This paper investigates the joint optimization of user association, base station (BS) beamforming, and reconfigurable intelligent surface (RIS) phase adjustment in an RIS-aided multi-BS multi-user-equipment (UE) millimeter wave (mmWave) network with load-balancing considerations. To address this complex problem, we propose a graph neural network (GNN)-based approach with enhanced generalizability to both varying numbers of BSs and UEs. An adaptive attention-based aggregation (AAA) mechanism is incorporated to mitigate oversmoothing in deep GNNs. By using uplink pilots as input, the proposed method avoids the need for explicit channel state information (CSI). Simulation results demonstrate the proposed scheme’s superior sum-rate performance compared to both optimization-based and learning-based benchmarks while satisfying load-balancing requirements, quantify the effectiveness of AAA in addressing oversmoothing, demonstrate the proposed scheme’s robustness to pilot correlation, and highlight the load-balancing benefits of deploying RISs.

This paper examines a reconfigurable intelligent surface (RIS)-assisted single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar system for detecting vital signs, particularly breath rates, of multiple targets. The proposed method employs manifold optimization (MO) to design RIS phase shifts, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received radar signal. The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is then applied to estimate the targets' vital signs. Simulation results show that the proposed scheme outperforms both the no-RIS scenario with an added singular value decomposition (SVD)-based processing at the radar receiver and a semidefinite relaxation-based RIS design approach, all while maintaining lower computational complexity.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhances millimeterwave (mmWave) communication by enabling full-space coverage, making it ideal for vehicle-to-everything (V2X) applications. This paper explores a multiuser multiple-input single-output (MU-MISO) mmWave non-orthogonal multiple access (NOMA) downlink vehicular environment aided by STAR-RIS, aiming to maximize the sum rate of the infrastructure-to-vehicle (I2V) links through the design of base station (BS) beamforming, power allocation, and STAR-RIS phase shifts under coupled phase constraints. We propose an unsupervised learning model for STAR-RIS phase design, complemented by analytical approaches for BS beamforming and power allocation. Simulations using the simulation of urban mobility (SUMO) software confirm the superior performance of the proposed scheme in various scenarios while meeting STAR-RIS and NOMA requirements.

Low Earth orbit (LEO) satellite communication (SatCom) is the key enabler to realize global coverage in future non-terrestrial networks (NTNs). However, the high mobility and wide serving area of LEO satellites cause fast timing-varying propagation delays and severe uplink timing misalignment, making the uplink timing synchronization particularly challenging in LEO SatCom. Existing works employ delay estimation and timing advance (TA) operations to align the arrival times of uplink signals. However, the fast time-varying nature of the delay is often overlooked, leading to inaccurate or outdated TA adjustments. Motivated by this, we propose a deep learning method incorporating a long short-term memory (LSTM) predictor to estimate uplink delays for TA in LEO SatCom uplinks. Simulation results show that the proposed method achieves low root-mean-square error (RMSE) and low bit error rate (BER), demonstrating its efficacy in addressing the timing misalignment in LEO SatCom uplinks.

This paper considers the joint optimization of user association (UA), base station (BS) transmit beamforming, and reconfigurable intelligent surface (RIS) phase adjustment in a RIS-aided multi-BS multi-user-equipment (UE) system with load balancing. The problem is challenging due to variable coupling and nonconvexity. We propose a novel graph neural network (GNN) approach, leveraging permutation equivariance and invariance properties of GNNs for enhanced generalization compared to conventional deep neural network (DNN) methods. By using uplink pilots as input, our proposed GNN method eliminates the need for explicit channel state information (CSI), which can be challenging to acquire in RIS-aided systems. Simulation results demonstrate the superior performance of our GNN-based method over traditional UA strategies in terms of sum rate and load-balancing violation penalty. Moreover, the benefits of deploying RIS are illustrated from a new perspective of facilitating load balancing without substantial compromises in sum-rate performance.

Federated learning (FL) is a promising paradigm that enables collaboration among edge devices to train a neural network while preserving data privacy. This paper considers a previously unexamined scenario of over-the-air FL systems with model heterogeneity, where clients with varying computing capacities adopt local models comprising subsets of global parameters, and over-the-air computation is employed to accelerate parameter aggregation. We investigate three key design considerations, namely, subnet creation, client selection, and client composition, and assess their impact on signal distortion resulting from over-the-air computation and on the accuracy of model learning. Our results provide insights into the effects of each design aspect on the FL system’s performance and convergence.

Navigation and positioning with low Earth orbit (LEO) satellites has become a popular topic in recent years. In contrast to traditional global navigation satellite system (GNSS), LEO satellite positioning suffers significantly smaller power attenuation. Furthermore, the concept of the signal of opportu-nity (SoOp) provides an opportunity to realize positioning with the existing dense LEO communication constellations. However, similar to GNSS, LEO satellite positioning can still be affected by varying environmental conditions and satellite geometry, resulting in unstable positioning performance. To enhance the positioning accuracy and stability, we design a federated Kalman filter (FKF)-based data fusion method that employs confidence levels in the integration of LEO satellite positioning and GNSS. Simulation results demonstrate that the proposed fusion method can attain 25% higher average accuracy compared to the GNSS-only case.

Federated learning (FL) is a cost-efficient and privacy-preserving distributed learning framework. To enhance communication efficiency during FL model aggregation, over-the-air computation has been introduced, which exploits the superposition property of wireless channels to facilitate concurrent local weight communication and global model computation. However, over-the-air computation may introduce signal distortion and global aggregation errors, and may face hindrances such as blockages between devices and the parameter server. This paper explores reconfigurable intelligent surface (RIS)-enabled over-the-air FL to address these issues. We present a novel deep reinforcement learning (DRL) algorithm for designing RIS phase shifts to minimize mean squared error (MSE) during over-the-air computation. Simulation results demonstrate lower MSE and higher test accuracy yielded by the proposed DRL algorithm compared to existing model-based approaches.

Deep learning-based device-free fingerprinting indoor localization faces the challenge of high data-labeling and training costs, especially when localization is required in multiple environments. A general model that can adapt to multiple environments and reduce these costs while maintaining data privacy is highly desirable. This paper proposes a federated meta-learning framework for device-free indoor localization, where each client, representing an environment or task, collaboratively train a general environment-agnostic model while preserving their data privacy. Fast adaptation to new environments is achieved by downloading the general model from the server and updating the model locally with only few labeled data. The proposed system is applicable to heterogeneous environments with varying layouts, dimensions, or numbers of locations. Real-world experiments demonstrate the effectiveness of the proposed method and its potential for significant data-labeling and training cost reductions.

