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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.

Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of the multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of the multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate regions than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel achievable scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region achieved by DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies. The proposed DPCRS acts as a new benchmark and the best-known achievable strategy for multi-antenna BC with partial CSIT., accepted by IEEE transactions on communications for publication

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 $10^{-5}$ .

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

We deal with the waveform design problem for the radar-communications coexistence (RCC) that ensures low levels of waveform correlations and meanwhile implements efficient information embedding (IE) via the fast-time waveform transmission. To tackle the problem, we propose two unimodular waveform designs, in which the integrated sidelobe level (ISL) is evaluated to find the optimal waveform correlations for the radar transmission and the attributes of waveform phases with large degrees of freedom are exploited to embed communication symbols. The first design is an offline strategy simply using the phase initialization for waveform generation to embed communication symbols. It requires only a predefined database that maps the waveform initialization to the emitted version of it, which gives the lowest ISL of the waveform. The second design serves as an online strategy employing the phase difference between any two waveform elements for the implementation of IE, and its accuracy to approaching desirable phases is elaborately transformed into a quadratic form and jointly designed with the minimization of ISL. We formulate this design as a nonconvex optimization problem, whose compound objective involves the outer product of the waveform vector that is to be designed. Moreover, we apply the majorization-minimization technique to solve the formulated problem, based on which we also devise an efficient algorithm that iterates with a closed-form solution. Simulation results verify the effectiveness of the proposed designs for the RCC.

In this paper we propose a statistical approach for the optimization of the phased codes used in radar. By considering each code as a realization of a random variable we show that we can build good codes if the distribution of the random variable is suitably chosen. We then consider sequences generated by the skew tent map and we show that for some values of the bifurcation parameter the invariant probability density coincides with the desired one. To confirm this idea we compute the maximal peak of the ambiguity function outside zero relative to the bifurcation parameter of the skew tent map.

We consider private information retrieval (PIR) for distributed storage systems with noncolluding nodes where data is stored using a non maximum distance separable (MDS) linear code. Recently, it was shown that when data is stored using certain non-MDS codes, the MDS-PIR capacity can be achieved, and is indeed the capacity of the system. In this paper, for storage codes not belonging to this class, we present a heuristic algorithm for their decomposition into punctured subcodes and a PIR protocol based on these punctured subcodes. The code decomposition is guided by the generalized Hamming weights of the storage code. We show that the proposed PIR protocol can achieve a larger PIR rate than that of all existing PIR protocols.

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loeve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantlv reduced.

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

The problem of communication over binary dirty paper (DP) using nested polar codes is considered. An improved scheme, focusing on low delay, short to moderate blocklength communication is proposed. Successive cancellation list (SCL) decoding with properly defined CRC is used for channel coding, and SCL encoding without CRC is used for source coding. The performance is compared to the best achievable rate of any coding scheme for binary DP using nested codes. A well known problem with nested polar codes for binary DP is the existence of frozen channel code bits that are not frozen in the source code. These bits need to be retransmitted in a second phase of the scheme, thus reducing transmission rate. We observe that the number of these bits is typically either zero or a small number, and provide an improved analysis, compared to that presented in the literature, on the size of this set and on its scaling with respect to the blocklength when the power constraint parameter is sufficiently large or the channel crossover probability sufficiently small., Accepted to ISIT 2019

Polar codes natively lack the flexibility that is desired for practical application. Namely, Arikan’s polar code definition can only achieve code lengths that are powers of two. Rate-matching techniques, known as puncturing and shortening, have been applied to polar codes to grant a flexible block length. By considering polarizing kernels of alternate dimensions, Multi-kernel polar codes improve natural block length flexibility. With the recent advent of the 3GPP 5th generation New Radio specification, there now exists an industry standard for length-flexible polar codes. This paper outlines various state-of-the-art flexible polar coding schemes, such as puncturing, shortening, and multi-kernel construction, and evaluates their efficacy with respect to the newly designed 3GPP standard. Simulations and an in-depth analysis are presented.

