Low Earth orbit (LEO) satellite constellations are emerging as a core enabler of sixth-generation (6G) wireless systems, providing global coverage and high-capacity connectivity. However, dense multi-beam LEO deployments introduce severe inter-beam and inter-satellite interferences, while rapid orbital motion results in frequent handovers and highly dynamic channels that challenge the real-time optimization. To address these issues, this paper has proposed a digital twin (DT)-driven multi-LEO network architecture that integrates ray tracing-based virtual simulations with intelligent on-orbit control. Within this framework, we develop a DT-driven Efficient handover and Multi-Agent Twin delayed deep deterministic policy gradient (DEMAT) scheme, which jointly optimizes beam training, handover, power allocation, and beamwidth adaptation to maximize energy efficiency (EE) while satisfying user throughput requirements. DEMAT leverages bidirectional DT-LEO parameter exchange and federated learning-enhanced agents for cooperative and low-latency resource management. Extensive simulations validate its convergence and scalability under diverse network configurations such as various time frame intervals and user densities. Notably, DEMAT achieves up to a 59.6% EE improvement over the Deep Deterministic Policy Gradient (DDPG) baseline and more than 40% of EE compared to the other DT-based benchmarks, demonstrating superior adaptability and coordination for the next-generation non-terrestrial networks.

Exploiting on-device data and computing power for machine learning at the network edge is challenged by constrained device resources, privacy requirements, and local data heterogeneity. To address the above gap, this work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized ML. BagChain integrates blockchain with distributed ML by replacing the computationally costly hash computing in proof-of-work with ML model training and validation, and does not rely on any trusted central servers. Individual miners in BagChain train base models by using their local computing resources and private data and further aggregate these base models, which could be very weak, into strong ensemble models. More specifically, we design a three-layer blockchain structure and associated generation and validation mechanisms to enable distributed ML among uncoordinated miners without revealing raw data. To reduce computational waste due to blockchain forking, we further propose the cross fork sharing mechanism for practical networks with lengthy delay and limited bandwidth. Extensive experiments illustrate the superiority and efficacy of BagChain when handling various ML tasks on both independently and identically distributed (IID) and non-IID datasets. BagChain remains robust and effective even when facing resource constrained mobile devices, heterogeneous private user data, and limited network connectivity. The source code of BagChain is released at: https://github.com/czxdev/BagChain.

The recent rise of semantic-style communications has fostered the development of goal-oriented communications (GO-COMs), facilitating remarkably efficient multimedia information transmissions. The concept of GO-COMs leverages advanced artificial intelligence (AI) tools to address the rising demand for bandwidth efficiency in applications, such as edge computing and the Internet of Things (IoT). Unlike traditional communication systems focusing on source data accuracy, GO-COMs provide intelligent message delivery catering to the special needs critical to accomplishing downstream tasks at the receiver. In this work, we present a novel GO-COM framework, namely LaMI-GO, that utilizes emerging generative AI for better quality of service (QoS) with ultrahigh communication efficiency. Specifically, we design our LaMI-GO system backbone based on a latent diffusion model followed by a vector-quantized generative adversarial network (VQGAN) for efficient latent embedding and information representation. The system trains a common-feature codebook for the receiver side. Our experimental results demonstrate substantial improvement in perceptual quality, accuracy of downstream tasks, and bandwidth consumption over the state-of-the-art GO-COM systems and establish the power of our proposed LaMI-GO communication framework.

Multiuser MIMO (MU-MIMO) deployment jointly serves multiple users over shared time-frequency Resource Blocks (RBs) to achieve substantial spectral efficiency improvement. To exploit the full potential of MU-MIMO, users should be scheduled on RBs with strong channels and low Co-Channel Interference (CCI). This paper leverages Variational Quantum Algorithm (VQA) framework, tailored for near-term Noisy Intermediate-Scale Quantum (NISQ) devices, for efficient user scheduling in broadband MU-MIMO systems, which is NP-hard. Specifically, our designed quantum circuit incorporates parameterized entangling gates to create controllable correlations between quantum states, which may explore the solution space more efficiently. To facilitate the challenging variational parameters optimization in VQAs, exacerbated by entangling operations, our framework adopts a two-stage approach. First, we utilize Bayesian Optimization (BO) which constructs a global surrogate model to find sets of promising parameters. Second, we perform local optimization to fine-tune the result of BO by leveraging Gaussian kernels to approximate the noisy optimization landscape with a limited amount of quantum shots. Through extensive simulations, we demonstrate the efficacy of our VQA in achieving near-optimal performance for broadband MU-MIMO user scheduling problems.

This paper introduces a novel method to estimate doubly selective channels for single-carrier (SC) communication systems in high-mobility environments. We propose a transmission frame structure featuring adaptive pilot insertions tailored to the Doppler spread, and an adaptive threshold-based channel estimation method that leverages the delay-Doppler (DD) domain channel characteristics. To mitigate the peak-to-average power ratio (PAPR) and reduce pilot overhead, we further generalize the frame structure from single-pilot scheme to multiple-pilot scheme and propose the design criteria for the pilot. By employing zero correlation zone (ZCZ) sequences with ZCZ sizes equal to or greater than the normalized maximum Doppler shift of the channel, we show that the multiple-pilot scheme effectively enhances estimation accuracy without inducing significant PAPR and pilot overhead increment. For both schemes, we also derive the asymptotically optimal thresholds for DD channel tap detection to minimize mean square error (MSE) of the estimation. Moreover, the closed-form approximation of Cramer-Rao Lower Bound for the channel estimation error is also derived, serving as a performance benchmark. Comparative analysis demonstrates that our proposed method significantly outperforms conventional interpolation techniques in estimating doubly selective fading channels, thereby illustrating its efficacy and practical utility in SC systems.

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

Recent advancements in generative artificial intelligence have introduced groundbreaking approaches to innovating next-generation semantic communication, which prioritizes conveying the meaning of a message rather than merely transmitting raw data. A fundamental challenge in semantic communication lies in accurately identifying and extracting the most critical semantic information while adapting to downstream tasks without degrading performance, particularly when the objective at the receiver may evolve over time. To enable flexible adaptation to multiple tasks at the receiver, this work introduces a novel semantic communication framework, which is capable of jointly capturing task-specific information to enhance downstream task performance and contextual information. Through rigorous experiments on popular image datasets and computer vision tasks, our framework demonstrates promising improvement compared to existing work, including superior performance in downstream tasks, better generalizability, ultra-high bandwidth efficiency, and low reconstruction latency.

Semantic communication marks a new paradigm shift from bit-wise data transmission to semantic information delivery for the purpose of bandwidth reduction. To more effectively carry out specialized downstream tasks at the receiver end, it is crucial to define the most critical semantic message in the data based on the task or goal-oriented features. In this work, we propose a novel goal-oriented communication (GO-COM) framework, namely Goal-Oriented Semantic Variational Autoencoder (GOS-VAE), by focusing on the extraction of the semantics vital to the downstream tasks. Specifically, we adopt a Vector Quantized Variational Autoencoder (VQ-VAE) to compress media data at the transmitter side. Instead of targeting the pixel-wise image data reconstruction, we measure the quality-of-service at the receiver end based on a pre-defined task-incentivized model. Moreover, to capture the relevant semantic features in the data reconstruction, imitation learning is adopted to measure the data regeneration quality in terms of goal-oriented semantics. Our experimental results demonstrate the power of imitation learning in characterizing goal-oriented semantics and bandwidth efficiency of our proposed GOS-VAE.

The rapid expansion of edge devices and Internet-of-Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusionbased GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing highquality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GO${}^{\mathbf{n}}$well-suited for real-time communications and downstream applications.

Earth exploration satellite service (EESS) plays a crucial role in environmental monitoring and weather forecasting by utilizing passive sensing technologies. However, the rapid expansion of terrestrial and satellite communication networks has introduced significant interference challenges, particularly in frequency bands that overlap with or are adjacent to EESS sensors. In this work, we develop a system model that explicitly characterizes EESS interference by considering reflected signal effects and spatial interference accumulation. Based on this model, we propose a EESS-aware resource allocation (EARA) framework that jointly optimizes power allocation and user association, while ensuring that interference to EESS sensors remains within acceptable limits. A non-convex joint optimization problem is formulated and efficiently solved leveraging the Lagrangian dual transform and Dinkelbach’s method. Simulation results demonstrate that the proposed EARA scheme achieves up to 26.3% higher sum rate compared to genetic algorithm and binary whale optimization algorithm, while strictly satisfying the ITU-defined interference threshold. This work establishes a foundation for future research on the coexistence of communication networks and passive Earth observation systems, offering practical strategies for interference mitigation and spectrum sharing in next-generation networks.

The coexistence of heterogeneous sub-networks in 6G poses new security and trust concerns and thus calls for a perimeterless-security model. Blockchain radio access network (B-RAN) provides a trust-building approach via repeated interactions rather than relying on pre-established trust or central authentication. Such a trust-building process naturally supports dynamic trusted services across various service providers (SP) without the need for perimeter-based authentications; however, it remains vulnerable to environmental and system unreliability such as wireless channel uncertainty. In this study, we investigate channel unreliability in the trust-building framework based on repeated interactions for secure wireless services. We derive specific requirements for achieving cooperation between SPs and clients via a repeated game model and illustrate the implications of channel unreliability on sustaining trusted wireless services. We consider the framework design and optimization to guarantee SP-client cooperation, given the worst channel condition and/or the least cooperation willingness. Furthermore, we explore the maximum cooperation area to enhance service resilience and reveal the trade-off relationship between transmission efficiency, security integrity, and cooperative margin. Finally, we present simulations to demonstrate the system performance over fading channels and verify our results.

Blockchain-based decentralized applications (DApps) offer enhanced security and decentralization features; nevertheless, their maintenance demands substantial computational resources and poses challenges for deployment in mobile networks. To address this, a number of studies have explored offloading blockchain mining tasks from mobile users to mobile edge computing (MEC) servers. However, the existing literature overlooks the fact that MEC servers can not only mine for mobile users but also mine for themselves, potentially explaining why MEC mining offloading has not gained broad acceptance within the industry. In this work, we exploit a more practical case and rethink the question of whether MEC servers lease computing power to mobile users by taking into account that MEC servers can mine for themselves. We establish a game model and apply backward induction to analytically characterize Nash equilibria for mining strategies adopted by MEC and mobile users. Our findings suggest that, if MEC can mine for self, MEC would mine for mobile users only under specific conditions where mobile users possess superior information gathering capability (at least better than the MEC server) or the whole blockchain system exhibits significant network value. We further provide a series of simulations to verify our conclusion and illustrate the impact of network parameters on the strategies of both sides.

Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. The main objective of this work is to solve this issue by introducing a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning upsampling methods significantly outperform traditional interpolation techniques or multiple state-of-the-art approaches by 8-13 dB and 2-10 dB, respectively, in terms of normalized mean square error.

To address the limited server coverage of traditional over-the-air federated learning (OA-FL), we propose a new OA-FL framework for MIMO-based cloud radio access network (Cloud-RAN), called MIMO Cloud-RAN OA-FL (MIMOCROF). The proposed MIMOCROF consists of three stages in each training round. The first stage of edge aggregation allows each access point (AP) to collect local updates from edge devices and construct an edge update using MIMO multiple access. In the second stage of global aggregation, the cloud server (CS) aggregates edge updates received from the APs to form a global update through a fronthaul network. In the third stage of model updating and broadcasting, the CS sends the updated global model parameters to the APs, and the latter then broadcast the parameters to their served devices. To effectively exploit inter-AP correlation, we model the global aggregation stage as a lossy distributed source coding (L-DSC) problem. Based on the rate-distortion theory, we further analyze the performance of the MIMOCROF framework. We formulate a communication-learning optimization problem to improve the system performance by considering the inter-AP correlation. To solve this problem, we develop an algorithm by using alternating optimization (AO) and majorization-minimization (MM). Furthermore, we propose a practical L-DSC that exploits inter-AP correlation. Numerical results show that the proposed practical L-DSC effectively utilizes inter-AP correlation and is superior to other baseline schemes in performance.

This paper investigates wireless federated learning in data heterogeneous scenarios, where device selection usually leads to a degradation in learning performance. This paper is motivated by the fact that while training deep learning networks using federated stochastic gradient descent (FedSGD) on non-independent and identically distributed (non-IID) datasets, device selection can generate gradient errors that accumulate, leading to potential weight divergence, which is further exacerbated with low device participation. To mitigate weight divergence, an age-weighted FedSGD algorithm is designed in this paper to scale local gradients according to the previous device selection results. Furthermore, by revealing the relationship between device participation and latency, an energy consumption minimization problem is formulated accordingly, which consists of resource allocation and sub-channel assignment. By transforming the resource allocation problem into convex and utilizing KKT conditions, we derive the optimal resource allocation solution. Moreover, this paper develops a matching based algorithm to generate the enhanced sub-channel assignment. Simulation results indicate that 1) age-weighted FedSGD is able to outperform conventional FedSGD in terms of convergence rate and achievable accuracy, and 2) the proposed resource allocation and sub-channel assignment strategies can significantly reduce energy consumption and improve learning performance by increasing device participation.

Goal-oriented communication (GO-COM) has recently emerged as an important concept in modern communications, owing partly to the insatiable demand for high bandwidth efficiency in edge networks and Internet-of-Things (IoT) systems. Unlike traditional communication systems that focus on packet transport and accuracy, GO-COM aims to convey information critical to the receiver’s goals. To leverage the strength of emerging generative artificial intelligence (AI) models within GO-COM, this work presents an ultra-efficient GO-COM design built upon the backbone of the diffusion models. This Diff-GO+ model features high spectrum efficiency and flexible feedback control. Specifically, we embed the key information within semantic conditions and incorporate dictionary learning to derive a noise codebook for forward diffusion at the transmitter, with which a corresponding receiver model regenerates messages via denoising. Our proposed compression-friendly semantic conditions and low-dimensional codewords achieve significant reduction in communication overhead and satisfactory message recovery. To control recovery quality, we introduce a “local generative feedback” (LGF) that enables the transmitter to anticipate recovery quality and ensure goal accomplishment at the receiver end. Our experimental results demonstrate that the proposed Diff-GO+ can achieve a better computation-bandwidth tradeoff with ultra-high spectrum efficiency and superior data recovery. Specifically, our Diff-GO+ can achieve 98% compression for image transmission of the Cityscape dataset.

To address limited server coverage in traditional over-the-air federated learning (OA-FL), we introduce the framework of multiple input multiple output (MIMO) cloud radio access network (Cloud-RAN) OA-FL (MIMOCROF). This framework involves a two-step model aggregation method in each training round. Firstly, each base station (BS) aggregates the local updates from its served devices, resulting in an edge update. Secondly, the cloud server (CS) aggregates the edge updates from the BSs. By modeling the second step as a lossy distributed source coding (L-DSC) process, we analyze the performance of MIMOCROF from the perspective of rate-distortion theory, resulting in a unified communication-learning design approach. In the proposed design, we jointly optimize rate resources and beamforming vectors, thereby leveraging the correlation inherent in FL to achieve performance gains. Numerical results show that, by solving the optimization problem, MIMOCROF performs comparably to the error-free bound and significantly outperforms other benchmark schemes.

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms and reduced feedback overhead. Common codebook-based approaches include Type II and eType II precoding methods defined in the 3GPP standards. Feedback in these systems is typically standardized per subband (SB), allowing user equipment (UE) to select the optimal precoder from the codebook for each SB, thereby reducing feedback overhead. However, this subband-level feedback resolution may not suffice for frequency-selective channels. This paper addresses this issue by introducing an uplink CSI-assisted precoder upsampling module deployed at the gNodeB. This module upsamples SB-level precoders to resource block (RB)-level precoders, acting as a plug-in compatible with existing gNodeB or base stations.

Federated learning (FL) has emerged as an effective paradigm for distributed learning systems owing to its strong potential in exploiting underlying data characteristics while preserving data privacy. In cases of practical data heterogeneity among FL clients in many Internet of Things (IoT) applications over wireless networks, however, existing FL frameworks still face challenges in capturing the overall feature properties of local client data that often exhibit disparate distributions. One approach is to apply generative adversarial networks (GANs) in FL to address data heterogeneity by integrating GANs to regenerate anonymous training data without exposing original client data to possible eavesdropping. Despite some successes, existing GAN-based FL frameworks still incur high communication costs and elicit other privacy concerns, limiting their practical applications. To this end, this work proposes a novel FL framework that only applies partial GAN model sharing. This new partially shared federated GAN (PS-FedGAN) framework effectively addresses heterogeneous data distributions across clients and strengthens privacy preservation at reduced communication costs, especially over wireless networks. Our analysis demonstrates the convergence and privacy benefits of the proposed PS-FEdGAN framework. Through experimental results based on several well-known benchmark data sets, our proposed PS-FedGAN demonstrates strong potential to tackle FL under heterogeneous (nonindependent identically distributed) client data distributions, while improving data privacy and lowering communication overhead.

Signal processing over single-layer graphs has become a mainstream tool owing to its power in revealing obscure underlying structures within data signals. However, many real-life datasets and systems, including those in Internet of Things (IoT), are characterized by more complex interactions among distinct entities, which may represent multi-level interactions that are harder to be captured with a single-layer graph, and can be better characterized by multilayers graph connections. Such multilayer or multi-level data structures can be more naturally modeled by high-dimensional multilayer graphs (MLG). To generalize traditional graph signal processing (GSP) over multilayer graphs for analyzing multi-level signal features and their interactions, this work proposes a tensor-based framework of multilayer graph signal processing (M-GSP). Specifically, we introduce core concepts of M-GSP and study properties of MLG spectral space, followed by fundamentals of MLG-based filter design. To illustrate novel aspects of M-GSP, we further explore its link with traditional signal processing and GSP. We provide example applications to demonstrate the efficacy and benefits of applying multilayer graphs and M-GSP in practical scenarios.

The advent of blockchain technology in wireless networking has spawned a novel decentralized paradigm that engenders the establishment of multi-party trust, secure and efficient resource sharing, and equitable distribution of benefits. However, blockchain often results in lengthy access latencies and can hardly guarantee the quality of off-chain services. The problems stem from the intrinsic flaws of blockchain in dealing with off-chain services such as wireless access. In this study, we propose a unified framework that guarantees service quality and low establishment latency for blockchain-based systems, and incentivizes clients and service providers (SPs) to cooperate without a trusted third party. We use wireless access service in blockchain radio access network (B-RAN) as a typical example and provide a detailed implementation design. We model the service delivery process in our framework and identify the necessary condition for achieving trusting cooperation. Interestingly, we find the necessary condition is also sufficient for cooperation if the service framework is properly designed. Furthermore, we uncover a trade-off between system robustness and transmission efficiency, which offers insightful guidance for framework design in practice. Finally, we present simulation results to illustrate the effectiveness of our proposed wireless access scheme.

Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending channel state information-reference symbols (CSI-RS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs.

Transmitter-side channel state information (CSI) is vital for large MIMO downlink systems to achieve high spectrum and energy efficiency. Existing deep learning architectures for downlink CSI feedback and recovery show promising improvement of UE feedback efficiency and eNB/gNB CSI recovery accuracy. One notable weakness of current deep learning architectures lies in their rigidity when customized and trained according to a preset number of antenna ports for a given compression ratio. To develop flexible learning models for different antenna port numbers and compression levels, this work proposes a novel scalable deep learning framework that accommodates different numbers of antenna ports and achieves dynamic feedback compression. It further reduces computation and memory complexity by allowing UEs to feedback segmented DL CSI. We showcase a multi-rate successive convolution encoder with under 500 parameters. Furthermore, based on the multi-rate architecture, we propose to optimize feedback efficiency by selecting segment-dependent compression levels. Test results demonstrate superior performance, good scalability, and high efficiency for both indoor and outdoor channels.

Channel State Information (CSI) feedback, powered by Deep Learning (DL) methodologies, exhibits significant promise in enhancing spectrum efficiency within massive MIMO systems. However, DL-based approaches typically necessitate substantial CSI datasets for each specific scenario, and managing multiple learned models demands considerable storage and updating bandwidth. To overcome this costly barrier, we develop a solution for efficient training and deployment enhancement of DL-based CSI feedback, which involves a lightweight translation model to cope with new CSI environments and introduces a novel dataset augmentation based on domain knowledge. Specifically, we first develop a deep unfolding CSI feedback network, SPTM2-ISTANet+, which incorporates spherical normalization to mitigate the challenge of path loss variation. Additionally, SPTM2-ISTANet+ integrates a trainable measurement matrix and residual CSI recovery blocks to enhance efficiency and accuracy. Employing SPTM2-ISTANet+ as a foundational feedback model, we introduce an adaptive CSI feedback architecture termed CSI-TransNet. CSI-TransNet features a scenario-adaptive plug-in module for CSI translation, composed of a sparsity aligning function and a compact DL module, facilitating the reuse of pretrained models in unencountered environments. To accommodate the small datasets, we propose a lightweight and general augmentation strategy based on domain knowledge. Test results demonstrate the efficacy and efficiency of the proposed solution for accurate CSI feedback given limited measurements for unseen CSI environments.

In this paper, a federated learning (FL) based system is investigated with non-independent and identically distributed (non-IID) dataset, where multiple devices participate in the global model aggregation through a limited number of sub-channels. By analyzing weight divergence and convergence rate, a new metric is proposed based on age-of-information (AoI), which incorporates latency and can provide an advanced device selection standard. After that, device selection, sub-channel assignment and resource allocation are jointly designed in an overall AoI minimization problem under the maximum energy consumption constraint. The formulated problem is decoupled into two sub-problems. After analyzing the feasibility, the resource allocation problem is transformed to a convex problem, and the closed-from solution is obtained based on KKT conditions. By introducing virtual sub-channels, device selection and sub-channel assignment are jointly solved by a matching based algorithm. Simulation results indicate that the proposed scheme is able to outperform all baselines in terms of both test accuracy and sum AoI, and the developed strategies can achieve significant improvements for all schemes.

Providing rich and useful information regarding spectrum activities and propagation channels, radiomaps characterize the detailed distribution of power spectral density (PSD) and are important tools for network planning in modern wireless systems. Generally, radiomaps are constructed from radio strength measurements by deployed sensors and user devices. However, not all areas are accessible for radio measurements due to physical constraints and security considerations, leading to non-uniformly spaced measurements and blanks on a radiomap. In this work, we explore distribution of radio spectrum strengths in view of surrounding environments, and propose two radiomap inpainting approaches for the reconstruction of radiomaps that cover missing areas. Specifically, we first define a propagation-based priority before integrating exemplar-based inpainting with radio propagation model for fine-resolution small-size missing area reconstruction on a radiomap. We next introduce a novel radio depth map and propose a two-step template-perturbation approach for large-size restricted region inpainting. Our experimental results demonstrate the power of the proposed propagation priority and radio depth map in capturing PSD distribution, as well as their efficacy in radiomap reconstruction.

Acquiring downlink channel state information (CSI) at basestation (gNB) is crucial for optimizing performance in massive MIMO FDD systems. Deep learning (DL) architectures have shown successes in enabling UE-side CSI feedback and gNB-side recovery, but often lack flexibility and/or require volumes of customized training data for specific RF channel environments and compression ratios. This work proposes a new CSI feedback architecture called zero-replacement (ZR). ZR is free from customized training and can be directly applied to new and unseen channel scenarios without pre-training and/or customization. It is also scalable and simple to implement, making it suitable for practical massive MIMO wireless deployment. We further generalize a Select-ZR algorithm, which switches between different sparse transformation techniques to enhance recovery performance. Our numerical results demonstrate that both proposed ZR and Select-ZR algorithms achieve competitive CSI recovery accuracy and feedback efficiency across various channels against highly complex data-driven DL models.

This work formulates a collaborative decision-making framework that exploits over-the-air computation to efficiently aggregate soft information from distributed sensors. This new AirCompFDM protocol approximates the sufficient statistic (SS) of optimum binary hypothesis testing at a server node in this distributed sensing environment under different operation constraints. Leveraging pre/post-processing functions on over-the-air aggregation of sensor log-likelihood ratios, Air-CompFDM significantly improves bandwidth efficiency with little detection loss, even from modest numbers of participating sensors and imperfect phase pre-compensation. Without phase pre-compensation, the benefit of over-the-air sensor aggregation diminishes but still can mitigate the effect of channel noise. Importantly, AirCompFDM outperforms the traditional bandwidth-hungry polling scheme, even under low SNR. Furthermore, we analyze the Chernoff information and obtain the approximate effect of sensor aggregation on the probability of detection error that can help develop advanced detection strategies.