Vehicle-to-everything (V2X) communication is an essential technology for future vehicular applications. It is challenging to simultaneously achieve vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, given the shared spectrum. Deep reinforcement learning (DRL)-based algorithms have been proposed for resource allocation in V2I and V2V designs. Existing DRL designs focus on the objectives of high-capacity V2I and high-reliability V2V links. In this study, a multi-agent DRL algorithm is proposed to maximize the sum capacity of V2I links while ensuring capacity fairness among the V2V links. The simulation results demonstrate the balance between the V2I–V2V objectives achieved by the proposed algorithm.

Frequency-modulation continuous wave (FMCW) radar has been employed to implement a non-contact vital sign monitoring system for future healthcare applications. This paper proposes a multi-target vital sign (heart rate and breath rate) detection scheme with limited channel information for single-input multiple-output (SIMO)-FMCW radar systems in multipath propagation environments. In the proposed method, multipath effect mitigation is first achieved by the decomposition of vital sign signals from self- and mutual-multipath interferences using the multichannel singular spectrum analysis (MSSA) algorithm. Then, the desired vital signs are estimated via the estimation of signal parameters via rotational invariance technique (ESPRIT). Simulation shows that the proposed scheme achieves superior performance in terms of the estimation error in multipath propagation environments.

In recent years, spectrum congestion has become a significant issue. Thus, significant attention has been paid to spectrum-sharing. The dual-function radar-communication (DFRC) system is an attractive solution for the spectrum-sharing problem. Existing studies primarily focus on transmitted beamforming at the base station (BS). In this study, we designed both the transmitted and received beamformers of a DFRC BS to perform multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communication employing hybrid beamforming. To address the difficulty of the primal problem, we recast the nonconvex design problem into a convex form and subsequently derive suboptimal solutions. Simulation results demonstrate satisfactory performance in terms of the sum-rate, interference mitigation, desired signal enhancement, and complexity of the proposed scheme.

Climate change has been identified as one of the most critical threats to human civilization and sustainability. Wildfires, which produce huge amounts of carbon emission, are both drivers and results of climate change. An early and timely wildfire detection system can constrain fires to short and small ones and yield significant carbon reduction. In this paper, we propose to use ground sensor deployment and satellite Internet of Things (loT) technologies for wildfire detection by taking advantage of satellites' ubiquitous global coverage. We first develop an optimal loT sensor placement strategy based on fire ignition and detection models. Then, we analyze the uplink satellite communication budget and the bandwidth required for wildfire detection under the narrowband loT (NB-IoT) radio interface. Finally, we conduct simulations on the California wildfire database and quantify the potential economical benefits by factoring in carbon emission reductions and sensorlbandwidth costs.

Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of labeled data from the current environment and reuses a large amount of existing labeled data previously collected in other environments, thereby significantly reducing the data collection and labeling cost for localization in each new environment. The core method lies in graph neural network (GNN) based few-shot transfer learning and its modifications. Experimental results conducted on real-world environments show that the proposed system achieves comparable performance to a convolutional neural network (CNN) model, with 40 times fewer labeled data.

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.

Designing hybrid beamforming transceivers in millimeter wave (mmWave) MIMO-OFDM systems with satisfactory performance and acceptable complexity is a challenging problem. The well-known weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm offers desired performance but has high computational complexity. In this paper, we propose to apply the deep unfolding technique to the WMMSE-MO algorithm. The proposed deep unfolding model yields faster convergence to better solutions as compared to the original algorithm. Simulation results demonstrate remarkable spectral efficiency performance with reduced computational time complexity for the proposed scheme, under different hardware (RF chains) and algorithmic (inner/outer iterations) settings for a massive MIMO-OFDM system.

This paper proposes a multiple-input multiple-output (MIMO) symbol detector that incorporates a deep reinforcement learning (DRL) agent into the Monte Carlo tree search (MCTS) detection algorithm. A self-designed deep reinforcement learning agent, consisting of a policy value network and a state value network, is trained to detect MIMO symbols. The outputs of the trained networks are adopted into a modified MCTS detection algorithm to provide useful node statistics and facilitate enhanced tree search process. The resulted scheme, termed the DeepMCTS detector, demonstrates significant performance and complexity advantages over the original MCTS detection algorithm under varying channel conditions.

Coexistence and interference management issues for machine-to-machine (M2M) and human-to-human (H2H) communications are crucial for the Internet of Things (IoT). This article considers beamforming and power allocation for M2M/H2H coexistence networks adopting the dynamic time division duplex (TDD) spectrum sharing scheme and energy harvesting (EH). The design objective is total system power minimization with device Quality-of-Service (QoS) constraints as well as EH constraints. Since the dynamic TDD introduces new types of interference, i.e., uplink/downlink cross-interference, the considered problem is a challenging nonconvex coupled problem. We first consider a simplified problem without the EH considerations. We propose a novel low-complexity algorithm based on uplink-downlink duality (UDD) and alternating optimization (AO) to tackle this problem. Then, we propose a second-order cone programming (SOCP) relaxation-based AO low-complexity algorithm to deal with the general problem. In the simulation, we study the performance of the QoS, the number of antennas, the number of users, and the power splitting ratio. Finally, the performance of the proposed algorithms have low-complexity than the classical convex optimization method.

Real-time device-free indoor localization is a key technology for many Internet of Things (IoT) applications. Fingerprinting-based localization schemes rely on the database constructed from an offline site survey. Constructing a fully labeled database is expensive, and therefore a fingerprinting-based method requiring only a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised) is highly useful. In this paper, we consider semi-supervised fingerprinting techniques based on the classic, generative model-based variational auto-encoder (VAE) and generative adversarial network (GAN). We conduct a comparative study of VAE and GAN in three real-world environments. Experimental results reveal that GAN generally outperforms VAE with various amounts of labeled data. Insights into how different generative mechanisms of these schemes, as well as environmental effects, affect the performance are provided.

This paper studies the previously unexamined problem of joint cooperation clustering and content caching in a cache-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) network that comprises a large number of access points (APs) collaboratively serving users without cell structure limitations. A joint cooperation clustering and content caching design is motivated by the observation that forming cooperation clusters (i.e., determining the sets of serving access points (APs) for users) based on channel quality alone or caching status alone is suboptimal. We develop a deep reinforcement learning (DRL)-based joint design scheme for dynamic CF-mMIMO networks. The proposed scheme demonstrates favorable network energy efficiency (EE) performance and does not require prior information such as user content preferences.

Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.

This paper investigates beamforming and power allocation problems in the dynamic time division duplex (TDD) cellular networks. Based on the dynamic TDD coexistence scheme, the network comprises uplink and downlink networks serving machine-type devices (MTDs) and human-type devices (HTDs), respectively. The design goal is to minimize the total system power consumption by optimizing the transmit/receive beamforming and uplink power under MTD and HTD quality-of-service (QoS) constraints. The resulting optimization problem is challenging to solve because the variables to be designed are tightly coupled in the constraints. By using the uplink-downlink duality (UDD) and alternating optimization (AO) algorithm, we propose a novel algorithm to overcome the difficulty in this work. Numerical results demonstrate the superiority of the proposed algorithm.

Coexistence and interference management issues for human-to-human (H2H) and machine-to-machine (M2M) communications are crucial for the Internet of Things (IoT). This paper considers beamforming and power allocation for H2H/M2M coexistence networks with dynamic time division duplex (TDD) spectrum sharing and energy harvesting. The design objective is total system power minimization with device quality-of-service (QoS) constraints as well as energy harvesting constraints. The resulting optimization problem is nonconvex and challenging to solve due to the new interference sources introduced by dynamic TDD spectrum management, which are nonexistent in conventional spectrum usage systems. To tackle this problem with tightly coupled design parameters in the constraints, we propose a second-order cone programming (SOCP) relaxation-based alternating optimization (AO) algorithm. Numerical results demonstrate the performance of the proposed algorithm from various perspectives.

In a question & answer (Q& A) system, credible users provide answers of higher correctness. However, in a distributed social network based Q& A (SNQ& A) system, an asker does not know a k-hop answerer's credibility, thus making it difficult for the asker to assess the answer correctness. Therefore, a credibility-enabled distributed SNQ& A system is crucial for determining the correctness of the answers. To this end, we propose CRED, a credibility-enabled distributed SNQ& A system, which facilitates each user to assess the correctness of the provided answers. CRED utilizes subjective logic to build interestwise friend-to-friend credibility opinions under uncertainties. The developed opinions are then accumulated by CRED to get each user's aggregated credibility opinion, which may reflect the user's real credibility. CRED forwards a question to users with highest credibility beliefs in the question interest category. Our evaluation results show that, on average, CRED accomplishes higher success ratio, higher answer correctness, and lower answer uncertainty by 12.1%, 16.4%, and 22.2%, respectively, as compared to the best-performing baseline systems.

Loop interference (LI) is the main performance-limiting factor in full-duplex (FD) relaying systems. In this paper, we propose using improper Gaussian signaling (IGS) in joint source and relay transmission to achieve a better LI resistance and, as a result, a better end-to-end system throughput in energy harvesting (EH) enabled FD relaying systems. An alternating optimization (AO) algorithm is proposed to solve the challenging design problem of finding the optimal transmission with IGS at both the source and relay, as well as the optimal power-splitting (PS) factor at the EH-enabled relay. The relationship between the PS factor and the optimal pseudo-variances is derived in closed form. Numerical results demonstrate the superior throughput performance yielded by IGS at the same LI level, and suggest the possibility of employing IGS as a replacement of a sophisticated LI canceller in practical FD relaying systems.

Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of DNN-based location estimators trained with Wi-Fi channel state information (CSI) fingerprints in a real-world experiment. We address such questions as 1) how well has the DNN learned and been trained, and 2) what critical features has the DNN learned to distinguish different classes, via visualization techniques. The results provide plausible explanations and allow for a better understanding of the mechanism of DNN-based wireless indoor localization.

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.

This paper proposes an economical, nonintrusive, and high-precision indoor localization scheme based on Wi-Fi fingerprinting that requires only a single Wi- Fi access point and a single fixed-location receiver. A deep neural network (DNN) based classification model is trained with Wi-Fi channel state information (CSI) fingerprints for localizing the target without any device attached (i.e., device-free). CSI provides finer-grained information than received signal strength (RSS). CSI pre- processing based on singular value decomposition (SVD), as well as data augmentation based on noise injection and inter-person interpolation, are incorporated into the proposed DNN framework for enhanced robustness and performance. Real-world experiments examine two scenarios with different degrees of target similarity and show that the proposed DNN-based system can consistently improve the localization performance as compared to the original DNN model.

This paper considers the precoder design for energy-efficient data transmissions in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid multiple-input multiple-output (MIMO) cellular networks. We aim to maximize the energy efficiency (EE) of the network, defined as the ratio of the system sum rate to the system power consumption, under EH and transmit power constraints for both cellular and D2D users. The considered problem is nonconvex due to the concave-convex and fractional form of the objective. We propose to apply the concave-convex procedure (CCCP) and the Dinkelbach method to find tractable, approximate solutions. Numerical results demonstrate the performance of the proposed method from various perspectives.

People/crowd counting is a critical technique in many people-centric Internet of Things (IoT) applications, e.g., security monitoring and energy management for smart homes. Device-free people counting systems can in general be categorized as image-based and non-image-based. Non-image-based methods have the advantages of being economical and nonintrusive, as only ambient wireless signals from off-the-shelf wireless devices such as Wi-Fi are used. In this paper, we propose a non-image- based people counting system based on the deep neural network (DNN) model using fine-grained physical-layer wireless signatures such as Wi-Fi channel state information (CSI). Only one Wi-Fi transmitter and one laptop receiver are required, and people are not required to wear or carry any equipment (i.e., device-free). A novel feature space expansion scheme that incorporates the dynamic information of CSI measurements is proposed for the DNN model to enhance its performance. Real testbed experiments showed that the proposed system can achieve as high as 88% average correct classification rate in estimating the exact number of the crowd of size up to nine people in the most general indoor scenario.

For rate optimization in interference limited network, improper Gaussian signaling has shown its capability to outperform the conventional proper Gaussian signaling. In this work, we study a weighted sum rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel (MIMO-IBC). To solve this nonconvex and NP-hard problem, we propose an effective two-stage algorithm. In the first stage, a weighted mean square error (MSE) minimization algorithm is adopted to compute the transmit covariance matrices, and in the second stage, an alternating direction method of multipliers (ADMM)-based distributed multi-agent algorithm is proposed to obtain the globally optimal pseudo-covariance matrices in a parallel and scalable fashion. Finally, simulation results are presented to demonstrate the superior performance of our algorithm under different signal-to-noise ratio (SNR) and various network scenarios.

