Precise detection of driver mental fatigue is critical for reducing traffic accidents and enhancing road safety. Compared with vision-based detection—which is susceptible to illumination and occlusion—multimodal physiological‑signal-based approaches integrate complementary information from diverse biosignals, delivering more faithful and objective fatigue assessments. However, adverse factors such as motion artifacts and environmental noise induce ceaseless deterioration to physiological signals, which markedly degrade the performance of existing multimodal fusion methods. To address this challenge, we propose Multimodal Uncertainty-based Self-driven Evolution, MUSE, reallocating modality contributions in real time via overall uncertainty minimization, thereby enabling efficient collaborative fusion of multi‐source predictions. Theoretically, MUSE guarantees a provably bounded cumulative error, and its generalization error approaches the Bayesian‑optimal fusion as iterations progress. Operating in a closed loop without labels or manual recalibration, MUSE presents superior suitability for real‑world driving scenarios compared to supervised algorithms. On the large‑scale driving fatigue dataset SEED‑VIG, MUSE outperforms existing models in both classification and regression tasks, substantiating its robustness and practicality as a promising driving fatigue detection solution.

This paper presents a novel binocular-encoded SSVEP (beSSVEP) method, leveraging binocular vision in virtual reality (VR) to enhance brain-computer interface (BCI) applications. We introduce the Binocular Periodically Repeated Component Analysis (bPRCA) algorithm, designed to address the unique characteristics of binocular-encoded targets, which include combinations of monocular single-frequency SSVEP units or void units, with frequency units being reused multiple times in the encoded interface. To further optimize performance, we propose the Fusion Component Analysis (FusionCA) framework, which integrates bPRCA with Task-Related Component Analysis (TRCA), effectively utilizing both steady-state periodic components and cross-trial aperiodic components. Experimental results demonstrate that ensemble-FusionCA achieves the highest information transfer rate (ITR) with an average accuracy of 71.39% and an ITR of 138.50 bits/min at 0.4 seconds, among the comparison with ensemble-bPRCA and ensemble-TRCA. Compared to traditional SSVEP approaches, beSSVEP significantly enhances frequency utilization, making VR-BCI systems more efficient and practical. This study highlights the application of physiological mechanisms of binocular vision to improve BCI systems, offering a new perspective for developing fast and scalable brain-computer interactions in VR environments.

In recent years, the diverse applications of electroencephalography (EEG) - based affective brain-computer interfaces (aBCIs) are being extensively explored. However, due to adverse factors like noise and physiological variability, the recognition capability of aBCIs can unforeseeably suffer abrupt declines. Since the timing of these aBCI failures is unknown, placing trust in aBCIs without scrutiny can lead to undesirable consequences. To alleviate this issue, we propose an algorithm for estimating the reliability of aBCI (primarily Graph Convolutional Network), synchronously delivering a probabilistic confidence score upon aBCI decision completion, thereby reflecting the aBCI's real-time recognition capabilities. Methodologically, we use the Maximum Softmax Probability (MSP) from EEG recognition networks as confidence scores and leverage the Scaling Operator to calibrate them. Then, the Projection Operator is employed to address confidence estimation biases caused by noise and subject variability. For the numerical concentration of MSP, we provide fresh insights into its causes and propose corresponding solutions. The derivation of the estimator from the Maximum Entropy Principle is also substantiated for robust theoretical underpinnings. Finally, we confirm theoretically that the estimator does not compromise BCI performance. In experiments conducted on public datasets SEED and SEED-IV, the proposed algorithm demonstrates superior performance in estimating aBCIs reliability compared to other benchmarks, and commendable adaptability to new subjects. This research has the potential to lead to more trustworthy aBCIs and advance their broader application in complex real-world scenarios.

Satellite network is envisioned as a key enabler for wide-area coverage and seamless connectivity. Nonetheless, satellite-terrestrial communications are confronted with critical challenges, including significant signal attenuation and complex inter-satellite interference, particularly in ensuring uplink performance for power-constrained ground users. Reconfigurable intelligent surface (RIS) is considered as a promising solution to compensate for the severe path loss, owing to their high-gain and dynamic beamforming capabilities. In addition, with the deployment of ultra-dense satellite constellations, multi-satellite cooperation is expected to effectively mitigate inter-satellite interference and enhance channel gains. In this paper, a novel RIS-aided multi-satellite cooperative reception scheme is proposed for multi-user uplink transmission. We first analyze five types of multi-satellite cooperative reception framework within cell-free paradigm and formulate a multi-user total energy efficiency (EE) maximization problem. Based on block coordinate descent method, this nonconvex optimization problem is decomposed into three subproblems: receiver detection vector design, RIS phase shift optimization, and multi-user transmit power control. Maximum ratio combining, zero forcing, and minimum mean square error are adopted for linear detection vector designs, respectively. Then the RIS phase shift is optimized based on Riemannian conjugate gradient algorithm. Furthermore, multi-user transmit power control is designed using Dinkelbach’s algorithm. Finally, the total EE optimization problem is solved in an iterative manner. Extensive simulation results validate the effectiveness of the proposed reception scheme and algorithm.