Multiuser MIMO (MU-MIMO) technologies can help provide rapidly growing needs for high data rates in modern wireless networks. Co-channel interference (CCI) among users in the same resource-sharing group (RSG) presents a serious user scheduling challenge to achieve high overall MU-MIMO capacity. Since CCI is closely related to correlation among spatial user channels, it would be natural to schedule co-channel user groups with low inter-user channel correlation. Yet, establishing RSGs with low co-channel correlations for large user populations is an NP-hard problem. More practically, user scheduling for wideband channels exhibiting distinct channel characteristics in each frequency band remains an open question. In this work, we proposed a novel wideband user grouping and scheduling algorithm named SC-MS. The proposed SC-MS algorithm first leverages spectral clustering to obtain a preliminary set of user groups. Next, we apply a post-processing step to identify user cliques from the preliminary groups to further mitigate CCI. Our last step groups users into RSGs for scheduling such that the sum of user clique sizes across the multiple frequency bands is maximized. Simulation results demonstrate network performance gain over benchmark methods in terms of sum rate and fairness.

Outdoor radio coverage map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. A radio map spatially describes radio signal strength distribution and provides network coverage information. A practical problem is to estimate fine-resolution radio maps from sparse radio strength measurements. However, nonuniformly positioned measurements and access constraints pose challenges to accurate radio map estimation (RME) and spectrum planning in many outdoor environments. In this work, we develop a two-phase learning framework for RME by integrating well-known radio propagation model and designing a conditional generative adversarial network (cGAN). We first explore global information to extract radio propagation patterns. Next, we focus on the local features to estimate the shadowing effect on radio maps in order to train and optimize the cGAN. Our experimental results demonstrate the efficacy of the proposed framework for RME based on generative models from sparse observations in outdoor scenarios.

The tremendous growth of mobile networking and Internet of Things (IoT) demands efficient and reliable service for massive wireless systems. Multi-input-multi-output (MIMO) technologies successfully utilize spatial diversity to substantially improve spectral efficiency by scheduling multiple devices for simultaneous spectrum access. Efficient solutions to the NP-hard problem of scheduling large number of users are vital to interference mitigation and spectrum efficiency. Despite successes of machine learning in tackling large-scale optimization problems, direct adoption of supervised learning in MIMO user scheduling is difficult as there is no optimum solution to use as labeled training data, and unsupervised learning would identify similar user channel features instead of promoting channel diversity. In this work, we propose an effective and scalable user scheduling paradigm based on unsupervised learning to enhance spatial diversity in both uplink and downlink. Given users’ channel state information (CSI), we first cluster CSIs over the Grassmannian manifold to identify users with high CSI similarity, before scheduling them into MIMO access groups with low co- channel interference. Our paradigm is generalizable to a variety of different simple and scalable unsupervised learning tools and different diversity optimization criteria. Numerical tests demonstrate substantial gain in terms of spectrum efficiency and interference suppression at modest computation complexity.

To achieve communication-efficient federated multi-task learning (FMTL), we propose an over-the-air FMTL (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). To overcome the inter-task interference inherent in the non-orthogonal transmission among tasks, we design a novel transmission method called model sparsification and random compression (MSRC) as well as a reception method called modified turbo compressed sensing (M-Turbo-CS). More specifically, at each edge device, the local model updates of all tasks are first sparsified andrandomlycompressed with different random compression matrices for different tasks, before being superimposed and sent over the uplink channel. Then the ES constructes the model aggregations of all the tasks from the channel observation data through a modified version of the turbo compressed sensing (Turbo-CS) algorithm called M-Turbo-CS. We analyze the performance of the proposed OA-FMTL framework with MSRC and M-Turbo-CS. Based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by adjusting the power allocation among the tasks at the edge devices. Numerical simulations show that our proposed OA-FMTL efficiently suppresses the inter-task interference to achieve a learning performance comparable to the inter-task interference free bound at a significantly reduced communication overhead. It is also shown that the proposed inter-task power allocation optimization algorithm further reduces the overall communication overhead by appropriately adjusting the power allocation among the tasks.

Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network.

We propose an over-the-air learning framework for collaborative decision making in wireless sensor networks. The low complexity framework leverages low-latency sensor transmission for a decision server to coordinate measurement sensors for hypothesis testing through over-the-air aggregation of sensor data over a multiple-access channel. We formulate several collaborative over-the-air hypothesis testing problems under different practical protocols for collaborative learning and decision making. We develop hypothesis tests for these network protocols and deployment scenarios including channel fading. We provide performance benchmark for both basic likelihood ratio test and generalized likelihood ratio test under different deployment conditions. Our results clearly demonstrate gain provided by increasing number of collaborative sensors.

Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems. Generally, radio strength measurements are collected to construct fine-resolution radio maps for analysis. However, certain protected areas are not accessible for measurement due to physical constraints and security considerations, leading to blanked spaces on a radio map. Non-uniformly spaced measurement and uneven observation resolution make it more difficult for radio map estimation and spectrum planning in protected areas. This work explores the distribution of radio spectrum strengths and proposes an exemplar-based approach to reconstruct missing areas on a radio map. Instead of taking generic image processing approaches, we leverage radio propagation models to determine directions of region filling and develop two different schemes to estimate the missing radio signal power. Our test results based on high-fidelity simulation demonstrate efficacy of the proposed methods for radio map reconstruction.

As applications of Internet-of-things (IoT) rapidly expand, unscheduled multiple user access with low latency and low cost communication is attracting growing more interests. To recover the multiple uplink signals without strict access control under dynamic co-channel interference environment, the problem of blind demixing emerges as an important obstacle for us to overcome. Without channel state information, successful blind demixing can recover multiple user signals more effectively by leveraging prior information on signal characteristics such as constellations and distribution. This work studies how forward error correction (FEC) codes in Galois Field can generate more effective blind demixing algorithms. We propose a constrained Wirtinger flow algorithm by defining a valid signal set based on FEC codewords. Specifically, targeting the popular polar codes for FEC of short IoT packets, we introduce signal projections within iterations of Wirtinger Flow based on FEC code infor-mation. Simulation results demonstrate stronger robustness of the proposed algorithm against noise and practical obstacles and also faster convergence rate compared to regular Wirtinger flow algorithm.

In this letter, we introduce over-the-air computation into the communication design of federated multi-task learning (FMTL), and propose an over-the-air federated multi-task learning (OA-FMTL) framework, where multiple learning tasks deployed on edge devices share a non-orthogonal fading channel under the coordination of an edge server (ES). Specifically, the model updates for all the tasks are transmitted and superimposed concurrently over a non-orthogonal uplink fading channel, and the model aggregations of all the tasks are reconstructed at the ES through a modified version of the turbo compressed sensing algorithm (Turbo-CS) that overcomes inter-task interference. Both convergence analysis and numerical results show that the OA-FMTL framework can significantly improve the system efficiency in terms of reducing the number of channel uses without causing substantial learning performance degradation.

Distributed deep learning (DL) plays a critical role in many wireless Internet of Things (IoT) applications including remote camera deployment. This work addresses three practical challenges in cyber-deployment of distributed DL over band-limited channels. Specifically, many IoT systems consist of sensor nodes for raw data collection and encoding, and servers for learning and inference tasks. Adaptation of DL over band-limited network data links has only been scantly addressed. A second challenge is the need for pre-deployed encoders being compatible with flexible decoders that can be upgraded or retrained. The third challenge is the robustness against erroneous training labels. Addressing these three challenges, we develop a hierarchical learning strategy to improve image classification accuracy over band-limited links between sensor nodes and servers. Experimental results show that our hierarchically-trained models can improve link spectrum efficiency without performance loss, reduce storage and computational complexity, and achieve robustness against training label corruption.

Vertical sectorization introduces considerable gain to particular scenarios where user equipment (UE) are distributed in 3D domain, i.e. high-rise buildings, in terms of coverage and network capacity thanks to availability of active antenna systems. However, due to the huge varieties in distribution of UEs and different physical environments, presenting a comprehensive analytical framework is quite challenging. From this aspect, most available studies on vertical sectorization are limited to present only empirical results. In this paper, we introduce a novel methodology to forecast the performance of NB-IoT systems over urban scenarios. In particular, a logistic distribution-based analytical framework is exploited in order to calculate the group probabilities for each available UE. Based on these probabilities, we propose a scheduling framework with beamforming which improves physical resource block (PRB) utilization by over 50% compared to the case with no scheduling.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting with a posteriori channel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we present an iterative algorithm to design the precoder. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

The past decade has witnessed the rising dominance of deep learning and artificial intelligence in a wide range of applications. In particular, the ocean of wireless smartphones and IoT devices continue to fuel the tremendous growth of edge/cloud-based machine learning (ML) systems, including image/speech recognition and classification. To overcome the infrastructural barrier of limited network bandwidth in cloud ML, existing solutions have mainly relied on traditional compression codecs such as JPEG that were historically engineered for human-end users instead of ML algorithms. Traditional codecs do not necessarily preserve features important to ML algorithms under limited bandwidth, leading to potentially inferior performance. This work investigates application-driven optimization of programmable commercial codec settings for networked learning tasks such as image classification. Based on the foundation of variational autoencoders (VAEs), we develop an end-to-end networked learning framework by jointly optimizing the codec and classifier without reconstructing images for a given data rate (bandwidth). Compared with the standard JPEG codec, the proposed VAE joint compression and classification framework achieves classification accuracy improvement by over 10% and 4%, respectively, for CIFAR-10 and ImageNet-1k data sets at data rate of 0.8 bpp. Our proposed VAE-based models show 65%–99% reductions in encoder size,$\times 1.5$–$\times 13.1$improvements in inference speed, and 25%–99% savings in power compared to baseline models. We further show that a simple decoder can reconstruct images with sufficient quality without compromising classification accuracy.

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though the blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms.

Efficient processing and feature extraction of large-scale point clouds are important in related computer vision and cyber-physical systems. This work investigates point cloud resampling based on hypergraph signal processing (HGSP) to better explore the underlying relationship among different points in the point cloud and to extract contour-enhanced features. Specifically, we design hypergraph spectral filters to capture multilateral interactions among the signal nodes of point clouds and to better preserve their surface outlines. Without the need and the computation to first construct the underlying hypergraph, our low complexity approach directly estimates hypergraph spectrum of point clouds by leveraging hypergraph stationary processes from the observed 3D coordinates. Evaluating the proposed resampling methods with several metrics, our test results validate the high efficacy of hypergraph characterization of point clouds and demonstrate the robustness of hypergraph-based resampling under noisy observations.

Channel state information (CSI) plays a vital role in scheduling and capacity-approaching transmission optimization of massive MIMO communication systems. In frequency division duplex (FDD) MIMO systems, forward link CSI reconstruction at transmitter relies on CSI feedback from receiving nodes and must carefully weigh the tradeoff between reconstruction accuracy and feedback bandwidth. Recent application of recurrent neural networks (RNN) has demonstrated promising results of massive MIMO CSI feedback compression. However, the cost of computation and memory associated with RNN deep learning remains high. In this work, we exploit channel temporal coherence to improve learning accuracy and feedback efficiency. Leveraging a Markovian model, we develop a deep convolutional neural network (CNN)-based framework called MarkovNet to efficiently encode CSI feedback to improve accuracy and efficiency. We explore important physical insights including spherical normalization of input data and deep learning network optimizations in feedback compression. We demonstrate that MarkovNet provides a substantial performance improvement and computational complexity reduction over the RNN-based work. We demonstrate MarkovNet’s performance under different MIMO configurations and for a range of feedback intervals and rates. CSI recovery with MarkovNet outperforms RNN-based CSI estimation with only a fraction of computational cost.

Distributed computing systems, such as Hadoop, have been widely studied and used for executing and analyzing large data. In this paper, we investigate an emerging resource allocation problem for wireless distributed computing systems consisting of multifunctional nodes in charge of both numerical computation and wireless communication with master nodes. We focus on a computation power consumption model based on CMOS devices and a communication power consumption model involving multiple antenna transceivers against mutual interference. We present a joint optimization problem for workload scheduling and power allocation for achieving maximum computational speed under total power constraint. We simplify the joint optimization into two sub-problems. For workload scheduling as an integer programming sub-problem, we relax the integer constraint and establish the equivalence between relaxed and original problems. For the power allocation sub-problem, we maximize a difference of convex functions by utilizing the concave-convex procedure. We prove our proposed algorithm to converge to a stationary point of the original program. Simulation results confirm the efficiency and near-optimal performance of our proposed algorithms.

Massive multiple-input and multiple-output (MIMO) enables ultra-high throughput and low latency for tile-based adaptive virtual reality (VR) 360° video transmission in wireless network. In this paper, we consider a massive MIMO system where multiple users in a single-cell theater watch an identical VR 360° video. Based on tile prediction, base station (BS) deliveries the tiles in predicted field of view (FoV) to users. By introducing practical supplementary transmission for missing tiles and unacceptable VR sickness, we propose the first stable transmission scheme for VR video. we formulate an integer non-linear programming (INLP) problem to maximize users’ average quality of experience (QoE) score. Moreover, we derive the achievable spectral efficiency (SE) expression of predictive tile groups and the approximately achievable SE expression of missing tile groups, respectively. Analytically, the overall throughput is related to the number of tile groups and the length of pilot sequences. By exploiting the relationship between the structure of viewport tiles and SE expression, we propose a multi-lattice multi-stream grouping method aimed at improving the overall throughput for VR video transmission. Moreover, we analyze the relationship between QoE objective and number of predictive tile. We transform the original INLP problem into an integer linear programming problem by setting the predictive tiles groups as some constants. With variable relaxation and recovery, we obtain the optimal average QoE. Extensive simulation results validate that the proposed algorithm effectively improves QoE.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting witha posteriorichannel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

The millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays (DLA) have received great attention due to their simple hardware implementation and excellent performance. In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure. The investigated non-convex problem with discrete variables and coupled constraints is challenging to solve and an efficient framework of joint neural network (NN) design is proposed to tackle it. Specifically, the proposed framework consists of a deep reinforcement learning (DRL)-based NN and a deep-unfolding NN, which are employed to optimize the beam selection and digital precoding matrices, respectively. As for the DRL-based NN, we formulate the beam selection problem as a Markov decision process and a double deep Q-network algorithm is developed to solve it. The base station is considered to be an agent, where the state, action, and reward function are carefully designed. Regarding the design of the digital precoding matrix, we develop an iterative weighted minimum mean-square error algorithm induced deep-unfolding NN, which unfolds this algorithm into a layer-wise structure with introduced trainable parameters. Simulation results verify that this jointly trained NN remarkably outperforms the existing iterative algorithms with reduced complexity and stronger robustness.

Three-dimensional (3D) point clouds are important data representations in visualization applications. The rapidly growing utility and popularity of point cloud processing strongly motivate a plethora of research activities on large-scale point cloud processing and feature extraction. In this work, we investigate point cloud resampling based on hypergraph signal processing (HGSP). We develop a novel method to extract sharp object features and reduce the data size of point cloud representation. By directly estimating hypergraph spectrum based on hypergraph stationary processing, we design a spectral kernel-based filter to capture high-dimensional interactions among point signal nodes and to better preserve object surface outlines. Experimental results validate the effectiveness of hypergraph in representing point clouds, and demonstrate the robustness of the proposed algorithm under noise.

We investigate a coded uplink non-orthogonal multiple access (NOMA) configuration in which groups of co-channel users are modulated in accordance with orthogonal time frequency space (OTFS). We take advantage of OTFS characteristics to achieve NOMA spectrum sharing in the delay-Doppler domain between stationary and mobile users. We develop an efficient iterative turbo receiver based on the principle of successive interference cancellation (SIC) to overcome the co-channel interference (CCI). We propose two turbo detector algorithms: orthogonal approximate message passing with linear minimum mean squared error (OAMP-LMMSE) and Gaussian approximate message passing with expectation propagation (GAMP-EP). The interactive OAMP-LMMSE detector and GAMP-EP detector are respectively assigned for the reception of the stationary and mobile users. We analyze the convergence performance of our proposed iterative SIC turbo receiver by utilizing a customized extrinsic information transfer (EXIT) chart and simplify the corresponding detector algorithms to further reduce receiver complexity. Our proposed iterative SIC turbo receiver demonstrates performance improvement over existing receivers and robustness against imperfect SIC process and channel state information uncertainty.

The continually rising demand for wireless services and applications in the era of Internet of things (IoT) and artificial intelligence (AI) presents a significant number of unprecedented challenges to existing network structures. To meet the rapid growth need of mobile data services, blockchain radio access network (B-RAN) has emerged as a decentralized, trustworthy radio access paradigm spurred by blockchain technologies. However, many characteristics of B-RAN remain unclear and hard to characterize. In this study, we develop an analytical framework to model B-RAN and provide some basic fundamental analysis. Starting from block generation, we establish a queuing model based on a time-homogeneous Markov chain. From the queuing model, we evaluate the performance of B-RAN with respect to latency and security considerations. We uncover a more comprehensive picture of the achievable performance of B-RAN by connecting latency and security. At last, we present experimental results via an innovative prototype and validate the proposed model.

This work proposes a fiber-enabled optical wireless communication (FE-OWC) system for bidirectional communications between the base station (BS) and a number of mobile user terminals (UTs) via full beam coverage, aimed at facilitating ultra-high data rate communications. The FE-OWC system comprises optical antennas, optical chains, and baseband units at both BS and UTs. The innovative optical antenna consists of an array of fiber ports and a transceiver lens, which can form a number of transmit and receive optical beams and provide a full beam coverage for simultaneous downlink and uplink connections with a number of UTs, respectively. We present analysis to characterize downlink and uplink channel models and gains, including both optical and electrical parts, between the BS and UTs and conduct a complete link budget analysis. We further design downlink and uplink multiuser multiple-input multiple-output (MIMO) as well as massive MIMO transmission protocols and develop asymptotically optimal schemes for a large number of fiber ports. Numerical results illustrate that the FE-OWC system has the potential to support over 10 Gbps data rate per UT and Tbps system throughput required in future 6G mobile communication systems.

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common applications of sampling and denoising of point clouds for which we provide specific hypergraph filter design and spectral properties. Experimental results demonstrate the strength of hypergraph signal processing as a tool in characterizing the underlying properties of 3D point clouds.

The recent emergence of orthogonal time frequency space (OTFS) modulation as a novel PHY-layer mechanism is more suitable in high-mobility wireless communication scenarios than traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have analyzed OTFS performance using theoretical and ideal baseband pulseshapes, a challenging and open problem is the development of effective receivers for practical OTFS systems that must rely on non-ideal pulseshapes for transmission. This work focuses on the design of practical receivers for OTFS. We consider a fractionally spaced sampling (FSS) receiver in which the sampling rate is an integer multiple of the symbol rate. For rectangular pulses used in OTFS transmission, we derive a general channel input-output relationship of OTFS in delay-Doppler domain without the common reliance on impractical assumptions such as ideal bi-orthogonal pulses and on-the-grid delay/Doppler shifts. We propose two equalization algorithms: iterative combining message passing (ICMP) and turbo message passing (TMP) for symbol detection by exploiting delay-Doppler channel sparsity and the channel diversity gain via FSS. We analyze the convergence performance of TMP receiver and propose simplified message passing (MP) receivers to further reduce complexity. Our FSS receivers demonstrate stronger performance than traditional receivers and robustness to the imperfect channel state information knowledge.

Optimization theory assisted algorithms have received great attention for precoding design in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant optimization algorithms are able to provide excellent performance, they generally require considerable computational complexity, which gets in the way of their practical application in real-time systems. In this work, in order to address this issue, we first propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed in matrix form to better solve the problems in communication systems. Then, we implement the proposed deep-unfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. Specifically, the iterative WMMSE algorithm is unfolded into a layer-wise structure, where a number of trainable parameters are introduced to replace the high-complexity operations in the forward propagation. To train the network, a generalized chain rule of the IAIDNN is proposed to depict the recurrence relation of gradients between two adjacent layers in the back propagation. Moreover, we discuss the computational complexity and generalization ability of the proposed scheme. Simulation results show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.

JPEG2000 (j2k) is a highly popular format for image and video compression. It plays a major role in the rapidly growing applications of cloud based image classification. Considering limited network bandwidth, we propose an end-to-end deep learning framework to achieve faster and more accurate classification by directly training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients from j2k-compressed images without image reconstruction. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy. Furthermore, we present DWT-centric augmentation transformations to achieve more accurate classification without added cost. Achieving faster and more accurate classification for j2k encoded images, the proposed solution is well suited for joint compression and cloud-based image and video classification over limited channel bandwidth.

Learning and processing of signals over hypergraph models have gained substantial traction owing to the ability of hypergraphs in characterizing multilateral interactions. In this work, we explore hypergraph spectral analysis and provide alternative definitions of frequency domain operations that are practically useful in image processing. We analyze hypergraph spectral properties and present several application examples, including compression, edge detection and segmentation. Successful experiment results demonstrate the effectiveness and the future prospect of the proposed hypergraph frequency operations in image processing.

We study low-overhead uplink multi-access algorithms for massive Internet-of-Things (IoT) that can exploit the MIMO performance gain. Although MIMO improves system capacity, it usually requires high overhead due to Channel State Information (CSI) feedback, which is unsuitable for IoT. Recently, a Pseudo-Random Beam-Forming (PRBF) scheme was proposed to exploit the MIMO performance gain for uplink IoT access with uniform channel and load, without collecting CSI at the BS. For non-uniform channel and load, new adaptive beamselection and random-access algorithms are needed to efficiently utilize the system capacity with low overhead. Most existing algorithms for a related multi-channel scheduling problem require each node to at least know some information of the queue length of all contending nodes. In contrast, we propose a new Low-overhead Multi-Channel Joint Channel-Assignment and Random-Access (L-MC-JCARA) algorithm that reduces the overhead to be independent of the number of interfering nodes. A key novelty is to let the BS estimate the total backlog in each contention group by only observing the random-access events, so that no queue-length feedback is needed from IoT devices. We prove that L-MC-JCARA can achieve at least `0.24`` of the capacity region of the optimal centralized scheduler for the corresponding multi-channel system.

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This article presents an energy-efficient edge processing framework to execute deep learning inference tasks at the edge computing nodes whose wireless connections to mobile devices are prone to channel uncertainties. Aimed at minimizing the sum of computation and transmission power consumption with probabilistic Quality-of-Service (QoS) constraints, we formulate the joint inference tasking and the downlink beamforming problem that is characterized by a group sparse objective function. We provide a statistical learning-based robust optimization approach to approximate the highly intractable probabilistic-QoS constraints by nonconvex quadratic constraints, which are further reformulated as matrix inequalities with a rank-one constraint via matrix lifting. We design a reweighted power minimization approach by iteratively reweighted ℓ1minimization with difference-of-convex-functions (DC) regularization and updating weights, where the reweighted approach is adopted for enhancing group sparsity whereas the DC regularization is designed for inducing rank-one solutions. The numerical results demonstrate that the proposed approach outperforms other state-of-the-art approaches.

Signal processing over graphs has recently attracted significant attention for dealing with the structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications, such as Internet of Things (IoT). In this article, we propose a new framework of hypergraph signal processing (HGSP) based on the tensor representation to generalize the traditional graph signal processing (GSP) to tackle high-order interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform (HGFT) and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.

Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud, and the multilateral relationship among their points can be naturally captured by the high-dimensional hyperedges. This work investigates the power of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds. We estimate, and order the hypergraph spectrum from observed point cloud coordinates. By trimming the redundancy from the estimated hypergraph spectral space based on spectral component strengths, we develop a clustering-based segmentation method. We apply the proposed method to various point clouds, and analyze their respective spectral properties. Our experimental results demonstrate the effectiveness and efficiency of the proposed segmentation method.

Bursty data links can benefit directly from the removal of pilot symbol transmission for channel estimation by improving the spectral efficiency. For such networking scenarios including data or paging signals, blind equalization for joint data detection and channel estimation with few or no pilot can improve spectrum efficiency. Though some existing works typically have attempted to take advantage of the sparsity of multipath channels, substantial performance improvement remains elusive. In this work, we develop an iterative Markov chain Monte Carlo algorithm based on Gibbs sampling designed for sparse channels. We incorporate the channel sparsity in the form of an l1type prior probability distribution, and derive the posterior channel distribution via stochastic sampling. Furthermore, we propose transmitter and receiver structures that could resolve unknown phase ambiguity in frequency-selective channels. This algorithm is also generalizable to non-sparse channels.

Channel state information (CSI) reporting is important for multiple-input multiple-output (MIMO) wireless transceivers to achieve high capacity and energy efficiency in frequency division duplex (FDD) mode. CSI reporting for massive MIMO systems could consume large bandwidth and degrade spectrum efficiency. Deep learning (DL)-based CSI reporting integrated with channel characteristics has demonstrated success in improving CSI compression and recovery. To further improve the encoding efficiency of CSI feedback, we develop an efficient DL-based compression framework CQNet to jointly tackle CSI compression, codeword quantization, and recovery under the bandwidth constraint. CQNet is directly compatible with other DL-based CSI feedback works for further enhancement. We propose a more efficient quantization scheme in the radial coordinate by introducing a novel magnitude-adaptive phase quantization framework. Compared with traditional CSI reporting, CQNet demonstrates superior CSI feedback efficiency and better CSI reconstruction accuracy.