Face detection is very important for video surveillance, human-computer interaction, and face recognition. In this paper, a very robust face detection algorithm that can well detect rotated, in-plane, and out-of-plane faces without large amount of training data is proposed. First, several techniques, including the entropy rate superpixel (ERS) and the skin filter, are applied to obtain face candidate regions. Then, angle compensation and non-maximum suppression are applied to improve the accuracy of face detection. Moreover, to find out-of-plane faces, one can apply the flipping-and-blending technique, i.e., blending the face candidate with its flipping version to create a face that is similar to the frontal one. With it, even if there are no training data for out-of-plane faces, one can successfully detect the faces in the out-of-plane case. Simulations on the FEI dataset and the BaoFace dataset show that the proposed algorithm is efficient and outperforms state-of-the-art face detection approaches.

This paper investigates the beamforming design for sum-rate maximization in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid cellular networks. In the considered system, each receiving cellular or D2D user performs EH while decoding information from the base station (BS) or the paired transmitting D2D user. The objective is to derive optimal beamforming strategies at the BS and power allocations at transmitting D2D users, such that the network sum rate is maximized under EH and transmit power constraints. The original nonconvex problem is convexified by the semidefinite relaxation technique and a reformulation of the objective function with first-order approximation in each algorithm iteration, and solved by an iterative algorithm, based on the concept of the Frank-Wolfe algorithm. Simulation provides numerical validation of the proposed method from various perspectives.

Social-network-based Question Answering (Q&A) systems are recently emanated due to their capabilities of outperforming classical search engines in answering non-factual questions. Social network users have different expertises and activity times, and identifying answerers with proper expertises, short response times, and high response rates is challenging for Q&A systems. To address this problem, we propose an optimal Q&A system that identifies answerers with required expertises and routes the questions with minimum possible response time in dynamic social networks. Our proposed system uses a hybrid model for estimating the expertise of each user, in order to identify the suitable answerers; besides, it avoids bottleneck answerers in the network, so as to increase the response rate. We conduct trace- driven simulations, which show that our Q&A system: (i) achieves up to 27% higher average response rate than the state-of-the-art systems, and (ii) reduces the average maximal response time by up to 60%. Moreover, the results show that, by varying the number of answerers, the number of keywords per question, the arrival rate of questions, and the predictability against the maximal response time, our Q&A system consistently outperforms the state-of-the-art systems.

This paper considers autonomous environment discovery for energy harvesting Internet of things (IoT) applications. A self-sustainable mobile micro-robot explores an unknown environment to collect data under energy constraints. The data are fed into a machine- learning model to construct an energy harvesting map of the environment, which will then be used by IoT devices for self-sustainable operations in the same environment. The objective is to develop an efficient robotic exploration algorithm that helps construct as accurate an energy harvesting map as possible. The challenge lies in the unknown environment and the sometimes conflicting goals of data collection and energy management during the robotic exploration. We propose an efficient exploration algorithm in combination with the support vector regression (SVR) model. Simulation validates the effectiveness of the proposed method and shows exemplary traces of robotic exploration to illustrate the core ideas.

Wireless indoor localization is a key technology for the future Internet of things (IoT) paradigm. In this paper, we perform an experimental comparative study of machine learning-based localization schemes, such as k-nearest neighbor (k-NN) and variants of support vector machine (SVM), based on the received signal strength (RSS) measurements of the ambient frequency modulation (FM) and digital video broadcasting- terrestrial (DVB-T) signals in three real testbed environments. The consideration of readily available, ambient radio signals frees the need for dedicated radio transmitters. Noise-reduction techniques such as feature selection and ensemble learning are proposed in conjunction with SVM. Our results examine the performance comparisons between SVM and k-NN, as well as the performance comparisons of SVM-based methods incorporating different noise-reduction schemes, with noisy RSS data. Insights into the performance of learning- based localization schemes working with real database collected from real environments are provided.

Consider a two-hop interference relay network with K multi-antenna transmitters and receivers, and a single multi-antenna relay. The relay employs amplify-and-forward (AF) strategy. The direct links between transmitters and receivers are assumed to be present. A distributed power allocation scheme is sought via the alternating direction method of multipliers (ADMM) algorithm at the transmitters under individual transmit power constraint to assign the minimum power to each encoded data stream such that a signal to interference plus noise ratio (SINR) target is met. Numerical results demonstrate that the proposed distributed scheme achieves the centralized solution.

This paper investigates nonlinear transceiver design for full-duplex multiple-input multiple-output (FD-MIMO) relay systems. A dual-hop amplify-and-forward relaying protocol is considered. At the destination, nonlinear successive-interference-cancellation (SIC) is used for signal detection. The goal is to find the source and relay precoders such that the symbol-vector error rate (SVER) can be minimized. Due to the loop interference (LI), optimizing the relay precoder in FD systems is much more involved. In this paper, we propose novel designs to solve this problem. Starting from the QR-SIC receiver, we theoretically show that the relay precoder can be solved with a closed-form expression even when the system incurs LI. Then, we consider the system with a minimum mean-squared-error SIC receiver, where the relay precoder design entails a different problem formulation and introduces new challenges. We propose a novel iterative method, with closed-form solutions in each iteration, to solve this problem. Simulations show that our designs can significantly improve the SVER performance for FD-MIMO relay systems.

This paper considers a device-to-device (D2D) communications underlaid multiple-input multiple-output cellular network and studies D2D mode selection from a previously unexamined perspective. Since D2D mode selection affects the network interference profile and vice versa, a joint D2D mode selection and interference management is desired but challenging. In this paper, we propose a holistic approach to this problem with interference-free considerations. We adopt the degrees-of-freedom (DoFs) as the mode-selection criterion and exploit the linear interference alignment technique for interference management. We analyze the achievable sum DoF of the potential D2D users according to their mode selections, and derive the probabilistic sum-rate relations between the proposed DoF-based mode selection scheme and the common received-signal-strength-index-based mode selection scheme in Poisson point process networks. Simulation illustrates the theoretical insights and shows the advantages of the proposed DoF-based mode selection scheme over conventional mode selection schemes from various perspectives. The proposed scheme presents a promising proposal for D2D mode selection in 5G communications.

