Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical.

Traditional crowd sensing based indoor localization methods rely on large scale pre-collected fingerprint data to construct a radio map with cumbersome prior preparation. However, when they lack floor plan information or only have a little of data is willing to share, the tracking accuracy degrades significantly. In this paper, we propose a singular value decomposition (SVD) track matching scheme to obtain an effective radio map based on small scale crowd sensing data, which is a non-learning based system (SVD-CSP). SVD-CSP fuses received signal strength indicator (RSSI), inertial measurement unit (IMU), and magnetic field strength to label surrounding WiFi access points as marker points. The proposed scheme uses SVD method to directly compute the rotation matrix and displacement vector among the crowd sensing trajectories and attain the reliable tracks. The radio map is constructed and users are tracked according to our developed bidirectional Bayesian filter, which contains forward filter and reverse filter. The density-based spatial clustering of applications with noise (DBSCAN) is embedded within the forward filter to improve the radio map quality. Meanwhile, the reverse filter fuses pedestrian dead reckoning (PDR) and radio map-based localization to track users. Experimental results demonstrate that SVD-CSP can achieve robust localization using extremely sparse crowd trajectories (e.g., 4 trajectories in a 648 m2scenario, 30 trajectories in a 2856 m2scenario) without deep learning training or infrastructure knowledge.

Hybrid reconfigurable intelligent surfaces (HRIS) can enable 6 G vehicle-to-everything (V2X) system to attain promising localization and communication performance due to its flexible beamforming feature. However, without jointly designing the HRIS control and system resource allocation scheme, the HRIS-V2X system can not adapt to the dynamic environment efficiently. In addition, the optimization of HRIS reflectivity and communication time slice are both non convex and nonlinear problems. In this paper, we propose an asynchronous time division multiplexing (ATDM) protocol for the HRIS-V2X system to meet integrated localization and communications requirements. We analyze the role of HRIS in signal transmission according to squared position error bound (SPEB) and achievable rate (AR). Then, we propose an adaptive block coordinate descent (ABCD) algorithm to optimize the localization accuracy and channel transmission capability, which includes two parts: the time optimization and the reflectivity optimization. Time optimization employs the iterative projection method to find the optimal time slice scheme satisfying AR constraints. Reflectivity optimization uses the Adagrad method with an adaptive learning rate to gradually achieve the optimal reflectivity scheme. The simulation results indicate that our proposed ABCD algorithm has achieved a maximum 94.1% reduction in SPEB compared to greedy algorithm, genetic algorithm (GA), artificial rabbits optimization (ARO) and particle swarm optimization (PSO).

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

In this work, we investigate the physical layer security (PLS) of ambient backscatter communication non-orthogonal multiple access (AmBC-NOMA) networks where non-colluding eavesdroppers (Eves) are randomly distributed. In the proposed system, a base station (BS) transmits a superimposed signal to a typical NOMA user pair, while a backscatter device (BD) simultaneously transmits its unique signal by reflecting and modulating the BS’s signal. Meanwhile, Eves passively attempt to wiretap the ongoing transmissions. Notably, the number and locations of Eves are unknown, posing a substantial security threat to the system. To address this challenge, the BS injects artificial noise (AN) to mislead the Eves, and a protected zone is employed to create an Eve-exclusion area around the BS. Theoretical expressions for outage probability (OP) and intercept probability (IP) are provided to evaluate the system’s reliability-security trade-off. Asymptotic behavior at high signal-to-noise ratio (SNR) is further explored, including the derivation of diversity orders for the OP. Numerical results validate the analytical findings through extensive simulations, demonstrating that both the AN injection and protected zone can effectively enhance PLS. Furthermore, analysis and insights of different key parameters, including transmit SNR, reflection efficiency at the BD, power allocation coefficient, power fraction allocated to desired signal, Eve-exclusion area radius, Eve distribution density, and backscattered AN cancellation efficiency, on OP and IP are also provided.