With the development of unmanned aerial vehicle (UAV) systems, multi-UAV cooperation has attracted noticeable attention. In response to the communication constraints faced in UAV swarm coordination, both the lazy and the eager strategies were proposed to enable swarm-wide reliable information exchange to further behavior coordination for UAV swarms. However, these two algorithms are only evaluated in a fixed and homogeneous network scenario. Hence, how to choose the proper information exchange strategy for a UAV swarm in realistic dynamic and heterogeneous network environments remains an open while interesting problem. Therefore, in this paper, we first evaluate the convergence and payload cost of both strategies for robotic swarms in realistic network scenarios. Then we propose a novel online adaptive information exchange strategy by adopting single relay selection schemes to ensure low payload and fast convergence in various network environments. Numerical results reveal our novel strategy performs well across different network scenarios in terms of convergence and payload cost, showing its robustness, adaptive capability and potential applications in UAV swarms.

Once the sparse representation-based classifier (SRC) was raised, it achieved a more outstanding performance than typical classification algorithm. Normally, SRC algorithm adopts \(l_1\)-norm minimization method to solve the sparse vector, and its computation complexity increases correspondingly. In this paper, we put forward a compressed sensing reconstruction algorithm based on residuals. This algorithm utilizes the local sparsity within figures as well as the non-local similarity among figure blocks to boost the performance of the reconstruction algorithm while remaining a median computation complexity. It achieves a superior recognition rate in the experiments of Yale facial database.

Cell tracking automatically in time-lapse image sequences is important for understanding the dynamic pattern of micro-cell. In this paper, we present a novel method for tracking cell with shape feature based on the delta generalized labeled multi-Bernoulli (delta-GLMB) filter which is of great research significance. The delta-GLMB filter with cell shape parameters can improve the tracking accuracy. This approach is evaluated and compared with raw detection using the generalized optimal sub-pattern assignment (GOSPA) metric on real N2DH-SIM cell sequences. Experiment results show that the delta-GLMB filter can provide the shape information as well as the better estimation than raw detection and KTH method.

In this paper, we study a multiple access monostatic backscatter communication (BackCom) system, where one reader talks to multiple backscatter nodes (BNs). We propose to use nonorthogonal multiple access (NOMA) to improve the efficiency of BackCom system. Specifically, the reader uses the feedback signal during the training phase to separate the multiple BNs into two groups. The reader then randomly pairs the BNs from these two groups to implement NOMA. We propose to set the reflection coefficients for these two groups differently to further distinguish the reflected signal power for the paired BNs. To characterize the network performance, we derive an analytical expression for the success rate which shows the percentage of bits that can be successfully decoded in one time slot. We also present the design guideline of the reflection coefficients for the two groups to maximize the system performance. Our numerical results show that increasing the readers transmit power has little impact on the system performance when the channel condition on the transmission link is less severe. Instead, the proper choice of the reflection coefficients for the two groups of BNs can greatly enhance the BackCom system.

In recent years, ultra-broadband communication using densification of millimeter-wave (mmWave) smallcells has attracted attention owing to its ability to accommodate increasing mobile data traffic. To make full use of mmWave access, we need to deploy ultra-broadband backhauling lines such as 10Gbps Ethernet everywhere at the expense of very high installation costs. The mmWave Edge Cloud system proposed in this paper is a solution to this problem. Based on achievable user context information e.g. mobility and traffic demand, user data can be transmitted and stored in storage (prefetching) installed at the smallcell site in advance. By using this, mmWave access can bring out sufficient performance even with low-capacity backhaul. For the analysis, we have developed a new mobility model, traffic model, and prefetching algorithm. Numerical results showed that the proposed method with 1Gbps-backhaul and prefetching can achieve 95% of the ideal system rate while reduce the average access latency by a factor of 67% as compared to the case without prefetching.

In spatial modulation (SM) and quadrature SM (QSM), the maximum-likelihood (ML) decoder provides the optimum solution with high decoding complexity at the receiver side. This paper presents a novel low-complexity algorithm for decoding the SM and QSM symbols, referred to as the min-max algorithm. This is an intelligent searching algorithm, particularly designed for the tree-search of the SM and QSM decoders. The proposed algorithm expands the minimum Euclidean distance (ED) by adding a single node at each step, without considering the order of the branches. The expanding process stops if the minimum ED occurs at the end of a fully expanded branch. It is shown that the proposed algorithm achieves the optimum ML bit error rate performance with a significant reduction in the decoding complexity comparing with SM-ML and QSM-ML, as well as other existing sphere decoding algorithms. Simulations and mathematical analysis are provided to assess the decoding performance and complexity of the proposed algorithm.