Frequency lowering technologies have demonstrated effectiveness in English speech recognition for English-speaking people with high-frequency hearing loss. Their effect on Mandarin speech has not been well investigated. This paper serves two important purposes: it 1) examines the effect of frequency transposition (FT), a category of frequency lowering technologies, on Mandarin speech recognition, and 2) proposes a dictionary-based FT framework based on nonnegative matrix factorization (NMF) that is transferable across languages. Our results show that the proposed NMF-FT improves Mandarin consonant identification as compared to the traditional FT, with particularly significant improvements in affricates and fricatives.

This paper considers a hybrid cochannel MIMO cellular network with both cellular and device-to-device (D2D) users, and studies, from a new perspective, mode selection (cellular or D2D mode) for the potential D2D users joining the network. We propose a new mode selection scheme that takes into account not only the interference caused to the potential D2D users, but also the interference caused by the potential D2D users to the network. We adopt the interference alignment technique for interference management in the network. We theoretically show the conditions under which the potential D2D users will select the cellular or D2D mode for a greater number of guaranteed interference-free transmissions. Simulation illustrates the theoretical insights and shows the advantages of the proposed interference-aware mode selection scheme over conventional mode selection schemes.

The performance of rate adaptive trellis-coded and uncoded M-ary quadrature amplitude modulation (M-QAM) over large open office indoor wireless environments is studied in this paper. An appropriate composite fading/shadowing channel model termed the joint fading and two-path shadowing (JFTS) model is adopted for such an indoor wireless environment, where mobility of users remains constrained within a small space. Mathematically tractable expressions for the spectral efficiency and average bit error rate (ABER) of adaptive coded and uncoded M-QAM over the JFTS channel are derived. Numerical results demonstrate that in contrast to conventional fading models like Rayleigh and Nakagami-m distributions, the error probability performance of rate adaptive M-QAM over a JFTS faded/shadowed link never approaches zero for lower channel signal-to-noise ratios (CSNRs).

This paper presents a 60 GHz CPW-fed patch antenna on multilayer LTCC- and LCP-based materials with new and compact AMC. The proposed antenna structure consists of top three layers of antenna and bottom three layers of AMC ground plane. First, a new and compact three- layer AMC is designed on both LTCC- and LCP-based substrates. The designed AMC is more compact and provides 37.5% higher in-phase bandwidth as compared to a state-of-the-art design. Then, a three-layer CPW- fed patch antenna is designed and optimized on LTCC- and LCP-based substrates on top of the three-layer AMC ground plane of the same material. We perform a gain- bandwidth comparative study of antennas on multilayer LTCC- and LCP-based substrates with AMC/PEC ground planes for 60 GHz. We observe that antennas on LCP- based substrate outperform those on LTCC-based substrate in terms of input impedance, bandwidth, and gains on both AMC and PEC ground planes, while antennas on LTCC-based substrate have smaller antenna and AMC dimensions.

Indoor localization technique is a key enabling technology for the future Internet of things (IoT) paradigm. Improving the precision of indoor localization will expand the horizon of indoor IoT applications. In this paper, we propose an enhanced machine-learning indoor localization scheme which incorporates access point (AP) selection and the proposed signal strength reconstruction to enhance robustness in noisy environments. The proposed signal strength reconstruction scheme estimates/reconstructs the received signal strength indicator (RSSI) values of the nonselected APs from those of the selected APs to increase the size of the feature space for enhanced noise robustness. The proposed concept can be applied to various machine-learning frameworks. Simulation results demonstrate improved precision yielded by the proposed method in conjunction with support vector regression (SVR), ensemble SVR, and artificial neural network (ANN) models, as compared to these machine- learning techniques alone.

In machine-to-machine (M2M) communication, a massive number of machine devices may transmit simultaneously in response to an event occurring in the system. Supporting massive device transmission while maintaining low congestion and low access delay is a challenging problem. This paper proposes a new transmission control scheme based on slotted ALOHA, with a practical consideration of partial information available at the data aggregator about the system. The proposed approximate maximum likelihood estimation ALOHA (AMLE-ALOHA) scheme incorporates an approximate ML estimation of the (unknown) number of active machines in the system. We apply the drift analysis to show the stability of the proposed control scheme. Simulation results demonstrate that the proposed AMLE- ALOHA outperforms an existing scheme in terms of the access delay and reaction time under bursty traffic with the same partial information, and compares favorably to the optimal control scheme with oracle knowledge of the number of active machines in the system.

This paper studies the information-theoretic secrecy rates of wireless two-way relay systems where two users wish to exchange information through a single relay with an eavesdropper observing all communications. We formulate and compare the achievable secrecy rates of the system that employs one of the three common relay protocols: conventional decode-and-forward (DF), DF with network coding (NC), and compute-and-forward (CF) based on physical-layer network coding (PNC). We show that CF based on PNC achieves the highest secrecy rate at high signal-to- noise ratio (SNR), while, interestingly, the other two protocols have mixed performance depending on the power allocation scheme and network topology. Our study offers insights into designing wireless two-way relay protocols from a secrecy perspective.

This paper studies joint subchannel allocation, power allocation, and beamforming for simultaneous wireless information and power transfer (SWIPT) in multiuser downlink orthogonal frequency-division multiple access (OFDMA) systems. We formulate a multi-objective optimization (MOO) problem where the objectives are to maximize both the information rate and the harvested power for all users in the system. We approach the MOO problem with two proposed methods, i.e., semidefinite relaxation-based weighted aggregation (SDR-WA) and multi-objective genetic algorithm (MOGA). Simulation compares the achievable Pareto optimal solution set yielded by these methods, and illustrates the tradeoffs of the sum information rate vs. the sum harvested power in the system.

In this paper, we consider the network coding (NC) enabled three-phase protocol for information exchange between two users in a wireless two-way (bidirectional) relay network. Modulo-based (nonbinary) and XOR-based (binary) NC schemes are considered as information mixture schemes at the relay while all transmissions adopt pulse amplitude modulation (PAM). We first obtain the optimal constellation mapping at the relay that maximizes the decoding performance at the users for each NC scheme. Then, we compare the two NC schemes, each in conjunction with the optimal constellation mapping at the relay, in different conditions. Our results demonstrate that, in the low SNR regime, binary NC outperforms nonbinary NC with 4-PAM, while they have mixed performance with 8-PAM. This observation applies to quadrature amplitude modulation (QAM) composed of two parallel PAMs.

