This paper presents a novel knowledge-assisted joint source-channel coding (JSCC) scheme that integrates implicit neural compression (INC) with analog residual transmission for high-quality and efficient image delivery. To improve compression efficiency and decoding delay, we introduce a knowledge base architecture that separates learning and inference: the global knowledge, implemented on the cloud, compresses image-specific latent representations and lightweight network parameters; the local knowledge, deployed at the edge, reconstructs side information using lightweight inference from the received bitstream. Although the side information enables coarse reconstruction, it often lacks the fine-grained visual details, i.e., high-frequency components. To supplement the missing high-frequency details, the residuals between the original and side information are transmitted via pseudo-analog modulation and reconstructed at the receiver. The residual transmission not only enables high-quality reconstruction of high-frequency components but also provides graceful improvement under instantaneous channel conditions. Experimental results on the Kodak and CLIC2020 datasets demonstrate that the proposed scheme significantly outperforms conventional and learned image codecs in terms of reconstruction quality and robustness to unreliable channel conditions.

Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA’s random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP’s passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43% than the competing protocols.

Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of pre-trained models, allowing anyone to publish without rigorous verification. This scenario creates a privacy threat, as pre-trained models can be intentionally crafted to compromise the privacy of fine-tuning datasets. In this study, we introduce a novel poisoning technique that uses model-unlearning as an attack tool. This approach manipulates a pre-trained language model to increase the leakage of private data during the fine-tuning process. Our method enhances both membership inference and data extraction attacks while preserving model utility. Experimental results across different models, datasets, and fine-tuning setups demonstrate that our attacks significantly surpass baseline performance. This work serves as a cautionary note for users who download pretrained models from unverified sources, highlighting the potential risks involved.

This paper addresses the integration of millimeter-wave (mmWave) Wi-Fi communication and sensing during data transmission intervals (DTIs). We leverage prior knowledge from codebook beam training conducted during preceding beacon transmission intervals (BTIs) and association beamforming training (A-BFT) intervals to design a transceiver array response that meets both requirements on downlink communication SNR and targeted sensing area. By formulating it as a first-order array response optimization with constraints on power, codebook, communication SNR, and limited RF chains, this paper introduces a two-stage solution. First, we introduce a two-way communication-sensing matching pursuit to determine a set of codewords that prioritize the communication SNR constraint. Then, using the selected codewords, we employ an alternating minimization over an auxiliary phase term and beamforming weights to further minimize an array-response distance loss. Numerical results validate the effectiveness of the proposed DMG beamforming design over baseline methods.

This paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (LoRA), Quantum-PEFT exploits an underlying full-rank yet surprisingly parameter efficient _quantum unitary parameterization_. With the use of Pauli parameterization, the number of trainable parameters grows only logarithmically with the ambient dimension, as opposed to linearly as in LoRA-based PEFT methods. Quantum-PEFT achieves vanishingly smaller number of trainable parameters than the lowest-rank LoRA as dimensions grow, enhancing parameter efficiency while maintaining a competitive performance. We apply Quantum-PEFT to several transfer learning benchmarks in language and vision, demonstrating significant advantages in parameter efficiency.

Wi-Fi channel measurements across different bands, e.g., sub-7-GHz and 60-GHz bands, are asynchronous due to the uncoordinated nature of distinct standards protocols, e.g., 802.11ac/ax/be and 802.11ad/ay. Multi-band Wi-Fi fusion has been considered before on a frame-to-frame basis for simple classification tasks, which does not require fine-time-scale alignment. In contrast, this paper considers asynchronous sequence-to-sequence fusion between sub-7-GHz channel state information (CSI) and 60-GHz beam signal-to-noise-ratio (SNR)s for more challenging tasks, such as continuous coordinate estimation. To handle the timing disparity between asynchronous multi-band Wi-Fi channel measurements, this paper proposes a multi-band neural dynamic fusion (NDF) framework. This framework uses separate encoders to embed the multi-band Wi-Fi measurement sequences to separate initial latent conditions. Using a continuous-time ordinary differential equation (ODE) modeling, these initial latent conditions are propagated to the respective latent states of the multi-band channel measurements at the same time instances for a latent alignment and a post-ODE fusion, and at their original time instances for measurement reconstruction. We derive a customized loss function based on the variational evidence lower bound (ELBO) that balances between the multi-band measurement reconstruction and continuous coordinate estimation. We evaluate the NDF framework using an in-house multi-band Wi-Fi testbed and demonstrate substantial performance improvements over a comprehensive list of single-band and multi-band baseline methods.

In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privacy risks of sharing gradients, our paper aims to provide a systematic approach to analyze private information leakage from gradients. We present a unified game-based framework that encompasses a broad range of attacks including attribute, property, distributional, and user disclosures. We investigate how different uncertainties of the adversary affect their inferential power via extensive experiments on five datasets across various data modalities. Our results demonstrate the inefficacy of solely relying on data aggregation to achieve privacy against inference attacks in distributed learning. We further evaluate five types of defenses, namely, gradient pruning, signed gradient descent, adversarial perturbations, variational information bottleneck, and differential privacy, under both static and adaptive adversary settings. We provide an information-theoretic view for analyzing the effectiveness of these defenses against inference from gradients. Finally, we introduce a method for auditing attribute inference privacy, improving the empirical estimation of worst-case privacy through crafting adversarial canary records.

Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g., a woman's photo) and a text description (e.g., “a woman is drinking water.”). Existing TI2V frameworks often require costly training on video-text datasets and spe-cific model designs for text and image conditioning. In this paper, we propose TI2V-Zero, a zero-shot, tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image, enabling TI2V generation without any optimization, fine-tuning, or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input, we propose a “repeat-and-slide” strategy that modulates the reverse denoising process, al-lowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity, we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets, where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore, we show that TI2V-Zero can seam-lessly extend to other tasks such as video infilling and pre-diction when provided with more images. Its autoregressive design also supports long video generation.

Point cloud delivery over wireless and mobile channels will be a key technology for untethered users to realize extended reality via wireless and mobile terminals. A key challenge of point cloud delivery is efficiently delivering the point cloud over unstable and band-limited channels. Graph Fourier Transform (GFT) is a potential solution to compress such non-uniformly and non-orderly distributed signals in a 3D space, whereas GFT-based solutions require large communication overhead to share the graph information with the receiver. This paper proposes a novel point cloud delivery scheme that introduces implicit neural representation (INR) to reduce the overhead. Specifically, the INR of the proposed scheme trains a mapping between the indices and the corresponding weight in the adjacency matrix and the proposed scheme sends the parameter set of the INR as the metadata. Evaluations demonstrate that the proposed scheme can improve the point cloud quality under the same amount of communication overhead because the proposed INR can predict most of the elements in the adjacency matrix using a small parameter set.

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression. To handle the timing disparity between the two channel measurements, we extend our recently proposed dual-decoder neural dynamic (DDND) framework with latent ordinary differential equations (ODEs), align the distinct latent dynamic states at the same time instances, and introduce a post-ODE fusion framework. The resulting neural dynamic fusion (NDF) framework is trained in an end-to-end fashion with a modified variational autoencoder loss function. Evaluation over a newly collected in-house multi-band Wi-Fi dataset shows the advantage of the proposed NDF method over frame-based and DDND methods.

Haptic communication will be a key technology for extended reality (XR), robotics, and remote manipulation. The deformation magnitude of 3D deformable objects is a key attribute for realizing fine-grained remote manipulation. The typical solutions for sending the deformation magnitudes over wireless channels are to perform digital compression and transmission considering the channel quality, i.e., digital source-channel coding. However, the key problems of the solutions are 1) still large traffic and 2) catastrophic quality degradation due to channel quality fluctuation. This paper proposes a graph-based analog joint source-channel coding for 3D haptic communication. Specifically, Graph Fourier Transform (GFT)-based energy compression efficiently removes the redundancy across deformation magnitudes. In addition, the integration of unequal error protection and analog modulation prevents catastrophic degradation and gradually improves the reconstruction quality according to the instantaneous channel quality. Evaluation results using the deformation magnitude of various 3D objects show that the proposed scheme prevents quality degradation due to channel quality fluctuations and provides accurate deformation magnitude for remote users.

