Data augmentation is widely utilized as an effective technique to enhance the generalization performance of deep models. However, data augmentation may inevitably introduce distribution shifts and noises, which significantly constrain the potential and deteriorate the performance of deep networks. To this end, we propose a novel information-preserving framework, namely IPF-RDA, to enhance the robustness of data augmentations in this paper. IPF-RDA combines the proposal of (i) a new class-discriminative information estimation algorithm that identifies the points most vulnerable to data augmentation operations and corresponding importance scores; And (ii) a new information-preserving scheme that preserves the critical information in the augmented samples and ensures the diversity of augmented data adaptively. We divide data augmentation methods into three categories according to the operation types and integrate these approaches into our framework accordingly. After being integrated into our framework, the robustness of data augmentation methods can be enhanced and their full potential can be unleashed. Extensive experiments demonstrate that although being simple, IPF-RDA consistently improves the performance of numerous commonly used state-of-the-art data augmentation methods with popular deep models on a variety of datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, CUHK03, Market1501, Oxford Flower, and MNIST, where its performance and scalability are stressed.

Large Vision-Language Models face growing safety challenges with multimodal inputs. This paper introduces the concept of Implicit Reasoning Safety, a vulnerability in LVLMs. Benign combined inputs trigger unsafe LVLM outputs due to flawed or hidden reasoning. To showcase this, we developed Safe Semantics, Unsafe Interpretations, the first dataset for this critical issue. Our demonstrations show that even simple In-Context Learning with SSUI significantly mitigates these implicit multimodal threats, underscoring the urgent need to improve cross-modal implicit reasoning.

Continual learning requires machine learning models to continuously acquire new knowledge in dynamic environments while avoiding the forgetting of previous knowledge. Prompt-based continual learning methods effectively address the issue of catastrophic forgetting through prompt expansion and selection. However, existing approaches often suffer from low accuracy in prompt selection, which can result in the model receiving biased knowledge and making biased predictions. To address this issue, we propose the Multiple Queries with Multiple Keys (MQMK) prompt matching paradigm for precise prompt selection. The goal of MQMK is to select the prompts whose training data distribution most closely matches that of the test sample. Specifically, Multiple Queries enable precise breadth search by introducing task-specific knowledge, while Multiple Keys perform deep search by representing the feature distribution of training samples at a fine-grained level. Each query is designed to perform local matching with a designated task to reduce interference across queries. Experiments show that MQMK enhances the prompt matching rate by over 30\% in challenging scenarios and achieves state-of-the-art performance on three widely adopted continual learning benchmarks. The code is available at https://github.com/DunweiTu/MQMK.

Generating interactive motion from texts has garnered significant attention in recent years. While text inputs offer greater flexibility, in many practical applications, there is a need to controllably impose strict constraints on the motion range or trajectory of virtual characters. However, existing trajectory-based methods are designed for single-actor scenarios and lack support for interactivity in interactive motions. Moreover, text-only methods struggle to accurately convey user-intended trajectories. The distribution shift between training and inference often leads to trajectory deviation and physical interpenetration. To address the questions mentioned, we introduce two key concepts: (1) Lead-Follow Paradigm: Inspired by role allocation in partner dancing, we decompose complex interactive motion tasks into a Lead-Follow paradigm. The leader's path is optimized first, and the follower's motion is subsequently adjusted for coherence and alignment. (2) Trajectory Guidance: We highlight the pivotal role of 3D trajectory guidance in interactive motion generation and accurately reflect user intentions. Through 3D trajectory control, we can more controllably generate the desired motion while avoiding physical interpenetration. In addition, we further investigate the refinement of motion scopes for interactive agents and propose an effective optimization strategy to enhance motion coherence and controllability. Experimental results show that the proposed approach, by more effectively using trajectory, outperforms existing methods in both realism and accuracy.

This study systematically uncovers and quantitatively evaluates the pervasive Orientalist biases in text-to-image (T2I) and text-to-video (T2V) generation models through a sociocultural lens grounded in postcolonial Orientalist theoretical frameworks. We identify systematic biases in the visual representations produced by multimodal generative models, including hyper-exoticization and temporal alienation. These biases mirror colonial-era narratives and undermine equitable sociocultural communication. Through empirical analysis of 8 mainstream T2I models and 4 T2V models, we demonstrate that culturally neutral prompts related to China consistently generate visual outputs embedded with Orientalist biases. We develop a novel visual question answering (VQA) framework as an evaluation metric, leveraging state-of-the-art vision-language model (VLM) to establish the first automated quantitative assessment methodology for such biases. A mitigation framework employing large language model (LLM) is proposed and experimentally validated. This interdisciplinary work illuminates the societal implications of multimodal generative models while advancing efforts toward fair and inclusive social computing.