Given the abundance of unlicensed spectrum in 5 GHz band, licensed-assisted-access (LAA) technology presents an efficient and simple approach to alleviate the spectrum crunch in wireless networks. The recent proposal of IEEE 802.11ax as an advanced WiFi standard to accommodate more high rate connections motivates the investigation with respect to the feasibility and benefits of LAA in coexistence with unlicensed user access under this new WiFi protocol. In this article, we first introduce an enhanced LAA and WiFi coexistence mechanism based on spatial multi-stream transmission within IEEE 802.11ax access. We then derive a stream selection and user replacement strategy based on the proposed synchronous coexistence scheme to improve LAA performance without sacrificing WiFi throughput. We further present a Lyapunov algorithm to optimize the LAA access intensity level with rapid convergence and low-complexity. Numerical results demonstrate the effectiveness of the proposed algorithms, and the mutual benefits of the proposed coexistence framework to both LAA and WiFi users.

The recent release of the cellular standard known as 5G New Radio (5G-NR) has adopted polar codes for error protection in the physical downlink control channel (PDCCH). We develop a novel joint detection and decoding algorithm for 5G multiple-input multiple-output (MIMO) transceivers to achieve robustness against practical obstacles including channel state information (CSI) errors, noise, interferences, and pilot contamination. To optimize the performance of PDCCH detection, we incorporate the polar code information during signal detection by transforming the Galois field code constraints into the complex signal field. Specifically, our novel joint linear programming (LP) formulation takes into consideration the transformed polar code constraints. Our proposed detector can also be integrated with effective successive cancellation list (SCL) decoders to deliver superior receiver performance and improve the computational efficiency compared to turbo approach joint detection decoding design. Moreover, similar to 4G-LTE, each active user equipment (UE) must blindly detect its own PDCCH in the downlink search space. We further introduce a metric readily available at the detector that can be used to eliminate most of wrong PDCCH candidates before sending them to the decoder and therefore, improve the computational efficiency of PDCCH blind detection for 5G-NR.

Signal classification plays an important role in cognitive communication networks to identify and avoid interference. Contrary to traditional cooperative spectrum sensing based on binary hypothesis testing, we study a network of cognitive radios that jointly perform linear cooperation based signal classification via M-ary hypothesis testing. To maximize the probability of successful classification subject to constraints on individual probabilities of misclassification, we divide the problem into M independent binary hypothesis testing subproblems in parallel before selecting the hypothesis that is most likely true. Furthermore, we consider a problem that maximizes the probability of successful classification subject to a constraint on the total probability of misclassification. We reformulate such an optimization problem into two different subproblems, where the optimal solution is obtained by alternating the two optimization sub-problems iteratively. Numerical simulations demonstrate the near-optimality of the proposed methods with low computational complexity for the cooperative signal classification problems.

The low cost and versatility of optical devices make it possible to pack a large number of optical transceivers into arrays and exploit massive multiple-input multiple-output (MIMO) transmission in optical wireless communications. Nevertheless, optical massive MIMO presents several distinct challenges such as, the line-of-sight propagation and intensity modulation, incompatible with existing radio frequency massive MIMO techniques. This paper presents a networked optical massive MIMO system that consists of multiple base stations (BSs), each equipped with a transmit lens and an optical transmitter array, cooperatively serving a number of user terminals (UTs), each equipped with a receive lens and a photodetector array. We establish the optical massive MIMO channel model, analyze its asymptotic behavior, and evaluate the potential of networked optical massive MIMO on system throughput improvement. To achieve high throughput, we propose optical beam division multiple access (BDMA) transmission schemes under the total and per transmitter power constraints with the asymptotic optimality. Our results show that the system sum rate increases proportionally to the number of BSs and UTs using the optical BDMA transmission. Further numerical results show that the proposed optical massive MIMO system along with the optical BDMA transmission is able to achieve high throughput with low complexity.

The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

A pseudo-random beamforming (PRBF) based random access (RA) system is proposed to enable uplink (UL) machine-type communications (MTC) with ultra low signaling overheads. Specifically, a pseudo random (PR) sequence is used as public information to coordinate the beamforming vectors used at the base station (BS) and the devices. Within the coherence time window, each device distributively determines in advance the ''good'' time slots and receiving beams for transmission. This UL protocol reduces the overheads due to the feedback of channel state information and the control signals for centralized scheduling. This paper derives the throughput and user scaling of the proposed M- PRBF-CA protocol for achieving spatial multiplexing gain, under both an i.i.d. slow fading channel and a correlated slow fading channel. Our simulation results confirm the analysis in both fading channel models.

Internet-of-Things (IoT) networks are envisioned to typically include a massive number of devices with sporadic and low-latency uplink service needs. This paper presents a blind demixing approach to support the data recovery of multiple simultaneous and unscheduled device transmissions without a priori channel state information (CSI). The proposed joint receiver leverages the group sparse bilinear characteristics of the underlying problem that involves active device detection and data recovery. We exploit the manifold geometry of rank-one matrices in the lifted bilinear equation and apply smoothed ℓ1/ℓ2-norm to induce the group sparsity for active device detection. We further develop a smoothed Riemannian algorithm to solve the sparse blind demixing optimization problem. Numerical results demonstrate the algorithmic advantage and desirable performance of the proposed algorithm.

Information cascades represent traces of information propagation in latent unknown networks. The underlying information diffusion dynamics are captured from collected cascade data and used to infer online social network connectivity and structure which is useful for several applications such as influence maximization and rumor blocking. Our goal is to make the network inference process robust to missing cascade entries. As a first approach, we attempt to recover the missing elements and use the recovered cascades to solve the inference problem. The second approach consists in using expectation maximization algorithm to jointly recover missing entries and solve the network inference problem iteratively. Experiments are implemented on synthetic as well as large scale real social networks to validate and compare the proposed methods.

This paper proposes an enhanced licensed-assisted-access (eLAA) and WiFi coexistence mechanism for LTE deployment in the unlicensed spectrum. Specifically, we utilize the Clear-to-Send-to-Self (CTS-to-Self) frame to seamlessly embed the eLAA transmission within the WiFi access protocol. Unlike traditional LAA ideas, our proposed eLAA is non-intrusive to WiFi users and beneficial to both eLAA and WiFi users. To fully consider the performance of WiFi users, we propose to insert the CTS-to-Self frame under the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. For further throughput enhancement, we adopt the Nash bargaining solution (NBS) to develop a fair and optimal unlicensed resource allocation scheme and also develop a low computational complexity algorithm to solve the NBS-based problem. Our numerical results corroborate the analytical results and demonstrate the strength of the proposed mechanism.

Packet-switched wireless networks such as 4G and 5G cellular systems apply RObust Header Compression (ROHC) to reduce PDCP header length and improve payload efficiency. Our recent works have demonstrated the benefit of applying a trans-layer approach that exploits lower layer information in ROHC control based on a partially observable Markov decision process (POMDP) formulation. The benefit of the POMDP solution comes at significant computation complexity beyond existing ROHC. The present work focuses on simplicity by designing ROHC compressors with lower layer awareness including channel adaptive transport block size as a result of link adaptation present in many wireless networks. Our new models directly address practical implementation and can deliver transmission efficiency close to an optimized POMDP compressor.

The rapid growth in storage capacity and computational power of mobile devices is making it increasingly attractive for devices to process data locally instead of risking privacy by sending them to the cloud or networks. This reality has stimulated a novel federated learning framework for training statistical machine learning models on mobile devices directly using decentralized data. However, communication bandwidth remains a bottleneck for globally aggregating the locally computed updates. This work presents a novel model aggregation approach by exploiting the natural signal superposition of wireless multiple-access channel. This over-the-air computation is achieved by joint device selection and receiver beamforming design to improve the statistical learning performance. To tackle the difficult mixed combinatorial optimization problem with nonconvex quadratic constraints, we propose a novel sparse and low-rank modeling approach and develop an efficient difference-of-convex-function (DC) algorithm. Our results demonstrate the algorithm's ability to aggregate results from more devices to deliver superior learning performance.

The performance of cloud based image classification depends critically on its allocated bandwidth. Traditional data compression methods can negatively impact classification accuracy under limited bandwidth. We investigate the design of bandwidth efficient quantization for image encoding and compression with minimum classification accuracy loss. This work develops a simple neural network framework for joint quantization and classification. The proposed 'QuanNet' can optimize the quantization intervals of JPEG2000 encoder to minimize the classification loss. We show that our quantizer optimization can achieve significant accuracy improvement for a given channel bandwidth. Similarly, significant bandwidth can be saved to achieve a desired accuracy for cloud based image classification.

Licensed-assisted-access (LAA) has recently emerged as a heterogeneous network technology to help mitigate the scarcity of licensed spectrum by extending the long-term evolution (LTE) network to unlicensed spectrum. This work proposes an enhanced LAA (eLAA) as a practical technique by exploiting the inherent transmit opportunity (TXOP) reservation via the Clear-to-Send-to-Self (CTS-to-Self) frame in Enhanced Distributed Channel Access (EDCA) to seamlessly integrate eLAA transmission within existing WiFi protocol. Unlike other LAA proposals, our eLAA is non-intrusive to unlicensed WiFi users and beneficial to both eLAA and WiFi networks. To further improve throughput, we analyze and derive an optimized unlicensed resource allocation scheme before deriving several intrinsic properties. Our results demonstrate that tagging CTS-to-Self frames as a higher-priority access category can substantially improve eLAA throughput without seriously degrading the per-user WiFi throughput.

In visible light communication (VLC), using multiple colors is an efficient way to enhance data rate, leading to multi-color VLC (MC-VLC). However, the performance of MC-VLC is jeopardized by the spectral overlaps of different colors. Thin-film optical filters, as the key component of MC-VLC systems, are usually adopted to separate colors. The passband bandwidth (BW) and center wavelength (CWL) of optical filters are critical to mitigate the crosstalk among colors and, thus, must be carefully designed. Moreover, due to the intrinsic wavelength shift of the CWL with the varying of the angle of incidence, it is challenging to support mobility for MC-VLC. In this paper, we consider a joint design of multiple optical filters for MC-VLC by properly selecting the BW and CWL of each filter. We first investigate the optical filter design for a fixed receiver location. Then, to support mobility, we propose two robust optical filter designs, namely, statistically and worst case robust designs, which do not rely on the exact receiver location. Efficient methods are developed to solve the corresponding design problems and obtain the optimized optical filters. Compared with the existing optical filters, the proposed optical filters exhibits much better performance in various scenarios.

This paper presents a novel massive multiple-input multiple-output (MIMO) transmission in beam domain for optical wireless communications. The optical base station equipped with massive optical transmitters communicates with a number of user terminals (UTs) through a transmit lens. Focusing on LED transmitters, we analyze light refraction of the lens and establish a channel model for optical massive MIMO transmissions. For a large number of LEDs, channel vectors of different UTs become asymptotically orthogonal. We investigate the maximum ratio transmission and regularized zero-forcing precoding in the optical massive MIMO system and propose a linear precoding design to maximize the sum rate. We further design the precoding when the number of transmitters grows asymptotically large and show that beam division multiple access (BDMA) transmission achieves the asymptotically optimal performance for sum rate maximization. Unlike optical MIMO without a transmit lens, BDMA can increase the sum rate proportionally to$2K$and$K$under the total and per transmitter power constraints, respectively, where$K$is the number of UTs. In the non-asymptotic case, we prove the orthogonality conditions of the optimal power allocation in beam domain and propose efficient beam allocation algorithms. Numerical results confirm the significantly improved performance of our proposed beam-domain optical massive MIMO communication approaches.

Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.

Non-orthogonal multiple access (NOMA) has been envisioned as a useful component of fifth generation (5G) mobile networks. As imperfect channel state information (CSI) due to channel estimation errors poses problems for most wireless receivers, it presents even greater challenges in successive interference cancellation (SIC) reception of NOMA signals. We present a novel approach to the multi-user detection problem by exploiting the important constraints of forward error correction (FEC) code word. We devise our new receiver based on the minimum output energy (MOE) criterion while preserving a distortionless response to the user equipment (UE) of interest. In particular, we efficiently adopt the UE signatures presented by the FEC channel codes under distinct permutations to separate desired signals of interest from interfering UEs. We formulate our receiver optimization into a quadratic-programming problem anchored with a set of code constraints. Our simulations demonstrate that the proposed code-anchored quadratic programming (CQP) receiver can accurately improve SIC performance and provide robustness to CSI errors better than other legacy schemes.

As a major tool for improving transport efficiency by reducing header redundancy, robust header compression (ROHC) plays an important role in modern packet-switched wireless networks. However, widespread ROHC deployment is in sharp contrast to extremely limited number of research works on ROHC analysis and design optimization. In this paper, we investigate a novel trans-layer approach in designing a bi-directional ROHC under unreliable wireless channel conditions. We propose a novel ROHC compressor design based on a new formulation in terms of a partially observable Markov decision process (POMDP). This new formulation robustly explores the lower protocol layer signaling to optimize the compressor actions dynamically on the header level and feedback polling. Our design approach improves the transmission efficiency and curtails the required ROHC feedback overhead. Furthermore, to reduce the complexity of our optimized POMDP design, we propose a low-complexity suboptimal ROHC compressor. Our novel trans-layer designs achieve more flexible trade-offs between transmission efficiency and feedback overhead than existing ROHC compressors. They also demonstrate a substantial performance improvement under poor channel conditions and long feedback delay.

Network densification is a natural way to support dense mobile applications under stringent requirements, such as ultra-low latency, ultra-high data rate, and massive connecting devices. Severe interference in ultra-dense networks poses a key bottleneck. Sharing channel state information (CSI) and messages across transmitters can potentially alleviate the interferences and improve the system performance. Most existing works on interference coordination require significant CSI signaling overhead and are impractical in the ultra-dense networks. This paper investigates the topological cooperation to manage interferences in message sharing based only on the network connectivity information. In particular, we propose a generalized low-rank optimization approach in a complex field to maximize the achievable degrees of freedom (DoFs) by establishing interference alignment conditions for the topological cooperation. To tackle the challenges of poor structure and non-convex rank function, we develop the Riemannian optimization algorithms to solve a sequence ofcomplexfixed-rank subproblems through a rank growth strategy. By exploiting the non-compact Stiefel manifold formed by the set of complex full column rank matrices, we develop the Riemannian optimization algorithms to solve the complex fixed-rank optimization problem by applying the semidefinite lifting technique and the Burer–Monteiro factorization approach. The numerical results demonstrate the computational efficiency and higher DoFs achieved by the proposed algorithms.

This paper investigates the joint power allocation with spectrum sharing for vehicle-to-everything (V2X) communications with Long Term Evolution Unlicensed (LTE-U) technology in a heterogeneous network. The vehicle users (VUEs) are classified into safety VUEs and non-safety VUEs based on the corresponding services. With the purpose to maximize the total throughput of CUEs, safety VUEs, and non-safety VUEs in contend free period (CFP) based LTE-U mode, a matching based resource allocation scheme is proposed under the constraints of fairness coexistence. The ergodic sum rate is considered with respect to statistical channel state information (CSI), and a lower bound evaluation of the objective function is presented with reduced computation complexity. Numerical results demonstrate the analysis and performance of the proposed strategy.

One important issue that confronts multicast service is multiple-user satisfaction under dynamic channel conditions over cellular networks. In practical Long Term Evolution (LTE) networks, a key mechanism that affects the transmission efficiency and robustness for each user is RObust Header Compression (ROHC). In this work, we improve the transmission performance of multicast aiming at satisfying all the users with a trans-layer design scheme. We first establish a practical system model for trans-layer multicast networks, with transport blocks (TBs) as the transmission units. Focusing on the optimization of ROHC, we formulate a novel trans-layer control framework in terms of partially observable Markov decision process (POMDP) to maximize the total number of successfully received payload bits among all users in one Multimedia Broadcast Multicast Service (MBMS). To address the complexity issue of the rigorous POMDP formulation, we further develop an Instantaneous Marginal Belief (IMB) policy algorithm to handle multiple-user. Our numerical results demonstrate substantial improvements by the proposed policy.

Wireless distributed computing presents new opportunities to execute intelligent tasks on mobile devices for low-latency applications, by wirelessly aggregating the computation and storage resources among mobile devices. However, for low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency therein, we establish a novel interference alignment condition by exploiting the locally computed intermediate values as side information. The low-rank optimization model is further developed to maximize the achieved degrees-of-freedom (DoFs). Unfortunately, existing convex relaxation based approach fails to yield satisfied performance due to the poor structure in the formulated low-rank optimization problem, for which we develop a novel difference-of-convex (DC) programming based algorithm. We show that this new approach can significantly improve communication efficiency and the achievable DoF is independent of the number of mobile devices.

In this paper, we address the problem of channel allocation for femtocells that share the use of regular macrocell spectrum. The femto basestation (FBS) scheduling problem is formulated in the form of restless multiarmed bandit (RMAB) framework. Our goal is to choose the arms/channels that maximize the total expected discounted reward over infinite horizon while minimizing the induced interference due to channel sharing with macrocell. Without direct observation of true channel state, we use the available macrocell user feedback known as channel quality indicator (CQI). In general, the RMAB problem is P-SPACE hard. We propose a heuristic low complexity indexing policy referred as approximated Whittle index to rank available channels for FBS. Although finding a closed form channel ranking solution typically involve dynamic programming, we show that based on the partial channel information within CQI, there exists a closed form for the channel index. Moreover, we demonstrate the performance advantage of the proposed indexing policy over a myopic policy.

In many packet-switched wireless systems including cellular networks, RObust Header Compression (ROHC) plays an important role in improving payload efficiency by reducing the number of header bits in a link session. However, there are only very few research works addressing the optimized control of ROHC. Our recent studies have demonstrated the advantage of a trans-layer ROHC design that exploits lower layer link status. We have presented a unidirectional ROHC design based on a partially observable Markov decision process formulation that enables the transmitter to decide the header compression level without receiver feedback. The present work considers the physical channel dynamics in an LTE environment and how they affect header decompressor status. Our new model takes into consideration the transport block (TBs) size defined in LTE transmission according to the modulation and coding scheme (MCS). Our novel and practical model can significantly improve the efficiency of the transmission when compared to a traditional timer-based ROHC control.

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.

This work develops a novel design of joint detection and decoding receiver for multiple-input multiple output (MIMO) wireless transmissions that utilizes polar codes in forward error correction (FEC). To optimize the overall receiver performance, we integrate the polar code constraints during signal detection by relaxing and transforming FEC code constraints from the original Galois field to the real field. We propose a novel joint linear programming (LP) optimization formulation that takes into consideration the transformed polar code constraints when designing a novel receiver robust against practical obstacles including channel state information (CSI) errors, additive noises, co-channel interferences, and pilot contamination. Our newly proposed joint LP formulation can also be integrated with reduced complexity polar decoders such as successive cancellation (SC) and successive cancellation list (SCL) decoders to deliver superior receiver performance at low cost.

Semi-definite relaxation (SDR) has demonstrated the capability of approaching maximum-likelihood (ML) performance. In this work, we first develop a new SDR-based detector that exploits forward error correction (FEC) code information in the detection stage. The joint SDR detector substantially improves overall receiver performance by generating highly reliable information to downstream decoder. For further performance improvement, we integrate the joint SDR detector with decoder using a feedback link to form an iterative turbo receiver. Meanwhile, we propose a simplified SDR receiver that solves only one SDR problem per codeword instead of solving multiple SDR problems in the iterative turbo processing. This simplification significantly reduces the complexity of SDR turbo receiver, while maintaining a similarly superior error performance.

Sustainable powering of wire-free sensors (WFSs) is a critical challenge in structure health monitoring for failure prevention applications, where battery replacement is practically difficult. Hence, it is of interest to develop means for remotely charging WFS devices using external power sources. In this paper, a novel stress wave-based optimization method is proposed for piezoelectric sensors wire-free charging. This method includes a novel waveform optimization scheme, a novel multiactuator, and a multichannel wire-free charging strategy to maximize energy transmission efficiency. Based on the measured channel characteristics of a tightly insulated environment, four different waveform design algorithms are implemented successfully to generate the optimized charging waveforms in accordance with the novel waveform optimization scheme. To testify the effectiveness of this novel optimized wire-free charging method, the piezoelectric sensor-based wire-free charging process is simulated. In the simulation process, a predefined waveform is applied in the piezoelectric actuator to generate a stress wave which is subsequently utilized to wire-free transfer energy to the piezoelectric sensors. Numerical results demonstrate the advantages of multichannel wire-free charging in significantly reducing input power loss and enhancing energy transmission efficiency.

Visible light communication (VLC) equipped with multiple light-emitting diodes (LEDs) can provide near ubiquitous indoor coverage for both communication and illumination. This paper introduces the concept of biased beamforming to explore the full potential of multicarrier multi-LED VLC systems. Biased beamforming includes two components to be jointly designed: a direct current (DC) bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact on clipping in multi-LED VLC systems. Next, we consider the joint optimization of beamforming and biasing to maximize data rate. We find the optimal beamformer and analytically characterize its structure. We further optimize the bias on each LED and provide the globally optimal solution in closed form for flat channels, which leads to several critical insights that are helpful for practical systems. We also derive a simplified near-optimal solution for dispersive channels and develop an efficient method for biased beamforming. The performance of the proposed biased beamforming design outperforms existing solutions.

Provides an overview of the technical articles and features presented in this issue. Our regular papers this month focus on 5G related topics such as multipleinput– multipleoutput transmission using finite input signals, and achieving ultrareliable and low-latency wireless communication.

Multiple antennas have played an essential role in spatial multiplexing and diversity transmission for a wide range of communication applications. Most advances in the design of high-speed wireless multiple-input-multiple-output (MIMO) systems have been based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. However, although the Gaussian signal is capacity-achieving, practical systems transmit signals belonging to finite and discrete constellations. Therefore, capacity-achieving transceiver processing based on a Gaussian input signal can be quite suboptimal for practical MIMO systems with discrete constellation input signals. To address this shortcoming, this paper aims to provide a comprehensive overview of MIMO transmission design with finite input signals. It first summarizes existing fundamental results for MIMO systems with finite input signals. Next, focusing on basic point-to-point MIMO systems, it examines transmission schemes based on the three most important criteria for communication systems: mutual-information-driven designs, mean-square-error-driven designs, and diversity-driven designs. In particular, a unified framework is developed for the design of low-complexity transmission schemes applicable to massive MIMO systems in forthcoming 5G wireless networks for the first time. Furthermore, adaptive transmission designs are proposed that switch among these criteria based on channel conditions to formulate the best transmission strategy. A survey is then given of transmission designs with finite input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, transmission designs with finite input signals are discussed for other multi-antenna systems. Finally, a number of technical challenges that remain unresolved at the time of writing are highlighted, and future trends in transmission design with finite input signals are discussed.

One important issue that confronts network service providers is the need to provide reliable multimedia data service efficiently over cellular networks for a large number of subscribers under dynamic channel conditions. In long term evolution (LTE) networks, multimedia broadcast multicast service (MBMS) is a bandwidth efficient data service to simultaneously support multiple users at high bandwidth efficiency. In this paper, instead of considering spectrum resource allocation, we investigate MBMS provisioning for each mobile user based on the higher layer robust header compression (ROHC) consideration in response to user channel quality to reduce packet losses. We formulate a profit maximization problem for two different MBMS channel models and further propose a new MBMS assignment scheme for each user to be assigned a target MBMS with optimal ROHC parameters. We further develop a dynamic programming algorithm for user assignment and ROHC parameters optimization to achieve maximal profit with high spectrum resource utility. Our numerical results demonstrate substantial profit gain achieved by the proposed method in LTE systems.

This paper investigates the resource allocation problem in device-to-device-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We use graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-D matching problem. We propose a suite of algorithms, including a baseline graph-based resource allocation algorithm, a greedy resource allocation algorithm, and a randomized resource allocation algorithm, to address the performance-complexity tradeoffs. We further investigate resource allocation adaption in response to slow fading CSI of all vehicular links and develop a low-complexity randomized algorithm.

Learning network topology from partial knowledge of its connectivity is an important objective in practical scenarios of communication networks and social-media networks. Representing such networks as connected graphs, exploring and recovering connectivity information between network nodes can help visualize the network topology and improve network utility. This work considers the use of simple hop distance measurement obtained from a fraction of anchor/source nodes to reconstruct the node connectivity relationship for large scale networks of unknown connection topology. Our proposed approach consists of two steps. We first develop a tree-based search strategy to determine constraints on unknown network edges based on the hop count measurements. We then derive the logical distance between nodes based on principal component analysis (PCA) of the measurement matrix and propose a binary hypothesis test for each unknown edge. The proposed algorithm can effectively improve both the accuracy of connectivity detection and the successful delivery rate in data routing applications.

This paper proposes a novel concept of biased beamforming to explore the full potential of multicarrier visible light communication (VLC) systems equipped with multiple light-emitting diodes (LEDs). Biased beamforming requires joint optimization of both: a direct current bias on each LED and a beamforming vector on each subcarrier. We first analyze the impact of clipping in multi-LED multicarrier systems, before carrying out the joint optimization of beamforming and biasing. We derive the optimal biased beamforming in closed form, along with several critical insights that are helpful for practical systems. The proposed biased beamforming strategy outperforms existing solutions, as shown by our simulation results.