Along with the fourth industrial revolution, industrial automation systems are evolving into a multi-tier end-edge computing architecture. Edge controllers, which are equipped with a larger computing capacity compared to local controllers, can communicate with local plants over mainstream wireless networks such as WirelessHART, Wi-Fi, and cellular networks. Well-known challenges induced by networks, such as uncertain time delays and packet drops, have been intensively investigated from various perspectives: control synthesis, network design, or control and network co-design. The status quo is that the industry remains hesitant to close the loop between the edge controller and the actuation side due to safety concerns. This work offers an alternative perspective to address the safety concern, by exploiting the design freedom of an end-edge computing architecture. Specifically, we present a smart actuation framework, which deploys (1) an edge controller, which communicates with physical plant via wireless network, accounting for optimality, adaptation, and constraints by conducting computationally expensive operations; (2) a smart actuator, which is co-located with the physical plant on the end tier and executes a local control policy, accounting for system safety in the view of network imperfections, (3) the end-edge control co-design strategies and cooperation logic for both performance and stability. For certain classes of plants, semi-globally asymptotic stability of the resulting end-edge control systems is established when the edge controller is the model predictive control (MPC), or policy iteration-based learning control. We also provide an adaptation strategy for the end-edge control systems facing model parameter mismatches when the edge controller employs reinforcement learning. Extensive simulations demonstrate the advantages of the proposed end-edge co-design and cooperation procedures. Note to Practitioners—Edge computing is gaining momentum in areas that require low latency and high efficiency, i.e., mobile computing, video analytics, and autonomous driving. Industrial automation systems are also evolving into a multi-tier end-edge computing architecture. It pays obvious dividends to leverage the cooperation between end and edge, benefiting from fast and reliable communication on the end side, and powerful computation capacity on the edge side. The current end-edge cooperation focuses on how to partition tasks and offload computation resources in order to minimize delay and energy consumption, as well as how to balance the tradeoff between them. However, the impacts of end-edge cooperation on the safety, optimality, and cost of industrial automation have not been systematically studied. This paper aims to tailor end-edge cooperation in a smart actuation framework, for industrial automation to reconcile the above aspects by leveraging co-design of end and edge controllers and their switching logic. Extensive pure and semi-physical simulations demonstrate the advantages in performance and system stability of the proposed end-edge co-design and cooperation procedures.

This paper investigates quantum machine learning to optimize the beamforming in a multiuser multiple-input single-output downlink system. We aim to combine the power of quantum neural networks and the success of classical deep neural networks to enhance the learning performance. Specifically, we propose two hybrid quantum-classical neural networks to maximize the sum rate of a downlink system. The first one proposes a quantum neural network employing parameterized quantum circuits that follows a classical convolutional neural network. The classical neural network can be jointly trained with the quantum neural network or pre-trained leading to a fine-tuning transfer learning method. The second one designs a quantum convolutional neural network to better extract features followed by a classical deep neural network. Our results demonstrate the feasibility of the proposed hybrid neural networks, and reveal that the first method can achieve similar sum rate performance compared to a benchmark classical neural network with significantly less training parameters; while the second method can achieve higher sum rate especially in presence of many users still with less training parameters. The robustness of the proposed methods is verified using both software simulators and hardware emulators considering noisy intermediate-scale quantum devices.

A key challenge in holographic-type communication is transmitting point cloud signals to users across diverse channel quality and bandwidths. Digital based point cloud coding efficiently reduces point cloud traffic. In contrast, the quantization and entropy coding in digital-based schemes causes quality degradation owing to channel quality fluctuations and diversity. This study proposes a novel scheme for point cloud delivery over wireless channels. The proposed scheme consists of a graph auto-encoder (GAE) architecture to compress the point cloud into coded symbols and restore the point cloud from the received symbols. The proposed scheme addresses the quality degradation due to channel quality fluctuations and bandwidth diversity via, the following two steps. First, the coded symbols are directly mapped onto transmission symbols, analog modulation, to ensure that the point cloud quality follows the instantaneous channel quality of each user. Second, a non-uniform dropout is introduced to realize a rateless property in the GAE architecture for gradually improving the point cloud quality according to the available bandwidth. Evaluation results demonstrate that the proposed GAE architecture can yield better point cloud quality than digital-based and analog-based schemes, even when users have varying available bandwidths.

Holographic-type communication, i.e., three-dimensional (3D) content delivery, will be a crucial application for modern wireless and mobile networks. In this paper, we propose a novel soft delivery scheme to realize efficient 3D content delivery. Specifically, the proposed scheme sends a single 2D image over error-prone wireless channels using discrete cosine transform followed by near-analog modulation. At the receiver, a 2D-to-3D decoder based on graph neural networks (GNN) reconstructs the corresponding 3D point cloud and mesh from the received 2D image. We verify that the proposed soft 2D-to-3D delivery scheme can reconstruct clean 3D data gracefully from the soft-delivered 2D image even in the presence of fading and noise distortion. In addition, the proposed scheme can generate higher-quality 3D data compared with direct 3D content delivery schemes.

In frequency-modulated continuous-wave (FMCW) lidar, the distance to an illuminated target is proportional to the beat frequency of the interference signal. Laser phase noise often limits the range accuracy of FMCW lidar, and existing frequency estimation methods make overly simplistic assumptions about the noise model. In this work, we propose an algorithm that performs frequency estimation via phase unwrapping by explicitly accounting for correlations in the phase noise. Given a candidate frequency, we approximately recover the maximum likelihood unwrapping sequence using the Viterbi algorithm and the phase noise statistics. The algorithm then alternates between unwrapping and frequency estimate refinement until convergence. Compared to state-of-the-art alternatives, our algorithm consistently achieves superior performance at long range or with large-linewidth lasers when the signal-to-noise ratio is sufficiently high.

We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and contention-based channel-time allocation, and (2) how to exploit underlying object dynamics to assist the localization. To this end, we formulate the trajectory estimation as a sequence regression problem. We propose a dual-decoder neural dynamic learning framework to simultaneously reconstruct Wi-Fi beam training measurements at irregular time instances and learn the unknown dynamics over the latent space in a continuous-time fashion by enforcing strong supervision at both the coordinate and measurement levels. The proposed method was evaluated on an in-house mmWave Wi-Fi dataset and compared with a range of baseline methods, including traditional machine learning methods and recurrent neural networks.

System logs record rich information for system events. Practical anomaly detection from system logs should be able to address three challenges: 1) understanding complicated attributes in event logs; 2) extracting complex context relations among events; and 3) providing concrete explanations to human analysts. In this paper, we develop an attention-equipped encoder-decoder system to capture context from system logs for explainable anomaly detection. For each target event, we collect its nearby events in chronological order as its context events. Instead of using a recurrent neural network-based encoder like previous works, we adopt a Transformer-based encoder to extract complex relations among context events and their attributes. Then, a context vector is generated and passed to the decoder, where an attention matrix is learned and used to weigh the context events for detecting the anomalies. Evaluation on the large-scale real-world Los Alamos National Laboratory dataset shows that, compared with existing works, our methods can provide fine-grained one-to-one attention to help explain the importance of each attribute in the context events to the prediction, without sacrificing detection performance.

Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models that perform plug-and-play generation, i.e., to use a predefined or pretrained model, which is not explicitly trained on the generative task, to guide the generative process (e.g., using language). However, such guidance is typically useful only towards synthesizing high-level semantics rather than editing fine-grained details as in image-to-image translation tasks. To this end, and capitalizing on the powerful fine-grained generative control offered by the recent diffusion-based generative models, we introduce Steered Diffusion, a generalized framework for photorealistic zero-shot conditional image generation using a diffusion model trained for unconditional generation. The key idea is to steer the image generation of the diffusion model at inference time via designing a loss using a pre-trained inverse model that characterizes the conditional task. This loss modulates the sampling trajectory of the diffusion process. Our framework allows for easy incorporation of multiple conditions during inference. We present experiments using steered diffusion on several tasks including inpainting, colorization, text-guided semantic editing, and image super-resolution. Our results demonstrate clear qualitative and quantitative improvements over state-of-the-art diffusion-based plug-and-play models while adding negligible additional computational cost.

The software distribution in advanced IoT networks is inevitable. However, distributing software in multi-hop wireless networks consumes enormous communication bandwidth and can also suffer from reliability challenge. This paper proposes an innovative dynamic relay point (DRP) protocol to reduce the number of software packet transmissions. It also introduces a network condition based rateless coding scheme to improve the packet transmission reliability. The NS3 simulator is employed for performance evaluation. The proposed DRP protocol outperforms multi-point relay (MPR) baseline by reducing the software packet transmissions and improving the effective throughput.

3D point cloud data formats are used to express three-dimensional (3D) information using numerous points in a 3D space. A key challenge is the delivery of high-quality 3D point cloud for the users under a diverse channel quality and available bandwidth to share the same 3D space across multiple untethered extended reality (XR) users. The existing digital-based schemes suffer from two issues owing to the diversity: cliff and leveling-off effects. This paper proposes a novel soft multicasting scheme of point cloud data for untethered XR users. The key ideas of the proposed scheme are three-fold: 1) integration of graph signal processing and analog modulation to adaptively improve the 3D reconstruction quality according to the channel quality for all individual XR users, 2) integration of Givens rotation and non-uniform adaptive quantization to reduce metadata overhead for the graph Fourier transform, and 3) prioritized transmission of the metadata to realize adaptive quality improvement based on the bandwidth available for each XR user. This paper reveals that the proposed scheme prevents cliff and leveling-off effects even when the XR users experience different channel qualities. Furthermore, the proposed transmission exhibits better 3D reconstruction quality compared with the state-of-the-art graph-based delivery scheme in band-limited environments.