With the rapid proliferation of Internet of Things (IoT) devices, overlapping spectrum signals (OSS) have become increasingly prevalent due to concurrent transmissions over the same frequency bands. Accurate recognition of OSS, including their categories, positions, and transmission powers, is crucial for spectrum management and interference mitigation in dense wireless environments. This paper proposes an integrated deep signal recognition (IDSR) architecture based on deep non-negative matrix factorization (Deep-NMF) for multi-parameter OSS estimation. To reduce computational complexity and accelerate iterative convergence, we further develop a collaborative multi-layer signal recognition (CMSR) architecture that decomposes OSS features through a three-layer structure, enabling successive estimation of category, position, and power. Additionally, we incorporate a Dempster-Shafer (D-S) based fusion strategy for signal category recognition and introduce a consensus-constrained NMF algorithm for collaborative localization across monitoring nodes. Particle swarm optimization (PSO) is employed to initialize the power and position estimation to enhance accuracy. Experimental results demonstrate that both IDSR and CMSR outperform traditional blind source separation methods (e.g., PCA, ICA, and standard NMF) and classical deep learning models (CNN and Transformer) in OSS recognition. Specifically, the proposed methods achieve up to 95.6% accuracy in signal classification under SNR = 4 dB, with comprehensive performance gains. Moreover, CMSR attains comparable accuracy to IDSR with lower computational complexity, indicating its potential for real-time edge deployment. The impact of monitoring node placement, signal power ratios, and deployment density is also analyzed, providing practical insights for spectrum monitoring in IoT networks.

This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

Human activity recognition (HAR) based on WiFi channel state information (CSI) has received a lot of attentions recently due to its nonintrusive nature. Most CSI-based HAR systems use a WiFi router and a computing terminal for centralized processing, which makes it difficult to achieve real-time wide-range recognition. Recently, lightweight Artificial Intelligence Internet of Things (AIoT) devices are widely deployed. The equipped WiFi chips within such devices can collect and process CSI data in a distributed way. Thus, the AIoT devices extend the detection range of collecting CSI and enrich the applications. However, the memories of the AIoT devices are constrained and lack of appropriate lightweight CSI processing strategies. To address these challenges, we propose the LiWi-HAR system which employs a comprehensive lightweight CSI processing strategy in WiFi-based AIoT devices. The proposed lightweight CSI processing strategy extracts the main related features while compressing the data size. Then, a double hidden layer BP neural network based on particle swarm optimization (PSO-BPNN) algorithm is developed for HAR. In this case, the computing memory occupation of the device is effectively reduced, and the real-time high-accurate recognition is achieved. Extensive experimental results present that the efficiency of our system significantly outperforms other centralized deep learning-based systems and the recognition accuracy achieves 91.7%.

Localization is essential for network management of the wireless backscattered sensor networks (WBSNs). In large-scale WBSN, the cooperative localization among the passive nodes effectively improves the localization accuracy. Meanwhile, reconfigurable intelligent surface (RIS) motivates nodes to gain better spatial channel using passive beamforming or phase modulation. In this article, we analyze the impact of passive beamforming for RIS on the localization accuracy of cooperative localization in the WBSN system. We derive the Fisher information matrix (FIM) and the spatial position error bound for the fully connected communication network system. We demonstrate that the phase modulation of RIS reflection units affect the localization accuracy of the cooperative localization WBSN system. However, RIS passive beamforming as a discrete and nonconvex integer programming problem is difficult to solve. Then, we propose a Monte Carlo-based random RIS passive beamforming to achieve the maximum localization accuracy. We apply Gibbs sampling and resampling methods to generate the phase shift vector samples of RIS. The sample with the highest localization accuracy is considered as the optimal solution. The simulation results demonstrate that our proposed method for RIS passive beamforming can improve 34.5% localization accuracy in the Line-of-Sight (LoS) case, while the genetic algorithm (GA) is 6.8%. In the Non-LoS (NLoS) environment, the localization accuracy improvement of our proposed method reaches 97%, and GA can only reach 85% as the comparison.