Auction-based service provisioning and resource allocation have demonstrated strong potential in Cloud-RAN wireless network architecture and heterogeneous networks for effective resource sharing. One major technical challenge is the integration of interference constraints in auction-based solutions. In this work we transform the interference constraint requirement into a set of linear constraints on each cluster. We tackle the generally NP-hard clustering problem by developing a novel practical suboptimal solution that can meet our design requirement. Our novel algorithm utilizes the properties of chordal graphs and applies Lexicographic Breadth First Search (Lex-BFS) algorithm for cluster splitting. This polynomial time approximate algorithm searches for maximal cliques in a graph by generating strong performance in terms of subgraph density and probability of optimal clustering without suffering from the high complexity of the optimal solution.

Cellular networks support a large number of wireless users with different service preferences in a variety of different channel conditions. To serve more users with better spectrum and power efficiency in Long Term Evolution (LTE) cellular networks, Multimedia Broadcast Multicast Service (MBMS) is an efficient service mode for multicasting to a large number of users. This work investigates user service provisioning of MBMS based on assessing the impact of the RObust Header Compression (ROHC) on MBMS service quality. In particular, we formulate a profit maximization problem for network operators that can optionally rely on MBMS. We propose a new user assignment scheme which aims to assign users into a MBMS group that utilizes shared, optimum ROHC parameters. We develop a Dynamic Programming (DP) algorithm for user assignment and ROHC parameter optimization to achieve high profit given insufficient amount of spectrum resources. Numerical results demonstrate the high profit achieved by the proposed user assignment and ROHC optimization in LTE MBMS.

Complementary code keying (CCK) is a high rate spread spectrum coded modulation designed for frequency selective channels. CCK has been shown as an effective signaling technology in underwater communications. To improve the performance of CCK receivers, this work presents a joint channel estimation and detection receiver based on a Markov Chain Monte Carlo (MCMC) approach. We simplify the receiver complexity by introducing a reduced state detector based on the concept of set partitioning for the CCK modulation and incorporated with the MCMC channel estimation mechanism. The proposed method demonstrates significant performance gain at modest computational complexity over several existing receiver algorithms.

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster‐sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike‐and‐slab prior model in their non‐parametric Bayesian learning framework. To avoid the need for a closed‐form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft‐input soft‐output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.

This paper investigates the transport capacity of full-duplex ad hoc networks based on stochastic geometry. Unlike the more traditional half duplex nodes, full duplex wireless equipment can exchange data simultaneously over the same spectrum band. While full duplex transmission represents a promising mechanism to improve spectrum efficiency, the inevitable rise of interference from more transmitting nodes can also lower the rate of successful transmissions in ad hoc full duplex networks. We study the transport capacity of ad hoc full duplex networks by analyzing the successful packet decoding rate in both the physical link model and the protocol link model. We derive a new upper bound and a new lower bound for the network transport capacity, which can be used to lower complexity approximate analysis of the network transport capacity. We further determine the optimal transmission probability for maximizing network throughput and quantify the potential benefit of full duplex nodes. In both physical and protocol link models, our analysis shows that full duplex networks can nearly double the transport capacity against half duplex only with relatively small paired link distance. As the paired link distance grows, the transport capacity gain of full duplex networks begins to degrade.

This paper addresses the problem of resource and power allocation for hybrid access femtocells. We introduce a refund mechanism to incentivize femtocell basestations (FBSs) to serve macrocell users (MUEs) suffering from low signal to interference and noise ratio (SINR) to enhance overall performance. Our goal is to guarantee quality of service for users, while allowing spectrum sharing between macrocell basestation (MBS) and the underlying FBSs. We exploit overheard user channel quality indicator (CQI) reports using different channel models in order to assess the interfered channel state and channel parameter distribution. We analyze the distribution of the SINR for both femtocell users and MUEs. Based on the analytical results, our solution decomposes the scheduling and power allocation problem into two sub-problems and tackles them sequentially. Using problem reduction/transformation, we convert the decomposed problems into well known reduced forms and provide solutions in accordance. Finally, we verify the presented results through simulations.

In packet-switched radio links, the little known Robust Header Compression (ROHC) has become an integral part of many wireless and particularly cellular communication networks. To strengthen existing schemes, this paper aims to improve ROHC performance in terms of payload efficiency for U-mode compression under poor wireless channel conditions. We first consider the parameter optimization of current ROHC systems, for which we propose a Markov compressor model suitable for realistic unidirectional (U-mode) ROHC. We present both the steady-state analysis and the transient behavior analysis of the ROHC. More generally, we propose a novel trans-layer ROHC design concept by exploiting lower cellular network layer status information to adaptively control header compression without dedicated feedbacks. Considering practical delay and inaccuracy when acquiring lower layer information, we develop a ROHC control framework in terms of a partially observable Markov decision process. Our results demonstrate the strength of our Markov ROHC compressor model in characterizing both stationary and transient behaviors, and the significant advantage of the proposed trans-layer ROHC design approach.

The upcoming big data era requires tremendous computation and storage resources for communications. By pushing computation and storage to network edges, fog radio access networks (Fog- RAN) can effectively increase network throughput and reduce transmission latency. Furthermore, we can exploit the benefits of cache enabled architecture in Fog-RAN to deliver contents with less latency. Radio access units (RAUs) need content delivery from fog servers through wireline links whereas multiple mobile devices need contents from RAUs wirelessly. This work proposes a unified low-rank matrix completion (LRMC) approach to solving the content delivery problem in both wireline and wireless parts of Fog-RAN. To attain a low caching latency, we present a high precision approach with Riemannian trust-region method to solve the challenging LRMC problem by exploiting the quotient manifold geometry of fixed-rank matrices. Numerical results show that the new approach has a faster convergence rate, is able to achieve optimal results, and outperforms other state-of-art algorithms.

In distributed inference, cooperation among networked agents can be exploited to enhance the performance of each individual agent. In this paper, we consider signal classification over a network of agents, where each agent observes a certain signal under a particular signal-to-noise ratio (SNR). Each agent produces a statistic that summarizes its observations over a time period and then forwards it to a fusion center for identifying the type of signal in a global manner. A linear cooperation strategy for signal classification is formulated as maximizing the classification probability subject to constrained misclassification probabilities. We show that this problem can be transformed into a convex problem under some conditions and linear cooperation is a simple but effective strategy that can greatly enhance the performance of signal classification over networked agents.

This work investigates robust receiver design against pilot-contaminated channel estimation in large multi-antenna systems. Given bounded channel estimate errors, we tackle the multi-user detection problem by presenting a novel idea of exploiting the forward error correction (FEC) code diversity. Unlike the traditional approach based on worst-case or probabilistic channel error, we distribute different channel codes among pilot-interfering users. We then develop a quadratic-programming receiver to utilize the special FEC signature of target user through a set of linear code constraints. Numerical results demonstrate substantial performance gain over existing robust detectors.

This paper studies power allocation optimization for D2D-underlay enabled cellular systems where only channel fading information is known at the eNB. Unlike instantaneous channel state information (CSI) assumed in many existing works, statistical (partial) CSI is more readily available from long term practical measurement. Within the partial CSI framework, we study the problem of maximizing ergodic weighted D2D sum rate under cellular outage constraints. While the ergodic sum rate maximization problem is NP-hard for interference channels, the outage constraints make the problem even more challenging. We propose a solution based on the framework of monotonic optimization (MO) by identifying the monotonicity in both rate and outage functions. We apply the branch-reduce-and-bound (BRB) algorithm to solve the MO problem and improve its numerical efficiency. We present simulation results to show the feasibility of proposed solution under various channel models.

Co-channel deployed small cell networks must overcome the effect of inter-cell interference. In this work, we formulate a joint rate and power optimization problem by considering both the utility functions for user equipments (UE) and the cost functions for base-stations (BS). For a class of UE utility and BS cost functions (including proportionally fair utility function and nearly any convex cost functions), we show the conversion of the problem into convex optimization. Instead of finding a direct solution, we propose a Frank-Wolfe (FW) based framework to tackle different utility functions in a unified way. The algorithm consists of a controller collecting information from both UEs and BS's for a series of iterative optimization steps. We further simplify the optimization procedure at the controller by using a fixed-point based updating algorithm. Furthermore, we also introduce a trading scheme to properly reimburse small cells for off-loading data and to provide simple access control to reduce the number of interfering links.

3GPP has recently accelerated the standardization process for Device-to-Device communications. Sidelink, which refers to the radio link between two user equipments (UEs), is a major update in 3GPP Release 12. In this paper, we will first introduce sidelink related structures and procedures in Release 12. Based on basic LTE signaling schemes, we propose methods of using demodulation reference signal (DMRS), which is used as reference signal in both uplink and sidelink, for advance D2D neighbor discovery and resource scheduling of D2D transmissions. Our study shows that reference signal of normal LTE channels can be well exploited to facilitate the newly proposed sidelink communications.

Long-term evolution in unlicensed spectrum (LTE-U) is an emerging technology for expanding cellular network capacity without additional spectrum cost. This paper investigates effective spectrum sharing for coexisting Wi-Fi and LTE-U services. Based on a novel hyper access point (HAP) we introduced for effectively embedding LTE-U in unlicensed Wi-Fi band, LTE-U can directly take advantage of the Wi-Fi point coordination function protocol. To facilitate the coexistence, our HAP dedicates a contention-free period to LTE-U users and allows a contention period (CP) for traditional Wi-Fi users. We investigate the optimization of joint user association and resource allocation to further improve system throughput and user fairness. We formulate a network utility maximization problem based on the Nash bargaining solution (NBS), for which we derive a closed-form expression for the optimal CP length under a given user association. We analyze this NBS-based utility maximization and the performance of the proposed algorithm under log-normal fading, Rayleigh fading, and Rician fading channel models, respectively. Our numerical results corroborate our analysis and demonstrate effective improvement of the system performance by the proposed HAP algorithm against traditional LTE-U deployment.

We consider a receiver design for a Hybrid Automatic-Repeat-reQuest (HARQ) system protected by polar codes against transmission errors. This integrated detection-decoding receiver targets a packet link for which channel state information during HARQ retransmission is unavailable at the receiver. Under such channel uncertainties, we propose a second-order cone programming (SOCP) approach for diversity combining without resorting to decision-directed channel estimation prone to error propagation. We formulate an integrated SOCP receiver by jointly exploiting the constraints from diversity channel models, subspace separation, and forward error correction codes. Unlike traditional turbo receiver algorithms that require iterative exchange of soft information between detector and decoder, our proposed SOCP receiver solves as a single-integrated convex optimization problem. This formulation is also versatile and extendable to a plurality of practical scenarios. We further investigate the means for enhancing the receiver performance and the HARQ throughput. Numerical results demonstrate the substantial performance benefits of the proposed joint SOCP receiver.

Traditional physical-layer (PHY)-based secret key generation schemes typically assume reciprocity of forward and reverse channels in order for facilitated shared secrecy. However, frequency-selective channels for frequency division duplex links as well as calibration difference of transceiver hardwares can invalidate reciprocity assumption and can cause severe key error floors between users. In this paper, we propose two practical PHY-based secret key generation schemes for multiple-input and multiple-output wireless links over non-reciprocal physical channels. One is pilot assistant secret key (PASKey) generation and the other is eigenvalue-based secret key (EVSKey) generation. In PASKey, both legitimate users estimate the same composite channel, while EVSKey exploits the properties of eigenvalues to generate secret bits. Both are effective against secrecy key theft by eavesdroppers. Furthermore, without adopting public training signals, EVSKey manages to generate secret bits even when nearby eavesdroppers collude. Our two schemes are shown to be more efficient and can achieve lower key error ratio than the existing methods.

In cellular communication systems with optional device-to-device (D2D) links, user equipments (UEs) can operate in either D2D mode or cellular mode for data transport. This work introduces mixed-mode D2D communication in which D2D links can operate in multiple modes through resource multiplexing. Within this framework, we study the problem of maximizing weighted D2D sum rate under cellular rate constraints by optimizing mixed-mode allocation and resource allocation in term of transmit power and subchannel assignment. Due to nonconvex cellular rate constraints and binary constraints of subchannel allocation, this problem is a nonconvex mixed-integer problem that is generally difficult to solve. We propose a two-step approach by introducing energy-splitting variables such that mixed-mode allocation and resource allocation can be decoupled and optimized independently. The resulting algorithm can be distributive, requires little signaling overhead, and has low computational complexity. We present extensive numerical results to demonstrate the practicality of our proposed algorithm with regard to various network parameters.

Massive multiple-input-multiple-output (MIMO) systems have been proposed to support high rate multiple access. As channel estimation in massive MIMO suffers from the well-known impairment of pilot contamination, we propose a novel approach to multi-user detection by exploiting forward error correction (FEC) code diversity. Unlike traditional approaches solely based on worst-case or probabilistic channel estimation errors, we develop a joint quadratic-programming (QP) receiver anchored with a set of FEC code constraints. Exploiting the user signatures presented by FEC channel codes of distinct permutations, our receiver can effectively recover signals from pilot-interfering users. The code-anchored robust design (CARD) method can also be applied to a chance-constrained receiver, which shows further performance gain compared with the direct integration of FEC code constraints in joint QP receiver. The effectiveness of CARD receivers is demonstrated by numerical results that establish substantial performance gain of the proposed receivers over existing robust designs. In addition, we present a distributed multi-cell processing scheme for enhanced performance via alternating direction method of multipliers.

Opportunistic scheduling exploits the multiuser diversity to improve the performance of wireless communications. The scheduling gain is enabled by the channel feedback sent from the receiver to the transmitter. In this paper, we consider the so-called opportunistic cumulative distribution function (CDF) scheduling with threshold-based selective feedback in the orthogonal frequency division multiple access downlink system. Among opportunistic scheduling techniques, the CDF scheduling is known to provide multiuser diversity gain while maintaining fair radio resource sharing among users. We first derive the exact and asymptotic average sum rates assuming antenna selection at transmitters. The expressions are valid for arbitrary number of inter-cell interference and number of transmit antennas. Moreover, a closed-form approximation for the user rate distribution is calculated using the exact moments of the user sum rate. The approximation is shown to provide an accurate estimate for the rate distribution, and the results can be utilized in rate adaptation at the transmitter.

Resource scheduling and power allocation for femtocell downlink when reusing regular macro-cell resources must ensure good signal quality at macrocell user equipment (MUE). This work considers the resource assignment and power allocation problem when femtocell can overhear the channel quality indicator (CQI) report from the MUE to its serving macro-base-station (MBS). Considering two different channel models, we study the distribution of signal to interference and noise ratio (SINR) at the MUE. Utilizing the available CQI report, we aim to maximize the throughput of femtocell users while limiting femtocell interference to the MUE receiver in terms of SINR and outage constraints. Our solution consists of two simple steps that first identify potentially valid channel assignment (scheduling) before solving it optimally using the well known Hungarian algorithm.

This paper considers a visible light system that serves the dual purpose of illumination and communication. We maximize the joint offset and power optimization in direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communications (VLC). We take both the optical and electrical power constraints into account. We analytically characterize the optimal solutions along with new insights on setting the direct current (DC) offset and information-carrying power in DCO-OFDM systems. We also investigate the relationship and impact of the optical and electrical power constraints, along with numerical results.

This work studies the transport capacity of the emerging full duplex wireless ad hoc networks. Unlike in isolated point-to- point transmission, in ad hoc networks, multiple nodes access the shared spectrum. The inevitable co-channel interference among neighboring nodes capable of full duplex transmission do not allow capacity doubling over half-duplex nodes even after canceling self interference. To assess the capacity gain, we extend the concept of transport capacity to full duplex ad hoc networks. We derive both an upper and a lower bounds for the network transport capacity in consideration of co-channel interference. Based on the analysis, we present numerical results to demonstrate the limit of transport capacity improvement between full duplex and half duplex networks. We also provide some helpful design guidelines for deploying ad hoc networks of full-duplex capable nodes.

We develop a novel diversity combiner for joint reception in wireless relay networks with unknown relay-to-destination channel. The partially available channel information poses an obstacle for utilization of the relay diversity. We tackle this problem by formulating a unified second-order cone programming (SOCP) receiver which jointly detects and decodes transmitted signals from both source and relay nodes. Our results demonstrate that the proposed diversity combiners can achieve performance close to that of the maximum-ratio combiner based on full channel information.

We study a multiuser multicarrier downlink communication system in which the base station (BS) employs a large number of antennas. By assuming frequency-division duplex operation, we provide a beam domain channel model as the number of BS antennas grows asymptotically large. With this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate before developing necessary conditions to asymptotically maximize the upper bound, with only statistical channel state information at the BS. Inspired by these conditions, we propose a beam division multiple access (BDMA) transmission scheme, where the BS communicates with users via different beams. For BDMA transmission, we design user scheduling to select users within non-overlapping beams, work out an optimal pilot design under a minimum mean square error criterion, and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. The proposed BDMA scheme reduces significantly the pilot overhead, as well as, the processing complexity at transceivers. Simulations demonstrate the high spectral efficiency of BDMA transmission and the advantages in the bit error rate performance of the proposed pilot sequences.

In Device-to-Device (D2D) enabled cellular networks, user pairs can select either direct mode or regular cellular mode for data transport. The optimum choice varies depending on power constraints, channel conditions, and QoS requirements. This work proposes a mixed-mode resource allocation strategy through which user pairs can multiplex different modes with disparate resource fraction and transmission power. We study the problem of maximizing D2D sum rate under cellular rate constraint. Successive convex approximation (SCA) algorithm is applied to solve the resulting non-convex joint optimization problem. Our theoretical analysis indicates that mixed-mode achieves superior performance over commonly adopted single mode selection. We further show the advantages of mixed-mode resource allocation in improving cellular rate constrained D2D link performance through extensive numerical tests.

In this paper, we present a method for resource allocation for small cells that integrate licensed and unlicensed RF operations motivated by the widespread WiFi hotspots and the common inclusion of WiFi interface in most cellular terminals. Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. We formulate an optimization problem that jointly allocates resources over both licensed and unlicensed bands with the goal of maximizing sum small cell user equipment (SUE) rate while achieving fairness among these user equipments and controlling inter-cell interference to neighboring macrocell users. The proposed solution further considers the quality of service (QoS) requirement of SUE traffics to be distributed over both licensed and unlicensed bands. We show the formulation of the proposed optimization problem as an efficient and low complexity linear programming. We further show that our problem formulation can be modified to maximize the revenue of mobile network operators. Our proposed solution achieves better performance than several existing solutions.

We consider a femto cellular network consisting of multiple neighboring femtocells, e.g., in an enterprise deployment such as shopping malls, stadiums, or corporate premises. We present a practical but suboptimal channel assignment and interference management algorithm for fractional frequency reuse (FFR) wireless networks. More specifically, we propose an adaptive graph coloring approach for resource allocation with the goal of interference management among femtocells as well as achieving fairness among users. While the global-optimum solution has exponential complexity, our proposed scheme has a linear complexity in the number of femtocells. Although suboptimal, we have evaluated our algorithm in small scenarios, where direct evaluation is possible, and found that the achieved minimum user rate using the proposed algorithm is 85% of the optimal minimum rate. Additionally, we have analyzed several practical design considerations of our proposal such as channel feedback, latency, and computational complexity. We demonstrate the performance of our proposed solution against various alternatives and show that it provides better performance under various environment parameters. For example, in a dense femtocell deployment, the performance was improved by 47% over a full frequency reuse scheme.

We study multicarrier multiuser multiple-input multiple-output (MU-MIMO) systems, in which the base station employs an asymptotically large number of antennas. We analyze a fully correlated channel matrix and provide a beam domain channel model, where the channel gains are independent of sub-carriers. For this model, we first derive a closed-form upper bound on the achievable ergodic sum-rate, based on which, we develop asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter. Furthermore, we propose a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams. By selecting users within non-overlapping beams, the MU-MIMO channels can be equivalently decomposed into multiple single-user MIMO channels; this scheme significantly reduces the overhead of channel estimation, as well as, the processing complexity at transceivers. For BDMA transmission, we work out an optimal pilot design criterion to minimize the mean square error (MSE) and provide optimal pilot sequences by utilizing the Zadoff-Chu sequences. Simulations demonstrate the near-optimal performance of BDMA transmission and the advantages of the proposed pilot sequences.

This work investigates the design of linear precoders in cooperative multi-cell MIMO downlink coverage for finite-alphabet source signals. Traditional design of multi-cell MIMO downlink precoder relies on Gaussian input assumption, which may lead to performance loss when true data inputs consist of discrete non-Gaussian symbols. This work presents optimized precoders for finite-alphabet input by maximizing the sum rate under per-base station power constraints. Specifically, we propose two distributed algorithms: a finite-alphabet signal Gaussian interference gradient projection algorithm and a block diagonalization alternating optimization algorithm while supporting interference cancellation. Our numerical results demonstrate considerable performance gain in terms of approximate transmission data rate as well as decoded bit error rate over precoding schemes designed by using the non-realistic Gaussian input assumption.

Spatial multiplexing is one of the multi-antenna techniques employed by current generation wireless systems. Based on existing research, spatial multiplexing is known to be susceptible to interference. However, it is not well known what the effect of spatial multiplexing on other transmissions is, i.e., how does user performance depend on whether a neighboring cochannel interferer applies a single (spatial) stream or a multi stream transmission. This work attempts to alleviate this shortcoming by analyzing the impact of interference rank (number of spatial streams) on a beamforming and orthogonal space-time block coded user transmission. We generalize existing analytical results on signal-to-interference-plus-noise-ratio distribution and outage probability under arbitrary number of unequal power interferers. Our results are in close form and include interference rank as a parameter, allowing for a thorough study of its impact. Analysis shows that higher rank interference causes lower outage probability, and can support higher outage threshold especially in the case of beamforming.

Spatially distributed transmissions in coordinated multipoint (CoMP) systems can lead to mean channel power imbalance (CPI) at the receiver. Similar imbalance also occurs in distributed antenna and co-located multi-antenna systems due to inaccurate antenna calibration. This paper studies performance impact of power imbalance on some practical CoMP methods with limited feedback. We derive approximate analytical expressions of asymptotic capacity, optimal amplitude weights, as well as signal-to-noise ratio gain for a few methods under analysis. Numerical results validate the analysis and show impacts of erroneous feedback under CPI. Results demonstrate that CPI has significant negative impact on the CoMP performance. Furthermore, our results reveal that amplitude information at transmitter is crucial and detrimental effect of CPI can be effectively compensated by using long-term amplitude information at transmitter. Moreover, additional short-term amplitude feedback shows insignificant gain when a large number of diversity antennas in base stations or CoMP suffer from feedback errors. In fact, a sparsely quantized phase and long-term power information feedback can lead to performance very close to the use of full channel state information at the transmitter.

Given channel statistics information, we develop a joint power control (PC) and resource-allocation (RA) scheme for outage balancing in a multicarrier femto/macro system to minimize the maximum femtocell user equipments' outage probability constrained on macrocell user equipments' outage requirements. The formulated problem falls into the category of nonconvex mixed-integer optimization, which is in general very hard to solve. In this paper, we propose an alternating optimization scheme to find at least a local optimal point in the sense that no single femto base station can increase the system performance by changing its RA and PC strategy. Starting from a special initial RA scheme to handle cross-tier interference, PC and RA are then updated alternately. In particular, we find the optimal power for fixed RA scheme distributedly during the PC subroutine and greedily move to the most beneficial neighboring RA scheme in the RA subroutine. The proposed algorithm converges by producing monotonic improvement of system performance. Furthermore, to reduce the implementation complexity and feedback overhead, we propose a variant local search algorithm, which greatly reduces the computational burden without causing a significant loss of performance in the simulations.

This paper investigates downlink distributed power control for femtocell networks with QoS provisioning for macro-cell user equipments (MUE). Specifically, we propose two non-cooperative game formulations: the Rate Maximization Game (RMG) and the Gradient-norm Minimization Game (GMG). We treat both the macro base-station (MBS) and femto base-stations (FBS) as active players capable of adjusting their respective transmission power in response to changing environments. With the same MBS pay-off function for both games, RMG lets each femtocell maximize its rate penalized by the price paid for its transmission, whereas GMG lets each femtocell minimize a weighted norm of the “local gradient” of the Lagrangian. We propose two different categories of algorithms, the iterative-waterfilling-based algorithms and the stochastic-approximation-based algorithms, to find the corresponding Nash equilibria (NE) of both games. We also characterize sufficient conditions for the convergence of both classes of algorithms under fixed price. With proper price choice, the NEs of both RMG and GMG are related to locally optimal solutions to the system rate maximization problem with the MUE QoS constraints. To further improve system performance for FBS's, we propose two price update methods with QoS provisioning of MUEs.

We consider the fairness problem of outage probability balancing based on statistical channel state information (CSI) in closed-access femtocell networks where multiple subchannels are shared among macro and femto user equipments. We aim to maximize a utility function of outage probabilities for each femto user equipment (FUE) in the network under a macro user equipment (MUE) outage constraint. In light of rate requirement for each FUE, we jointly optimize the power allocation for both FUEs and MUEs as well as the rate allocation for FUE in each sub-channels. Using successive convex approximation, we develop a centralized algorithm to iteratively tackle a series of geometric programming problems. For base-stations connected through back-haul, we further design a distributed algorithm to update optimization variables locally through message passing.