In this paper, a control method of double-swing-arm tracked robot based on deep reinforcement learning is proposed to solve the problem of stable operation of the robot on uneven terrain. A control algorithm without complex kinematics analysis is designed, so that the robot can learn independently and keep balance on various irregular terrain. This paper mainly studies the stability of the robot when crossing the terrain, so as to reduce the damage to the robot hardware. The main contributions are as follows: (1) Combining the hierarchical control strategy with the curiosity module, and testing in the simulation environment has achieved good performance; (2) By adding stability design, the robot can pass through uneven terrain more smoothly; (3) Three kinds of terrain task scenes are developed in the simulation environment, and the effectiveness of the control algorithm is verified. The experimental results show that this method can effectively improve the robot’s ability to cross complex terrain while maintaining high stability.

In this paper, a conventional multiuser space-time line code (MSTLC) transmission scheme is enhanced to a new enhanced MSTLC (EMSTLC). By introducing the balancing factors at receivers’ antennas to adjust the achievable rates among users, the EMSTLC precoder and balancing factors are designed in a unified minimum mean square error framework to maximize either sum rate or rate balancing. Numerical results verify that the proposed EMSTLC systems achieve a higher sum rate and better minimum user rate than baseline multiuser transmission methods, including the conventional MSTLC scheme. Furthermore, the designed low-complexity algorithms can significantly reduce the computational complexity of the EMSTLC system.

In this study, a novel single-tone-based channel estimation method is proposed for orthogonal time-frequency-space (OTFS) systems. The channel parameters, including the delay taps, Doppler taps, and channel gains, are sequentially estimated in a delay-Doppler domain. Concretely, the delay taps are estimated using a threshold method that provides a constant probability of false alarms (Neyman-Pearson criterion). The integer and fractional Doppler taps and the channel gains of each separable multipath component are obtained using a single-tone parameter estimation method. The estimated parameters are then used to reconstruct the channel matrix. The simulation results demonstrate that the proposed method outperforms conventional low-complexity channel estimation methods in terms of the normalized mean squared error of the channel estimation and the bit error rate (BER) of the communications at the cost of a marginal increase in computational complexity. Further, the BER performance of the proposed method is almost identical to that of perfect channel estimation.

Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods employ augmentation operations with fixed or random magnitudes throughout the training process. While this fosters data diversity, it can also inevitably introduce uncontrolled variability in augmented data, which could potentially cause misalignment with the evolving training status of the target models. Both theoretical and empirical findings suggest that this misalignment increases the risks of both underfitting and overfitting. To address these limitations, we propose AdaAugment, an innovative and tuning-free adaptive augmentation method that leverages reinforcement learning to dynamically and adaptively adjust augmentation magnitudes for individual training samples based on real-time feedback from the target network. Specifically, AdaAugment features a dual-model architecture consisting of a policy network and a target network, which are jointly optimized to adapt augmentation magnitudes in accordance with the model's training progress effectively. The policy network optimizes the variability within the augmented data, while the target network utilizes the adaptively augmented samples for training. These two networks are jointly optimized and mutually reinforce each other. Extensive experiments across benchmark datasets and deep architectures demonstrate that AdaAugment consistently outperforms other state-of-the-art DA methods in effectiveness while maintaining remarkable efficiency. Code is available at https://github.com/Jackbrocp/AdaAugment.

Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity. However, previous data augmentation methods have the disadvantages of disrupted syntactic structures, token-label mismatch, and requirement for external knowledge or manual effort. To address these issues, we propose Robust Prompt-based Data Augmentation (RoPDA) for low-resource NER. Based on pre-trained language models (PLMs) with continuous prompt, RoPDA performs entity augmentation and context augmentation through five fundamental augmentation operations to generate label-flipping and label-preserving examples. To optimize the utilization of the augmented samples, we present two techniques: self-consistency filtering and mixup. The former effectively eliminates low-quality samples with a bidirectional mask, while the latter prevents performance degradation arising from the direct utilization of labelflipping samples. Extensive experiments on three popular benchmarks from different domains demonstrate that RoPDA significantly improves upon strong baselines, and also outperforms state-of-the-art semi-supervised learning methods when unlabeled data is included.