We present a CRC-aided LDPC coding scheme that can outperform the underlying LDPC code under ordered statistic decoding (OSD). In this scheme, the CRC is used jointly with the LDPC code to construct a candidate list, instead of conventionally being regarded as a detection code to prune the list generated by the LDPC code alone. As an example we consider a (128,64)5 G LDPC code with BP decoding, which we can outperform by 2 dB using a (128,72) LDPC code in combination with a 8-bit CRC under OSD of order t=3. The proposed decoding scheme for CRC-aided LDPC codes also achieves a better performance than the conventional scheme where the CRC is used to prune the list. A manageable complexity can be achieved with iterative reliability based OSD, which is demonstrated to perform well with a small OSD order.

We propose a binary message passing decoding algorithm for product codes based on generalized minimum distance decoding (GMDD) of the component codes, where the last stage of the GMDD makes a decision based on the Hamming distance metric. The proposed algorithm closes half of the gap between conventional iterative bounded distance decoding (iBDD) and turbo product decoding based on the Chase–Pyndiah algorithm, at the expense of some increase in complexity. Furthermore, the proposed algorithm entails only a limited increase in data flow compared to iBDD.

In this paper, a flow network based backhaul path planning algorithm (FBPA) is proposed for mmWave small cell networks, to obtain the backhaul path with minimum energy consumption on the basis of maximum backhaul traffic. Firstly, the backhaul path planning problem is formulated as an integer programming (IP) problem, which is always an NP-hard problem. Then, to obtain the near-optimal solution of the proposed IP problem, a liner relaxation technique is used to make it be a liner problem. Finally, the FBPA algorithm is proposed to find the minimum energy consumption solution on the basis of maximum backhaul traffic based on the flow network theory for the IP. Extensive simulations are conducted and the simulation results show that the FBPA outperforms other traditional backhaul path planning algorithm in terms of energy efficiency and backhaul traffic.

This paper considers a media access control (MAC) scheme, which employs Reed-Solomon codes for error-correction, protocol sequences for packet scheduling, and successive interference cancellation (SIC) for demodulation. The construction of the protocol sequences is based on the Chinese remainder theorem (CRT), and a bound on the pairwise Hamming cross-correlation is established. After proving some properties of Hamming cross-correlation of CRT sequences, we illustrate how to allocate the code rates for a system with SIC. A lower bound on system throughput is established.

In this paper, we propose a novel low-complexity signal detection technique based on compressed sensing for a full-duplex generalized spatial modulation (FD-GSM) system, where a communication node transmits data symbols via some antennas and receives data symbols via the remaining antennas at the same time. Thus, each antenna operates as either transmitter or receiver in each symbol time, which implies FD-GSM can be implemented with half-duplex antennas. In particular, parallel orthogonal matching pursuit (POMP) algorithm is exploited as a sparse signal recovery algorithm for detecting GSM signals. The self-interference (SI) due to full-duplex operation is assumed to be completely removed by help of the recently proposed SI cancellation techniques. The proposed signal detection technique significantly outperforms the conventional OMP algorithm in terms of symbol error rate (SER). Interestingly, there exists an optimal number of active antennas for maximizing effective throughput.

International audience; This paper analyses the security of dirty paper trellis (DPT) watermarking schemes which use both informed coding and informed em- bedding. After recalling the principles of message embedding with DPT watermarking, the secret parameters of the scheme are highlighted. The security weaknesses of DPT watermarking are then presented: in the wa- termarked contents only attack (WOA) setup, the watermarked data-set exhibits clusters corresponding to the different patterns attached to the arcs of the trellis. The K-means clustering algorithm is used to estimate these patterns and a co-occurrence analysis is performed to retrieve the connectivity of the trellis. Experimental results demonstrate that it is possible to accurately estimate the trellis configuration, which enables to perform attacks much more efficient than simple additive white Gaus- sian noise (AWGN).

Costa's "writing on dirty paper" capacity result establishes that full state pre-cancellation can be attained in the Gel'fand-Pinsker channel with additive state and additive Gaussian noise. The "carbon copy onto dirty paper" channel is the extension of Costa's model to the compound setting: here a common message is to be decoded at M receivers, each observing the sum of the channel input, Gaussian noise and one of M possible state sequences. As in the Gel'fand-Pinsker channel, the state sequences are anti-causally known at the encoder, which can thus pre-code its transmissions so as to minimize the adverse effects of the channel states. In this paper we derive the capacity to within 2.25 bits-per-channel-use of the M-user "carbon copying onto dirty paper" channel in which the state sequences are Gaussian distributed with the same variance and the same pairwise correlation.

