to show the effectiveness of the scheme proposed in this paper.

Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

Due to the unavoidable influence of sparse and Gaussian noise during the process of data acquisition, the quality of hyperspectral images (HSIs) is degraded and their applications are greatly limited. It is therefore necessary to restore clean HSIs. In the traditional methods, low-rank and sparse matrix decomposition methods are usually applied to restore the pure data matrix from the observed data matrix. However, due to the fact that the optimization of the l0-norm for the sparse modeling is a non-convex and NP-hard problem, convex relaxation and regularization parameters are usually introduced. However, convex relaxation often leads to inaccurate sparse modeling results, and the sensitive regularization parameters can lead to unstable results. Thus, in this paper, to address these issues, an accurate multi-objective low-rank and sparse denoising framework is proposed for HSIs to achieve accurate modeling. The l0-norm is directly modeled as the sparse noise and is optimized by an evolutionary algorithm, and the denoising problem is converted into a multi-objective optimization problem through simultaneously optimizing the low-rank term, the sparse term, and the data fidelity term, without sensitive regularization parameters. However, since the low-rank clean image and sparse noise of the HSI are encoded into a solution, the length of the solution is too long to be optimized. In this paper, a subfitness strategy is constructed to achieve effective optimization through comparing the objective function values corresponding to each band for each solution. The experiments undertaken with simulated images in 11 noise cases and four real noisy images confirm the effectiveness of the proposed method.