This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.

Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802.11ad/ay standards have shown to be effective in terms of overhead and channel measurement granularity for these indoor sensing tasks. In this paper, we investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks (DNN) for the future quantum-ready society. The effectiveness of both DNN and QNN is validated by an in-house experiment for human pose recognition, achieving greater than 90% accuracy with a limited data size.

This paper introduces a new quantum computing framework integrated with a two-step compressed sensing technique, applied to a joint channel estimation and user identification problem. We propose a variational quantum circuit (VQC) design as a new denoising solution. For a practical grant-free communications system having correlated device activities, variational quantum parameters for Pauli rotation gates in the proposed VQC system are optimized to facilitate to the non-linear estimation. Numerical results show that the VQC method can outperform modern compressed sensing techniques using an element-wise denoiser.

Optical coherence tomography (OCT) using Fourier domain processing can resolve micrometer-scale depth information. However, the conventional volumetric reconstruction approach is unnecessary for opaque samples with only one reflector per lateral position, and the required sample interpolation degrades performance. In this paper, we show that surface depth profilometery with a Fourier-domain OCT system simplifies to a sinusoidal parameter estimation problem. We derive approximate maximum likelihood estimators for the sample depth and reflectivity, which can easily be computed by backprojecting the data without interpolating. Iterative refinement further improves results at high signal-to-noise ratio (SNR). We demonstrate the performance of the technique compared to the conventional Fourier transform approach on both simulated and experimental data collected with a spectral-domain OCT system. Our results show that maximum likelihood profilometry is fast and more robust to noise than the Fourier approaches at moderate SNR.

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.

The variational autoencoder (VAE) has been used in a myriad of applications, e.g., dimensionality reduction and generative modeling. VAE uses a specific model for stochastic sampling in latent space. The normal distribution is the most commonly used one because it allows a straightforward sampling, a reparameterization trick, and a differentiable expression of the Kullback–Leibler divergence. Although various other distributions such as Laplace were studied in literature, the effect of heterogeneous use of different distributions for posterior-prior pair is less known to date. In this paper, we investigate numerous possibilities of such a mismatched VAE, e.g., where the uniform distribution is used as a posterior belief at the encoder while the Cauchy distribution is used as a prior belief at the decoder. To design the mismatched VAE, the total number of potential combinations to explore grows rapidly with the number of latent nodes when allowing different distributions across latent nodes. We propose a novel framework called AutoVAE, which searches for better pairing set of posterior-prior beliefs in the context of automated machine learning for hyperparameter optimization. We demonstrate that the proposed irregular pairing offers a potential gain in the variational Rényi bound. In addition, we analyze a variety of likelihood beliefs and divergence order.

Complementary to the fine-grained channel state information (CSI) and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes [i.e., beam SNR (bSNR)] during the millimeter-wave (mmWave) beam training phase were recently repurposed for Wi-Fi sensing applications, such as human activity recognition and indoor localization. This article proposes a multiband Wi-Fi sensing framework to fuse features from both CSI from 5-GHz bands and the mid-grained bSNR at 60 GHz with feature granularity matching (GM) that pairs feature maps from the CSI and bSNR at different granularity levels with learnable weights. To address the issue of limited labeled training data, we propose to pretrain an autoencoder-based multiband Wi-Fi fusion network in an unsupervised fashion. For specific sensing tasks, separate sensing heads can be attached to the pretrained fusion network with fine-tuning. The proposed framework is thoroughly validated for three sensing applications using in-house experimental data sets: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to a list of baseline methods demonstrates the effectiveness of GM. An ablation study is performed as a function of the amount of labeled data, the latent space dimension, and learning rates.

Point cloud is an emerging data format useful for various applications such has holographic display, autonomous vehicle, and augmented reality. Conventionally, communications of point cloud data have relied on digital compression and digital modulation for three-dimensional (3D) data streaming. However, such digital-based delivery schemes have fundamental issues called cliff and leveling effects, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast+, to overcome cliff and leveling effects. Specifically, our method utilizes hybrid digital-analog coding, integrating digital compression and analog coding based on graph Fourier transform (GFT), to gracefully improve 3D reconstruction quality with the improvement of channel quality. We demonstrate that HoloCast+ offers better 3D reconstruction quality in terms of the symmetric mean square error (sMSE) by up to 18.3 dB and 10.5 dB, respectively, compared to conventional digital-based and analog-based delivery methods in wireless fading environments.

This paper proposes an anomaly detection algorithm for a factory automation system, which jointly performs data pre-processing and time-delay autoencoder (TDAE) with a hybrid loss function. The source data are pre-processed by digital filters before feeding into a TDAE for anomaly detection. The digital filters extract analog signals from a variety of frequency bands to facilitate identifying anomalies. The pre-processed data then takes time-delay reform to explore temporal relationship of data signals. In addition, two anomaly diagnosis algorithms, a statistical based method and an autoencoder based method, are presented. Numerical results show that time-delay reform can improve the anomaly detection accuracy compared to the conventional autoencoder. Data pre-processing can further improve the anomaly detection accuracy. Moreover, we confirm that our anomaly diagnosis algorithms outperform traditional method that does not perform data pre-processing and time-delay reform.

In typical point cloud delivery, a sender uses octree-based and graph-based digital video compression to send three-dimensional (3D) points and color attributes. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed a wireless point cloud delivery called HoloCast inspired by soft delivery. Although the HoloCast realizes graceful quality improvement according to instantaneous wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better 3D reconstruction quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct a clean 3D point cloud with low overheads by removing fading and noise effects.

Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between optimal robust learning and the privacy-utility tradeoff problem, which is a generalization of the rate-distortion problem. The saddle point of the game between a robust classifier and an adversarial perturbation can be found via the solution of a maximum conditional entropy problem. This information-theoretic perspective sheds light on the fundamental tradeoff between robustness and clean data performance, which ultimately arises from the geometric structure of the underlying data distribution and perturbation constraints.

Adversarial examples have recently exposed the severe vulnerability of neural network models. However, most of the existing attacks require some form of target model information (i.e., weights/model inquiry/architecture) to improve the efficacy of the attack. We leverage the information-theoretic connections between robust learning and generalized rate-distortion theory to formulate a universal adversarial example (UAE) generation algorithm. Our algorithm trains an offline adversarial generator to minimize the mutual information between the label and perturbed data. At the inference phase, our UAE method can efficiently generate effective adversarial examples without high computation cost. These adversarial examples in turn allow for developing universal defenses through adversarial training. Our experiments demonstrate promising gains in improving the training efficiency of conventional adversarial training.

We propose a new probabilistic amplitude shaping (PAS) scheme based on short linear block codes. In the proposed system, the capacity-approaching signal distribution is generated in the process of decoding linear block codes for a given information bit sequence. We associate the design problem of shaping codes with the classical covering problem, suggesting the use of good covering codes for shaping. From simulation results, it is demonstrated that by selectingperfectbinary codes as our shaping codes, the proposed scheme offers a shaping gain of around 0.3–1.0 dB. By comparing with the enumerative sphere shaping (ESS) of the same block length, we verify that the proposed scheme achieves significantly lower storage complexity and computational complexity at the receiver even with comparable block error rate performance.

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.

Screen-camera communications, using a liquid crystal display (LCD) screen and camera image sensors, have been attractive variants of visible light communications (VLC) since any external light-emitting modules and photo detectors are required for recent mobile devices, which are usually equipped with display and camera. A major issue in screen-camera communications is a performance loss in transmission rate due to nonlinear channel impairments with ambient noise. To improve transmission rates, we investigate the impact of nonlinear channel equalization, nonbinary channel coding, probabilistic shaping, and nonlinear precoding for high-order modulation schemes. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.8-3.3 times higher transmission rates compared to existing schemes for a communication distance of 60-160 cm.

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained using our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmarks is visible, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on mulitple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.

In this paper, we propose a new polar coding scheme with molecular programming, which is capable of highly parallel implementation at a nano-scale without the need for electrical power sources. We designed chemical reaction networks (CRN) to employ either successive cancellation (SC) or maximum-likelihood (ML) decoding schemes for short polar codes. From differential equation analysis of the proposed CRNs, we demonstrate that SC and ML decoding achieve accurate computations across fully-parallel chemical reactions. In terms of the number of required chemical reactions, we verify the superiority of ML decoding over SC decoding for very short block lengths.