We develop a linear programming based approach for the joint detection and decoding of LDPC coded distributed space-time signaling transmitted in a wireless relay network. Traditional receivers typically decouple the detection and decoding processes as two separate blocks or require iterative turbo exchange of extrinsic information between the soft detector and decoder. We exploit the constraints imposed on the channel input signals and jointly consider the training symbols as well as the LDPC code information by formulating a unified linear programming (LP) receiver. Moreover, in consideration of the vast amount of LDPC parity check inequalities, we present an adaptive procedure to significantly reduce the complexity of the proposed LP receiver.

This work studies the design of linear precoders for cooperative multi-cell MIMO downlink systems with finite alphabet inputs. Traditionally, multi-cell MIMO downlink precoder designs rely on Gaussian input assumption, which may lead to performance loss when the true inputs admit discrete non-Gaussian symbols. In this work, we present optimized precoders by maximizing weighted sum rate (of finite-alphabet-input) under a set of single base station power constraints. Specifically, we propose a simple gradient algorithm for general multi-cell MIMO downlink channel and a block diagonalization gradient algorithm while supporting interference cancellation.

In this paper, we propose a resource allocation scheme for interference management in heterogeneous networks. We particularly consider downlink interference from a Home eNB (HeNB) to macrocell user equipments (MUEs) in the coverage of the traditional macrocell basestation (MBS). By overhearing uplink feedback information from MUEs and Home User Equipments (HUEs) together with downlink control information (DCI) from the MBS, the HeNB formulates a dynamic programming problem with the objective of maximizing the total reward over an infinite horizon. By exploiting the feedback information as well as the DCI at the HeNB, we show that the solution of the dynamic programming problem follows a greedy policy at the HeNB, which simplifies the solution of the infinite horizon dynamic programming problem. We also examine the effect of uncertainty in the feedback information on the performance of our proposed scheme.

The 15 papers in Part II of this special issue focus on the spectrum and energy efficient design of wireless communication networks.

This paper studies interference mitigation in heterogeneous cellular networks consisting of traditional macrocells and newly envisioned femtocells. The mutual interference between macrocells and femtocells arises as a result of decentralized femtocell deployment and backhaul delay. To mitigate downlink interference between the femtocell clients, known as Home User Equipments (HUEs), and macrocell clients, known as Macrocell User Equipments (MUEs), we present methods of dynamic distributed beamforming that are fully compatible with MIMO precoding mechanisms in existing LTE standard releases. We develop three MIMO beamforming schemes for interference mitigation that take into account the Quality of Service (QoS) requirement of both femtocell and macrocell clients. These new heterogeneous MIMO precoding strategies improve flexibility in resource provisioning and signaling requirement while responding to different QoS needs. We also present MUE mean throughput analysis by applying order statistics to our proposed methods. Moreover, we provide an approximate closed form for the mean throughput in terms of basic transmitter, channel, and receiver parameters. Furthermore, we extend our proposed interference control precoding schemes to spatial multiplexing for MIMO transmissions. Finally, we extend our solution to tackle the more general case involving multiple MUEs, multiple HUEs, and multiple femtocells.

We investigate the problem of cooperative self-navigation (CSN) for multiple mobile sensors in the mixed line-of-sight (LOS) and nonline-of-sight (NLOS) environment based on measuring time-of-arrival (TOA) from the cooperative sensing. We first derive an optimized recursive Bayesian solution by adopting a multiple model sampling-based importance resampling particle filter for the development of CSN. It can accommodate nonlinear signal model and non-Gaussian position movement under different levels of channel knowledge. We also utilize a Rao-Blackwellization particle filter to split the original problem by tracking the channel condition with a grid-based filter and estimating the position with a particle filter. The CSN with position and channel tracking exhibits advantage over the noncooperative methods by utilizing additional cooperative measurements. It also shows improvement over the methods without channel tracking. Simulation results validate that both schemes can take the advantage of cooperative sensing and channel condition tracking in mixed LOS/NLOS environments, which motivates future research of cooperative gain for navigation and localization in a more general environment.

We study the power control for femtocell networks in a heterogeneous spectrum-sharing network consisting of macrocells and femtocells. We propose a new approach that utilizes macrocell queue state information for femtocell power control. We formulate the underlying stochastic optimization problem to maximize average femtocell throughput under the constraint of macrocell queue stability by using the macrocell queue length information as a Lagrange weight of macrocell throughput. This formulation considers both cross-tier interference between femtocells and macrocells and intra-tier interference among femtocells. We design a centralized algorithm and a distributed algorithm for femtocells to decide downlink power for each sub-channel dynamically. Our simulation tests show that using local information, the distributed algorithm implementation can also achieve similar average femtocell throughput as the centralized algorithm by paying a penalty of larger macro queue length variation.

This work studies constrained rate maximization problem for clustered device-to-device (D2D) communication in cellular networks. In the cluster, only one capable user equipment (cluster head) downloads data from the base station. D2D communication is used for local data sharing between the cluster head and other user equipments in the cluster. When sharing and reusing cellular spectrum resources, D2D links must control the potential impact of mutual interference. In a scenario that D2D is only allowed by eNB when the cochannel cellular QoS constraint can be achieved, we investigate joint precoding strategy of D2D and cochannel cellular transmission subject to the cellular outage probability constraint. Based on estimated cellular channel state information (CSI), we analyze the feasibility and optimality of the constrained rate maximization problem and provide optimized solutions for different scenarios. Our simulation demonstrates the advantage of the proposed precoding strategy in satisfying cellular QoS and improving D2D rate.

This paper investigates the linear precoder design for multiple-input multiple-output (MIMO) K-user interference channels with finite alphabet inputs. We first obtain the general explicit expressions of the achievable rate of each user in MIMO interference channel systems. We study optimal transmission strategies in both high signal-to-noise ratio (SNR) and low SNR regions. We show that given finite alphabet inputs, a simple power allocation design can achieve optimal performance. In contrast, the well-known interference alignment technique for Gaussian input scenarios, only utilizes a partial interference-free signal space for transmission and leads to a constant performance loss when it is applied to finite-alphabet input scenarios. We determine this constant rate loss at high SNR. Moreover, we establish necessary conditions for the linear precoder design of the weighted sum-rate maximization. We also develop an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results show that for the practical digital modulated signals from discrete constellations, the proposed iterative algorithm achieves considerably higher sum-rate than the existing methods.

WiFi interfaces have been recently incorporated in most cellular user equipments (UEs). In current practice, the UE selects either the licensed band for cellular technologies or the unlicensed WiFi band depending on the signal quality of both bands. At the same time, small (pico or femto) cells have also become popular means to offload traffic from traditional macro-cell networks and to improve cell coverage. This work presents a method for dynamic switching and aggregation of licensed and unlicensed bands in small cells for traffic offloading and per-user throughput enhancement. Our proposed method allows small cells to jointly control transmission in both licensed and unlicensed bands in order to maximize the sum of small cell user throughputs over both bands while constraining the interference effect to maintain the Quality of Service (QoS) requirements for macrocell user equipments. Performance evaluation shows that our proposed scheme outperforms other existing solutions.

We study a classic problem in wireless data communications in which receivers generally rely on training pilots for equalization of multi-path dispersive channel distortions. Traditional equalizers often require a substantial number of pilot symbols for effective channel compensation. To conserve limited channel bandwidth, we investigate novel equalization algorithms that can make more efficient use of fewer pilot symbols. We present a linear programming algorithm that takes better advantage of the QAM constellation. We further exploit the information inherent in the LDPC forward error correction codes within the linear programming framework to improve the equalization performance and convergence properties.

As more advanced multi-antenna user equipment becomes widespread, multi-input-multi-output (MIMO) Device-to-Device (D2D) communications can provide substantial rate improvement when properly designed and implemented. This work studies MIMO precoding schemes for D2D and cellular downlink when D2D links reuse the channel resources of cellular downlink. Due to the co-channel interference resulting from shared resources, the selection of D2D precoder and cellular DL precoder become highly inter-dependent. We propose various precoding schemes in the distributed system where users only have their own channel information. These schemes enable cooperative precoding via signaling in the cellular context. We provide extensive simulations for different precoding schemes and illustrate their performance and overhead tradeoffs.

We develop and analyze a time-domain bi-level power control scheme using Almost Blank Subframe (ABS) for downlink cross-tier interference mitigation. Even for only one femto cell and macro cell, the optimization problem given rate constraint is nonconvex. We prove that by relaxing the integer requirement for subframe division, the optimal strategy is using either power control or ABS individually, and a joint scheme is not needed in the optimal solution. Imposing the integer subframe number requirement, this conclusion is no longer true. However, we show that the optimal solution also assumes simple structure by choosing from one of three feasible solutions. Simulation results show that the joint power control and ABS scheme outperforms traditional single-level power control scheme for downlink cross-tier interference mitigation with low computational complexity.

In this paper, we consider a two-tier heterogeneous network that locally consists of a multi-antenna macrocell base station and a multi-antenna femtocell base station (FBS) each serving separate single-antenna users. We investigate an optimal transmission strategy with maximum degree of freedom for transmit power minimization of the FBS under outage-based quality-of-service (QoS) constraints for the femtocell user equipment (FUE) and macrocell user equipment (MUE). Specifically, we examine the scenario that the FBS receives no instantaneous channel estimate from the FUE, and relies on only statistical information of downlink multiple-input single-output (MISO) channels. Although the outage constrained problem has no closed-form probabilistic constraints and may not be convex in general, we propose a transmission strategy and prove its optimality under a given condition. Our simulation results also demonstrate that the proposed transmission strategy can significantly save power compared to beamforming strategies.

This work studies dual-access-technology femtocells equipped with both cellular and non-cellular air interfaces with the goal of increasing per-user throughput as well as achieving user fairness. We target enterprise environments characterized by high data rates and/or dense user terminals such as corporate premises, shopping malls, or stadiums. To meet this high data demand, multiple femtocells are deployed for which the major challenge is interference management. We propose an architecture for interference management and resource allocation over both cellular and non-cellular bands. Numerical evaluation shows that our proposed solution outperforms other existing solutions.

This work considers worst-case utility maximization (WCUM) problem for a downlink wireless system where a multiantenna base station communicates with multiple single-antenna users. Specifically, we jointly design transmit covariance matrices for each user to robustly maximize the worst-case (i.e., minimum) system utility function under channel estimation errors bounded within a spherical region. This problem has been shown to be NP-hard, and so any algorithms for finding the optimal solution may suffer from prohibitively high complexity. In view of this, we seek an efficient and more accurate suboptimal solution for the WCUM problem. A low-complexity iterative WCUM algorithm is proposed for this nonconvex problem by solving two convex problems alternatively. We also show the convergence of the proposed algorithm, and prove its Pareto optimality to the WCUM problem. Some simulation results are presented to demonstrate its substantial performance gain and higher computational efficiency over existing algorithms.

This work studies the design of robust transmit beamforming under receiver outage constraint for limited feedback multiuser multiple-input multiple-output (MIMO) downlinks. We propose a new stochastic model to characterize the channel uncertainty resulting from vector quantization-based limited feedback schemes. With this uncertainty model, we develop two convex optimization formulations to minimize the transmitted power subject to per user quality of service (QoS) constraints that are defined in terms of outage probability of the signal-to-interference-and-noise ratio (SINR) at a target level. Our numerical results, based on solving the design problem using known convex optimization algorithms, show that both design formulations lead to desired outcomes and satisfy the specified QoS requirements.

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks.

Conventional approaches to linear equalization of data transmissions under frequency selective channels simply treat the problem as a system identification issue. However, practical data communication systems impose additional conditions on the channel input signals such as LDPC error correction coding and known data constellation. We study a unified framework to integrate these known constraints into the channel equalization procedure and propose a linear programming-based approach to channel equalization for QAM signals that can succeed with significantly reduced pilots. In addition to utilizing the information of QAM constellation, our method exploits the code information of LDPC forward error correction coding to improve the equalization performance and convergence properties.

This work investigates joint estimation of symbol timing synchronization and channel response in two-way relay networks (TWRN) that utilize amplify-and-forward (AF) relay strategy. With unknown relay channel gains and unknown timing offset, the optimum maximum likelihood (ML) algorithm for joint timing recovery and channel estimation can be overly complex. We develop a new Bayesian based Markov chain Monte Carlo (MCMC) algorithm in order to facilitate joint symbol timing recovery and effective channel estimation. In particular, we present a basic Metropolis-Hastings algorithm (BMH) and a Metropolis-Hastings-ML (MH-ML) algorithm for this purpose. We also derive the Cramer-Rao lower bound (CRLB) to establish a performance benchmark. Our test results of ML, BMH, and MH-ML estimation illustrate near-optimum performance in terms of mean-square errors (MSE) and estimation bias. We further present bit error rate (BER) performance results.

This work investigates the joint detection and decoding of MIMO-OFDM signals. Traditional receivers either utilize disjoint serial detector and decoder or require turbo message passing between the two functional blocks of detection and decoding. We present a novel approach that can jointly achieve detection and decoding of low density parity check (LDPC) coded multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) signals as a unified optimization algorithm. Our receiver integrates the MIMO-OFDM signal detection and the decoding of LDPC coded data by formulating a linear programming problem regardless of affine or nonaffine quadrature amplitude modulation (QAM) mapping. The proposed joint MIMO-OFDM detector and decoder achieves substantial performance gain over existing joint detection receivers of comparable computational complexity. Furthermore, the proposed receiver also substantially outperforms the more traditional turbo receiver with only modest cost in complexity.

This paper investigates the linear precoder design for K-user interference channels of multiple-input multiple-output (MIMO) transceivers under finite alphabet inputs. We first obtain general explicit expressions of the achievable rate for users in the MIMO interference channel systems. We study optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions. Given finite alphabet inputs, we show that a simple power allocation design achieves optimal performance at high SNR whereas the well-known interference alignment technique for Gaussian inputs only utilizes a partial interference-free signal space for transmission and leads to a constant rate loss when applied naively to finite-alphabet inputs. Moreover, we establish necessary conditions for the linear precoder design to achieve weighted sum-rate maximization. We also present an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results demonstrate that the proposed iterative algorithm achieves considerably higher sum-rate under practical QAM inputs than other known methods.

This work studies the problem of tracking signal-emitting mobile targets using navigated mobile sensors based on signal reception. Since the mobile target's maneuver is unknown, the mobile sensor controller utilizes the measurement collected by a wireless sensor network in terms of the mobile target signal's time of arrival (TOA). The mobile sensor controller acquires the TOA measurement information from both the mobile target and the mobile sensor for estimating their locations before directing the mobile sensor's movement to follow the target. We propose a min-max approximation approach to estimate the location for tracking which can be efficiently solved via semidefinite programming (SDP) relaxation, and apply a cubic function for mobile sensor navigation. We estimate the location of the mobile sensor and target jointly to improve the tracking accuracy. To further improve the system performance, we propose a weighted tracking algorithm by using the measurement information more efficiently. Our results demonstrate that the proposed algorithm provides good tracking performance and can quickly direct the mobile sensor to follow the mobile target.

In heterogeneous cellular scenarios with macrocells, femtocells or picocells users may suffer from significant co-channel cross-tier interference. To manage this interference 3GPP introduced almost blank subframe (ABSF), a subframe in which the interferer tier is not allowed to transmit data. Vulnerable users thus get a chance to be scheduled in ABSFs with reduced cross-tier interference. We analyze downlink scenarios using stochastic geometry and formulate a condition for the required number of ABSFs based on base station placement statistics and user throughput requirement. The result is a semi-analytical formula that serves as a good initial estimate and offers an easy way to analyze impact of network parameters. We show that while in macro/femto scenario the residue ABSF interference can be well managed, in macro/pico scenario it affects the number of required ABSFs strongly. The effect of ABSFs is subsequently demonstrated via user throughput simulations. Especially in the macro/pico scenario, we find that using ABSFs is advantageous for the system since victim users no longer suffer from poor performance for the price of relatively small drop in higher throughput percentiles.

This work studies femtocell resource allocation in shared spectrum heterogeneous networks. We propose an enhanced shadow chasing method for femtocell resource allocation to achieve interference mitigation in heterogeneous networks serving both Macrocell User Equipments (MUEs) and Home User Equipments (HUEs). Shadow chasing home eNB (HeNB) uses Downlink Control Information (DCI) together with over-heard macrocell user ACK/NAK feedbacks and CQI reports to assign its own downlink resources to mitigate downlink interference to MUEs. Since the HeNB receives outdated DCI due to backhaul delay, we derive a likelihood metric for each resource unit being either empty or assigned to a (low-interference) outdoor MUE based on a finite-state Markov chain model for each resource unit. By dynamically separating MUE and HUE assignments, the enhanced shadow chasing can better control the downlink interference to MUEs for QoS assurance. It effectively reduces the probability of resource collision and MUE interference compared to schemes not considering backhaul delay effect or user feedbacks.

In this paper, we investigate distributed control of multiple secondary users attempting to access the channel of a high priority primary user. Our aim is to maximize the sum cognitive (secondary) user throughput under the constraint of primary user's queue stability. We consider the effect of primary user link adaptation that allows the primary transmitter (PTx) to adapt its transmission rate in response to the secondary interference-level at the primary receiver (PRx). To control the sum secondary interference to PRx beyond the traditional collision-avoidance paradigm, we propose a novel power-control algorithm for secondary nodes to function. To develop such a distributed algorithm and to improve secondary user adaptability, we allow secondary nodes to monitor primary's radio link control information on the feedback channel. We present practical schemes that approximate the optimum solution without relying on global channel information at each secondary node.

Hybrid ARQ transmissions are effective means for on-demand error correction in wireless networks against unpredictable channel distortions and interferences. By requesting retransmission of failed data packets upon detecting packet errors, receivers can integrate original and retransmitted packet signals for more effective packet detection and error correction. Often, only a small fraction of the packet bits is in error. For channels with sufficiently high capacity such as in MIMO wireless systems, it is typically unnecessary to reuse the entire channel for hybrid-ARQ retransmissions in order to correct the few remaining errors. To improve the wireless channel utility, we propose and investigate a shared hybrid ARQ (SHARQ) mechanism in MIMO systems where failed data can piggyback on new data frames over the diversity MIMO channel. To guarantee certain quality of HARQ retransmission, we design a linear transmit precoder based on channel state information at the transmitter and receiver to achieve a predetermined {minimum} signal-to-noise ratio (SNR) gain for the SHARQ signals by minimizing the transmission power. Our proposed SHARQ system takes advantage of a simple successive interference cancellation receiver to facilitate a simpler and better transmitter.

To improve wireless heterogeneous network service via macrocell and femtocells that share certain spectral resources, this paper studies the transmit beamforming design for femtocell base station (FBS), equipped with multiple antennas, under an outage-based quality-of-service (QoS) constraint at the single-antenna femtocell user equipment characterized by its signal-to-interference-plus-noise ratio. Specifically, we focus on the practical case of imperfect downlink multiple-input single-output (MISO) channel state information (CSI) at the FBS due to limited CSI feedback or CSI estimation errors. By characterizing the CSI uncertainty probabilistically, we formulate an outage-based robust beamforming design. This nonconvex optimization problem can be relaxed into a convex semidefinite programming problem, which reduces to a power control problem when all CSI vectors are independent and identically distributed. We also investigate the performance gap between the optimal transmission strategy (that allows maximum transmission degrees of freedom (DoF) equal to the number of transmit antennas) and the proposed optimal beamforming design (with the DoF equal to one) and provide some feasibility conditions, followed by their performance evaluation and trade-off through simulation results.

ABSTRACT We investigate the problem of precoding optimization in an amplify‐and‐forward multiple‐input‐multiple‐output relay system. Most reported works on this problem focus chiefly on the design of relay precoder without simultaneously optimizing the direct link. In this paper, we propose a method for joint source/relay precoder design, taking both direct and relay links into account. Our design is based on maximizing the mutual information (MI) under limited transmission power constraints at the source and relay, respectively. We first formulate a constrained optimization problem before relaxing the original cost function for tractability and derive a MI lower bound. This elaborate bound can asymptotically approach the exact expression of MI in an iterative fashion. In contrast to previous strategies, we then prove that the optimal structure of the source and relay precoders jointly convert the multiple‐input‐multiple‐output relay channel into a bank of single‐input‐single‐output relay channels without having to assume a beamforming structure to simplify the derivation. Specifically, the linear precoding design problem degenerates into power loading among multiple single‐input‐single‐output relay channels. Applying standard Lagrange technique results in a scalar convex optimization, and it can be readily solved by iterative water filling. Numerical examples demonstrate that the proposed scheme, either exploiting partial or full channel state information, significantly outperforms the existing methods. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we propose an enhancement to a resource allocation scheme known as shadow chasing for interference mitigation in heterogeneous networks composed of Macrocell User Equipments (MUEs) and Home User Equipments (HUEs). In shadow chasing, the Home eNB (HeNB) uses Downlink Control Information (DCI) together with the overheard ACK/NAK feedback and CQI reports to assign its HUEs the Physical Resource Blocks (PRBs) that are assigned to outdoor MUEs. Since the HeNB receives an outdated DCI due to backhaul delay, our solution is to derive a likelihood metric for each PRB being either empty or assigned to an outdoor MUE by using a MC model for the state of each PRB. By dynamically separating the MUE and HUE PRB assignments, enhanced shadow chasing can better constrain the downlink interference to the MUE. Our results show effective reduction of probability of PRB assignment collision and MUE interference compared to schemes that do not integrate the delay effect or exploit user feedbacks.

In this work1, we present a new multiple-input-multiple-output (MIMO) receiver that integrates the MIMO signal detection and the decoding of low density parity check coded data. This joint MIMO detector and decoder utilizes linear programming and achieves about 9.0 dB gain over existing works in terms of bit error rate (BER) of 4 × 10−5with comparable computational complexity. The proposed detector also outperforms the classic turbo equalizer by achieving up to 4.0 dB improvement over turbo equalizer at frame error rate (FER) of 1 × 104. In fact, we can achieve further gain by improving the proposed joint detector through the use of redundant parity checks.

In this work, we study the localization of mobile signal emitters using time of arrival (TOA) measurement and additional urban street information. Two algorithms are proposed to improve the localization performance by integrating street information with the TOA measurement. The first algorithm exhaustively searches of all possible road paths. For each possible path, the source location is estimated based a semidefinite programming (SDP) algorithm by minimizing the maximum error measurement between the observed propagation time and the modeled propagation time. Only location on a street that satisfies the minimum mean square error yields estimation output. To reduce complexity, our second joint optimization algorithm combines the two steps together and jointly optimizes the path selection and source location. Numerical results show that both algorithms can improve the localization performance. Our proposed joint optimization approach is more suitable for practical use because of lower complexity and good performance.

In this paper1, we study interference mitigation in cellular networks with femtocells. We propose to use adaptive distributed beamforming to mitigate downlink interference between femtocell users, known as Home User Equipments (HUEs), and macrocell users, known as Macrocell User Equipments (MUEs). We develop three MEVIO beamforming schemes for interference mitigation that take into account the QoS requirement of femtocell and macrocell clients. These new heterogeneous MIMO precoding strategies improve flexibility in resource provisioning and signaling requirement in response to QoS need. The proposed schemes minimize the interference at the MUE or maximize the throughput at the HUE depending on the network traffic and QoS constraints. We also analyze the MUE mean throughput by applying order statistics theory.

Recent works have established that MIMO systems optimized for Gaussian source signals may suffer unexpected performance loss when practical inputs are in fact discrete QAM sources. There is a practical need in the optimization of MIMO related systems of various networking scenarios to specifically target source signals of finite QAM alphabet. In this work, we investigate the precoding optimization of wireless two-hop non-regenerative three-node MIMO relay networks driven by finite-alphabet inputs. Exploiting a known optimal structure for the precoder at relay and a special convexity property, we propose an iterative two-step numerical optimization algorithm. This algorithm is a general solution, not only for arbitrary source signals but also for cooperative networks with or without direct link. Simulation results demonstrate substantial performance improvement by the new precoder over precoders optimized under the Gaussian input assumption.

We investigate the design of linear transmit precoding for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna configurations. For the case where all the users employ the same modulation scheme, we further present a weighted sum rate upper-bound of the MIMO BC with identical transmit precoding matrices. The resulting bound demonstrates a serious performance loss due to multi-user interference for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region, which motivates the use of simple precoding to combat this multiuser interference. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Numerical results illustrate that our proposed algorithm provides significant gains over other conventional precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input assumption.