In this paper, we consider a UAV-assisted wireless network, where a multi-antenna ground base station (GBS) provides communication services to a group of users through a set of UAVs in a disaster area using in-band wireless backhaul. We propose to optimize the multi- UAV assisted wireless network specifically focusing on the joint optimization of UAV deployments and wireless resources. This problem is complicated because the access link capacities of the network and its backhaul capacities are coupled, and the possible locations of UAVs in the airspace are infinite. We take into account the deployment cost of UAVs in the system. Our objective is to minimize the total system cost subject to the QoS requirements of users and the backhaul constraints. A block coordinate descent algorithm combined with successive convex approximation is introduced to solve the complicated problem by jointly optimizing the number of required UAVs, their locations, the UAV-user association, and the allocation of wireless resources. Simulation results show that the proposed method is adaptable to wireless backhaul constraints and achieves better performance compared to other benchmark methods.

There has been a growing interest in using unmanned-aerial-vehicles (UAVs) for high-rate wireless communications due to their flexibility in deployment and relatively low costs. In this article, we consider an UAV-assisted wireless network, where a multiantenna ground base station provides communication services to a group of users through a set of UAVs using the in-band wireless backhaul. We propose a novel framework for optimizing the multi-UAV-assisted wireless network and consider the following two problems: one minimizes the total system cost and the other maximizes the system utility. In contrast to the previous studies, we consider the deployment cost of UAVs and the impact of the UAV locations on the backhaul capacity for both the problems. The two problems are complicated mixed-integer nonlinear programming (MINLP) problems, which involve the joint optimization of the UAV selection and their locations, the UAV-user association, and the wireless resource allocations subject to the Quality of Service requirements of users and the wireless backhaul capacity constraints. Therefore, we propose the block coordinate descent-based algorithms combined with successive convex approximation to solve the two problems. Simulation results show that the proposed methods are adaptable to the wireless backhaul constraints and outperform the other benchmark methods.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enables full-space manipulation of signal propagation. In this work, we consider a multi-STAR-RIS-assisted multiple-input multiple-output (MIMO) system for multi-users and investigate the impact of different STAR-RIS schemes on the system performance. We jointly optimize the beamforming matrix of the base station and the transmitting and reflecting coefficient (TRC) matrix of each STAR-RIS to maximize the sum rate of users. We propose a block coordinate descent (BCD) algorithm to optimize the beamforming matrix and the TRC matrices. We reformulate the optimization problem and optimize the beamforming matrix using the Lagrange duality method to reduce the computational complexity and then employ the constrained concave-convex procedure to tackle the optimization problem for the TRC matrices. When the energy splitting (ES) scheme is applied, our proposed method achieves a remarkable improvement of up to 17.97% in the user sum rate compared to the mode switching and the equal ES schemes in the multi-STAR-RIS-assisted system. Furthermore, when compared with systems assisted by multiple conventional RISs, our system can achieve a substantial gain of 38.99%. The improvement is even more pronounced compared to systems with a single STAR-RIS or a single conventional RIS.

In conventional recognition tasks, models are only trained to recognize learned targets, but it is usually difficult to collect training examples of all potential categories. In the testing phase, when models receive test samples from unknown classes, they mistakenly classify the samples into known classes. Open set recognition (OSR) is a more realistic recognition task, which requires the classifier to detect unknown test samples while keeping a high classification accuracy of known classes. In this paper, we study how to improve the OSR performance of deep neural networks from the perspective of representation learning. We employ supervised contrastive learning to improve the quality of feature representations, propose a new supervised contrastive learning method that enables the model to learn from soft training targets, and design an OSR framework on its basis. With the proposed method, we are able to make use of label smoothing and mixup when training deep neural networks contrastively, so as to improve both the robustness of outlier detection in OSR tasks and the accuracy in conventional classification tasks. We validate our method on multiple benchmark datasets and testing scenarios, achieving experimental results that verify the effectiveness of the proposed method.