In addition to coarse-grained received signal strength indicator (RSSI) measurements and fine-grained channel state information (CSI), a mid-grained channel measurement - spatial beam signal-to-noise ratios (SNRs) - that are inherently available during the millimeter wave (mmWave) beam training as defined in mmWave fifth-generation (5G) and IEEE 802.11ad/ay standards, were recently utilized for fingerprinting-based indoor localization. In this paper, we extend the beam SNR fingerprinting-based indoor localization to more challenging scenarios in non-line-of-sight (NLOS) propagation. Particularly, multi-channel beam covariance matrix (BCM) images are used as the fingerprinting signature and fed into a beam covariance learning (BCL) network to identify the position and estimate the coordinate. Using our in-house testbed with commercial off-the-shelf (COTS) 60-GHz WiFi routers, real-world mmWave BCMs are fingerprinted in several NLOS locations-of-interest in an enclosed L-shape conference room. Given a fingerprinting grid-size of 30 cm, preliminary performance evaluation shows the position classification accuracy can be above 90% using classical classification methods and a coordinate estimation error around 11 cm with the BCL approach.

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.

The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs. Neural networks flexibly allow for data-driven optimization, but are often employed as black boxes detached from direct application of domain knowledge. Our work considers learning-based approaches to end-to-end design of modulation and signal detection for the non-coherent multi-input multi-output (MIMO) channels. We demonstrate that simulation-driven optimization can outperform traditional Grassmann designs. Additionally, we show the feasibility of non-coherent MIMO communications over extremely short channel coherence time, with as few as two time slots, which have never been explored in existing literature due to design hardness.

Soft image delivery uses pseudo-analog modulation for wireless image transmissions to prevent cliff and leveling effects subject to channel quality fluctuation and to realize graceful quality improvement according to wireless channel quality. Despite its attractive feature of graceful performance, the conventional soft image delivery suffers from low image quality when the analog-modulated symbols are severely impaired by fading and strong channel noise. In this paper, we propose a novel scheme of soft image delivery to reconstruct high-quality images from its low-quality observations. Specifically, the proposed scheme integrates deep convolutional neural network (DCNN)-based image restoration, i.e., deep image prior, into soft image delivery. The deep image prior learns a mapping function from the noisy image to the clean image based on user's perception-aware loss function using multiple training images in prior to soft delivery. The mapping function can restore a clean image even when the received image is distorted by strong fading and additive noise. From the evaluation results, the proposed scheme can remove fading and noise effects from the received images by using DCNN-based image restoration. For example, the proposed scheme achieves up to 0.44 improvement compared with the conventional soft image delivery in terms of structural similarity (SSIM) index at a deep fading channel.

Conventional point cloud delivery schemes use graph-based compression to stream three-dimensional (3D) points and the corresponding color attributes over wireless channels for 3D scene reconstructions. However, the graph-based compression requires a significant communication overhead for graph signal decoding, i.e., inverse graph Fourier transform (IGFT), and such large overhead causes a low 3D reconstruction quality due to power and rate losses. We propose a novel scheme of point cloud delivery to significantly reduce the amount of overhead while allowing a small degradation in the 3D reconstruction quality. Specifically, the proposed scheme exploits Givens rotation for the graph-based transform basis matrix to compress the basis matrix into quantized angle parameters. Even when the angle parameters are strongly quantized for compression, the receiver can reconstruct a clean point cloud by using the basis matrix obtained from the quantized angle parameters. Evaluation results show the Givens rotation in the proposed scheme achieves overhead reduction with a slight quality degradation. For example, the proposed scheme achieves 89.8% overhead reduction with 1.3 dB quality degradation compared with the conventional point cloud delivery scheme.

We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion. In the proposed RL-AEs, instead of a deterministic bottleneck architecture, we use an over-complete representation that is stochastically regularized with weighted dropouts. Our RL-AEs employ monotonically increasing dropout rates across the latent representation nodes such that the latent variables become sorted by importance like in principal component analysis (PCA). This is motivated by the rateless property of conventional PCA, where the least important principal components can be discarded to realize variable rate dimensionality reduction that gracefully degrades the distortion. Our proposed stochastic bottleneck framework enables seamless rate adaptation with high reconstruction performance, without requiring predetermined latent dimensionality at training. We experimentally demonstrate that the proposed RL-AEs can achieve variable dimensionality reduction while retaining nearly optimal distortion compared to conventional AEs.

Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of physiological sensing lies in the variability of biosignals across different users and tasks. To address this issue, we propose an adversarial feature extractor for transfer learning to exploit disentangled universal representations. We consider the trade-off between task-relevant features and user-discriminative information by introducing additional adversary and nuisance networks in order to manipulate the latent representations such that the learned feature extractor is applicable to unknown users and various tasks. Results on cross-subject transfer evaluations exhibit the benefits of the proposed framework, with up to 8.8% improvement in average accuracy of classification, and demonstrate adaptability to a broader range of subjects.

A distribution matcher (DM) maps a binary input sequence into a block of nonuniformly distributed symbols. To facilitate the implementation of shaped signaling, fast DM solutions with high throughput and low serialism are required. We propose a novel DM architecture with parallel amplitudes (PA-DM) for which m-1 component DMs, each with a different binary output alphabet, are operated in parallel in order to generate a shaped sequence with m amplitudes. With negligible rate loss compared to a single nonbinary DM, PA-DM has a parallelization factor that grows linearly with m, and the component DMs have reduced output lengths. For such binary-output DMs, a novel constant-composition DM (CCDM) algorithm based on subset ranking (SR) is proposed. We present SR-CCDM algorithms that are serial in the minimum number of occurrences of either binary symbol for mapping, and fully parallel for demapping. For distributions that are optimized for the additive white Gaussian noise (AWGN) channel, we numerically show that PA-DM combined with SR-CCDM can reduce the number of sequential processing steps by more than an order of magnitude, while having a rate loss that is comparable to conventional nonbinary CCDM with arithmetic coding.

Simultaneous screen-to-camera and screen-to-eye communications, i.e., watermarking, have been proposed in visible light communications. The main purpose of such communications is to provide many data bits for camera devices and visual information for human eyes by using a common displayed image. To this end, the existing studies leverage the capability discrepancy and distinctive features between the human vision system and camera devices. However, the existing techniques mainly require high refresh rates in both screen and camera devices to achieve better throughput while keeping high visual quality. In this paper, we propose a novel transmission scheme for efficient simultaneous screen-to- camera and screen-to-eye communications without a need of high refresh rates. Specifically, we use deep convolutional neural networks (DCNN)-based watermark encoder and decoder to embed many bits into high-quality images, and then to maximize throughput from the bit-embedded image. With end-to-end adversarial learning, the encoder networks learn a mapping function to embed digital data into an original image based on a perceptual loss function while the decoder networks also learn a mapping function from the bit-embedded image to the data bits based on a cross-entropy loss function. From the evaluations, we show that the proposed watermark encoding and decoding networks yield high throughput from the bit-embedded images compared with a simple DCNN-based watermarking. In addition, the bit-embedded images on the screen achieve high quality for human perception.

Soft delivery, i.e., analog transmission, has been proposed to provide graceful video/image quality even in unstable wireless channels. However, existing analog schemes require a significant amount of metadata for power allocation and decoding operations. It causes large overheads and quality degradation due to rate and power losses. Although the amount of overheads can be reduced by introducing Gaussian Markov random field (GMRF) model, the model mismatch can degrade reconstruction quality. In this paper, we propose a novel analog transmission scheme to simultaneously reduce the overheads and yield better reconstruction quality. The proposed scheme uses a deep neural network (DNN) for metadata compression and decompression. Specifically, the metadata is compressed into few variables using the proposed DNN-based metadata encoder before transmission. The variables are then transmitted and decompressed at the receiver for high-quality video/image reconstruction. Evaluations using test images demonstrate that our proposed scheme reduces overheads by 80.0% with 11.2 dB improvement of reconstruction quality compared to the existing analog transmission schemes.

The central challenge in supporting massive IoT connectivity is the uncoordinated, random access by sporadically active devices. The random access protocol and activity detection have been widely studied, while the auxiliary procedures, such as synchronization, channel estimation and equalization, have received much less attention. However, once the protocol is fixed, the access performance can only be improved by a more effective receiver, through more accurate execution of the auxiliary procedures. This motivates the pursuit of joint synchronization and channel estimation, rather than the traditional approach of handling them separately. The prohibitive complexity of the conventional analytical solutions leads us to employ the tools of deep learning in this paper. Specifically, the proposed method is applied to the random access protocol of Narrowband IoT (NB-IoT), preserving its standard preamble structure. We obtain excellent performance in estimating Time-of-Arrival (ToA), Carrier-Frequency Offset (CFO), channel gain and collision multiplicity from a received mixture of transmissions. The proposed estimator achieves a ToA Root-Mean-Square Error (RMSE) of 0.99 us and a CFO RMSE of 1.61 Hz at 10 dB Signal-to-Noise Ratio (SNR), whereas a conventional estimator using two cascaded stages have RMSEs of 15.85 us and 8.05 Hz, respectively.