In this paper, we design and analyze a dual-band rectangular microstrip patch antenna with defected ground plane for 2.5 GHz and 3.5 GHz communications. The design concept is to have two slots etched out from the ground plane of a microstrip patch antenna designed for 2.5 GHz operation to enable second frequency band (3.5 GHz) operation. The positions of etched slots are determined using parametric analysis to meet the objectives of large impedance bandwidth and high gains in the desired bands and mismatching in the undesired bands. The proposed antenna provides wide impedance bandwidths of 13.56% (2.3-2.7 GHz) and 10.36% (3.3-3.7 GHz) at center frequencies of 2.5 GHz and 3.5 GHz, respectively. The gains in E-Plane are 6.7 dB and 5.1 dB, and the gains in H-Plane are 6.5 dB and 4.88 dB, for 2.5 GHz and 3.5 GHz, respectively. The proposed dual-band antenna shows monopole-like radiation patterns with higher gains as compared to monopole antennas.

In this paper, we study spectrum trading in cognitive radio (CR) networks with multiple primary services (PSs) and multiple secondary services (SSs) from a game-theoretic perspective. We propose a multistage Bayesian game-based trading model which accounts for unknown private information of players (for example, the number of user connections in PSs may be unknown to the SSs) as in practical network scenarios. The perfect Bayesian equilibrium (PBE) is derived by solving an involved sequential optimization problem. We formulate the joint Karush-Kuhn-Tucker (KKT) conditions and use the KKT translation technique to obtain the PBE at each stage. Simulation demonstrates the convergence of the sequence of strategies in the multistage Bayesian game.

Dynamic spectrum sharing with minimum spectrum leakage is actively considered to meet the requirements of future generation networks which are expected to support huge amount of data traffic beyond 2020 with limited spectrum. In this paper, we propose a spectral leakage suppression technique for orthogonal frequency division multiplexing (OFDM)-based overlay cognitive radio (CR). We apply the Bohman window-based pulse shaping, which has a high sidelobe fall rate and low highest sidelobe power, to the edge subcarriers rather than the entire waveform of the secondary users (SUs) utilized spectrum. We allocate antipodal symbol pairs to the Bohman-windowed subcarriers to overcome the problem of a slightly large 3-dB bandwidth of the Bohman window. The power spectral density (PSD) of the proposed scheme rolls off asymptotically as of f-8, as compared to the current state-of-the-art where the sidelobe rolls off asymptotically as of f-4. Simulation results show improved bit-error-rate (BER) performances of the primary user (PU) and the SU in the proposed scheme due to improved spectral leakage suppression. The practicality of the proposed scheme is validated by field programmable gate array (FPGA) prototyping.

We consider the correlated data gathering problem in machine-to-machine communications. The machines implement distributed source coding and transmit their gathered data to the data aggregator. The data aggregator has limited radio resources and thus only a subset of machines are selected for transmission. Missing data from nonselected machines are reconstructed at the aggregator by exploiting data correlation. We first propose a data distortion measure based on information loss to characterize the reconstruction, and derive its relationship with the traditional mean squared error distortion analytically. Then, we formulate the machine selection problem with the objective of minimizing the overall data distortion given some resource constraints. We decouple the problem into subproblems and solve them by the proposed algorithm based on the cross entropy method. Numerical results demonstrate improved data fidelity by implementing distributed source coding, and better network coverage and energy efficiency for the proposed machine selection scheme.

In this paper, we study the channel selection problem for selfish and altruistic precoding in multilink multiple-input single-output (MISO) networks from a distributed game-theoretic perspective. Our goal is to find for each link a proper channel selection strategy that is robust against time-varying channel states. This motivates the development of stochastic learning that finds Nash equilibrium (NE) of an expected game. The convergence properties of the proposed learning algorithm are theoretically and numerically verified. The proposed algorithm demonstrates good sum-rate performance in the system-level simulation of a multilink MISO network based on the 3GPP-LTE model.

This paper studies spectrum trading in cognitive radio networks in which multiple service providers (SPs) sell unused spectrum to multiple unlicensed secondary users (SUs). Motivated by the nature of the problem with new considerations, spectrum trading is modeled as a multi-leader multi-follower expected Stackelberg game with two levels of competition. The SPs as leaders compete in offering subscription prices (upper-level subgame) and the SUs as followers compete in selecting service from the SPs (lower-level subgame). The lower-level subgame incorporates the time-varying spectrum availability as the external state so that the proposed scheme does not require knowledge of dynamic spectrum availability. To achieve self-organized network operation, we propose decentralized, stochastic learning-based algorithms for the game. The convergence properties of the proposed algorithms toward the Nash equilibrium (NE) are theoretically and numerically studied. The proposed scheme demonstrates good utility performance for the SUs as compared to other service selection schemes.

This paper considers a multi-way relay network in which multiple users intend to achieve full information exchange with one another with the aid of a single relay. A general method for designing the transmission protocol with binary physical- layer network coding (PNC) is developed based on a tree representation. Different transmission schemes are analytically and numerically examined in terms of the decoding strategy, throughput performance, and energy consumption. It is shown that distributing the load of transmissions unevenly among users may achieve a better network throughput performance although some users will consume remarkably more energy than others. A systematic approach to designing the transmission scheme such that minimum error probability is achieved while some specified energy constraint is satisfied is also proposed.

This paper considers the optimal constellation design for the generalized spatial modulation (GSM). The signal and spatial parts of the constellation are jointly designed where the signal part is not restricted to the conventional amplitude/phase modulation constellation points. The design problem is formulated as an optimization problem where a set of multi-dimensional constellation points that minimizes the average symbol error rate (SER) is selected. A gradient search algorithm is proposed to solve the optimization problem. Numerical results demonstrate the improved SER performance and increased energy efficiency of the proposed generalized signal- spatial constellation (GSSC) scheme as compared to previous spatial modulation (SM), GSM, and conventional MIMO schemes.

This paper considers a noncoherent distributed space-frequency coded (SFC) wireless relay system with multiple relays. Each relay adopts a censoring scheme to determine whether the relay will decode and forward the source's information toward the destination. We analytically obtain the achievable diversity for both cases of perfect and imperfect relay censoring. With perfect censoring, we show that the same diversity of a conventional noncoherent SFC MIMO-OFDM system is achievable in the considered noncoherent distributed SFC system with maximum-likelihood (ML) decoding, regardless of whether partial information of channel statistics and relay decoding status is available at the destination. With imperfect censoring, we analytically investigate how censoring errors affect the achievability of the system's diversity. We show that the two types of censoring errors, which correspond to useless and harmful relays, respectively, can decrease the achievable diversity significantly. Our analytical insights and numerical simulations demonstrate that the noncoherent distributed system can offer a comparable diversity as the conventional MIMO-OFDM system if relay censoring is carefully implemented.