In this paper, we investigate the secrecy rate of finite alphabet communications over multiple-input, multiple-output, multiple-antenna eavesdropper (MIMOME) systems. Traditional precoder designs at the transmitter for achieving secrecy capacity (maximum secrecy rate) for MIMOME systems are developed according to the assumption of Gaussian input signals. Such designs may risk substantial secrecy rate loss when Gaussian inputs are replaced by practical finite alphabet inputs. To address this issue, we propose a linear precoding design to directly maximize the secrecy rate for MIMOME systems under the constraint of finite alphabet input. Exploiting convex optimization and matrix calculus, we present necessary conditions required of the optimal precoding design and develop an iterative algorithm for finding an efficient precoder. With finite alphabet input signals, maximum transmission power no longer corresponds to maximum secrecy rate as in the case of Gaussian input. We further derive closed-form results on the optimal transmission design for maximizing secrecy rate in low signal-to-noise ratio (SNR) region and near-optimal transmission power in a high SNR region.

This paper presents a resource allocation strategy for maximizing the lifetime of amplify-and-forward (AF) dualhop cooperative OFDM networks. The high computational complexity for optimization necessitates our study of suboptimal approaches. Our centralized scheme is implemented in two steps. Specifically, during each transmission interval, source-relay selection and subcarrier pairing can be decided according to channel state information of the relay links and residual energy information at each terminal. Next, energy-aware power loading can be solved by employing standard Lagrange techniques. Numerical examples verify the effectiveness of our proposal.

We present investigation results on joint estimation of symbol timing synchronization and channel response in two-way multiple antenna relay networks that utilize amplify-and-forward (AF) relay strategy. With practically unknown relay channel gains and timing offset, optimum maximum likelihood (ML) estimation for joint timing recovery and channel estimation can be prohibitively complex. We develop a new Bayesian based Markov chain Monte Carlo (MCMC) algorithm and generalize our previous principle to a multiple antenna relay network to accomplish joint symbol timing recovery and effective channel estimation. Simulation results are provided to demonstrate the performance of the proposed algorithm.

In the literature, there exists a number of blind signal recovery algorithms that are implemented as stochastic gradient descent (SGD)-based adaptive schemes. SGD typically has low complexity at the expense of slower convergence. On the other hand, packet-based data transmission in many practical digital communication systems makes it attractive to develop steepest descent (SD) implementation in order to speed-up convergence. This work aims at developing SD implementation of several well-known blind signal recovery algorithms for multi-channel equalisation and source separation. The authors SD formulation is more amenable to additional parametric and signal subspace constraint for faster convergence and superior performance.

Cognitive radio systems capable of opportunistic spectrum access represent a new paradigm for improving the efficiency of current spectrum utilisation. In this work, the authors present a novel cognitive channel access method based on learning from both primary channel transmissions and the receiver ARQ feedback signals. This new sensing-plus-confirmation scheme constitutes a non-trivial generalisation of the more traditional ‘Listen-Before-Talk’ (LBT) strategy that merely listens to and yields to primary transmissions regardless of primary receiver (PRx) responses. Our new method exploits the bi-directional and interactive nature of most wireless communication links to facilitate better opportunistic secondary access while achieving PRx protection. By allowing the secondary users to learn from both primary transmissions and the corresponding receiver confirmations, our approach allows secondary cognitive users to exploit critical information that PRxs regularly send to their transmitters. The authors show that, by monitoring both primary transmissions and receiver feedback signals, secondary radio access can improve throughput over the traditional LBT while limiting the probability of collision with primary user signals.

This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.

This work investigates the effect of finite-alphabet input constraint on the secrecy rate of a multi-antenna wiretap channel. Most existing works have characterized maximum achievable secrecy rate or secrecy capacity for single and multiple antenna systems based on Gaussian source signals and secrecy code. For practical considerations, we study the effect of finite discrete-constellation on the achievable secrecy rate of multiple-antenna wire-tap channels. Our proposed precoding scheme converts the underlying multi-antenna system into a bank of parallel channels. Based on this precoding strategy, we develop a decentralized power allocation algorithm based on dual decomposition to maximize the achievable secrecy rate. In addition, we analyze the achievable secrecy rate for finite-alphabet inputs in low and high SNR regions. Our results demonstrate substantial difference in secrecy rate between systems given finite-alphabet inputs and systems with Gaussian inputs.

This work investigates the optimization of power allocation for hybrid-ARQ (H-ARQ) retransmissions of non-Gaussian inputs over a bank of independent parallel Gaussian channels. We establish a general solution for maximizing generic transceiver objective utility functions that are monotonically non-decreasing and concave function with respect to the accumulated signal to noise ratio (SNR). Specifically, we investigate optimized solutions under two performance metrics, namely, the mutual information (MI) and the union bound of symbol error rate (UBSER) under maximal ratio combining (MRC) reception. We establish that efficient utilization of parallel channels in H-ARQ retransmissions requires sequential updating of signal-channel pairing as well as optimizing power allocation. Applying geometric analysis of power loading for H-ARQ retransmission, we show that for i.i.d. inputs that are not necessarily Gaussian, the optimum pairing policy should match signals of the lowest cumulative signal-to-noise ratio with channels of the best quality in each transmission, which is consistent with a similar result of , for Gaussian input signals. We further propose a generalized mercury/waterfilling algorithm for the optimal power assignment problem in H-ARQ. Simulation results illustrate substantial improvements over designs based on Gaussian input assumptions.

We investigate the design of linear transmit precoder for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We first derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna number. We further present a weighted sum rate upper bound of the MIMO BC with identical transmit precoding matrices. The resulting bound exhibits a serious performance loss because of the non-uniquely decodable transmit signals for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region. This performance loss motivates the use of a simple precoding to combat the non-unique decodability. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Our { simulation} results under the practical transmit symbols of discrete constellations demonstrate significant gains by the proposed algorithm over other precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input signals. For the low-density parity-check coded systems, our precoder provides considerably coded BER improvement through iterative decoding and detection.

We study the design optimization of linear precoders that maximize the mutual information in complex-valued vector Gaussian channels under finite alphabet inputs. It is well known that mutual information of a vector channel with discrete constellation source is a highly nonlinear and no concave function of the linear precoder matrix G, thereby complicating the precoder design optimization. In this paper, we show that the mutual information is a concave function of W = GhHhHG, where H is the complex-valued channel matrix and superscript "h" represents conjugate transpose. We further propose an iterative algorithm for solving the globally optimal linear precoder G. Illustrative results show that the proposed iterative algorithm is very robust and efficient for global convergence.

We consider an opportunistic channel access problem over multiple primary bands. We exploit primary ACK/NAK packets overhearing to overlay secondary communications on top of permanently busy primary channels. Through the theory of multi-armed restless bandit processes (MARB), we prove optimality of the myopic policy with a simple structure that does not require channel state-transition probabilities knowledge at the secondary users under certain conditions.

Interference alignment has emerged as new tool for achieving high capacity in multi-user and multi-channel wireless interference channels. Based on maximizing a degree of freedom (DOF) metric defined for asymptotic signal to noise ratio (SNR), interference alignment encounters limitations in practical applications because of limited SNR. In addition, perfect interference alignment requires strict antenna-user number condition which may not always hold true as user number increases. In this work, we address these two practical issues in interference alignment. We propose a new definition of interference alignment metric designed to assess the effectiveness of non-ideal interference alignment. We apply the new interference alignment metric to investigate the "best effort" interference alignment in multi-user OFDM systems with limited SNR and limited dimension (subcarriers).

Practical transmission of orthogonal frequency division multiplexing (OFDM) signals suffers from high peak-to- average power ratio (PAPR) at the transmit antenna(s). Among various solutions, tone injection is an effective approach to mitigate this problem without incurring bandwidth loss. However, due to its computational complexity, finding the optimal tone injection becomes intractable for OFDM systems with a large number of subcarriers. In this work we present a novel convex optimization approach to numerically determine the near-optimal tone injection solution based on a semi-definite relaxation. This approach leads to significant PAPR reduction with polynomial complexity. An STBC-OFDM extension is also proposed.

Multicarrier transmission over time varying frequency selective fading channels is investigated and a model for such a transmission scheme with emphasis on Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems is developed. The fast fading can destroy the orthogonality of subcarriers and cause serious InterCarrier Interference (ICI), thus leading to significant performance degradation that becomes severe as Doppler shift increases. To tackle this problem, a frequency domain iterative ICI mitigation algorithm is mathematically derived. By fully exploiting the separation property of Channel Transfer Function (CTF) matrix, ICI can be iteratively subtracted from received symbols and then Parallel Interference Cancellation (PIC) detection is performed to suppress multistream interference among different antennas. In particular, the complexity of the proposed strategy can be drastically reduced by restricting the interference computation to limited eighteen adjacent subcarriers, whereas the performance loss is quite minor. Simulation results illustrate that this scheme can effectively alleviate the impairment of ICI and asymptotically approach the ICI free performance at low-to-moderate Signal-to-Noise Ratio (SNR).

We investigate the problem of precoding optimization in an amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay system. Most reported works on this problem focus chiefly on the design of relay precoder without simultaneously optimizing the direct link. In this paper, we propose a method for joint source/relay precoder design, taking both direct and relay links into account. Our design is based on maximizing the mutual information (MI) under limited transmission power constraints at the source and relay, respectively. We first formulate a constrained optimization problem before relaxing the original cost function for tractability and derive a MI lower bound which asymptotically approaches the exact expression of MI in an iterative fashion. In contrast to previous strategies, we then prove that the optimal structure of the source and relay precoders jointly convert the MIMO relay channel into a bank of single-input-single-output (SISO) relay channels without having to assume a beamforming structure to simplify the derivation. Specifically, the linear precoding design problem degenerates into power loading among multiple SISO relay channels. Applying standard Lagrange technique results in a scalar convex optimization which can be readily solved by iterative water filling. Numerical examples demonstrate that the proposed scheme, either exploiting partial or full channel state information (CSI), significantly outperforms the existing methods.

This work on cognitive radio access ventures beyond the more traditional "listen-before-talk" paradigm that underlies many cognitive radio access proposals. We exploit the bi-directional interaction of most primary communication links. By intelligently controlling their access parameters based on the inference from observed link control signals of primary user (PU) communications, cognitive secondary users (SUs) can achieve higher spectrum efficiency while limiting their interference to the PU network. In one specific implementation, we let the SUs listen to the PU's feedback channel to assess their own interference on the primary receiver, and adjust radio power accordingly to satisfy the PU's interference constraint. We propose a discounted distributed power control algorithm to achieve non-intrusive secondary spectrum access without either a centralized controller or active PU cooperation. We present an analytical study of its convergence property. We show that the link control feedback information inherent in many two-way primary systems can be used as important reference signal among multiple SU pairs to distributively achieve a joint performance assurance for primary receiver's quality of service.

A new class of quasi-cyclic LDPC codes whose parity-check matrices are arrays of circulant permutation matrices constructed based on cyclic subgroups of finite fields is presented. This class of codes contains several known classes of algebraic quasi-cyclic LDPC codes as subclasses. Experimental results show that the codes constructed perform very well over the AWGN channel when decoded with iterative decoding based on belief propagation. This class of new QC-LDPC codes contains a subclass of codes which have large minimum distances. Combinatorial expressions for the ranks of the parity-check matrices of a subclass of codes constructed based on fields of characteristic two are given.

We investigate the problem of source localization based on measuring time difference of signal arrivals (TDOA) from the source emitter. Taking into account the colored measurement noise, we adopt a min-max principle to develop two lower complexity semidefinite relaxation algorithms that can be reliably solved using semidefinite programming. The reduction of algorithm complexity is achieved through a simple, but effective method to select a reference node among participating measurement nodes such that only selective time differences of signal arrival are exploited. Our estimation methods are insensitive to the source locations and can be used either as the final location estimate or as the initial point for more traditional search algorithms.

We investigate the secrecy performance of a codebook based transmission beamforming for a sensitive data link against passive eavesdropping. We characterize the secrecy outage probability of a communication link that is being eavesdropped. We consider a limited feedback scenario where the transmitter is using a predefined codebook known to both transmitter and receiver for beamforming, and analyze the secrecy outage probability of the link when it being eavesdropped. Our analysis also provides the secrecy outage probability of the beamforming transmission in the direction of the intended receiver. We characterize how the secrecy outage probability improves as the number of transmit antennas increases. We further analyze the effect of codebook design on secrecy enhancement and provide bounds on the secrecy outage probability of codebook beamforming.

We propose a new joint receiver for the blind detection of synchronous co-channel multiuser signals modulated with orthogonal space-time block codes (OSTBC). We exploit the algebraic structure of the orthogonal space-time block codes and the constellation feature of Quadrature Amplitude Modulation (QAM) signals to develop a blind zero-forcing (ZF) detector based on quadratic programming (QP) with desirable convergence properties. Focusing on high rate space-time block codes in synchronous co-channel multiuser systems, we propose a successive processing joint receiver based on the proposed blind detector with low computational complexity and fast convergence. For signal detection in single user MIMO systems, the proposed QP algorithm has performance similar to that of maximum-likelihood receiver with wireless channel information. Unlike many existing techniques, our new receiver is applicable to full-rate orthogonal space-time block codes such as the practically popular Alamouti code. Furthermore, our QP receiver for multiuser detection relaxes stringent conditions on the number of receive antennas of the multi-input-multi-output (MIMO) transceiver systems.

Blind equalization typically achieves parameter optimization through cost minimization using stochastic gradient descent in both batch and adaptive algorithms. In general, stochastic descent algorithms typically require large number of iterations or long data samples to converge. The batch approach is generally based on data reuse (recycling) and re-filtering to recompute the cost gradient after each iterative parameter update, thereby causing long processing delays. In this work, we present a novel steepest descent batch algorithm that does not require data recycling. We consider the popular Constant Modulus Algorithm and the Minimum Entropy Deconvolution for normalized cumulant maximization. Both algorithms utilize 4-th order cumulants. The proposed steepest descent batch implementation of both algorithms converge rapidly in a few iterations and deliver superior performance without the delay due to data recycling and refiltering.

In this work, we propose a new criterion for blind channel shortening based on high order statistical information. This method overcomes the weakness of two existing channel shortening criteria derived from only second order output statistics of the unknown channel. It is applicable to both single-input-single-output and single-input-multiple-output frequency selective channels. The optimization criterion can be achieved through either a stochastic gradient descent algorithm or a batch algorithm. Simulation results demonstrate successful shortening of wireless channels that the existing criteria fail to shorten.

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate source localization based on the practical time of arrival (TOA) measurement model. Unlike most existing works that transform TOA measurement into time differences before processing, we consider the original measurement model and investigate three methods for direct source localization. We also derive the Cramér-Rao lower bound (CRLB) under the TOA model and establish its connection with the CRLB under the more commonly used time-difference of arrival (TDOA) signal model. We present results that illustrate the performance advantage of source localization based on the original TOA model over the commonly used TDOA pre-processing.

We venture beyond the "listen-before-talk" strategy that is common in many traditional cognitive radio access schemes. We exploit the bi-directional nature of most primary communication systems. By intelligently choosing their transmission parameters based on the observation of primary user (PU) communications, secondary users (SUs) in a cognitive network can achieve higher spectrum usage while limiting their interference to the PU. Specifically, we propose that the SUs listen to the PU's feedback channel to assess their interference on the primary receiver (PU-Rx), and adjust radio power accordingly to satisfy the PU's interference constraint. We investigate both centralized and distributed power control algorithms without active PU cooperation. We show that the PU feedback information inherent in many two-way primary systems can be used as important coordination signal among multiple SUs to distributively achieve a joint performance guarantee on the primary receiver's quality of service.

Wireless source localization has found a number of applications in wireless sensor networks. In this work, we investigate robust and low complexity solutions to the problem of source localization based on the time-difference of arrivals (TDOA) measurement model. By adopting a min-max approximation to the maximum likelihood source location estimation, we develop two low complexity algorithms that can be reliably and rapidly solved through semi-definite relaxation. Our approach hinges on the use of a reference sensor node which can be optimized according to the Cramer-Rao lower bound or selected heuristically. Our low complexity estimate can be used either as the final location estimation output or as the initial point for other traditional search algorithms.

We investigate the problem of downlink user scheduling in multiple-input multiple-output (MIMO) systems withMtransmit antennas andKhomogeneous multiantenna receiving users. We develop a simple algorithm to schedule a subset ofMactive users for data transmission. We first identify a set ofLcandidate users that should provide channel information feedback. We then selectMactive users among theLcandidates based on joint consideration of their effective channel gains and directions. We derive an asymptotic upper bound for the sum rate gap between the maximum sum rate of theMselected active users, achieved by dirty paper coding, and the full sum capacity of the original MIMO broadcast channel. Utilizing the upper bound, we show that the sum rate gap can be reduced to meet user requirement by suitably choosing a large but still finiteL. Furthermore, we also investigate the performance of the low complexity zero-forcing beamforming (ZFBF) for theMactive users.

An error-burst correcting algorithm is developed based on a circulant parity-check matrix of a cyclic code. The proposed algorithm is more efficient than error trapping if the code rate is less than about 2/3. It is shown that for any (n, k) cyclic code, there is an n × n circulant parity-check matrix such that the algorithm, applied to this matrix, corrects error bursts of lengths up to the error-burst correction limit of the cyclic code. This same matrix can be used to efficiently correct erasure bursts of lengths up to n - k. The error-burst correction capabilities of a class of cyclic low-density parity-check (LDPC) codes constructed from finite geometries are also considered.

We investigate the problem of eavesdropping control in wireless communications networks. When a confidential message is transmitted through a wireless network, there is a potential security breach or 'information leakage' due to the network's susceptibility to eavesdropping. In this work, we quantify the information leakage problem in the context of resource allocation. By limiting the signal to interference and noise ratio (SINR) at the eavesdropper receiver, we develop a cooperative power allocation algorithm to mitigate such security vulnerability. The resulting power allocation algorithm is readily applicable in the physical layer of wireless ad hoc and mesh networks as part of a multi-step security measure.

We study the problem of channel shortening in multicarrier modulation systems without training. We reformulate two existing methods, the sum-squared and the sum-absolute autocorrelation minimization algorithms (SAM and SAAM), into semidefinite programming to overcome their shortcoming of local convergence. We present the original SAM and SAAM cost functions into as a batch optimization problem before relaxing the original problem into globally convergent semidefinite programming algorithms. Our batch processor is superior to the original stochastic gradient algorithms in terms of achievable bit rate and signal to interference and noise ratio (SINR).

Network component analysis (NCA) has been established as a promising tool for reconstructing gene regulatory networks from microarray data. NCA is a method that can resolve the problem of blind source separation when the mixing matrix instead has a known sparse structure despite the correlation among the source signals. The original NCA algorithm relies on alternating least squares (ALS) and suffers from local convergence as well as slow convergence. In this paper, we develop new and more robust NCA algorithms by incorporating additional signal constraints. In particular, we introduce the biologically sound constraints that all nonzero entries in the connectivity network are positive. Our new approach formulates a convex optimization problem which can be solved efficiently and effectively by fast convex programming algorithms. We verify the effectiveness and robustness of our new approach using simulations and gene regulatory network reconstruction from experimental yeast cell cycle microarray data.

We investigate the design of MIMO linear precoders for multiuser broadcast channels under signal leakage constraints. This power leakage constraints reflect both an important security consideration and the need for interference suppression at receivers. Defining a convex optimization problem, we propose two sequential semi-definite program algorithms. In principle, our algorithms exploit both spatial diversity and multiuser diversity to enhance the overall system performance. The resulting MIMO precoded transmission demonstrates superior performance over time-division multiplexed multiuser transmission in conjunction with the traditional space-time transmission.

We present a new approach to blind equalization for generalized orthogonal space-time block codes. Our method takes the form of linear programming (LP) and is globally convergent. We exploit the implicit structure of orthogonal space-time block codes to cast the problem as linear programming that can be solved efficiently. Unlike several known methods, the proposed technique is applicable to many full-rate orthogonal space time codes such as the popular Almouti code. Our algorithm allows receiver detection of full diversity codes without channel knowledge with detection performance comparable to the optimum maximum-likelihood (ML) detection.

In cognitive wireless networks where secondary users (SUs) opportunistically access spectral white spaces of primary users (PUs), there exists an inherent tradeoff between sensing and transmission due to the competing goals of PU protection and SU access maximization. This paper studies means of sensing-transmission for SUs to better manage the competing goals by defining utility function to reward the SU for successful packet transmissions and to penalize it for colliding with PU. To maximize the SU utility, we present a threshold-based sensing-transmission structure that is optimal under a technical constraint. Both perfect sensing and imperfect sensing are considered, with or without SU acknowledgement of reception. This SU access scheme optimizes SU access efficiency while protecting PU performance. It sets a benchmark and provides insight for the design of sensing-transmission control in cognitive networks such as IEEE 802.22.

The authors study the problem of limited channel information feedback in multiple-input single-output (MISO) broadcast channels serving heterogeneous users. The heterogeneous users have different feedback channel capacities because of their physical constraints and limitations. Our objective is to design an optimised limited feedback strategy for multiple users by using a set of multi-resolution codebooks such that, under varying and quantised channel information feedback, the multiuser system can maximise its achievable sum rate. By exploiting the receive antenna combining technique, the authors further generalise the proposed scheme to the multiuser beamforming case with multi-antenna users. Finally, the authors verify the proposed scheme by numerical methods. Simulation results show that the sum rate performance for heterogeneous users can be effectively improved by using the proposed limited feedback scheme, which is optimised according to only statistic channel information of users.

In source localization, one estimates the location of a source using a variety of relative position information. Many algorithms use certain powers of distances to effect localization. In practice, exact distance measurement is not directly available and must be estimated from information such as received signal strength (RSS), time of arrival, or time difference of arrival. This letter considers bias and variance issues in estimating powers of distances from RSS affected by practical log-normal shadowing. We show that the underlying estimation problem is inefficient and that the maximum likelihood estimate yields a bias and a mean-square error (MSE) that both increase exponentially with the noise power. We then characterize the class of unbiased estimates and show that there isonlyoneestimatorinthisclass, but that its MSE also grows exponentially with the noise power. Finally, we provide the linear minimum mean-square error (MMSE) estimate and show that its bias and MSE are both bounded in the noise power.

Transmission precoding in MIMO wireless systems is an effective technique that can significantly improve system capacity and diversity gain. In general, the effectiveness of MIMO precoding depends critically on the knowledge of channel state information available at the transmitter. In this work we describe a novel method for the downlink channel estimation based on a novel feedback mechanism. To minimize processing need at mobile units, the receiver in this new framework returns a fraction of its received data back for the base-station to extract downlink channel information in the form of a quadratic channel product. The base-station can then utilize the quadratic channel product to design channel-aware downlink precoders. This bandwidth efficient feedback scheme applies to both flat fading and frequency selective MIMO channels.

This paper presents algebraic methods for constructing high performance and efficiently encodable non-binary quasi-cyclic LDPC codes based on flats of finite Euclidean geometries and array masking. Codes constructed based on these methods perform very well over the AWGN channel. With iterative decoding using a fast Fourier transform based sum-product algorithm, they achieve significantly large coding gains over Reed-Solomon codes of the same lengths and rates decoded with either algebraic hard-decision Berlekamp-Massey algorithm or algebraic soft-decision Kotter-Vardy algorithm. Due to their quasi-cyclic structure, these non-binary LDPC codes on Euclidean geometries can be encoded using simple shift-registers with linear complexity. Structured non-binary LDPC codes have a great potential to replace Reed-Solomon codes for some applications in either communication or storage systems for combating mixed types of noise and interferences.

Cognitive radio offers a promising technology to mitigate spectrum shortage in wireless communications. It enables secondary users (SUs) to opportunistically access low-occupancy primary spectral bands as long as their negative effect on the primary user (PU) access is constrained. This PU protection requirement is particularly challenging for multiple SUs over a wide geographical area. In this paper, we study the fundamental performance limit on the throughput of cognitive radio networks under the PU packet collision constraint. With perfect sensing, we develop an optimum spectrum access strategy under generic PU traffic patterns. Without perfect sensing, we quantify the impact of missed detection and false alarm, and propose a modified threshold-based spectrum access strategy that achieves close-to-optimal performance. Moreover, we develop and evaluate a distributed access scheme that enables multiple SUs to collectively protect the PU while adapting to behavioral changes in PU usage patterns. Our results provide useful insight on the trade-off between the protection of the primary user and the throughput performance of cognitive radios.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

We investigate the design of a limited feedback mechanism for multiple-input-multiple-output (MIMO) broadcast systems that are equipped with automatic repeat request (ARQ) capabilities. Given multiple antennas at the base-station (BS) and single antenna subscribers, we focus on maximizing the ergodic system sum capacity and propose an adaptive feedback bit allocation scheme under the constraint of total feedback bandwidth. Furthermore, we develop two efficient feedback allocation schemes based on asymptotic analysis. We present simulation results that verify the effectiveness of the proposed adaptive allocation schemes.

We consider a heterogeneous multiuser OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. We propose several efficient algorithms for subcarrier/cluster and power allocation. Our strategy is to maximize the total network utility of the best-effort user while satisfying the high-priority users. We define the best-effort user utility function in two different ways and propose two different novel, efficient and optimal cluster allocation algorithms. We also propose an efficient and optimal power allocation algorithm based on the modification of existing algorithms.

In iterative decoding of LDPC codes, trapping sets often lead to high error floors. In this work, we propose a two-stage iterative decoding to break trapping sets. Simulation results show that the error floor performance can be significantly improved with this decoding scheme.