Millimeter-wave (mmWave) communications is an emerging technology expected to bring unprecedented data rates and throughput. WiFi operating at unlicensed 60 GHz range is envisioned to become an ubiquitous technology and the IEEE 802.11ad standard is an initial attempt in that direction. Al-though spatial and temporal resolution of mmWave signals make them suitable for location estimation, a variety of hardware-related issues and commonly encountered difficulties in extract-ing channel measurements from commercial chipsets, challenge opportunistic use of commercial mmWave WiFi chips for indoor localization. We propose in this paper an indoor localization method that fingerprints transmit beam indices that a pair of WiFi transceivers employ to establish a mmWave link, as well as the resulting received signal strength (RSS). In particular, we develop an algorithm that learns possible probabilistic models from the fingerprint data and leverages them to perform indoor localization in the online stage. The proposed algorithm is experimentally evaluated using commercial 60 GHz WiFi routers in an office space area and localization error of around 30 cm is demonstrated.

In this paper, we propose a variational Bayesian inference approach for a low-complexity symbol detection for massive MIMO systems with symbol- dependent transmit-side impairments. This study is motivated by observations that realworld communication transceivers are often affected by the hardware impairments, such as non-linearities of power amplifiers, I/Q imbalance, phase drifts due to non-ideal oscillators, and carrier frequency offsets. Particularly, symbol-dependent perturbations are fully accounted into the designed hierarchical signal model as unknown model parameters. The developed variational Bayesian symbol detector is able to learn the unknown perturbations in an iterative fashion. Numerical evaluation confirms the effectiveness of the proposed approach.

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements from the MAC layer. In this paper, we propose to use an intermediate channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available during the beam training phase as defined in the IEEE 802.11ad standard - to construct the feature space for location-and-orientation-dependent fingerprinting database. We build a 60GHz experimental platform consisting of three access points and one client using commercial-off-the-shelf routers and collect realworld beam SNR measurements in an office environment during regular office hours. Both position/orientation classification and coordinate estimation are considered using classic machine learning approaches. Comprehensive performance evaluation using real-world beam SNRs demonstrates that the classification accuracy is 99.8% if the location is only interested, while the accuracy is 98.6% for simultaneous position-and-orientations classification. Direct coordinate estimation gives an average root-mean-square error of 17.52 cm and 95% of all coordinate estimates are less than 26.90 cm away from corresponding true locations. This concept directly applies to other mmWave band (e.g., 5G) devices where beam training is also required.

This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the misspecified Cramér-Rao bound (CRB) when system designers choose existing efficient estimation algorithms designed for an independent (decoupled) mixture model due to hardware limits. Our analysis provides an analytical tool to conveniently evaluate performance loss if the implemented system ignores the coupling effect. The achievability of the misspecified CRB is verified by numerical examples.

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.

This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node. We train DNNs such that the cross entropy loss is directly minimized, and thus it maximizes the likelihood, rather than considering the Euclidean distance of the constellations. The proposed scheme can be extended to higher level constellations with slight modification of the DNN structure. Simulation results demonstrate a significant performance gain in terms of the achievable sum rate over conventional relaying schemes. Furthermore, since our DNN demodulator directly outputs bit-wise probabilities, it is straightforward to concatenate with soft-decision channel decoding.

Motivated by the recent advancement of quantum processors, we investigate quantum approximate optimization algorithm (QAOA) to employ quasi-maximum-likelihood (ML) decoding of classical channel codes. QAOA is a hybrid quantum-classical variational algorithm, which is advantageous for the near-term noisy intermediate-scale quantum (NISQ) devices, where the fidelity of quantum gates is limited by noise and de-coherence. We first describe how to construct Ising Hamiltonian model to realize quasi-ML decoding with QAOA. For level-1 QAOA, we derive the systematic way to generate theoretical expressions of cost expectation for arbitrary binary linear codes. Focusing on [7], [4] Hamming code as an example, we analyze the impact of the degree distribution in associated generator matrix on the quantum decoding performance. The excellent performance of higher-level QAOA decoding is verified when Pauli rotation angles are optimized through meta-heuristic variational quantum eigensolver (VQE). Furthermore, we demonstrate the QAOA decoding performance in a real quantum device.

Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG. Furthermore, recent methods have mostly trained and evaluated based on single session EEG data. We address this problem from an invariant representation learning perspective. We propose an adversarial inference approach to extend such deep learning models to learn session-invariant person-discriminative representations that can provide robustness in terms of longitudinal usability. Using adversarial learning within a deep convolutional network, we empirically assess and show improvements with our approach based on longitudinally collected EEG data for person identification from half-second EEG epochs.

Distribution matching is a fixed-length invertible mapping from a uniformly distributed bit sequence to shaped amplitudes and plays an important role in the probabilistic amplitude shaping framework. With conventional constant-composition distribution matching (CCDM), all output sequences have identical composition. In this paper, we propose multiset-partition distribution matching (MPDM), where the composition is constant over all output sequences. When considering the desired distribution as a multiset, MPDM corresponds to partitioning this multiset into equal-sized subsets. We show that MPDM allows addressing more output sequences and, thus, has a lower rate loss than CCDM in all nontrivial cases. By imposing some constraints on the partitioning, a constructive MPDM algorithm is proposed which comprises two parts. A variable-length prefix of the binary data word determines the composition to be used, and the remainder of the input word is mapped with a conventional CCDM algorithm, such as arithmetic coding, according to the chosen composition. Simulations of 64-ary quadrature amplitude modulation over the additive white Gaussian noise channel demonstrate that the block-length saving of MPDM over CCDM for a fixed gap to capacity is approximately a factor of 2.5-5 at medium to high signal-to-noise ratios.

Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast. FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0-25 dB at a transmission symbol rate of 37.5 Msymbols/s.

State-of-the-art base stations can be equipped with a massively large number of antenna elements, often several hundreds of elements, thanks to the rapid advancement of wideband radio-frequency (RF) analog circuits and compact antenna design techniques. With massive antenna systems, a relatively large number of users can be served at the same time by means of analog and digital beamforming and spatial multiplexing. We investigate such a large-scale multi-user multiple-input multiple-output (MU-MIMO) wireless system employing an orthogonal frequency- division multiplexing (OFDM)-based downlink transmission scheme. The use of OFDM causes a high peak-to-average power ratio (PAPR), which usually calls for expensive and power-inefficient RF components at the base station. In this paper, we propose a nullspace vector perturbation (VP) which integrates both nonlinear lattice and linear subspace precoding approaches. By exploiting high degrees of freedom available in massive MU-MIMO OFDM systems, the signal PAPR can be significantly reduced with the proposed method. We also introduce a Gaussian process (GP) regression approach to be robust against the imperfect channel knowledge, which is required for the VP operation, in time-varying fading channels. Our analysis of outage capacity reveals that the proposed VP with GP regression offers a significant improvement in sum-rate spectral efficiency while reducing the PAPR.

A growing number of applications may benefit from embedding receiver with the capability to separate collided packets directly on a physical layer. For example, in IoT's random access channel scenario, a number of IoT devices are placed in the same cell, occasionally transmit messages, and are assigned non-orthogonal channel resources, giving rise to packet collisions. While channel access techniques for sharing a common channel are usually employed in most multi-user communication systems, they cause channel underutilization and increased latency. This paper considers a scenario where multiple users asynchronously transmit packets over a shared channel and are not subject to a random access mechanism, or the mechanism itself fails to prevent packet collisions. Assuming that each packet consists of a common preamble and information bearing payload, and experiences random delay, frequency offset, phase noise variation and block flat fading channel, we propose a packet separation algorithm which recovers collided packets, estimates their parameters and detects corresponding payload symbols. The performance of the proposed method is validated using simulations and benchmarked against bounds.

In this paper, we propose a Bayesian inference approach to extract the binary reflectance pattern of samples from compressed measurements in the terahertz (THz) frequency band. Compared with existing compressed THz imaging methods relying on the sparsity of the reflectance pattern, the proposed Bayesian approach exploits the non-negative binary nature of the reflectance without any assumption on its spatial pattern information and enables a pixel-wise iterative inference approach for fast signal recovery. Numerical evaluation confirms the effectiveness of the proposed approach.

A major challenge of screen-camera visual multiinput multi-output (MIMO) communications is to increase the achievable throughput by reducing nonlinear channel effects including perspective distortion, ambient lights, and color mixing. To mitigate such nonlinear effects, an existing transmission method uses linear or simple nonlinear equalizations in decoding operations. However, the throughput improvement from the equalization techniques is often limited because the effects are composed of a combination of various nonlinear distortions. In addition to the above issue, the existing studies consider specific environments, such as indoor and static communications, although screen-camera communications can be used for a variety of applications including outdoor and mobile scenarios. In this study, we propose 1) deep neural network (DNN)- based decoding for screen-camera communications to increase the achievable throughput and 2) Unity 3D-based evaluation methodology to synthetically learn the DNN for being robust against many different screen-camera environments. The DNN finds the best nonlinear kernels for equalization from numerously captured images, and then decodes original bits from newly captured images based on the trained nonlinear kernels. In the Unity-based evaluation tool, we can easily capture numerous photo-realistic images in different screen-camera scenarios to learn the impact of perspective distortion, screen- to-camera distance, motion blur, and ambient lights on the throughput since Unity-based environment can freely set programmable screens, cameras, and ambient lights on a 3D space. As an initial proof of concept, we demonstrate that the proposed DNN-based decoder scheme improves the achievable throughput by up to 148% compared to existing methods by equalizing nonlinear effects.