In long-term video object tracking (VOT) tasks, most long-term trackers are modified from short-term trackers, which contain more and more machine learning modules to improve their performance. However, we empirically find that more modules do not necessarily lead to better results. In this paper, we make the long-term tracking framework simple by carefully selecting the cutting-edge trackers. Specifically, we propose a new long-term VOT framework that combines the benefits of two mainstream short-term tracking pipelines, i.e., the discriminative online tracker and the one-shot Siamese tracker, with a global re-detector awakened when the target is lost. Such a framework fully exploits existing advanced works from three complementary perspectives. Experimental results show that by exploiting the capabilities of existing methods instead of designing new neural networks, we can still achieve remarkable results on seven long-term VOT datasets. By introducing a continuous adjustable speed control parameter, our tracker reaches 20+FPS with only a small performance loss. The refine module not only improves the bounding box estimations but also outputs segmentation masks, so that our framework can handle the video object segmentation (VOS) tasks by using only VOT trackers. We obtain a trade-off between time and accuracy on two representative VOS datasets by only using bounding boxes as the initial input.

Deep neural networks are powerful, but they also have short-comings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. However, we find that the specific robustness is often improved at the sacrifice of the additional robustness or generalization ability of the neural network model. In particular, adversarial training methods significantly hurt the generalization performance on unperturbed data when improving adversarial robustness. In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models. Finally, we validate the effectiveness of AugRmixAT on the CIFAR-10/100 and Tiny-ImageNet datasets. The experiments demonstrate that AugR-mixAT can improve the model's generalization performance while enhancing the white-box robustness, black-box robustness, common corruption robustness, and partial occlusion robustness.

Face hallucination is a task of generating high-resolution (HR) face images from low-resolution (LR) inputs, which is a subfield of the general image super-resolution. However, most of the previous methods only consider the visual effect, ignoring how to maintain the identity of the face. In this work, we propose a novel face hallucination model, called C-Face network, which can generate HR images with high visual quality while preserving the identity information. A face recognition network is used to extract the identity features in the training process. In order to make the reconstructed face images keep the identity information to a great extent, a novel metric, i.e., C-Face loss, is proposed. We also propose a new training algorithm to deal with the convergence problem. Moreover, since our work mainly focuses on the recognition accuracy of the output, we integrate face recognition into the face hallucination process which ensures that the model can be used in real scenarios. Extensive experiments on two large scale face datasets demonstrate that our C-Face network has the best performance compared with other state-of-the-art methods.

In order to quickly discover the low-dimensional representation of high-dimensional noisy data in online environments, we transform the linear dimensionality reduction problem into the problem of learning the bases of linear feature subspaces. Based on that, we propose a fast and robust dimensionality reduction framework for incremental subspace learning named evolutionary orthogonal component analysis (EOCA). By setting adaptive thresholds to automatically determine the target dimensionality, the proposed method extracts the orthogonal subspace bases of data incrementally to realize dimensionality reduction and avoids complex computations. Besides, EOCA can merge two learned subspaces that are represented by their orthonormal bases to a new one to eliminate the outlier effects, and the new subspace is proved to be unique. Extensive experiments and analysis demonstrate that EOCA is fast and achieves competitive results, especially for noisy data.

Neurobiologists recently found the brain can use sudden emerged channels to process information. Based on this finding, we put forward a question whether we can build a computation model that is able to integrate a sudden emerged new type of perceptual channel into itself in an online way. If such a computation model can be established, it will introduce a channel-free property to the computation model and meanwhile deepen our understanding about the extendibility of the brain. In this article, a biologically inspired neural network named artificial evolution (AE) network is proposed to handle the problem. When a new perceptual channel emerges, the neurons in the network can grow new connections to connect the emerged channel according to the Hebb rule. In this article, we design a sensory channel expansion experiment to test the AE network. The experimental results demonstrate that the AE network can handle the sudden emerged perceptual channels effectively.

Image clustering is more challenging than image classification. Without supervised information, current deep learning methods are difficult to be directly applied to image clustering problems. Image clustering needs to deal with three main problems: 1) the curse of dimensionality caused by highdimensional image data; 2) extracting the effective image features; 3) combining feature extraction, dimensionality reduction and clustering. In this paper, we propose a new clustering framework called Deep Embedded Dimensionality Reduction Clustering (DERC) via Probability-Based Triplet Loss, which effectively solves the above issues. To the best of our knowledge, the DERC is the first framework that effectively combines image embedding, dimensionality reduction, and clustering into the image clustering process. We also propose to incorporate a novel probability-based triplet loss measure to retrain the DERC network as a unified framework. By integrating the reconstruction loss and the probability-based triplet loss, we can improve the image clustering accuracy. Extensive experiments show that our proposed methods outperform state-of-the-art methods on many commonly used datasets.