Interference pre-cancellation as in the “writing onto dirty paper” channel crucially depends on the transmitter having exact knowledge of the way in which input and channel state combine to produce the channel output. The presence of even a small amount of uncertainty in such knowledge, gravely hampers the ability of the encoder to pre-code its transmissions against the channel state. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that state decoding, instead of state pre-cancellation, can be approximately optimal for a channel with discrete states when only partial channel knowledge is available. More specifically, we consider a variation of the “writing onto dirty paper” channel in which a discrete-valued state sequence is multiplied by a fast fading process and derive conditions on the fading distribution for which state decoding closely approaches capacity. This channel model is a special case of the Gelf'and-Pinsker channel and our results show an instance of this problem in which state decoding is approximately optimal.

International audience; Dirty Paper Trellis Codes (DPTC) watermarking, published in 2004, is a very efficient high rate scheme. Nevertheless, it has two strong drawbacks: its security weakness and its CPU computation complexity. We propose an embedding space at least as secure and a faster embedding. The embedding space is built on the projections of some wavelet coefficients onto secret carriers. It keeps a good security level and has also good psycho-visual properties. The embedding is based on a dichotomous rotation in the Cox, Miller and Boom Plane. It gives better performances than previous fast embedding approaches. Four different attacks are performed and revealed good robustness and rapidity performances.

This paper studies a variant of the classical problem of ``writing on dirty paper'' in which the sum of the input and the interference, or dirt, is multiplied by a random variable that models resizing, known to the decoder but not to the encoder. The achievable rate of Costa's dirty paper coding (DPC) scheme is calculated and compared to the case of the decoder's also knowing the dirt. In the ergodic case, the corresponding rate loss vanishes asymptotically in the limits of both high and low signal-to-noise ratio (SNR), and is small at all finite SNR for typical distributions like Rayleigh, Rician, and Nakagami. In the quasi-static case, the DPC scheme is lossless at all SNR in terms of outage probability. Quasi-static fading broadcast channels (BC) without transmit channel state information (CSI) are investigated as an application of the robustness properties. It is shown that the DPC scheme leads to an outage achievable rate region that strictly dominates that of time division., To appear in IEEE International Symposium on Information Theory 2007

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

Highly realistic visualization becomes crucial in numerous applications such as: medical imaging, simulation and entertainment systems. In this article, we describe the image/video capture system aimed for autostereoscopic multiview displays, as well as view interpolation and image restoration algorithms used to enhance the image quality. We evaluate the proposed algorithms on a head tracking based stereoscopic multiview display.

To develop algorithms and protocol for wireless mesh networks, one needs good models, especially capturing the defining aspect of the wireless medium: interference. The model should be simple and general, yet realistic. We survey here the various modeling aspects, focusing particularly on recent work involving physical models.

We describe a new binning technic for informed data hiding problem. In information theoretical point of view, the blind watermarking problem can be seen as transmitting a secret message M through a noisy channel on top of an interfered host signal S that is available only at the encoder. We propose an embedding scheme based on Low Density Parity Check(LDPC) codes, in order to quantize the host signal in an intelligent manner so that the decoder can extract the hidden message with a high probability. A mixture of erasure and symmetric error channel is realized for the analysis of the proposed method.

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.

In this paper, we propose a practical low delay digital scheme for lossy transmission of a Gaussian source over Gaussian broadcast channel when there is correlated side information at the receivers. Focusing on two receivers scenario, a bit-filling criterion is applied at the transmitter, to establish a connection between the common layer and refinement layer data. Then, we complete the dirty paper coding according to jointly typicality and finally superimpose the two layers for transmission. At the receiver, we propose a modified log-likelihood ratio-belief propagation decoding algorithm to utilize side information. And a typical-set based decoding algorithm is introduced to be an effective supplement if the decoding decision of the successive canceling method fails. Simulated results show that the proposed typical set decoding algorithm plays an excellent part in refinement layer recovery, and on the whole, the proposed digital scheme renders better reconstruction accuracy than the corresponding low delay separate source channel coding.