Hybrid active and passive wireless sensor networks (HWSNs) gain advantages in extending the network lifetime and reducing the overall cost. Because the passive sensors without battery harvest the energy from distributed active sensor signal beam, and only a few active sensors can maintain a large-scale network. Thus, how to track the passive sensor’s location is essential for network management. Since the active sensors are sparsely deployed, cooperative localization which employs passive sensors to locate themselves together is a promising solution. In this article, we analyze the energy beam generated by the active sensors on the cooperative localization accuracy of the passive sensors. We consider the spatial–temporal cooperative localization based on the received signal strength (RSS) model with unknown Tx power information of each sensor due to the limited processing capabilities, circuit complexity, and energy constraints. We formulate the Fisher information matrix (FIM) and the corresponding Cramér–Rao lower bound (CRLB) for the static fully connected network and dynamic spatial–temporal recursive network. Accordingly, energy beamforming schemes are proposed to optimize localization accuracy and energy efficiency problems. For the optimal localization problem, we derive the closed-form solution of the optimal energy beamforming wave. For the optimal energy efficiency problem, we propose a semidefinite programming (SDP) solution to achieve optimal energy consumption with a self-calibration method, which can address the over-relax problem. Extensive simulation results indicate that our proposed beamforming schemes have high localization accuracy and lower power consumption compared with the existing power allocation-based schemes.

Large scale location management in wireless powered communication networks (WPCNs) can benefit the network performance, and it can also provide location based services for IoT applications without maintaining the batteries. However, it is difficult to attain accurate node positions only based on ranging information from anchors. Thus, cooperative localization is an effective way to increase the node positioning accuracy. In this paper, we mainly investigate the performance of cooperative localization in WPCNs, in which the nodes require energy from remote energy access point (E-AP). We firstly analyze the Cramer-Rao Lower Bound (CRLB) for the full connected´ network and the spatial recursive form for a single joint node respectively. Then we propose beamforming schemes to further optimize the cooperative localization performance, which are designed to achieve the minimum localization errors. The simulations demonstrate the highly accurate localization performance of our proposed schemes, which outperform the existing power allocation schemes.

Two functions are essential and necessary for the wireless-powered communication network, which are energy beamforming and localization. On one hand, energy beamforming controls the wireless energy waves of the energy access point (E-AP) in order to activate the nodes for transmitting information. On the other hand, locating the nodes is important to network management and location-based services in the wireless power communication network (WPCN). For a large-scale network, cooperative localization that employs neighborhood nodes to participate in positioning unknown target nodes is highly accurate and efficient. However, how to use energy beamforming to achieve highly accurate localization is not fully investigated yet. In this article, we analyze the impacts of energy beamforming on the cooperative localization performance of WPCNs. We formulate the Fisher information matrix (FIM) and the corresponding Cramér-Rao lower bound (CRLB) for the full connected network and a single node, respectively. Then, we propose beamforming schemes to optimize the cooperative localization and the power consumption. For optimal localization problems, we derive the closed-form expression of the optimal energy beamforming. For the optimal energy efficiency problems, we propose semidefinite programming (SDP) solutions to achieve the minimum power consumption while using calibrations to approach the actual localization requirements. Further, we also analyze the impacts of channel uncertainty. Through extensive simulations, the results demonstrate the dominant factors of the localization performance, and the performance improvements of our proposed schemes, which outperform the existing power allocation schemes.

Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0. 4m, which is highly accurate for locating a target in a large scale of indoor environment.