We consider the resource allocation and beamforming design for system utility maximization in a cloud radio access network (C-RAN) under given fronthaul rate constraints and quality of service (QoS) requirements at users. The considered problem is known to be NP-hard and thus it is difficult to obtain global optimum solutions. Under practical assumptions, we approximate the considered problem using a quasi-geometric-programming problem. By analyzing its optimality conditions, we propose a fast iterative algorithm with low complexity to solve it. We show that the proposed algorithm is locally convergent. Simulations are carried out to verify the proposed algorithm. Compared to using standard convex optimization software, the proposed algorithm can reduce the processing time to as low as 1/700 while achieving the same final results.1

We consider a heterogeneous cellular network with densely underlaid small cell access points (SAPs). Wireless backhaul provides the data connection from the core network to SAPs. To serve as many SAPs and their corresponding users as possible with guaranteed data rates, admission control of SAPs needs to be performed in wireless backhaul. Such a problem involves joint design of transmit beamformers, power control, and selection of SAPs. In order to tackle such a difficult problem, we apply$\ell_1$-relaxation and propose an iterative algorithm for the$\ell_1$-relaxed problem. The selection of SAPs is made based on the outputs of the iterative algorithm, and we prove such an algorithm converges locally. Furthermore, this algorithm is fast and enjoys low complexity for small-to-medium sized systems. However, its solution depends on the actual channel state information, and resuming the algorithm for each new channel realization may be unrealistic for large systems. Therefore, we make use of the random matrix theory and also propose an iterative algorithm for large systems. Such a large-system iterative algorithm can produce the asymptotically optimum solution for the$\ell_1$-relaxed problem, which only requires large-scale channel coefficients irrespective of the actual channel realization. Near optimum results are achieved by our proposed algorithms in simulations.

We address uplink user admission control problems in multiuser wireless systems with multiple single-antenna users and a multi-antenna receiver. Our aim is to select the maximum number of users that can simultaneously satisfy the given quality of service (QoS) and power constraints from a large number of candidates in the system. Both sum and per-user transmit power constraints are considered. By utilizing uplink-downlink duality, we formulate the uplink user admission problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose novel methods to tackle them by first using convex relaxation and then applying fast iterative methods on the convex-relaxed solutions. Simulations show that the proposed algorithms achieve excellent performance. The numbers of admitted users are close to the optimum ones obtained by exhaustive search when the QoS requirement is medium or low. Compared to a greedy algorithm, the proposed methods can admit up to 24% more users in the simulations.

Joint transmission (JT) is an attractive coordinated multipoint (CoMP) downlink transmission technique in multi-cell networks. It offers high system throughput but needs to distribute each user data to multiple base stations (BSs), which can lead to huge backhaul signaling overhead when the number of users is large. In this paper, we address the problem of jointly optimizing the BS assignment for each user data and the BS transmit beamformer design subject to per-BS power constraints and given quality-of-service (QoS) requirements. Our aim is to minimize the backhaul user data transfer from the data center to the BSs for the JT approach. We formulate such a problem into a sparsity-maximization problem. However, this problem is NP-hard. We propose to tackle it by first applying convex relaxation to an equivalent form of the original problem and improving the obtained results by solving a series of convex ℓ1-norm minimization problems. Compared to full BS cooperation, the proposed method saves about 13%–35% backhaul user data transfer in our simulations for moderate number of users and QoS requirement.

Coordinated multipoint (CoMP) transmission has been proposed as an important strategy to improve the signal quality for cell-edge users in future mobile cellular standards. However, when the joint transmission technique is applied in the CoMP downlink, the user data transferred from the data center to the base stations (BSs) can lead to a huge backhaul signaling overhead. Subject to quality-of-service (QoS) and per-BS power constraints, minimizing the user data transfer in the backhaul needs to jointly optimize the beamformer design and the BS assignment for each user data. We show that this problem can be cast into a sparsity-maximization problem, which is NP-hard. A semi-distributed algorithm is proposed for that problem to obtain suboptimal solutions. Such an algorithm is based on the uplink-downlink duality in multicellular networks. It utilizes the projected subgradient method and iterative function evaluation. Simulations show that the proposed method can reduce the backhaul user data transfer by 10%–30% compared to full cooperation for moderate number of users and QoS requirement.