In this work, we develop a joint ARQ receiver design for channel estimation and equalization under bandwidth efficient automatic repeat request (ARQ) in multiple-input multiple- output (MIMO) system. Our retransmission proposal improves bandwidth efficiency by sending a partial response of the original data sequence and by potentially omitting training sequence. To estimate unknown channels and to detect the transmitted data, our design of joint ARQ receivers can implement semiblind channel estimation and joint detection based on multiple transmissions. We demonstrate bandwidth savings without significant performance loss. We also propose a low complexity joint ARQ equalizer that minimizes the mean square error and provides a good design trade-off between performance and complexity.

We investigate new scheduling algorithms for MIMO broadcast systems to maximize the achievable sum rate of block diagonalization transceivers. In contrast to conventional schemes which select users directly, we investigate a more efficient type of scheduling algorithms, named as multiuser eigenmode transmission (MET). It is based on the selection of eigenmodes of all users. Utilizing an iterative procedure to calculate the Moore-Penrose generalized inverse of a matrix consisting of the already selected eigenmodes, we propose a low complexity multiuser eigenmode selection method based on the greedy algorithm. Then we show that the multiuser dominant eigenmode transmission can achieve the same sum rate scaling law as that of DPC. Therefore, to further reduce the complexity, we restrict our search space to the set of dominant eigenmodes and derive an alternative eigenmode selection algorithm with lower complexity at the cost of little performance loss, which we name as the low-complexity multiuser dominant eigenmode selection algorithm.

This paper investigates the scheduling of user signals in MIMO systems with spatially correlated channels. By maximizing an upper bound of the sum capacity, we propose a scheduling scheme which requires only a single scalar feedback from each receiving user. To further reduce the need for user information feedback and better explore the spatial correlation information, a more efficient scheduling method is also developed. This new approach only requires a 1-bit indicator from each user and selects users according to the slowly varying MIMO channel correlation information. Numerical results verify the effectiveness of our proposed schemes.

Driven by regulatory initiatives and radio technology advances, opportunistic spectrum access has the potential to mitigate spectrum scarcity and meet the increasing demand for spectrum. In this paper, we consider a scenario where secondary users can opportunistically access unused spectrum vacated by idle primaries. We introduce two metrics to protect primary performance, namely collision probability and overlapping time. We present three spectrum access schemes using different sensing, back-off, and transmission mechanisms. We show that they achieve indistinguishable secondary performance under given primary constraints. We provide closed form analysis on secondary user performance, present a tight capacity upper bound, and reveal the impact of various design options, such as sensing, packet length distribution, back-off time, packet overhead, and grouping. Our work sheds light on the fundamental properties and design criteria on opportunistic spectrum access.

This paper investigates error control at the physical layer of a multicast network using low-density parity-check (LDPC) codes. Packets for transmission are encoded into LDPC codewords. A joint iterative message-passing scheme for decoding LDPC codewords at a receive node in the network is proposed to improve error performance. Also proposed is a split-codeword transmission to provide equal error protection for all transmitted packets. Density evolution analysis and some simulation results are also presented.

MOTIVATION: Recently developed network component analysis (NCA) approach is promising for gene regulatory network reconstruction from microarray data. The existing NCA algorithm is an iterative method which has two potential limitations: computational instability and multiple local solutions. The subsequently developed NCA-r algorithm with Tikhonov regularization can help solve the first issue but cannot completely handle the second one. Here we develop a novel Fast Network Component Analysis (FastNCA) algorithm which has an analytical solution that is much faster and does not have the above limitations. RESULTS: Firstly FastNCA is compared to NCA and NCA-r using synthetic data. The reconstruction of FastNCA is more accurate than that of NCA-r and comparable to that of properly converged NCA. FastNCA is not sensitive to the correlation among the input signals, while its performance does degrade a little but not as dramatically as that of NCA. Like NCA, FastNCA is not very sensitive to small inaccuracies in a priori information on the network topology. FastNCA is about several tens times faster than NCA and several hundreds times faster than NCA-r. Then, the method is applied to real yeast cell-cycle microarray data. The activities of the estimated cell-cycle regulators by FastNCA and NCA-r are compared to the semi-quantitative results obtained independently by Lee et al. (2002). It is shown here that there is a greater agreement between the results of FastNCA and Lee's, which is represented by the ratio 23/33, than that between the results of NCA-r and Lee's, which is 14/33. AVAILABILITY: Software and supplementary materials are available from http://www.eee.hku.hk/~cqchang/FastNCA.htm

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

This work investigates the design of linear precoders for ARQ packet retransmissions in multi-input multi-output (MIMO) systems. We consider transmitter precoder design based on partial MIMO channel information in the form of their covariance feedback. Our objective is to maximize the ergodic mutual information provided by multiple (re)transmissions of a packet subject to transmission power constraint. We propose a set of near-optimal successive linear ARQ precoders for flat fading MIMO channels. This progressive linear ARQ precoder combines the appropriate power loading and the reverse-order pairing of singular values in the current retransmission with previous transmissions. This reverse-order pairing is a special feature unique to our sequential ARQ preceding approach with demonstrated performance gains.

We present an effective method for time domain channel estimation of wireless orthogonal frequency division multiplexing (OFDM) system. Relying on a bent-pipe mechanism, the mobile receiver sends a fraction of the received data back to the base station which can then estimate both the forward link and reserve link channel impulse responses. Given knowledge on the forward link channel response, the resource rich base station can employ effective adaptive modulation schemes to increase OFDM system capacity. In this paper, closed-form expressions for channel estimation Cramer Rao lower bound are derived for the feedback system. Impact of feedback parameters on channel estimation performance is discussed through Cramer Rao bound analysis and simulation. Identifiability issues associated with power loaded multicarrier systems are also addressed. Simulation results on the proposed feedback channel estimation scheme are shown.

We investigate the joint optimization of automatic repeat request (ARQ) and MIMO diversity for multi-user wireless communication systems. Exploiting MIMO for diversity gain and ARQ for error correction, our goal is to better integrate MIMO and ARQ in the transceivers to improve multi-user uplink performance. Specifically, we design optimal linear precoders for ARQ retransmissions by multiple wireless uplink terminals equipped with MIMO transmitters and we rely on joint detection of multiple retransmissions at the receivers. To better utilize ARQ diversity, we derive a set of progressive linear ARQ precoders for flat fading multiple-access channels based on the criterion of maximizing the mutual information of multiuser (re)-transmissions. We propose two efficient iterative numerical algorithms to determine the optimal precoders.

Cognitive radio has recently been identified as a potential relief to spectrum scarcity by improving temporal spectral efficiency. We investigate a flexible non-intrusive cognitive radio network based on smart antenna technologies. The proposed scheme exploits transmit beamforming to enable better spectral sharing between primary users and cognitive (secondary) users. As proposed, the cognitive transmitter equipped with antenna array forms transmit beamforming to keep the interference to primary receiver below a given threshold. By also adopting smart antennas at primary transmitters, we can significantly boost the successful transmission probability of cognitive users; thereby improving the spectrum utilization efficiency of the wireless communication networks.

In distributed wireless access networks, the short-term unfairness of IEEE 802.11 distributed coordination function (DCF) has been revealed by many works. In this paper, a modified DCF, based on the principle of priority collision resolution (PCR), is proposed to improve the short-term fairness in distributed access wireless networks. Our PCR-DCF achieves short-term fairness improvement without estimating behaviors of other users and the system contention level, such as the number of active users. Only the capability to identify collision is required. Theoretical analysis is carried out to validate the performance of PCR-DCF based on a slotted system model. Both simulations and analyses show that PCR-DCF has a much smaller packet transmission delay jitter than IEEE 802.11 DCF, with little degradation on the average packet transmission delay. Moreover, the analysis and simulation results indicate that PCR- DCF also experiences a much lower packet drop rate than IEEE 802.11 DCF in almost all load ranges with reasonable retransmission limit.

Frequency hopping systems have been widely used in military communications to prevent hostile jamming, interception and detection. In traditional frequency hopping (FH) systems, hopping frequency selection at the transmitter end is controlled by a pseudo-random code sequence, and the receiver operates accordingly in exact synchronization with the transmitters hopping pattern. In an effort to meet the ever increasing requirement on information capacity and reduce the burden of synchronization, in this paper, an innovative message-driven frequency hopping (MDFH) system is proposed. By embedding part of information into the process of hopping frequency selection, the spectral efficiency of the FH system can be significantly improved. Quantitative analysis on the proposed scheme is presented to demonstrate its superior performance and enhanced security features.

Transmission precoding in MIMO wireless systems can significantly improve system capacity and diversity gain. The effectiveness of MIMO precoding depends critically on accurate knowledge of channel state information available to the transmitter. In this paper we describe a novel method for the feedback of downlink channel estimation from an end user unit. Instead of letting the mobile receivers first identify and then send back the downlink channel parameters, a receiver returns portions of its received data back to the base station for channel identification. The base-station can then utilize all the channel information to design channel-aware downlink precoders. Simulation results are provided to illustrate the performance of the proposed methods.

This paper presents the use of space-time coding techniques to fully exploit polarization diversity in coherent optical systems without the need for polarization tracking. Our approach is analogous to a situation of two transmit antennas and two receive antennas in a wireless communication system. Utilizing the two polarizations intelligently, the highly effective technique of orthogonal space-time block coding is applied to combat polarization uncertainty and signal losses due to free- space non-idealities. The use of space-time block coding and differential space-time block coding are modeled and tested successfully. Significant performance gain is demonstrated by our new coherent optical multiple-input multiple-output (COMIMO) system over many traditional coherent optical communication techniques.

This paper investigates cyclic codes for correcting bursts of errors from a new point of view. A simple iterative algorithm for correcting bursts of errors is developed. This algorithm is optimal in the sense that it corrects burst of errors of lengths up to the burst-error-correction limit of a cyclic code. Also included in the paper is an iterative process for correcting bursts of erasures.

We investigate the design of optimal linear transceiver for multi-input-multi-output (MIMO) systems under channel mean feedback. We consider the case in which the receiver has perfect channel knowledge while the transmitter has only mean and variance information of the channel coefficients. We aim to minimize the mean square error of symbol detection by jointly designing the transmitter linear precoder and the receiver linear decoder. We present the optimal transceiver structure for any rank of channel mean matrix together with the sufficient and necessary conditions for optimal power loading. Furthermore, we provide a numerical optimal power loading strategy using water-filling principle, as well as one sub-optimal solution based on Jensen's inequality.

We propose a low complexity H-ARQ utilizing Bit Interleaved Coded Modulation (BICM). Our aim is to improve the reliability and bandwidth efficiency of wireless communications using high rate H-ARQ enabled by the proposed BICM option. This type-II hybrid ARQ generates incremental redundancy and diversity by varying the bit-to-symbol mapping during retransmission without relying on FEC design. The joint detection at the receiver through maximum likelihood demapping before error correction is simple and effective. Using identical the interleaver and convolutional FEC encoder, this scheme is inexpensive and is largely independent of the outer code selection.

Differential space-time coded (DSTC) modulation is a viable scheme for MIMO communication systems that do not utilize channel information at the receiver either because of rapid channel variation and/or limited training data. The drawback of DSTC is the 3 dB performance loss because of incoherent detection. In order to alleviate this performance loss, we develop a semi-coherent receiver structure in which partial channel information is acquired and utilized to generalize the traditional non-coherent receiver. This partial channel information takes the form of a subspace where the channel parameter vector must reside in. It is blindly extracted from the receive data without additional pilot assistance.

We present an effective time domain method for channel identification in wireless orthogonal frequency division multiplexing (OFDM) systems. Generalizing a novel bent-pipe mechanism, the mobile receiver decimates and returns (a fraction of) its received data back to the base station. Both the downlink and uplink channel impulse response can be estimated at the base OFDM transmitter. With knowledge of the downlink channel response, the resource rich base station can employ effective adaptive modulation schemes to exploit the channel capacity. In this paper we analyze the impact of various feedback parameters on channel estimation performance. Numerical simulation results are provided to verify the effectiveness of the approach.

This work investigates the design of robust precoders for ARQ packet retransmissions in Multi-Input Multi-Output (MIMO) systems under channel uncertainty. In practice, it is common for transmitters to only have imperfect Channel State Information (CSI). More specifically the channel uncertainty is characterized by the Frobenius norm between the actual and estimated channel. Given a known bound on the channel knowledge error, our objective is to sequentially design the robust precoder for each (re)-transmission that minimizes the Mean Square Error (MSE) for the worst case channel within CSI error bound. This min-max problem is solved by Lagrangian optimization. The robust precoders are proposed in this paper with simulation results demonstrating the worst case performance gain.

This paper investigates the optimal linear precoder design for packet retransmissions in multi-input-multi-output (MIMO) systems. To fully utilize the time diversity provided by automatic repeat request (ARQ), we derive a sequence of successive optimal linear ARQ precoders for flat fading MIMO channels, which minimize the mean-square error between the transmitted data and the joint receiver output. The optimization is subject to an overall transmit power constraint. This progressive linear ARQ precoder combines the appropriate power loading and the optimal pairing of channel matrix singular values in the current retransmission with previous transmissions. This optimal pairing is a special feature unique to our sequential ARQ precoding approach. Simulation results demonstrate the effectiveness of this optimized ARQ precoding in reducing symbol MSE and detection bit-error rate.

Space-time block coded (STBC) transmission has been established as an efficient tool to enhance communication performance over wireless fading channels. The success of STBC decoding relies on accurate channel knowledge at receivers. In this work, we present a channel estimation approach that does not require training data to estimate unknown channels. Focusing on STBC from orthogonal designs, we present channel identification conditions that are largely verifiable in terms of the code and the antenna array configuration. We also develop a simple subspace-based algorithm to identify the unknown space-time channel matrix for complex transmission. Finally, we present simulation test results to illustrate the performance of the proposed method.

In this work, we present some simple techniques for enhancing the diversity provided by retransmissions in OFDM and DMT modulations. The primary techniques of interest are symbol interleaving and mapping diversity for retransmissions. Symbol interleaving involves retransmitting packet symbols through different subcarriers of the same channel, while mapping diversity involves adapting the bit-to-symbol mapping for each retransmission. An analysis of the ability of interleaving to produce lower bit error rates is provided. Also, a discussion of optimally adapting the mappings and their application to OFDM and DMT is presented. Simulation results validate the efficiency of these methods in reducing BER and increasing throughputs.

A power and bandwidth efficient dual channel identification scheme based on decimated signal feedback was recently established by Xu et al for duplex wireless communication systems. Instead of estimating and feeding back channel parameters, the mobile station returns a portion of its received data back to the base station for channel identification. In this paper, we generalize the forward-link channel estimation concept to multiple-input-multiple-output communication systems. More importantly, we incorporate a simple ambiguity resistant FIR filter at the mobile station to simplify the channel identification ambiguity matrix to a scalar. Numerical simulation results are provided to illustrate the performance of the proposed method.

This paper investigates code assignment and transmission scheduling schemes for code-limited optical code division multiple access (O-CDMA) networks, where the number of nodes exceeds the number of codes. We present an architectural design of a coordinated access scheme based on a centralized gated-polling controller, and evaluate its performance in terms of scheduling delays and achievable throughputs. In particular, this paper proposes a list-scheduling algorithm for code assignment and analyzes its near-optimal performance. With theoretic analyses and simulations, we derive the relationships among system capacity, traffic load, and network performance, and establish an analytical framework to address practical issues regarding O-CDMA network planning. Performance results indicate that the code-limited system can achieve high throughputs at the expense of scheduling delays. The capacity requirement of the control channel is also investigated to ensure system stability. These results provide an in-depth insight into network dynamics of code-limited systems and can serve as guidelines for network planning and algorithm design.

In this paper, we present new approaches to blind channel estimation and equalization based on second order statistics (SOS). We first consider the case of minimum phase channels where the equalizer is designed based on the criterion of autocorrelation matching. We cast the problem as a convex optimization program that can be efficiently solved using interior point methods. Then we consider the equalization of single-input multiple-output (SIMO) channels. Due to oversampling, the equivalent channel matrix possesses a particular structure which enables us to estimate the channel based only on the information contained in the covariance matrix at zero delay. Simulation examples are provided to demonstrate the performance advantage of the proposed algorithms compared to existing techniques.

Wireless communications often exploit guard intervals between data blocks to reduce inter-block-interference in frequency selective channels. Here we propose a dual-branch transmission scheme that utilizes guard intervals for blind channel estimation and equalization. Unlike existing diversity schemes, in which different antennas transmit delayed, zero-padded, or time reversed versions of the same signal, we use two antennas to transmit independent data streams. It is shown that for systems with two transmit antennas and one receive antenna, blind channel estimation can be carried out based only on the second order statistics of the symbol rate sampled channel output. The proposed approach involves no pre-equalization and has no requirement on channel coprimeness. It is also shown that in combination with the T-BLAST structure and turbo coding, significant improvement can be achieved in the overall system performance.

We investigate a bandwidth efficient ARQ scheme in which the medium access control (MAC) only requests a partial retransmission after packet reception error. To reduce channel usage, the original packet is punctured prior to retransmission(s), thereby forming a modified ARQ scheme which we name punctured automatic repeat request (PARQ). The puncturing in the system can be viewed as a form of "diversity" to blindly estimate the channel. We focus on the detection of the original data sequence by presenting a maximum likelihood (ML) algorithm and a simpler multirate linear equalizer. We also investigate the BER performance based on estimated channel responses from both the blind and the semiblind channel estimation algorithms.

The paper presents a method for enhancing wireless ARQ system performance by combining packet retransmissions through frequency-selective channels. As a measure of system performance, we derive the total mutual information (or channel capacity) provided by multiple transmissions of a packet through different channels. Retransmissions are uniquely linear precoded and/or truncated in relation to the original transmission. With the objective of maximizing total throughput, optimal linear precoders are developed for zero-padded packet transmissions. These precoders effectively implement power loading with the right singular vectors of the original transmission channel serving as basis vectors. Simulation results validate the increase in the overall mutual information and throughput from optimized precoding.

The paper investigates the constrained capacity of sequential ARQ linear precoding in flat fading MIMO systems. To utilize fully the time diversity provided by ARQ, we derive the optimal linear ARQ precoders in a flat fading MIMO channel with the objective of maximizing the mutual information delivered by multiple transmissions of the same packet. The capacity-maximizing ARQ precoders combine water-pouring power loading and the optimal pairing of singular vectors in the current retransmission with those from previous transmissions. This optimal pairing is a special feature unique to sequential ARQ precoding and plays important role in maximizing the mutual information.

Broad-band wireless communication systems can effectively utilize transmitter precoding to compensate severe channel distortions based on forward link channel estimate. We demonstrate the integration of precoded transmission into a duplex channel estimation mechanism using bent-pipe signal feedback. We also present an effective fractionally spaced precoder design based on the base station transmitter estimation of the downlink channel. We focus on feedback of fractionally spaced samples and demonstrate the significant improvement when the channel estimator is given access to the precoder and filters used by the wireless transmitters.

In this letter, a new hybrid automatic repeat request (ARQ) approach is presented to enhance receiver performance for communication systems employing forward error-correction codes in frequency-selective fading environments. This new approach involves a simple modification to the traditional turbo equalizer by combining multiple ARQ transmissions via integrated channel equalization. This modification leads to better computational efficiency, better exploitation of channel diversity, better channel-estimation ability, and improved performance (frame-error rates) when concatenated with an outer code. These improvements are verified through evaluations of extrinsic information transfer charts and ARQ simulations when compared with iterative combining of multiple transmissions.

In this paper, we present a simple, but effective method of enhancing and exploiting diversity from multiple packet transmissions in systems that employ nonbinary linear modulations such as phase-shift keying (PSK) and quadrature amplitude modulation (QAM). This diversity improvement results from redesigning the symbol mapping for each packet transmission. By developing a general framework for evaluating the upper bound of the bit error rate (BER) with multiple transmissions, a criterion to obtain optimal symbol mappings is attained. The optimal adaptation scheme reduces to solutions of the well known quadratic assignment problem (QAP). Symbol mapping adaptation only requires a small increase in receiver complexity but provides very substantial BER gains when applied to additive white Gaussian noise (AWGN) and flat-fading channels.

Blind signal detection in multiuser code division multiple access (CDMA) system is particularly attractive when only the desired user signature is known to a given receiver. A problem common to several existing blind multiuser CDMA detectors is that the detection performance is very sensitive to the signature waveform mismatch (SWM) which may be caused by channel distortion. In this paper, we consider the design of a blind multiuser CDMA detector that is robust to the SWM. We present a convex formulation for this problem by using the second-order cone (SOC) programming. The resulting SOC problem can be solved efficiently using the recently developed interior point methods. Computer simulations indicate that the performance of our new robust blind multiuser detector is superior to those of many existing methods.

We present a new and simple iterative equalization scheme for the enhanced data rates for global evolution (EDGE) wireless system. This scheme can significantly reduce the number of trellis states in a maximum a posteriori probability (MAP) equalizer via delayed decision-feedback (DDF) approach. Relying on the mechanism of decision feedback in the DDF trellis, bidirectional processing can be applied to exploit the time diversity of the received signal bursts for a more reliable equalizer output. With mild increase in computational complexity over the more traditional single-directional (Si) DDF turbo equalizer, the bidirectional turbo equalizer can achieve a significant performance gain.

We present a performance analysis of an ultra-wideband (UWB) system. The UWB system utilizes a new pulse design that made the performance analysis possible, since the new pulse not only has a short duration to reduce collision, but is also spectrally compliant to the recent U.S. Federal Communications Commission regulation on UWB systems. We present the performance analysis of UWB systems based on different pulse shapes and number of users. New simulation results on multiuser performance of the impulse radio are also presented.

This paper proposes a generalized approach for throughput analysis and demonstrates its application for performance evaluation in OCDMA networks. The proposed analysis approach takes into account not only the characteristics of the physical layer to support arbitrary BER models, but also the nodal architecture design at higher layers. In particular, we investigate the effects of packet corruption, channel collision, and destination contention on network performance, and obtain steady-state estimation for effective throughput. This throughput estimation sets a tight upper bound for achievable throughput in OCDMA networks with random access schemes. The proposed analytic approach leads to results consistent with simulation experiments. We demonstrate the effectiveness of this approach by analyzing a representative OCDMA scheme with various system parameters. The derived performance metrics have proven effective in characterizing the OCDMA network dynamics.

We study the channel estimation of a bandwidth efficient automatic repeat request (ARQ) system in which re-transmissions are symbol or bit-wise punctured. Unlike simple ARQ in which an erroneous packet is retransmitted, many hybrid ARQ systems aim to conserve bandwidth by only retransmitting a punctured or recoded data packet. We formulate a joint semi-blind channel estimation algorithm for the punctured retransmission in a hybrid ARQ network. We show that the joint semi-blind estimation is quite simple and naturally combines training in the first transmission with retransmitted data statistics. We show that bandwidth savings resulting from puncturing the retransmission only lead to a marginal degradation to the accuracy of channel estimation.

We define an ARQ protocol for MIMO flat-fading channels which varies the bit-to-symbol mapping per retransmission. We begin by defining a model for distinctly mapped transmissions through MIMO channels, and the effect this mapping diversity has on a sphere decoder receiver. Varying the symbol mapping complicates the sphere decoding process, particularly for the enumeration of candidate solutions within the sphere. A technique that promises quick candidate enumeration is suggested, borrowing concepts from existing closest point search schemes. The value of mapping diversity, in reducing BER and reducing computational complexity, is analyzed.

Linear space-time block codes (STBC) are well established as an efficient means to improve the performance of wireless MIMO communication systems. Their success is contingent upon the accurate knowledge of the channel parameters. In this paper we present a blind channel estimation scheme for general linear STBC. We furnish a set of identification conditions that are largely verifiable in terms of the code parameters and the antenna array configuration. We also present a simple algorithm to estimate the unknown channel matrix. Finally we supply some simulation results to illustrate the performance of the proposed scheme.

This paper considers blind channel identification and signal separation in long-code CDMA systems. First, by modeling the received signals and MUIs as cyclostationary processes with modulation introduced cyclostationarity, long-code CDMA system is characterized using a time-invariant system model. Secondly, based on the time-invariant model, multistep linear prediction method is used to reduce the intersymbol interference introduced by multipath propagation, and channel estimation can then be performed using the non-constant modulus precoding technique and the matrix pencil approach. After channel estimation, equalization is carried out using cyclic Wiener filter. Simulation examples arc provided to illustrate the proposed approaches.

This paper addresses the problem of efficient and robust transmission of video over a hierarchical wireless infrastructure. First, we compress the data generated by the video sources using a multi-resolution wavelet coder. Subsequently, the generated bitstreams are transmitted over a wireless infrastructure to multiple regional processing centers (RPC), which then send the distorted bitstreams to a common higher-level processing center/receiver. We propose a symmetric framework for the joint compression and error protection of the correlated video bitstreams received by the RPC. We show that significantly improved video performance can be obtained by our framework over a variety of channel conditions.