Magnetic MIMO is a wireless power transfer (WPT) system that employs multiple magnetic resonance coils to provide high efficient wireless power in the near field. Magnetic energy beamforming is a typical scheme to control the currents or voltages of the transmitter coils in order to achieve some objectives. Thus, the magnetic channel information is essential to magnetic beamforming (MagBF), and it needs complicated circuits and communication protocols to feedback such information. Such information may be not available due to the circuit limits or privacy concerns. In addition, the performance will be degraded with imperfect channel estimation in the noisy and mobile dynamic environment. In this case, only some limited feedback information is available, e.g., received power. In this article, we propose a random MagBF method to achieve maximum received power efficiency and simplify the system architecture. This scheme employs iterative Monte Carlo sampling and resampling to search an optimal beamforming solution based on the received power feedbacks. We design an online training protocol to implement the proposed scheme. It is computationally light and requires only limited feedback information, which avoids complex channel estimation or AC measurements. The simulation and real experimental results indicate that our algorithm can effectively increase the received power and approach the optimal performance with a fast convergent rate.

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

Using IoT devices to locate targets is widely applied in many scenarios. However, replacing the batteries of these devices is time and labor consuming. In this article, we propose a wireless power-driven positioning system (WP2S) that employs MIMO-based wireless power transfer access points to supply energy to batteryless anchors. In this case, the IoT localization devices will have unlimited power. We formulate the equivalent Fisher information matrix (EFIM) as a fundamental tool to analyze the system performance. Then, we propose resource allocation schemes for optimal location estimation and energy efficiency problems by relaxing the objectives as semidefinite programming problems. In addition, we also analyze the impacts of channel uncertainty, anchor uncertainty, and NLOS for the performances of location estimation and energy consumption. The robust algorithms are developed according to uncertainty models. Both the analysis and simulations demonstrate that the estimation accuracy relies heavily on the transmitted power and the uncertainty models will consume more power to meet the location requirements.

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

The radio-frequency (RF) enabled wireless power transfer (WPT) system can be benefit from the MIMO technique. However, due to the limited resource, internet of things (IoT) devices can only feedback partial information which is received signal strength (RSS) value instead of channel state information (CSI). Thus, channel estimation based beamforming scheme from receiver side is not applicable for real applications. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to supply multiple batteryless IoT devices with high received power efficiency. Our algorithm does not require the complex channel estimation and adapts the beamforming scheme only according to the partial feedback information. We employ Gibbs sampling and re-sampling methods to generate several random beamforming weight vectors, and choose the optimal one. A simulated annealing algorithm is employed to control the convergence rate. We use the proposed algorithm to supply power in two cases: the maximum power transmission and robust power transmission. The simulation results indicate that this algorithm can fast converge to an optimal value and provide far-field power to multiple IoT devices.

The integration of two complementary technologies, millimeter-wave (mmWave) communications and massive multiple-input multiple-output (MIMO), will play a key role in enabling gigabit access in 5G systems. However, implementing mmWave massive MIMO using the traditional fully digital architecture will lead to prohibitive hardware complexity as it requires a massive number of RF chains matching antennas in number. To address this issue, the hybrid beamforming architecture has been recently proposed for efficient implementation of mmWave massive MIMO. Specifically, large-scale MIMO beamforming is implemented in the analog domain, called analog beamforming, that exploits the sparsity in mmWave channels for dramatic dimension reduction for digital MIMO signal processing. The typical phase-array implementation of analog beamforming introduces the uni-modulus constraints on the beamforming coefficients and renders the classic MIMO techniques unsuitable. This motivates the novel design framework, called Kronecker analog beamforming, proposed in this paper for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factor vectors with uni-modulus elements. Exploiting the properties of Kronecker product, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Thereby, Kronecker analog beamforming achieves interference nulling and signal enhancement both in the analog domain as well as dimension reduction for digital beamforming.

Using multi-antennas can improve the received energy efficiency of the radio-frequency (RF) enabled wireless power transfer (WPT) system. However, for the resource constrained internet of things (IoT) devices, only partial information which is received signal strength (RSS) value instead of channel state information (CSI) can be fed back. In this paper, we propose an adaptive random beamforming algorithm based on Monte-Carlo method to achieve the maximum received power efficiency. The proposed algorithm does not require any complicated channel estimation and it adapts the beamforming scheme only according to the RSS values. Gibbs sampling is used to generate the random beamforming weight vectors and re-sample them according to the feedback RSS values in an iterative manner. In addition, we employ a simulated annealing algorithm to control the convergence rate. The simulation results indicate that this algorithm can fast converge to an optimal value and achieve the maximum received power.