When the joint processing technique is applied in the coordinated multipoint (CoMP) downlink transmission, the user data for each mobile station needs to be shared among multiple base stations (BSs) via backhaul. If the number of users is large, this data exchange can lead to a huge backhaul signaling overhead. In this paper, we consider a multi-cell CoMP network with multi-antenna BSs and single antenna users. The problem that involves the joint design of transmit beamformers and user data allocation at BSs to minimize the backhaul user data transfer is addressed, which is subject to given quality-of-service and per-BS power constraints. We show that this problem can be cast into an \ell_0-norm minimization problem, which is NP-hard. \changedv{Inspired by recent results in compressive sensing, we propose two algorithms to tackle it. The first algorithm is based on reweighted \ell_1-norm minimization, which solves a series of convex \ell_1-norm minimization problems. In the second algorithm, we first solve the \ell_2-norm relaxation of the joint clustering and beamforming problem and then iteratively remove the links that correspond to the smallest transmit power. The second algorithm enjoys a faster solution speed and can also be implemented in a semi-distributed manner under certain assumptions.} Simulations show that both algorithms can significantly reduce the user data transfer in the backhaul.

When the joint processing technique is applied in the coordinated multipoint (CoMP) transmission downlink, the user data for each mobile station (MS) needs to be distributed among multiple base stations (BSs), which incurs the exchange of user data via the backhaul. In order to alleviate the requirement on the backhaul capacity, we propose to minimize the exchange of user data among the BSs, subject to certain QoS requirements and power constraints. We formulate this problem into an l0-“norm” minimization problem, and propose two heuristic methods to solve it. Simulations show that both methods can significantly reduce the data exchange among BSs in the backhaul, only at the cost of marginal increase in the sum transmit power at the BSs.

We propose a novel transmission scheme for the broadcast phase of two-way relaying systems. The proposed scheme employs network coding at the relay and is able to transmit with asymmetric data rates to the receivers according to their individual link qualities. The idea is that the weaker link receiver exploits a priori bit information in each transmit symbol, so that it only needs to decode on a subset of the transmit symbol constellation. Subject to the same bit error rate constraint, the weaker link receiver can decode at lower signal-to-noise ratio compared to the stronger link. The signal labeling used for mapping bits to symbols at the relay is shown to be crucial for the performance at the receivers, and we provide the criterion and method for finding the optimized labeling schemes. Simulations show that the proposed transmission scheme can be applied to practical scenarios with asymmetric channel qualities, and the optimized labeling greatly outperforms conventional ones at both receivers.

In this paper, we consider the broadcast (BRC) phase of two-way decode-and-forward (DF) relaying systems. The channel in that phase is called the bidirectional broadcast channel. Its achievable rates are calculated when the self-interference aided channel estimation scheme is applied. We consider a block- fading channel model in the BRC phase and exploit the self- interference that is inherent in two-way relaying techniques to get an initial estimate of the channel at the receiving terminals. This initial channel estimate is utilized to decode the data in the first several time slots of each coherence interval. Data- aided approaches are then employed to improve the channel estimates in the following time slots of each coherence interval. The spectral efficiency improvement for systems employing this self-interference aided channel estimation scheme is quantified by comparing its achievable rates to that of the traditional pilot- aided channel estimation scheme.

In this paper, we propose a novel channel estimation scheme for the broadcast phase of the newly invented two- way relaying technique. Instead of using pilot sequences, we exploit theself-interference, which contains the data known at the receivers, to get a first estimate of the channel. Then a decision- directed iterative estimation process is started to improve the accuracy of the channel estimates. We consider a block fading channel model. The simulation results show that the proposed scheme has similar performance as pilot-aided channel estimation schemes in our simulation environment. Since pilot sequences are no longer needed in our proposed scheme, higher spectrum efficiency is achieved without performance loss.

We consider a low mobility cellular relaying system downlink where two mobile users are served by two neighboring decode-and-forward relays concurrently using the same frequency channel. We propose two cooperative relaying transmission schemes where each relay can choose proper precoding vectors based on its local channel knowledge to transmit data in the second hop. Each user can receive its own data without interference, which simplifies the user receiver design. We show that the diversity of each user's received data signal can be improved by receiving data from multiple relays. In addition, higher array gain can be achieved by the first scheme at the cost of higher synchronization accuracy requirements. Furthermore, we show that the two transmission schemes achieve higher transmission rate than serving different users in separate channels.

Placing relays around the base station (BS) to assist wireless communication is an effective way of extending coverage in cellular systems. This paper provides a quantitative analysis of coverage extension by using decode-and-forward (DF) relays. To describe the relation between the number of relays and the coverage range extension, we introduce the concept of coverage angle and coverage range. We provide analytical upper and lower bounds for the coverage range in a cellular system for any given coverage angle. By means of simulations, we show the tightness of our analytical approach.