Time reversed space-time block code (TR-STBC) was originally proposed to handle frequency selective fading channels. Its detection requires accurate channel knowledge at the receiver side. In this work, we present a channel estimation approach that does not require training data under TR-STBC encoding. We provide identification conditions that are based on the known code parameters as well as channel matrix rank. We present a simple subspace algorithm for channel estimation. Additionally, simulations results are presented to highlight the performance of the estimation scheme.

A recent development by Xu et al. presented an effective forward channel identification method in duplex wireless mobile communications. By letting the mobile receiver send a fraction of the received data back to the base station, both forward and reverse channel identification can be achieved at the base transmitter. In this work, we extend this bent-pine feedback approach to accommodate fractionally spaced sampling and to utilize known pulse shape for better performance. We demonstrate that the new method requires minor modification on the system structure. Furthermore, with the fractionally spaced channel output, we can use the known information such as the pulse shaping filter and anti-aliasing filter to improve the channel identification and significantly reduce the needed data sample size.

Practical communication systems often involve frequency selective linear channels and high efficiency amplifiers that exhibits memoryless nonlinearity. In this paper we present a new Gibbs sampling method for the equalization and estimation of data communication signals under a nonlinearity in frequency selective channels. A channel model is developed for an unknown finite impulse response (FIR) distortion under a known nonlinearity. In order to mitigate the effects of the nonlinearity, an improved method for drawing samples from the unknown channel response is presented. This method is shown to closely match the MAP performance of a linear system with the same channel.

The channel estimation of a bandwidth efficient automatic repeat request (ARQ) system in which retransmissions are symbol or bit-wise punctured is presented in this paper. Unlike simple ARQ in which an erroneous packet is retransmitted, many hybrid ARQ systems aim to conserve bandwidth by only retransmitting a punctured or recoded data packet. In this paper, we formulate a joint subspace based blind channel estimation algorithm for a punctured hybrid ARQ network. We show that this new method can improve the identifiability conditions for channel estimation as well as the overall system performance.

We present a novel dual-channel identification approach for mobile wireless communication systems. Unlike traditional channel-estimation methods that rely on training symbols, we propose a bent-pipe feedback mechanism which requires the mobile station (MS) to send portions of its received signal back to the base station (BS) for wireless channel identification. Using a filter-bank decomposition concept, we introduce an effective algorithm that can identify both the forward and the reverse channels based only on this feedback information. This new method permits transfer of computational burden from the MS to the resource-rich BS, and leads to significant savings in bandwidth-consuming training signals.

We present a blind array beamforming method for the detection of aperiodic CDMA signals in the presence of an unknown carrier drift. This method employs a differential array beamforming algorithm (DABA), which utilizes the known spreading code of the desired user to estimate the signal subspace under carrier drift. Successful beamforming enables better carrier drift estimation before detecting user data symbols. Simulations are performed in order to compare this method with an existing subspace technique, as well as two baseline (Weiner filter and single element matched filter) detectors.

We present a simple, but effective, method of enhancing and exploiting diversity from multiple packet transmissions in systems that employ nonbinary linear modulations such as PSK and QAM. This diversity improvement results from redesigning the symbol mapping for each packet transmission. Using a general framework for evaluating the upper bound of bit error rate (BER) with multiple transmissions, a criterion to obtain optimal symbol mappings is attained. The optimal adaptation scheme reduces to solving the well known quadratic assignment problem (QAP). Symbol mapping adaptation only requires a small increase in receiver complexity but provides very substantial BER gains when applied to flat-fading channels.

For long code DS-CDMA systems, where the spreading codes are aperiodic and extending over a large number of data symbols, chip-rate sampled signals and MUI (multiuser interferences) are generally modeled as time-varying vector processes. This complicates the application of traditional blind multiuser detectors, since consistent estimation of the needed signal statistics can not be obtained by time-averaging over received data record. In this paper, we propose an equivalent time-invariant system model for long code CDMA, in which the received signals and MUI are modeled as cyclostationary processes with modulation introduced cyclostationarity. Based on knowledge of the desired user's code sequences, channel estimation is carried out using a frequency domain subspace method.

We present a simple, but effective, method of creating and exploiting diversity from packet retransmissions in systems that employ nonbinary modulations such as PSK and QAM. This diversity results from differing the symbol mapping for each packet retransmission. By developing a general framework for evaluating the bit error rate (BER) upper bound with multiple transmissions, a criterion to obtain optimal symbol (re)mappings is attained for memoryless AWGN channels. The optimal adaptation scheme reduces to solutions of the quadratic assignment problem (QAP). Symbol mapping adaptation only requires a small increase in receiver complexity but provides very substantial BER gains.

We have previously presented a novel dual channel identification approach for mobile wireless communication systems (Xu, H. et al., Proc. ICC, vol.8, p.2443-8, 2001). Unlike traditional channel estimation methods that rely on training symbols, this approach used a bent-pipe feedback mechanism requiring the mobile station (MS) to send portions of its received signal back to the base station (BS) for wireless channel identification. Using a filter-bank decomposition concept, we introduced an effective algorithm for identifying both the forward and the reverse channels based only on this feedback information. This new method permits transfer of the computational burden from the MS to the resource rich BS and leads to significant savings in bandwidth consuming training signals. This paper proposes a more informative feedback method leading to significant performance improvement over our earlier scheme.

This paper presents a simple, yet effective method of enhancing the inherent diversity among packet retransmissions in digital communication systems that employ high-order modulations such as M-PSK and M-QAM through frequency-selective channels. Diversity is enhanced by uniquely redefining the bit-to-symbol mapping for each retransmission. Finding the optimal mappings leads to iterative solutions of the quadratic assignment problem. More importantly, jointly equalizing transmission with different mappings only requires a marginal increase in complexity, while providing substantial gains in bit error rates (BER) and frame error rates (FER).

We describe a new method to integrate the time-reversal diversity in bi-directional decision feedback equalizers (DFE) for the improvement of detection performance. Bi-directional DFE consists of a traditional DFE in parallel with another DFE operating on the time-reversed sequence of the received signal samples. We propose a new approach based on maximum likelihood sequence estimating to resolve the contradictory blocks in the two DFE detection outputs. We demonstrate that, with only linear computation complexity, bi-direction DFE under this new arbitration strategy can achieve significant BER improvement over the classical DFE as well as bi-directional DFE with linear soft output combining.

We present a new and simple iterative equalization scheme for EDGE wireless systems. The new method improves the detection reliability through bi-directional processing on the DDF-SOVA equalizer. Relying on the mechanism of decision feedback in the DDF (delayed decision feedback) equalizer, bi-directional processing can be performed to exploit the time diversity of the received signal bursts for more reliable equalizer output. A special case of DFE-SISO (soft-input/soft-output) equalizer is also discussed. Simulation results show that, with modest increase in computational complexity, the bi-directional turbo equalizer can achieve significant performance gain over the single-directional turbo equalizer.

In this paper, a new approach is presented to enhance receiver performance for ARQ communication systems that use systematic codes in rapidly-changing environments. This new approach involves a simple modification to the traditional turbo-equalizer by integrating multiple ARQ transmissions for joint equalization of systematic codewords. This modification provides a computationally efficient joint receiver that improves frame error rates and throughput when compared with a few other simple approaches. To analyze the receiver, simulation results are presented for two types of systematic codes: recursive systematic codes (RSC) and low-density parity check codes (LDPC). Results are also presented for a hybrid ARQ system where subsequent transmissions contain only portions of the original transmission.

We study the blind identification of single input single output linear finite impulse response (FIR) channels via output higher-order cumulant information. Our aim is to find simple and effective approaches for channel estimation. The proposed algorithms identify channel impulse response by solving a cumulant matrix pencil. Compared with several existing linear approaches, the new methods are computationally simpler and less sensitive to channel order over-estimation. We also present simulation results that demonstrate the robustness of the new methods to various channel conditions in practical applications.

A common problem with the existing blind multiuser CDMA detectors is that their performance is very sensitive to the signature waveform mismatch (SWM) caused by channel distortion. We consider the problem of designing a blind multiuser CDMA detector which is robust to the SWM. We present a convex formulation for this problem by using the second order cone (SOC) programming. We also propose the use of recently developed interior point methods to efficiently solve the resulting SOC problem. Computer simulations indicate that the performance of our new robust blind multiuser detector is superior.

We present a novel dual channel identification approach for fast time-varying wireless communication systems. Unlike traditional channel methods that rely on training symbols, we propose a bent-pipe feedback mechanism which requires the mobile unit to send portions of its received signal back to the transmitter for wireless channel identification. Using a filter-bank decomposition concept, we introduce an effective algorithm that can identify both the forward and the reverse channels based only on this feedback information without reverse-link training. This new method has great potential in the design of future high data rate wireless communication systems.

Input signal recovery from frequency-selective fading channels is a problem of great theoretical and practical importance. We present several new blind algorithms that utilize second-order statistics for direct multichannel equalization. The algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix. For a multiuser system, user signal separation can be achieved based on partial information of the composite channel response. These equalization algorithms do not rely on the precise separation of signal and noise subspaces and therefore tend to be less sensitive to channel order (or column rank) estimation errors. Equalization is directly achieved without channel identification. Furthermore, the equalizability conditions of these algorithms are discussed.

Potential applications of blind channel identification and equalizationinwireless communication systems have been recently explored. For multi-user systems that areirreducible and column-reduced,second order statistical methods normally can identify channel dynamics up to a unitary mixing matrix. In this paper, we investigate the equalizability of desired users and the cancellation of unwanted interfering signals basedonsecond order output statistics. We show that a desired user channel can beequalizedifithas the longest memory. Furthermore, interfering user signals can becancelled under a morerelaxed multi-user channel condition.

An algorithm for blind ISI cancellation in multi-user systems is developed in this paper. By minimizing the distance between the output of a filter and a reference signal with different time delay for two filters, the output of one filter turns out to be exactly equal to the ISI component of the output of the other filter. The difference between the output of these filters is the ISI-free estimate of the signal. With an appropriate selection of the time delay introduced for the reference signal, any coefficient of the channel impulse response which determines the scale of ISI-free estimate, can be anchored accordingly. Computer simulation has been conducted to illustrate the performance of this new method.

This paper presents a subspace method for blind channel estimation based on A a special output correlation matrix. The approach relies on the i.i.d. assumption of the data sequence and uses the cyclic prefix redundancy present in OFDM systems or single-carrier systems with frequency domain equalization. This method has an important feature that allows channels to be longer than the cyclic prefix. In addition, unknown frame synchronization can be accommodated. There is no constraint on the zero locations of the channel and the performance is asymptotically independent of white noise. The method is compared with a simple correlation approach and a deterministic subspace method.

In this paper, we study the linear zero-forcing equalizability of a communication channel. Necessary and sufficient conditions are given in terms of zeros of the transfer function. Detailed procedures of equalizer design for minimum sensitivity with respect to modeling errors and white noise are also presented. It is found that the worst-case effect of channel mismatch on the performance may be modeled as equivalent losses in SNR.

This paper presents a new method to the blind equalization of a class of linear time-varying mobile channels for differentially encoded channel input signals. Through re-parameterization, the discrete system can be modeled equivalently as a single input multiple output system. Unlike some existing methods, this new algorithm does not require prior knowledge on the cyclic frequency of the channel response. The algorithm is simple and relies only on a decision feedback structure and the differential encoding nature of the channel input signals. The convergence analysis of the decision feedback system is presented along with simulation examples.

We study the blind separation problem of non-Gaussian source signals when there is only one desired signal to be recovered from its instantaneous mixtures with another source signals. Two sets of equations are built, both of which can be solved to blindly extract one non-Gaussian source signal. When there are n source signals, the number of equations in each equation sets is 3(n-1) that is much less than that for the separation of all source signals. The contribution of this paper lies in that it develops a principle for the design of blind single source separation algorithm.

This paper presents a new method for the blind symbol estimation of digital communication signals under linearly distortive channels. The algorithm blindly recovers input user symbols by exploiting second order statistics of the channel output signal collected from two data windows of different size. It does not require an intermediate step of channel identification. Moreover, this differential correlation algorithm (DCA) can directly demodulate differentially encoded data without using the more costly Viterbi algorithm. This method is directly applicable to a class of time-varying channels. Simulation examples are presented to demonstrate the performance of this scheme.

We study the linear zero-forcing equalizability of a communication channel. Necessary and sufficient conditions are given in terms of the zeros of the transfer function. Detailed procedures of equalizer design for minimum sensitivity with respect to modeling errors and white noise are also presented. It is found that the worst case effect of channel mismatch on the performance may be modeled as equivalent losses in signal-to-noise ratio.

A fast implementation of a special non-MSE cost function for blind equalization is presented here. This baud-rate equalization algorithm is based on a convex cost function coupled with a simple linear constraint on the equalizer parameters. For a generic class of channels with persistently exciting quadrature amplitude modulation input signals, this new algorithm allows the convergence of equalizer parameters to a unique global minimum achieving intersymbol interference suppression and carrier phase recovery.

The recovery of input signals in a frequency selective fading channel is a problem of great theoretical and practical interest. We present several new blind algorithms that utilize second order statistics for multichannel equalization. The algorithms are based on the subspace extraction of a preselected block column of the channel convolution matrix. For multiuser system, user signal separation can be achieved based on partial information of the composite channel response. The equalization algorithms do not rely on the signal and noise subspace separation and therefore tend to be more robust to channel order estimation errors.

A family of new MMSE blind channel equalization algorithms based on second order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known subspace methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single or multiple user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given and simulation examples are provided to demonstrate their superior performance over some existing algorithms.

We investigate the application of blind adaptive equalizers in GSM wireless communication systems. Based on a single-input two-output linear approximated model, a diversity based constant modulus algorithm (CMA) and super-exponential algorithm (SEA) adaptive equalizers are implemented to GSM systems without additional antennas. These diversity based CMA and SEA equalizers are robust to channel order mismatch that cannot be overcome by SOS blind channel algorithms. They are also applicable to time-variant mobile channels due to utilization of statistical information from the entire burst. The condition for the adaptive equalizers to converge is given. All simulation results are based on a more practical wireless channel model that is quasi-time-variant (piecewise time-invariant).

We propose a direct blind zeroforcing approach to cancel intersymbol interference (ISI) in multiple user finite impulse response (FIR) channels. By selectively anchoring columns of the channel convolution matrix, we present two column-anchored zeroforcing equalizers (CAZE), one without output delay and one with a chosen delay. Unlike many known blind identification algorithms, these equalizers do not need an accurate estimate of the channel orders. Exploiting second-order statistics (SOS) of the received signals, they can retain preselected d columns in the channel convolution matrix (d is the number of users) and force the remaining columns to zero. CAZE can effectively equalize single-input-multiple-output (SIMO) systems and can reduce dynamic multiple-input-multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. Simulation results show that the CAZE is not only effective for blind equalization of linear quadrature amplitude modulation (QAM) systems, but it is also applicable to the nonlinear GMSK modulation in the popular wireless GSM systems when computational cost severely limits the use of nonlinear methods such as the Viterbi algorithm.

Asynchronous decorrelating detector is a linear multiuser detector in code-division multiple-access (CDMA) environment. It has relatively good performance and low-computational complexity. However, the ideal asynchronous decorrelating detector requires the observation of the entire input bit sequence. Implementation of this ideal detector based on a long bit sequence results in long delays and high computational complexity. In fact, if the observed window does not cover the entire data sequence, the classical asynchronous decorrelating detector is no longer near-far resistant. In this paper, we propose a method using initial decisions on bits for both edges of the finite observed window. These initial decisions are used to assist subsequent decisions of the whole sequence inside the observed window based on the decorrelating method. This new edge decision assisted decorrelator (EDAD) method is shown to be near-far resistant.

The problem of blind source separation in additive white noise is an important problem in speech, array and acoustic signal processing. In general this problem requires the use of higher order statistics of the received signals. However for many signal sources, such as speech with distinct non-white power spectral densities, second order statistics of the received signal mixture can be exploited for signal separation. While previous approaches often assume that additive noise is absent or that the noise correlation matrix is known, we propose a simple and yet effective signal extraction method for signal source separation under unknown white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output auto-correlation matrices at different delays. Simulation examples are presented.

Blind channel equalization has been a very active research topic due to its potential application in mobile communications and digital TV systems. In this paper, we present a new blind zero-forcing equalizer that utilizes second order statistics from the multi-channel configuration. The algorithm is simple and relies only on nullspace decomposition. It can actively select the desired delay of the equalizer output signal. The performance of this new algorithm is demonstrated through simulation examples.

In this paper, we present a semi-blind channel identification scheme for the GSM system. Even though the GMSK signal has almost zero excess bandwidth (oversampling will give no more information), two diversity channels for each GMSK signal can be generated using a de-rotation scheme without additional antennas. Based on this single input and two output system, the semi-blind algorithm is applied to GSM signals under multipath distortions successfully. Simulation results are presented.

We focus on the design of blind equalization receiver for the phase modulated GSM systems without relying on additional antennas. We derive an equivalent baseband PAM model for the GMSK signal used in GSM systems. More importantly, we are able to use a de-rotation scheme to create two sub-channels for each GMSK signal even though the GMSK signal has almost zero excess bandwidth. The significance of the first discovery is that traditional QAM blind equalization algorithms can now be applied to GSM systems. The significance of the second discovery reduces the necessary RF receivers (sensors) by half without increasing any additional hardware or computational costs. Simulation results are presented for several second order statistical (SOS) and high order statistical (HOS) methods.

This paper focuses on the study of blind equalization global system for mobile communications (GSM) systems using a single antenna. In order to utilize the well-known linear system model in conventional studies of blind equalization, an equivalent baseband quadrature amplitude modulation (QAM) approximation is used for the nonlinear GMSK signal in GSM systems. Since the GMSK signal in GSM has very little excess bandwidth to warrant oversampling, a derotation scheme is developed to create two subchannels for each received GMSK signal sampled at the baud rate. Linear approximation of the GMSK signal makes the traditional QAM blind equalization system model applicable for GSM. Derotation induces channel diversity without an additional antenna and reduces the number of necessary radio frequency (RF) receivers (sensors) without increasing hardware or computational costs. Several second-order statistical and higher order statistical methods of blind equalization are adopted for GSM signals.

In a multi-user system where training is not available, blind channel identification and equalization become essential. We present anew method that utilize second order statistics for channel parameter estimation and optimum filtering. The identification algorithm is simple and relies on an outer-product decomposition and partial information of the desired signal channel. It allows the identification of individual user channels for which partial information is known under a specific condition. An optimum receiver structure can then be established for the desired signal channel.

The performance and complexity of blind algorithms in a digital receiver is dependent on the prefilter prior to discretization of the received continuous time signal and the sampling rate. This paper shows that symbol spaced blind equalization algorithms are in general sub-optimal, since a matched filter cannot be used. We show that, for fractionally spaced equalizers, the prefilter can be a general low-pass filter and does not need to be matched to the unknown channel. This flexibility on choosing the prefilter can result in different discrete time models with different complexities for the signal processing algorithms to follow. As for example, a simpler whitening filter design which is needed for the success of several important blind equalization algorithms can be realized using this flexibility.

Blind adaptive beamforming is often used in communication systems to combat co-channel interference. Among a number of techniques, the constant modulus algorithm (CMA) has proven to be an effective tool for blind beamforming of uncorrelated signals. Unfortunately, CMA beamformers encounter problems for correlated signals and interferences. In this paper, we consider correlated co-channel input signals as a result of multipath. We present two adaptation methods based on CMA to capture distinct signal sources. One approach is to use an orthogonal projection constraint on the beamformer parameters. The other approach relies on the independence of source signals and exploit a Gram-Schmidt orthogonalization at beamformer outputs. The performance of our methods is shown through computer simulations.

We study the convergence analysis of fractionally-spaced adaptive blind equalizers. We show that based on the trivial and nontrivial nullspaces of a channel convolution matrix, all equilibria, can be classified as channel dependent equilibria (CDE) or algorithm dependent equilibria (ADE). Because oversampling provides channel diversity, the nullspace of the channel convolution matrix is affected. We show that fractionally spaced equalizers (FSE) does not possess any CDE if a length-and-zero condition is satisfied. We characterize the global convergence ability of several popular blind adaptive algorithms simply based on their ADE. We also present an FSE implementation of the super-exponential algorithm. We show that the FSE implementation does not introduce any non-ideal approximation.

This paper presents a new method of beam-space direction of arrival (DOA) estimation for multiple far-field broadband signals. A novel multi-rate beamforming structure having a frequency invariant property is applied to the array outputs. Using this multi-rate structure, only a single vector of parameters is required to implement a desired frequency invariant beam pattern regardless of the bandwidth or number of sensors in the array. This frequency-invariant beamformer performs focusing over a very wide bandwidth without resorting to the frequency bin design approach of other methods in which the solution of a multiparameter optimisation problem is required to design the beam-space focusing matrices.

Several works on blind channel identification and equalization rely on a system model with multiple sub-channels driven by a single input. These methods depend on a critical condition that no common zero exists among the sub-channels. We aimed at establishing the physical significance of this critical zero condition and understanding the limitation of methods based on second-order statistics, By deriving a number of sufficient conditions, we characterize several classes of band-limited channels that are not identifiable from only the second-order statistics of the channel output.

The problem of exploiting cyclostationary statistical information for the purpose of array processing is addressed. Techniques for exploiting second order periodic information are proposed for the enhancement of cyclic-MUSIC and self coherent restoration (SCORE) algorithms. The robustification of both cyclic MUSIC and SCORE can be accomplished by increasing the amount of cyclic information used. Simulation results are presented which show the performance improvement by the modified algorithms.

Blind channel identification has been a popular research subject in recent years. We introduce the concept of knowledge-based blind channel identification. By relying on known information such as the pulse shaping filter and the antialiasing filter responses, the performance of channel identification and equalization can be significantly enhanced in digital communication systems. We present two simple methods: one in time-domain and one in frequency domain. Our simulation results demonstrate the performance of these two approaches.

We present simplified derivations of some results given by Balaban and Salt (see ibid., vol.40, no.5, p.885-894 and p.895-907, 1992). We propose an equivalent single channel representation of linear diversity channels. Based on J. Salz's earlier work (1973), we obtain a simple method to derive various forms of optimum diversity combining and equalization in digital data transmission.>

A joint minimum mean square error (MSE) criterion is presented for blind adaptive channel equalization of QAM signals. By jointly estimating the transmitted sequence and the equalizer parameters through the minimization of MSE, the equalizer parameters can be optimized and the channel input sequence can be restored at the equalizer output. To improve computational efficiency, a reduced constellation recursive least squares (RLS) algorithm is introduced for blind equalisation. Simulation results show the success of both the least square and the reduced constellation RLS algorithms.>

An early use of recursive identification in blind adaptive channel equalization is an algorithm developed by Y. Sato (1975). An important generalization of the Sato algorithm with extensive analysis appears in the work of A. Benveniste et al. (1980). These generalized algorithms have been shown to possess a desirable global convergence property under two idealized conditions. The convergence properties of this class of blind algorithms under practical constraints common to a variety of channel equalization applications that violate these idealized conditions are studied. Results show that, in practice, when the equalizer is finite-dimensional and/or the input is discrete (as in digital communications) the equalizer parameters may converge to parameter settings that fail to achieve the objective of approximating the channel inverse. It is also shown that a center spike initialization is insufficient to guarantee avoiding such ill-convergence. Simulations verify the analytical results.>

A set of memoryless blind adaptive equalization algorithms for nonminimum phase complex data systems is proposed and evaluated on the basis of admissibility. The algorithms are based on variations of a minimax cost on the equalizer output and actually take the form of gradient descent of linearly constrained convex cost functions. These investigations represent a systematic study based on nontrivial generalizations of admissible designs developed for real data systems. It is shown that for one such candidate generalization admissibility holds.>

It is shown, through the analysis of nullspace for the channel convolution matrix, that the behavior of a finitely parameterized Godard equalizer in general can never achieve or approximate the desirable global convergence performance of an infinitely parameterized noncausal Godard equalizer. Nullspace theory is supported by a simple example showing ill-convergence of the Godard algorithm.>

The existence of stable undesirable equilibria for the Godard algorithm is demonstrated through a simple autoregressive (AR) channel model. These undesirable equilibria correspond to local but nonglobal minima of the underlying mean cost function, and thus permit the ill-convergence of the Godard algorithms which are stochastic gradient descent in nature. Simulation results confirm predicted misbehavior. For channel input of constant modulus, it is shown that attaining the global minimum of the mean cost necessarily implies correct equalization. A criterion is also presented for allowing a decision at the equalizer as to whether a global or nonglobal minimum has been reached.>

Clarification is presented of the property that adaptive blind equalization algorithms may not always result in desired convergence. Those algorithms that can always converge (perhaps with the aid of a special initialization strategy) to desired equilibria offering an open eye pattern are classified as admissible. Various classes of existing blind equalization algorithms are described and the possibility of ill-convergence for these algorithms is illustrated. The general inadmissibility and the reason for earlier misconception regarding their admissibility development of new, admissible algorithms and certain initialization guidelines that can prevent weakly admissible algorithms from converging to undesirable equilibria is established.>