Many exist localization algorithms are unbiased estimators. However, the estimation performance presents biased feature in the real location systems. On the other hand, many biased location estimators show advantages that unbiased estimators can not achieve, e.g., robust to the noise, more accurate estimation and low complexity. In this paper, we propose a biased localization estimator and a hybrid Kalman filtering algorithm. The proposed algorithm is robust to the complicated environment with high accuracy. Both theoretical analysis and experimental evaluation indicate that the proposed algorithm outperform the unbiased optimal estimation methods.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.

In the absence of a control channel, the idle channel utilization (ICU) is dictated by both the spectrum sensing strategy and the packet access protocol in a distributed cognitive radio (CR) network. In this paper, we investigate a distributed multi-channel random access CR network under imperfect spectrum sensing, where each secondary user (SU) randomly selects a fixed number of channels for sensing and transmits on arbitrary detected idle channels. Based on this model, we first develop a slotted opportunistic access aloha protocol for multiuser random access. Then, the distribution of the successful packet transmissions is derived, according to which we obtain the expression of the average ICU. Finally, the analytical results are validated by substantial simulations. We show that their impacts on ICU by adjusting the number of the sensed channels and packet transmission probability, along with the distance from SUs to primary users.

SUMMARY Although the demand for battery capacity on mobile devices has grown with the increase in high data rate applications, battery technology has not kept up with this demand. Therefore, the growth in energy demand coupled with global warming provide a new trend in wireless communication known as energy‐efficient transmission. In this paper, the energy‐efficient resource allocation for a two‐hop uplink multiuser relay‐based system is studied. We adopt the orthogonal frequency division multiplexing as the physical layer modulation technique. Assuming that the base station has all the channel state information, an energy efficiency optimization problem by joint subcarrier assignment, bit and power allocation is formulated. We first develop a near‐optimal resource allocation scheme to maximize the overall energy efficiency; then, an efficient resource allocation algorithm is provided to solve the problem with relatively low computational complexity. Furthermore, fairness constraint among users is imposed on the system to guarantee each user's QoS. Our joint resource allocation algorithm is proved to achieve a considerable improvement in terms of energy‐saving and simultaneously decreases outage probability by simulation results. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we investigate a novel slotted ALOHA-based distributed cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU. Based on that, the analytical average system throughput is derived in both saturation and non-saturation networks. Second, the relationship between the average system throughput and the number of sensing channels is attained. We show that the optimal number of sensed channel in a given number of SUs is dependent on the number of licensed channels, the number of idle channels, and the transmission probability of each SU. Finally, the analytical results are validated by substantial simulations.

In this paper, the energy-efficient resource allocation in relay-aided uplink multiuser system is studied. We adopt the orthogonal frequency division multiplexing (OFDM) as the physical layer modulation technique. Assuming that the BS has all the channel state information (CSI), an energy-efficiency optimization problem through joint subcarrier assignment, bit and power allocation is formulated. Due to the computational complexity restriction, a suboptimal solution based on decomposition is presented to solve the problem with low complexity. Considering the fairness constraint, a joint resource allocation algorithm is further proposed, which is proved to achieve a considerable improvement in terms of energy-saving and simultaneously decreases rate outage probability by simulation results.

In this paper, we investigate a novel slotted ALOHA-based distributed access cognitive network in which a secondary user (SU) selects a random subset of channels for sensing, detects an idle (unused by licensed users) subset therein, and transmits in any one of those detected idle channels. First, we derive a range for the number of channels to be sensed per SU access. Then, the analytical average system throughput is attained for cases where the number of idle channels is a random variable. Based on that, a relationship between the average system throughput and the number of sensing channels is attained. Subsequently, a joint optimization problem is formulated in order to maximize average system throughput. The analytical results are validated by substantial simulations.