We consider a state reconstruction problem where the additive state process of a Gaussian Broadcast Channel (GBC) is to be reconstructed at both receivers, subject to some distortion constraints. The state process we consider is Gaussian, independently and identically distributed, and known fully a-priori at the encoder. Each receiver has to perform a lossy estimation, to within some prescribed distortion tolerance under a squared error distortion criterion. The set-up also involves communicating messages across to the receivers, in addition to the estimation. The optimal trade-off between the distortions incurred at the receivers and the rates at which messages can be communicated, is of interest. We provide a complete characterization of the optimal rate vs. distortion performance. Keywords: Broadcast channel, State transmission, Dirty paper coding, MMSE estimation

In this paper, we consider the downlink precoder design for two-user power-domain multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) systems. The proposed precoding scheme is based on simultaneous triangularization and decomposes the MIMO-NOMA channels of the two users into multiple single-input single-output NOMA channels, assuming low-complexity self-interference cancellation at the users. In contrast to the precoding schemes based on simultaneous diagonalization (SD), the proposed scheme avoids inverting the MIMO channels of the users, thereby enhancing the ergodic rate performance. Furthermore, we develop a power allocation algorithm based on the convex-concave procedure, and exploit it to obtain the ergodic achievable rate region of the proposed MIMO-NOMA scheme. Our results illustrate that the proposed scheme outperforms baseline precoding schemes based on SD and orthogonal multiple access for a wide range of user rates and performs close to the dirty paper coding upper bound. The ergodic rate region can further be improved by utilizing a hybrid scheme based on time sharing between the proposed MIMO-NOMA scheme and point-to-point MIMO., Accepted for presentation at the 2021 IEEE Wireless Communications and Networking Conference. This paper is the conference version of arXiv:2006.06471 with 6pp, 2 figures, for code, see https://gitlab.com/aravindh.krishnamoorthy/mimo-noma

Dirty paper coding (DPC) is a non-linear transmit scheme in presence of known interference, which is capacity achieving. Unfortunately, the implementation complexity of DPC is usually prohibitive. A sub-optimal yet practical method to implement DPC in inter-symbol interference channels is Tomlinson-Harashima precoding (THP). However, THP is not applicable to the constant-envelope phase shift keying (PSK) modulation. In this study, a modified Tomlinson-Harashima precoder is proposed to implement DPC for PSK modulation. The technique involves design of decision region at the receiver side and an algorithm to select the phase of transmitted symbol optimally at the transmitter side accordingly. Various patterns, namely, striped, checked, hexagonal, and radial are proposed and examined. Simulation results show that by using the proposed equaliser, the error floor is eliminated and significantly smaller bit-error rate is achieved. Numerical and simulation results show that the striped pattern outperforms other decision region designs. Moreover, its implementation and computational complexities is almost the same as the Tomlinson-Harashima precoder.

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

Random unitary beamforming (RUB) achieves multiuser diversity gain over multiple-input multiple-output broadcast channels with partial channel state information (CSI). RUB can asymptotically achieve the same growth of rate $M\log \log K$ as the optimal dirty paper coding or sub-optimal zero-forcing beamforming, which need full CSI at the transmitter. In this paper, we propose energy efficient RUB systems that select the optimal number of streams and transmission power to maximize energy efficiency (EE). In contrast to other beamforming schemes that use full CSI, optimizing RUB in terms of EE is a difficult task due to the CSI constraints. To solve this problem, we first select the EE-optimal number of streams and transmission power by using the statistical characteristics of RUB, which have been studied in previous works. Next, based on partial CSI feedback from users, we propose the method whereby the transmitter of RUB can adaptively control the transmission power. Simulation results demonstrate that our proposed systems can improve the EE performance of RUB and that the analytical results derived in this paper are accurate.

It is difficult to ensure that the scanned document will not be skewed due to the high speed scanning of the paper document by the mechanical feed or the document itself. This will not only affect the visual effects, but also seriously affect the accuracy and speed of subsequent processing. Therefore, this paper presents a Hough transform algorithm based on automatic scanning image correction and cropping, the first to determine whether the scanned image is a text or non-text image, the image preprocessing based on different types of scanned images of the image binarization, Compared with the traditional algorithm has a good anti-interference ability, can accurately detect the scan image skew angle.

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

The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers, the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.