Ultra-reliable forward error correction (FEC) codes approaching the Shannon limit have played an important role in increasing spectral efficiency of wireless communications. In addition to the error correction performance, both low-power and low-latency decoding are demanded for the fifth generation (5G) wireless applications. In this paper, we introduce turbo product codes (TPC) consisting of multiple polar codes to enable highly parallel decoding for high- throughput and low-latency FEC. With turbo iterative decoding, the proposed polar-TPC can outperform the conventional BCH-based TPC by 0.5 dB, and can approach the performance of the corresponding long polar code within 0.2 dB with a capability of 256- times faster decoding. In addition, we apply irregular polar codes, whose polarization units are pruned, to further reduce the computational complexity by 50% and decoding latency by 80% without sacrificing performance. We analyze the impact of list size, turbo iteration count, and fading channels to demonstrate the potential of the polar-TPC for 5G wireless systems.

Soft video delivery, i.e., analog video transmission, has been proposed to provide high video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations causing large overhead and quality degradation due to rate and power losses. To reduce the overhead while keeping the video quality high, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that the proposed scheme reduces overhead by 99.7% with 1.2-dB improvement of video quality (in terms of peak signal-to-noise ratio) compared to the existing analog video transmission scheme. We also investigate the impact of bandwidth limitation, showing a significant gain up to 2.7 dB for narrow-band systems.

Recent advancements in polar coding have achieved practical performance competitive with other capacity-achieving codes such as low-density parity-check codes. In this paper, we propose a new family called irregular polar codes, where polarlization units are irregularly inactivated to achieve additional degrees of freedom for code design. We first discuss the code construction for irregular polar-coded modulation by taking non-uniform bit-reliability into consideration. We then apply the proposed polar codes to wireless massive multiple-input multiple- output (MIMO) communication channels. Simulation results show that the irregular polar codes can significantly reduce encoding/decoding complexity up to 50% while also yielding a marginal improvement in error rate performance.

Molecule loss is a critical reliability issue in diffusive molecular communications. This paper proposes a communica- tion framework that allows biologically-enabled machines (bio- nanomachines) to transmit and receive information-carrying molecules (information molecules) in a robust manner against molecule losses. The proposed framework, called molecular fountain, employs deoxyribonucleic-acid (DNA) molecules as information carriers and leverages molecular fragmentation (i.e., packetization) between transmitter (Tx) and receiver (Rx) bio-nanomachines. It performs feedback-aided rateless erasure coding that considers biochemical constraints in DNA synthesis and sequencing to generate molecular packets. The Tx bio- nanomachine repeatedly generates molecular packets with Luby transform codes and transmits them to the Rx bio- nanomachine until it receives an acknowledgment from the Rx bio-nanomachine. The Rx bio-nanomachine can reconstruct lost molecular packets from other packets that have been successfully transmitted. Simulation results show that molecular fountain enhances robustness against molecular packet losses and in turn improves communication performance such as transmission latency, jitter, error rate, and coding overhead.

This paper investigates a method to improve performance of diffusive molecular communications between biologically-enabled nanomachines in in-vivo aqueous environment. The proposed method exploits periodic flow, e.g., induced by repeated heart pumping. We make an analysis of channel impulse response (CIR) for such drift- diffusion fluid systems. In order to take the cyclic CIR into account, the proposed method optimizes the release timing and size of information molecules so that highest equalization gain can be achieved. We reveal that error rate performance can be significantly improved with adaptive molecule loading by taking care of the cyclic CIR.

In millimeter wave (mmWave) communication, analog and hybrid beamforming systems are proposed to reduce the number of RF chains when a large antenna array is utilized to achieve a high beamforming gain. In this paper, a new transmission scheme is first proposed by leveraging the hardware architecture of analog and hybrid beamforming to improve the spectral efficiency. The new scheme is called spatial scattering modulation (SSM), since it exploits the spatial scattering dimension to modulate information bits. And then, an adaptive transmission strategy (ATS) which chooses the best transmission scheme under instantaneous channel state information, is proposed to improve the performance. Link-level simulation results demonstrate the superiority of the proposed ATS in all the simulated signal-to-noise (SNR) values.

Wireless video streaming for multi-view plus depth (MVD) frames enables free viewpoint video on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. To transmit MVD frames over wireless links, existing schemes use digital-based compression to achieve better compression efficiency. However, the digital-based schemes have an issue called cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to allocate quantization levels and transmission power across MVD frames is cumbersome. To facilitate the MVD video streaming, we propose an analog-based transmission scheme. Our scheme directly transmits linear-transformed signals based on five-dimensional discrete cosine transform (5D-DCT), without relying on quantization and entropy coding. The proposed scheme achieves graceful video quality with the improvement of wireless channel quality. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling transmission power assignment. It is demonstrated with test MVD video sequences that the analog-based scheme offers a great advantage over the conventional digital-based scheme. For instance, the video quality of one intermediate virtual viewpoint is approximately 2.1 dB higher than digital-based schemes at a wireless channel quality, i.e., signal-to-noise ratio (SNR), of 15 dB.

We investigate non-coherent multi-antenna signal processing which requires no channel state information (CSI) at either transmitter or receiver ends. With non-coherent constellations over Grassmannian manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. The conventional GLRT relies on the assumption that the wireless channel is time-invariant during a block. We propose an improved GLRT algorithm which employs a novel two-dimensional basis expansion model (2D-BEM) to cope with doubly-selective fading channels. The proposed method uses sequential GLRT for multi-symbol detection to keep high performance yet low complexity. Furthermore, we introduce Fourier-Legendre product basis to be robust against hardware impairments including carrier frequency and timing offsets. We demonstrate that the proposed scheme significantly improves performance in rapid fading channels, and realizes highly spectrum-efficient transmission up to 6 bps/Hz without any pilots.

Machine-to-machine (M2M) communications play an important role for applications that involve connections between a massive number of heterogeneous devices in home and industrial networks. For M2M networks, realizing low latency and high reliability is of great importance. In this paper, we show the great potential of polar-coded orthogonal frequency-division multiplexing (OFDM) to fulfill those requirements. We show that polar codes with list decoding plus cyclic redundancy check (CRC) can outperform state-of-the-art low-density parity-check (LDPC) codes at short block lengths. In addition, we introduce an efficient interleaver and constellation shaping for polar-coded high-order modulations, where a coded sequence is carefully mapped across subcarriers and modulation bits to exploit non-uniform reliability for higher diversity gains. Through computer simulations, we demonstrate that a significant gain greater than 2 dB can be achieved by quadratic polynomial permutation (QPP) interleaver with optimized parameters in comparison to the conventional random interleaver for high-order 256-ary quadrature-amplitude modulation (QAM) OFDM transmission in frequency-selective wireless channels.

In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature, we propose a two-dimensional sparse Bayesian learning method without a priori knowledge of two-dimensional angular spread patterns. It essentially couples the channel path power at one angular direction with its two-dimensional AoD-AoA neighboring directions. Compared with existing sparse mmWave channel estimation methods, the proposed method is numerically verified to reduce the training overhead and channel estimation error.

Compression of multispectal images is of great importance in an environment where resources such as computational power and memory are scarce. To that end, we propose a new extremely low-complexity encoding approach for compression of multispectral images, that shifts the complexity to the decoding. Our method combines principles from compressed sensing and distributed source coding. Specifically, the encoder compressively measures blocks of the band of interest and uses syndrome coding to encode the bitplanes of the measurements. The decoder has access to side information, which is used to predict the bitplanes and to decode them. The side information is also used to guide the reconstruction of the image from the decoded measurements. Our experimental results demonstrate significant improvement in the rate-distortion trade-off when compared to coding schemes with similar complexity.

Screen-camera communication, which uses a liquid crystal display (LCD) screen and camera image sensors, has been an attractive variant of visible light communications (VLC) since display and camera have been equipped with in various mobile devices. To improve transmission rates, we investigate the impact of nonlinear channel equalization and nonbinary channel coding as well as high-order modulation schemes. Equalization techniques can reduce the effect of channel impairments, such as color mixing. Nonbinary coding improves reliability of high-order modulation and thus increases the transmission rate. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.6-2.4 times higher transmission rates compared to existing schemes for a communication distance of 40-100 cm.

Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations. It causes large overheads and quality degradation because of rate and power losses. To reduce the overheads while keeping high video quality, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that our proposed scheme reduces overheads by 97 % with 3.4 dB improvement of video quality compared to the existing analog video transmission scheme.