In this paper, we study the sum degrees of freedom (DoF) of an uplink two-cell multiuser MIMO interference network with asymmetric number of users in the cells. The achievable DoF is devised based on a two-dimensional space-time spreading code framework with linear precoding/decoding design and finite channel extension. The derivation of the achievable DoF is shown related to a rank minimization problem, which corresponds to the minimization of the dimension of the interference subspace. The problem is solved by the proposed grouping algorithm (GA) based on aligning interfering signals into a low-dimensional subspace as a group and attaining the minimum number of groups. The achievable sum DoF derived based on the proposed GA is shown to be greater than prior arts and achieves the theoretic upper bound in several cases. We also give a closed-form expression of the maximum achievable sum DoF when there is the maximum number of admissible users in the considered finite diversity environment.

This paper proposes a clustering-based blind detector for multiple-input multiple-output system using space shift keying modulation. First, we convert the blind detection problem to a clustering problem while considering block fading channel. Second, we use the well-known k-means clustering algorithm to design the blind detector. Third, the proposed k-means clustering detector for a blind receiver can provide comparable performance to that of the optimal receiver with perfect channel state information under the conditions of sufficient channel coherent time and sufficient random initializations of the k-means clustering algorithm. Simulations are conducted to demonstrate the performance of the proposed detector.

Virtual multiple-input multiple-output (VMIMO) enables the implementation of conventional MIMO on mobile devices equipped with insufficient numbers of antennas via cooperation. This paper considers a spectral efficient broadcasting network in which selected mobile devices form a VMIMO system to relay the broadcasted data to help other devices decode the source data more reliably. In particular, the relay selection problem, a fundamental issue in the construction of VMIMO, is examined. We first review existing selection schemes for users operating in the amplify-and-forward (AF) mode. We then propose a distributed selection scheme based on post-processing SNR. In the proposed scheme, each user individually finds the most favorable candidates for VMIMO construction and then all users obtain a joint decision through a voting process. Simulation results show that the proposed distributed scheme outperforms existing distributed selection schemes and achieves a near-optimal performance with lower complexity compared to the centralized scheme.

In this paper, we consider a two-way relay network in which two users exchange messages through a single relay using a physical-layer network coding (PNC) based protocol. The protocol comprises two phases of communication. In the multiple access (MA) phase, two users transmit their modulated signals concurrently to the relay, and in the broadcast (BC) phase, the relay broadcasts a network-coded (denoised) signal to both users. Nonbinary and binary network codes are considered for uniform and nonuniform pulse amplitude modulation (PAM) adopted in the MA phase, respectively. We examine the effect of different choices of symbol mapping (i.e., mapping from the denoised signal to the modulation symbols at the relay) and bit mapping (i.e., mapping from the modulation symbols to the source bits at the user) on the system error-rate performance. A general optimization framework is proposed to determine the optimal symbol/bit mappings with joint consideration of noisy transmissions in both communication phases. Complexity-reduction techniques are developed for solving the optimization problems. It is shown that the optimal symbol/bit mappings depend on the signal-to-noise ratio (SNR) of the channel and the modulation scheme. A general strategy for choosing good symbol/bit mappings is also presented based on a high-SNR analysis, which suggests using a symbol mapping that aligns the error patterns in both communication phases and Gray and binary bit mappings for uniform and nonuniform PAM, respectively.

ABSTRACT A graph‐based framework is proposed in this paper to implement dynamic fractional frequency reuse (D‐FFR) in a multicell Orthogonal Frequency Division Multiple Access (OFDMA) network. FFR is a promising resource‐allocation technique that can effectively mitigate intercell interference (ICI) in OFDMA networks. The proposed D‐FFR scheme enhances the conventional FFR by enabling adaptive spectral sharing as per cell‐load conditions. Such adaptation has significant benefits in practical systems where traffic loads in different cells are usually unequal and time‐varying. The dynamic adaptation is accomplished via a graph framework in which the resource‐allocation problem is solved in two phases: (1) constructing an interference graph that matches the specific realization of FFR and the network topology and (2) coloring the graph by use of a heuristic algorithm. Various realizations of FFR can easily be incorporated in the framework by manipulating the first phase. The performance improvement enabled by the proposed D‐FFR scheme is demonstrated by computer simulation for a 19‐cell network with equal and unequal cell loads. In the unequal‐load scenario, the proposed D‐FFR scheme offers significant performance improvement in terms of cell throughput and service rate as compared to conventional FFR and previous interference management schemes. Copyright © 2011 John Wiley & Sons, Ltd.

The detection problem of the space shift keying (SSK) signaling and its generalized form (namely, generalized SSK or GSSK) in the emerging large-scale multiple-input multiple-output (MIMO) systems is discussed in this paper. First, we explicitly formulate the tree search and column search detection schemes achieving optimal maximum likelihood (ML) performance, and discuss their pros and cons in the context of large MIMO systems where the size of the GSSK modulation alphabet increases significantly. Secondly, we propose two useful suboptimal detection methods for large MIMO systems and large-alphabet GSSK signaling based on convex relaxation, which induce an approximately 2-4 dB performance penalty as shown through experimental results.

A new multiple-input multiple-output (MIMO) detection scheme combining minimum-mean-square-error (MMSE) detection and the K-best detection algorithm is proposed. The proposed scheme leverages the MMSE detection results to ease the demand of a large K in the conventional K-best algorithm to achieve satisfactory performance. The post-detection SNR obtained after MMSE detection is consulted to determine the symbols upon which a reduced-dimension K-best algorithm (h-best algorithm) is performed to obtain final detection results. Parameters associated with the proposed scheme are empirically chosen to make a fair comparison with the conventional K-best algorithm. Extensive Monte Carlo simulation demonstrates that the hybrid approach exhibits significant performance gain over both MMSE and K-best detection schemes.

In this paper, a MIMO detection scheme is proposed based on a combination of Monte Carlo technique and list detection. Specifically, a list of Gaussian samples are first generated to determine the search range of constellation points in which the transmitted symbol is most likely to locate. Linear equalizations are then applied to equalize the effect caused by the channel mixing, and a list detector is used to search within the determined search range. By varying the parameters in the Monte Carlo method, different symbol error rate (SER) versus complexity tradeoff can be obtained to account for different system design requirements. Simulation results also show that near-ML SER performance with considerably less computational complexity can be achieved by the proposed scheme compared to the exhaustive search.