In this paper, we consider a four-node relay-based OFDMA system. The expression of the upper bound for the achievable rate in Successive Relaying (SUR) protocol is derived by using the cut-set theorem for half-duplex systems. Based on this expression, the near-optimal solution of the achievable rate is obtained in the joint power and subcarrier allocation constraint, according to the dual problem decomposition approach and the subgradient method. Moreover, we make a comparison on the achievable rate between SUR and Simultaneous Relaying (SIR) in the pathloss model accepted by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) Advanced system. The simulation results demonstrate the enhancement of the achievable rate in SUR protocol is expanded in high signal-to-noise ratio (SNR) compared with the achievable rate in SIR protocol under both the symmetric and asymmetric cases. And for SUR protocol , the achievable rate in symmetric case performs better in high SNR.

In this paper, we consider cooperative beamforming in an amplify-and-forward (AF) cooperative network with multiple relay nodes. Specially, only "appropriate" relay nodes are selected to perform cooperative beamforming. Source node can determine the relay nodes with just mean channel gain, which will reduce the complexity of obtaining instantaneous channel state information (CSI). This scheme guarantees that the energy is allocated to those "appropriate" relay nodes, and accordingly improves the energy efficiency. Therefore, it is able to provide superior diversity over the conventional cooperative beamforming. We also prove that power allocation (PA) between source and selected relay nodes is a convex problem, which can be resolved with lower computational complexity. Simulation results demonstrate that our scheme achieves an essential improvement in terms of outage performance, as well as high energy-efficiency for energy-constrained networks.

Cooperative communication has been recently proposed as a way to improve the performance of wireless communication. However, due to the half duplex constraint, most of the relay networks face a fundamental challenge in terms of multiplexing loss. In this paper, we present an adaptive cooperation via relay selection (ACRS) protocol that improves spectral efficiency. This protocol lets the source transmit most of the time and always allows source transmission to be forwarded only by the best transmitter available (maybe source itself) if needed. ACRS has the feature of adapting the system to the fluctuation of the wireless channel and trying to make a packet transmission finished in just one time slot. The diversity-multiplexing tradeoff (DMT) is used to show a remarkable improvement over previous relaying schemes. Simulation results are presented to verify our analysis.

In this paper, we consider a two-hop orthogonal frequency division multiplexing (OFDM) relay link, and investigate the system capacity in hybrid forward (HF) relay through the power allocation (PA). The expression of the system capacity in HF relay is provided. Two situations about the power constraint are concerned, and under each situation, the selection criterion among different forwarding schemes in HF relay is proposed by means of capacity comparison on the subcarrier pair. From the selection criterions, the source or relay can adaptively choose the best scheme for every subcarrier pair according to the channel condition. Then the optimal solutions of the relay PA and the joint PA are derived to maximize the capacity. In the simulation, the advantage of the HF relay is shown in terms of capacity improvement compared with the conventional forward relay.

The performance of a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system can be improved when the channel state information (CSI) is available for beamforming at the transmitter. However, even with the quantization of the CSI, the feedback requirement is still prohibitively unaffordable in a frequency division duplexing based MIMO-OFDM system with a large number of subcarri- ers. In this paper, two schemes with limited feedback, i.e., a nonlinear interpolation and a modified clustering based transmit beamforming schemes, are proposed. In the first method, the beamforming vectors for the non-pilot subcarriers in each cluster are constructed via that of the pilot subcarriers through nonlinear interpolation. In the second method, the beamforming vector for each cluster is selected to optimize the performance of a group of subcarriers which are chosen from that cluster based on equal spacing or a predefined random matrix. Simulations demonstrate that the proposed nonlinear interpolation and the modified clustering based transmit beamforming schemes outperform the existing limited feedback methods with the same feedback.