Aiming to co-channel interference (CCI) and multi-stream for LTE downlink multi-user multiple input multiple output orthogonal frequency division multiple (MIMO-OFDM) system, a successive signal to leakage plus noise ratio (SLNR) with geometric mean decomposition (GMD) for downlink multi-user multi-stream MIMO systems based on pre-processing matrix is proposed. The algorithm utilizes precoding matrix based on SSLNR scheme to process emission signal at the transmitter, and the known leakages is canceled by dirty paper coding (DPC) algorithm. GMD is applied to cancel the known leakages, as all subchannels for this system have identical signal noise ratio (SNR), it is more convenient to reduce the influence of gain. Moreover, the received signal is treated by preconditioned matrix, the interference between different users is eliminated in further, the sum-capacity is improved by pre-processing Matrix. Meanwhile, the proposed scheme has no restriction of the number of antennas. The performance improvement is verified by simulation, the proposed scheme has superior bit error rate (BER) performance compared with the existing algorithm, when BER is 10−4, SNR is improved about 4 dB.

The capacity of frequency-flat multiuser MIMO downlinks is achieved with nonlinear dirty paper coding (DPC). Linear processing techniques, such as block diagonalization (BD), offer relatively low computational cost with respect to DPC and can also achieve capacity in certain cases. However, BD is not easily translated to frequency-selective channels since the channel matrix has a space-time structure (unlike the space-only information in frequency-flat channels), which induces an additional inter-symbol interference (ISI) component in the received signal. In this paper, we demonstrate that BD is feasible in frequency- selective channels when the transmitted block length is sufficiently large and the number of transmit antennas is greater than or equal to the number of users. We also present three novel techniques based on BD for frequency-selective MU- MIMO systems: time-reversal-based BD (TRBD), equalized BD (EBD), and joint processing BD (JPBD). These techniques use perfect channel state information at the transmitter (and also at the receiver, in the case of JPBD) to mitigate or eliminate both ISI and inter-user interference. We demonstrate that TRBD and EBD yield zero multiplexing gain (high SNR regime) but perform well under low SNR, while JPBD has a multiplexing gain approximately equal to the number of users for a sufficiently large transmit block length. Extensive numerical simulations show that the achievable rate performance of the proposed techniques improves that of time-reversal beamforming, a common technique for frequency- selective MU-MIMO downlinks.

This paper studies the downlink of a wireless system with an M-antenna base station, K single-antenna legitimate users and a single-antenna eavesdropper. A limited feedback-based scheduling and precoding scenario is considered that employs the orthogonal random beamforming (ORBF). The sum-rate of ORBF exhibits an identical growth rate as that of dirty paper coding for a large number of users. Unfortunately, for more realistic (low to moderate) K values, ORBF yields degraded performance. It is shown that having as many active beams as the number of transmit antennas is not always optimal for sparse network, thus scheduling strategies for ORBF with beam selection is investigated. Several user-beam selection algorithms featuring different complexity levels are proposed and their performance in terms of secrecy sum-rate is assessed. Numerical results are provided to illustrate the impact of the number of active beams on the secrecy performance.

In this paper, a two-stage filter for removing salt-and-pepper noise using noise detector based on characteristic difference parameter and adaptive directional mean filter is proposed. The first stage firstly detects the noise corrupted pixels by combining characteristic difference parameter and gray level extreme, then develops an improved adaptive median filter to firstly restore them. The second stage introduces a restoration scheme to further restore the noise corrupted pixels, which firstly divides them into two types and then applies different restoration skills for the pixels based on the classification result. One type of pixels is restored by the mean filter and the other type of pixels is restored by the proposed adaptive directional mean filter. The new filter firstly adaptively selects the optimal filtering window and direction template, then replaces the gray level of noise corrupted pixel by the mean value of pixels on the optimal template. Experimental results show that the proposed filter outperforms many existing main filters in terms of noise suppression and detail preservation.

본 논문에서는 다른 셀로부터 발생하는 간섭을 고려하여 다중사용자 다중안테나 하향링크 채널에서 최소평균제곱오차(MMSE: minimum mean-squared error)에 기반한 비선형 연속 프리코딩(precoding) 기법을 제안한다. 기존의 zero forcing(ZF) 기반의 방법과 달리 제안하는 방식은 다른 셀로부터의 간섭을 고려하여 셀 내의 다중사용자 간섭을 억제하는 방식을 취하여 수신단에서의 신호 대 간섭 잡음비를 향상시킬 수 있다. 모의 실험 결과를 통해 제안하는 최소평균제곱오차에 기반한 비선형 전처리 기법이 다양한 셀 간섭 환경에서 기존 방법보다 좋은 성능을 가지는 것을 확인할 수 있다.