The quality of wireless video delivery is susceptible to wireless channel instability, including channel fading, interference, noise, and packet loss. To improve the video quality in such wireless channels, analog transmission schemes have been proposed recently. However, the existing analog schemes have two drawbacks in high energy of video signals and loss resilience. To overcome the issues, we propose a new hybrid digital-analog transmission scheme, which jointly uses digital coding and analog coding based on compressive sensing. Digital coding generates the residual signals between the original and the encoded signals to decrease the energy of data for analog coding. Compressive sensing redistributes the energy across whole video packets to increase the resistance to packet loss. We show that our proposed hybrid scheme improves video quality by up to 11.6 dB compared to the existing analog scheme in an erasure wireless channel. In addition, our proposed scheme also improves video quality by 1.5 dB compared to the existing hybrid scheme.

Machine-to-machine (M2M) communications have been considered as an important application to connect a massively large number of different devices in networks. In particular for M2M wireless networks, low latency and high reliability are of great importance. To fulfill the requirements, a diversity technique exploiting limited resources is proposed in this paper for short-message transmissions. The proposed method uses multiple stages of fast unitary transforms and diagonal phase rotations to achieve full- diversity gain. A monomial phase rotation is also proposed to facilitate an optimization of the precoding matrix. It is verified that the proposed four-stage precoding provides universal diversity gain irrespective of channel selectivity in time and frequency, without changing monomial parameters. In addition, it is shown that the four-stage precoding based on the discrete Haar transform (DHT) achieves a full diversity while the computational complexity is significantly reduced from a log- linear order to a linear order compared to the other unitary transforms such as the discrete Fourier transform (DFT).

We propose an unsupervised, received signal strength (RSS)- based indoor localization method, which as an infrastructure uses commercial WiFi chipsets and does not require any changes in the existing hardware. The method relies on path loss model for measured RSS levels where path loss coefficient is treated as a discrete random variable which takes values from some finite alphabet. The unknown location and path loss coefficient corresponding to each access point are jointly estimated using the Expectation Maximization (EM) approach. The algorithm is experimentally tested in an office space area of dimensions 32-by-52 m (1600 m2) with only five access points and the achieved average localization error is below 4.5 m.

Probabilistic Data Association (PDA) algorithm has shown promising performance in symbol detection and interference cancellation in different communication schemes. This paper proposes new algorithms that build on PDA and introduce modifications in the way the symbol being detected is treated. While PDA models this symbol as a discrete sample from a constellation, PDA with symbol uncertainty (SU-PDA) views it as a sum of a deterministic symbol and random noise, while the Gaussian PDA (G-PDA) models it as a random variable with either a single Gaussian or Gaussian mixture distribution. The proposed algorithms are tested via computer simulations on both simulated and experimentally measured channels. The performance study reveals that the SU-PDA and G-PDA outperform the conventional PDA with the performance gain ranging from few dBs on measured channel with block fading up to and exceeding 10 dB on the simulated channel with fast fading.

We study the iterative decoding threshold performance of non-binary spatially-coupled low-density parity-check (NB-SC-LDPC) code ensembles for both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise channel (BIAWGNC), with particular emphasis on windowed decoding (WD). We consider both (2, 4)-regular and (3, 6)-regular NB-SC-LDPC code ensembles constructed using protographs and compute their thresholds using protograph versions of NB density evolution and NB extrinsic information transfer analysis. For these code ensembles, we show that WD of NB-SC-LDPC codes, which provides a significant decrease in latency and complexity compared to decoding across the entire parity-check matrix, results in a negligible decrease in the near-capacity performance for a sufficiently large window size W on both the BEC and the BIAWGNC. Also, we show that NBSC-LDPC code ensembles exhibit gains in the WD threshold compared to the corresponding block code ensembles decoded across the entire parity-check matrix, and that the gains increase as the finite field size q increases. Moreover, from the viewpoint of decoding complexity, we see that (3, 6)-regular NB-SC-LDPC codes are particularly attractive due to the fact that they achieve near-capacity thresholds even for small q and W.

This paper presents a novel framework, which enables a design of rateless codes for binary input additive white Gaussian noise (BIAWGN) channels, using the ripple-based approach known from the works for the binary erasure channel (BEC). We reveal that several aspects of the analytical results from the BEC also hold in BIAWGN channels. The presented framework is applied in a code design example, which shows promising results compared to existing work. In particular it shows a great robustness towards variations in the signal-to-noise power ratio (SNR), contrary to existing codes.

In this paper, we consider network coded (NCed) Hybrid-ARQ (HARQ) for multiple unicast flows. The main contribution of the paper is the derivation of throughput expressions for NCed HARQ with arbitrary number of users in identical i.i.d. channels amid packets for all users. We apply the result to Rayleigh fading channels and two packet combining schemes: incremental redundancy (IR) and chase combining (CC). We verify the analytical results with simulations and observe substantial SNR improvements over NCed ARQ and HARQ. The SNR gains in the moderate/high and low throughput regimes are mainly due to network coding and packet combining, respectively. For low/moderate SNRs, NCed HARQ with IR surpasses the CC performance. In addition, we introduce a novel re-transmission strategy that makes the network coding more efficient at low SNR.

This paper proposes a concept called rateless feedback coding. We redesign the existing LT and Raptor codes, by introducing new degree distributions for the case when a few feedback opportunities are available. We show that incorporating feedback to LT codes can significantly decrease both the coding overhead and the encoding/decoding complexity. Moreover, we show that, at the price of a slight increase in the coding overhead, linear complexity is achieved with Raptor feedback coding.

In this paper, we present an analytical framework for designing LT codes in additive white Gaussian noise (AWGN) channels. We show that some of analytical results from binary erasure channels (BEC) also hold in AWGN channels with slight modifications. This enables us to apply a ripple-based design approach, which until now has only been used in the BEC. LT codes designed by this way show promising performance which is near the Shannon limit even with short codewords.

We study precoder optimization gains and multiuser diversity gains with interference alignment in a two-cell wireless network. In this paper, we propose algorithms to improve the achievable sum rate by optimizing alignment directions. The proposed iterative algorithm can provide a substantial gain in the achievable rate while it requires only a few iterations. In addition, we investigate interference alignment to exploit multiuser diversity gains. A new criterion of user selection scheduling is proposed. This user selection can be done independently in different cells. Therefore, both the searching space and the information exchanged between base stations are significantly reduced compared to joint user scheduling over two cells.

It has been shown that the secrecy capacity of a wireless network is pushed rapidly towards zero as the number of users in the network grows. To deal with this issue, a secure method which intentionally superposes precoded jamming signals has been proposed. In this paper, we analyze its advantage to realize a secure wireless network in the presence of a large number (e.g., hundreds) of eavesdropping users, for transmitting confidential messages. Our analysis and simulations demonstrate that the jamming superposition achieves high secrecy rate even for undesired cases where the channels for many eavesdroppers are noise-less and highly correlated with the intended receiver.

We develop a recursive least-squares (RLS) algorithm which employs L1-Lqregularized sparse regressions to estimate a sparse channel matrix in frequency-and-time selective fading for multi-input multi-output (MIMO) wireless communications. We propose an improved sparse RLS by using an order extension technique for rapid fading channels. Simulation results demonstrate that the proposed sparse RLS algorithm offers a significant improvement over the conventional RLS algorithm.

We investigate non-coherent multi-input multi-output (MIMO) signal processing which requires no channel state information (CSI) at either the transmitter or the receiver. With non-coherent codes on Grassmann manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. However, the conventional GLRT suffers from a severe performance degradation when the channel changes fast within a coding block duration.We propose an improved GLRT algorithm referred to as high-order super-block techniques. The super-block scheme makes effective use of correlated channels for adjacent blocks in slow fading, whereas the high-order scheme can overcome the channel fluctuation during a block in fast fading. We demonstrate that the proposed scheme significantly improves performance for MIMO-OFDM with non-coherent Grassmann space-frequency block codes (SFBC).

In this paper, we propose a distributed interference alignment which employs physical-layer network coding and superposition coding for successive-cancelling multiuser detection (MUD) receivers in K-pair bidirectional relaying networks. The proposed scheme enables the transmitter to align only partial interference while strong interference is cancelled by MUD, and the transmitter can have more degrees of freedom in controlling the filter designs. Simulation results demonstrate that our proposed scheme significantly improves sum-rate performance in multiuser bidirectional relaying systems.