Energy-efficient communication using a class of spatial modulation (SM) that encodes the source information entirely in the antenna indices is considered in this paper. The energy-efficient modulation design is formulated as a convex optimization problem, where minimum achievable average symbol power consumption is derived with rate, performance, and hardware constraints. The theoretical result bounds any modulation scheme of this class, and encompasses the existing space shift keying (SSK), generalized SSK (GSSK), and Hamming code-aided SSK (HSSK) schemes as special cases. The theoretical optimum is achieved by the proposed practical energy-efficient HSSK (EE-HSSK) scheme that incorporates a novel use of the Hamming code and Huffman code techniques in the alphabet and bit-mapping designs. Experimental studies demonstrate that EE-HSSK significantly outperforms existing schemes in achieving near-optimal energy efficiency. An analytical exposition of key properties of the existing GSSK (including SSK) modulation that motivates a fundamental consideration for the proposed energy-efficient modulation design is also provided.

The tree representation of the multiple-input multiple-output (MIMO) detection problem is illuminating for the development, interpretation, and classification of various detection methods. Best-first detection based on Dijkstra's algorithm pursues tree search according to a sorted list of tree nodes. In the first part of the paper, a new probabilistic sorting scheme is developed and incorporated in a modified Dijkstra's algorithm for MIMO detection. The proposed sorting exploits the statistics of the problem and yields effective tree exploration and truncation in the proposed algorithm. The second part of the paper generalizes the results in the first part and removes some limitations. A generalized Dijkstra's algorithm is developed as a unified tree-search detection framework. The proposed framework incorporates a parameter triplet that allow the configuration of the memory usage, detection complexity, and sorting dynamic associated with the tree-search algorithm. By tuning different parameters, desired performance-complexity tradeoffs are attained and a fixed-complexity version can be produced. Simulation results and analytical discussions demonstrate that the proposed generalized Dijkstra's algorithm shows abilities to achieve highly favorable performance-complexity tradeoffs.

A unified tree-search detection scheme based on Dijkstra's algorithm is developed for MIMO systems. The proposed framework generalizes the original Dijkstra's algorithm by allowing the memory usage, detection complexity, and sorting dynamic associated with the algorithm to be customized. By tuning different parameters, desired performance- complexity tradeoffs are attained and a fixed- complexity version can be produced to facilitate hardware implementation. Simulation results demonstrate that the proposed algorithm shows abilities to achieve highly favorable performance- complexity tradeoffs.

The tree representation of the MIMO detection problem is illuminating for the development, interpretation, and classification of various detection methods. One method, based on the Dijkstra's search algorithm, pursues tree exploration according to a bounded, sorted list of tree nodes. Since sorting directly affects tree exploration and truncation in this tree-search method, it is critical to the performance. Motivated by the observation that sorting according to nodes' path metric, as in the conventional algorithm, does not adequately represent the "goodness" of nodes, a new probabilistic sorting rule is developed by innovatively exploiting the statistical properties of the path metric to yield more effective sorting. The relationship between the probabilistic sorting and the conventional one is established, and new features of the probabilistic sorting are presented. The effectiveness of the proposed method is demonstrated by computer simulation, where the new method outperforms the previous tree-search method in achieving near-ML detection performance, and meanwhile offers significant complexity reduction compared to the previous tree-search method.

A low-complexity, effective detection method for multiple-input multiple-output (MIMO) systems based on eigenspace search and linear equalization-based detection schemes is proposed in this paper. Based on the observation that solutions yielded by linear detectors are corrupted by color noise, the proposed method introduces a new constellation search procedure to augment linear detectors. Specifically, calibrated search is conducted around the initial solution yielded by linear detectors, in the directions guided by the eigenvectors corresponding to the dominant eigenvalues of the covariance matrix of the color noise to identify improved solutions. Complexity analysis is performed to understand the cost of this search procedure. Simulation results demonstrate that the proposed scheme yields an approximate 5 dB gain over linear equalization-based detectors in terms of symbol error rate (SER), at moderate additional computational cost.

A modified sphere decoding (SD) scheme is proposed for multiple-input multiple-output (MIMO) communication systems in this paper. The conventional SD goes from the lower dimension to the higher dimension to examine whether a lattice point lies inside the sphere of some radius, which remains fixed for all dimensions. Since the sphere radius directly affects the search range and thus the complexity, it is an important parameter to design. The proposed scheme employs a set of dimension-dependent sphere radii, which performs more aggressive search-space reduction in low dimensions. The proposed method is shown by computer simulation to offer substantial complexity benefits with little symbol error rate (SER) performance loss (0.5 dB), compared to the optimal maximum likelihood (ML) decoding. The contribution of this paper includes the complexity advantage yielded by the proposed scheme as well as the introduction of a systematic approach to sphere radius design and control.

A graph-based framework for dynamic fractional frequency reuse (FFR) in multi-cell OFDMA networks is proposed in this work. FFR is a promising resource allocation technique that can effectively mitigate inter-cell interference (ICI) in OFDMA networks. The proposed scheme enhances the conventional FFR by enabling adaptive spectral sharing per cell load conditions. Such adaptation has significant benefits in a practical environment where traffic load in different cells may be asymmetric and time-varying. The dynamic feature is accomplished via a graph approach in which the resource allocation problem is translated to a graph coloring problem. Specifically, in order to incorporate various versions of FFR in our framework, we construct a graph that matches the specific version of FFR and then color the graph using the corresponding graph algorithm. The performance improvement enabled by the proposed dynamic FFR scheme is further demonstrated by computer simulation for a 19-cell network with asymmetric cell load. For instance, the proposed dynamic FFR scheme can achieve a 12% and 33% gain in cell throughput and service rate over conventional FFR, and render a 70% and 107% gain in cell throughput and service rate with respect to the reuse-3 system.

In this paper, we propose a cost-effective scheme for robust wireless multi-party video conferencing based on network coding (NC). The main idea is the adoption of a NC scheme to enhance robust transmission, to simplify the erasure protection procedure, and to reduce the downlink bandwidth by leveraging the properties of opportunistic NC and wireless broadcasting. We design a pipelining schedule to meet the delay requirement for real-time video conferencing. The proposed NC method outperforms the opportunistic network coding method by a significant margin and reduces the downlink bandwidth of the overall video bit rate.