Block transmission with cyclic prefix is a promising technique to realize high-speed data rates in frequency-selective fading channels. Many popular linear precoding schemes, including orthogonal frequency-division multiplexing (OFDM), single-carrier (SC) block transmission, and time-reversal (TR), can be interpreted as such a block transmission. This paper presents a unified performance analysis that shows how the optimal precoding strategy depends on the optimization criterion such as capacity, mean-square error, and secrecy. We analyze three variants of TR methods (based on maximum-ratio combining, equal-gain combining and selective combining) and two-types of pre-equalization methods (zero-forcing and minimum mean-square error). As one application of our framework, we derive optimal precoding (i.e., OFDM with optimal power and phase control) in the presence of interference limitation for distributed antenna systems; we find that without power/phase control, OFDM does not have any capacity advantage over SC transmissions. When comparing SC and TR, we verify that for single-antenna systems in the high SNR regimes, SC has a capacity advantage; however, TR performs better in the low SNR regime. For distributed multiple-antenna systems, TR always provides higher capacity, and the capacity of TR can approach that of optimal precoders with a large number of distributed antennas. Furthermore, we make an analysis of secrecy capacity which shows how high-rate messages can be transmitted towards an intended user without being decoded by the other users from the viewpoint of information-theoretic security. We demonstrate that TR precoding can be the best candidate among the non-optimal precoders for achieving high secrecy capacity, while the optimal precoder offers a significant gain over those non-optimal precoders.

We investigate physical-layer network coding for two-way relaying systems which employ multiple-input multiple-output (MIMO) techniques. For single-antenna cases, it has been previously verified that adaptive network coding, whose mapping rule is dependent on channel state information (CSI), can offer good performance for two-stage relaying protocols. In this paper, we evaluate gains achieved by adaptive network coding over the conventional network coding in MIMO channels. We consider several different scenarios: any CSI is not available, only local CSI is available, and global CSI is available at transmitters. Optimal precoding is derived for each scenario to minimize pairwise error probability. We confirm that the adaptive network coding still outperforms the fixed network coding even for MIMO communications.

We investigate non-coherent multi-input multi-output (MIMO) signal processing which requires no channel state information (CSI) at either the transmitter or the receiver. With non-coherent codes on Grassmann manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. However, the conventional GLRT suffers from a severe performance degradation when the channel changes fast within a coding block duration. We propose an improved GLRT algorithm referred to as high-order super-block techniques. The super-block scheme makes effective use of correlated channels for adjacent blocks in slow fading, whereas the high-order scheme can overcome the channel fluctuation during a block in fast fading. We demonstrate that the proposed scheme significantly improves performance for MIMO-OFDM with non-coherent Grassmann space-frequency block codes (SFBC).

In this paper, we consider network coded (NCed) Hybrid-ARQ (HARQ) for multiple unicast flows. The main contribution of the paper is the derivation of a throughput expression for NCed HARQ with arbitrary number of users in i.i.d. channels. We apply the result to Rayleigh fading channels for incremental redundancy (IR) and chase combining (CC) based NCed HARQ. We verify the analytical approach with simulations and observe substantial SNR (or energy) improvements over regular ARQ with network coding as well as classical (H)ARQ. The SNR gains in the high and low throughput regimes are mainly due to the network coding and HARQ aspects, respectively. For low SNRs, NCed HARQ with IR surpass the CC performance.

In this paper, we present the impact on specific absorption ratio (SAR) distribution by dispersive media in wireless body area networks (BAN). For BAN systems, ultrawideband (UWB) transmissions have received much attention because of its low power spectrum and its small antenna size. When we adopt the UWB technique to BAN systems, the SAR analysis is highly demanded for predicting the exposure level prior to experiments and products. It is well-known that the dielectric properties of human tissues have a great spectrum dependency, that renders a difficulty of an SAR analysis by electromagnetic field simulations through the finite-difference time-domain (FDTD) methods for wideband signals. For frequency-dependent analysis, the piecewise linear recursive convolution (PLRC) method is useful for dealing with the dispersive media. To employ the PLRC method, we re-formulate the dielectric spectrum properties of human organs approximating the so-called 4-Cole-Cole model. We reveal that we need to consider the dispersive effect of human tissues to analyze the SAR according to the UWB bandwidth.

We propose a precoding strategy which controls amplitude and phase of receiving signals to improve throughput for two-stage bidirectional relaying. We consider the case when the nodes know channel state information (CSI) and can adopt adaptive modulation techniques. We introduce a novel scheme termed adaptive modulation and network coding (AMNC), which jointly optimizes modulations and network coding based on the CSI. For dynamic bit loading and power allocation, we propose a practical time-sharing method called the segmented precoding, in which a packet is split into several sub-packets, and a set of modulation and network coding is optimized in conjunction with amplitude and phase controls for each sub-packet. It is demonstrated that our proposed scheme can offer a significant improvement of achievable throughput for two-way relaying.

In this paper, we present a forwarding strategy for two-stage bidirectional relaying in which trellis-coded modulation (TCM) is employed. We reveal that adaptive network coding cannot resolve distance shortening occurred at specific channel conditions when a certain TCM is used. To overcome this issue, we introduce an improved amplify-and-forward (AF) scheme termed pseudo AF (PAF). The proposed strategy adaptively switches network coding and PAF according to the channel information. Computer simulations demonstrate that the proposed approach can improve throughput performance.

We investigate on multi-dimensional QAM constellations optimized by sphere packing with the known densest lattices. We propose a greedy design method assisted by the sphere detection. It is demonstrated that the optimized constellations can significantly increase the squared minimum distance in comparison to the conventional QAM constellations. In addition, we analyze the optimized QAMs through the use of an extrinsic information transfer (EXIT) chart for iterative decoding.

We investigate modulation schemes optimized for two-way wireless relaying systems, for which network coding is employed at the physical layer. We consider network coding based on denoise-and-forward (DNF) protocol, which consists of two stages: multiple access (MA) stage, where two terminals transmit simultaneously towards a relay, and broadcast (BC) stage, where the relay transmits towards the both terminals. We introduce a design principle of modulation and network coding, considering the superposed constellations during the MA stage. For the case of QPSK modulations at the MA stage, we show that QPSK constellations with an exclusive-or (XOR) network coding do not always offer the best transmission for the BC stage, and that there are several channel conditions in which unconventional 5-ary constellations lead to a better throughput performance. Through the use of sphere packing, we optimize the constellation for such an irregular network coding. We further discuss the design issue of the modulation in the case when the relay exploits diversity receptions such as multiple-antenna diversity and path diversity in frequency-selective fading. In addition, we apply our design strategy to a relaying system using higher-level modulations of 16QAM in the MA stage. Performance evaluations confirm that the proposed scheme can significantly improve end-to-end throughput for two-way relaying systems.

In multiple-input multiple-output systems, an ordered successive interference canceller, termed the vertical Bell laboratories layered space-time (V-BLAST) algorithm, offers good performance. This letter presents a low-complexity V-BLAST scheme suited for parallel implementation. The proposed scheme, using a greedy ordering, can achieve a performance comparable to that of V-BLAST with optimum ordering, while its computational complexity is lower than a linear detector.

We propose a low-complexity equalizer whose performance approaches that of the optimal maximum-likelihood estimators in wideband multiple-input multiple-output (MIMO) channels. The proposed algorithm makes use of a bit-flipping refinement procedure preceded by a frequency-domain equalizer and is based on local-optima searching algorithms. Through performance evaluations, it is demonstrated that the proposed equalizer can perform well when a large number of diversity branches are available in severely dispersive fading channels.

In this paper, we propose a low-complexity equalizer whose performance approaches that of the optimum maximum-likelihood (ML) estimator, in ultra-wideband (UWB) multiple- input multiple-output (MIMO) channels. Although the ML estimation offers the excellent performance, it is generally of high complexity due to the large size of searching space. Through a theoretical search-space analysis, we reveal that local searching algorithms may approximate the ML estimator when a large number of diversity branches is available. Based on the analysis, we develop a novel equalizer with simple bit-flipping for severely frequency-selective fading in UWB-MIMO channels.

We investigate on the design of modulation schemes suited for two-way wireless relaying systems that apply network coding at the physical layer. We consider network coding based on denoise-and-forward (DNF), which consists of two stages: multiple access (MA) stage and broadcast (BC) stage. For the case of QPSK constellation in the MA stage, we introduce the modulation-related problems in DNF. We propose two approaches to solve those issues. One uses only QPSK constellations at the BC stage. The other allows the use of unconventional 5-ary modulations, optimized according to the channel condition. The performance evaluation shows that a significant improvement in end-to-end throughput can be achieved, in particular for Nakagami-Rice fading channels.

This paper investigates on an accurate channel estimation scheme for fast fading channels in multiple-input multiple-output (MIMO) mobile communications. A high-order exponential-weighted recursive least-squares (EW-RLS) method has been known as a good channel estimation scheme in rapid fading. However, there exists a drawback that we need to properly adjust the estimation parameters of a forgetting factor and an estimation order according to the channel environment. In this paper, we theoretically derive an optimum-weighted LS (OW-LS) channel estimation based on the statistical knowledge of the spatio-temporal channel correlation. Through the analysis, we reveal that the zero-th order polynomial becomes optimal when the optimum-weighting is employed. Furthermore, we propose an efficient recursive algorithm for channel tracking in order to reduce the computational complexity. Since the proposed scheme automatically adapts the weighting coefficients to the channel condition, it has a significant advantage in mean-square error (MSE) performance compared to the EW-RLS scheme.