As the essential complements to terrestrial networks, satellite networks have attracted widespread attention due to their potential to enable seamless coverage. However, satellite-terrestrial communications face several critical challenges, including severe path loss and inter-satellite interference, which pose significant obstacles, especially for energy-constrained uplink transmissions. To overcome these problems, a reconfigurable intelligent surfaces (RIS)-aided multi-satellite cooperative reception scheme is proposed in this paper. Specifically, an energy efficiency (EE) optimization problem for multi-user uplink transmission is first formulated. Based on block coordinate descent method, the total EE maximization problem is decomposed into three subproblems: receive beamforming design, RIS phase shift optimization, and multi-user transmit power optimization. Furthermore, the aforementioned subproblems are respectively tackled by applying linear receiver design, the Riemannian conjugate gradient algorithm, and the Dinkelbach’s algorithm. The superiority of the proposed scheme is validated by numerical results, which reveal significant gains in sum rate and total EE.

Currently, LEO satellite networks enable global connectivity and emergency services, yet face dynamic topology changes and resource constraints. Traditional routing struggles with orbital dynamics and latency-sensitive demands. To address challenges related to data processing and transmission efficiency in dynamic satellite networks, this study proposes a hierarchical satellite routing architecture that decouples the roles of computing and communication satellites, enabling resource specialization and self-organized management. Additionally, an active edge caching mechanism is introduced, significantly reducing redundant data transmissions and processing delays across the network. Furthermore, the paper develops a deep reinforcement learning framework that integrates intelligent weight analysis to optimize packet routing decisions. Experimental results demonstrate that the proposed method achieves superior performance in terms of average delay and task completion rate compared to traditional approaches, with even greater advantages observed upon the integration of mobile edge computing.

The super-refractive characteristics of evaporation ducts confine electromagnetic waves to a thin layer near the sea surface, significantly reducing energy attenuation and enabling beyond-line-of-sight transmission. Accurately predicting the evaporation duct height (EDH) is crucial for the deployment of maritime communication systems. In this paper, a meteorological factor-driven framework for predicting EDH is proposed. Specifically, a voting-based ensemble model is introduced to capture complex nonlinear relationships and characterize local abrupt changes in meteorological data. Moreover, an evolutionary strategy is devised to optimize the model’s hyperparameters, thereby enhancing convergence performance. Numerical results demonstrate that the proposed method outperforms state-of-the-art models in EDH prediction and exhibits superior cross-regional generalization capabilities under complex meteorological and oceanic conditions.

Low earth orbit (LEO) satellite networks are becoming increasingly important for the development of sixth-generation mobile communication systems along with the concept of integrated space-air-ground networks. However, LEO satellite networks face numerous challenges due to their dynamic topology, large number of satellite nodes, unbalanced load distribution, and differences in communication capabilities. These factors make it difficult for traditional routing strategies to effectively handle the complex network environment and meet the requirements for low latency. To solve these problems, this paper proposes a deep reinforcement learning (DRL)-based routing algorithm for LEO satellite networks, aimed at minimizing the overall end-to-end delay through joint optimization of routing and wavelength resources. The algorithm combines DRL with a wavelength switching model and queuing theory to dynamically optimize routing decisions in response to the existing network state. Simulation results demonstrate that the algorithm exhibits strong convergence properties and effectively reduces full-path delay compared to traditional routing algorithms.

Satellite communication is widely recognized as a solution to achieve global coverage. However, due to the high mobility of the Low Earth Orbit (LEO) satellites, user equipment (UE) needs to handover frequently to maintain a stable connection. Furthermore, the complex topology of satellite networks exacerbates the challenge of user mobility management. This paper leverages artificial intelligence (AI) to optimize the satellite handover process, proposing a multi-objective optimization strategy to minimize handover frequency, balance satellite load, and maximize system throughput. A novel reward function is designed to enhance the stability of the user service, and a lightweight multilayer neural network is introduced to reduce computational complexity. The simulation results show that the proposed method outperforms state-of-the-art approaches in improving system throughput and reducing average handover times.

Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant potential for integration into Internet of Things (IoT) environments, enabling seamless interaction with users and external devices. In MI decoding, graph neural networks (GNNs) are increasingly employed due to their capacity to model the structures of non-grid data. However, current GNN approaches predominantly emphasize pairwise relationships, failing to account for the multi-node interactions essential for accurate decoding. To overcome these limitations, we propose NexusNet, a lightweight graph modeling network tailored for MI-BCI and IoT integration. NexusNet incorporates multi-node routing and Nexus Fusion, facilitating the modeling of intricate relationships that extend beyond pairwise connections. It effectively captures global information using a shortest path algorithm while complementing it with localized neighboring relationships. This architecture ensures efficient feature extraction and decoding while maintaining low computational overhead, making it suitable for resource-constrained scenarios. Experimental evaluations demonstrate that NexusNet achieves state-of-the-art performance, attaining 78.78% accuracy on BCIC-IV-2a, 87.21% on BCIC-IV-2b, and 94.12% on High Gamma Dataset with only 3.44K learnable parameters and 0.88M floating-point operations. These results highlight its potential for practical applications. Further analyses validate the contributions of its multi-node components, with visualizations illustrating their indispensable roles in decoding. NexusNet represents a step forward in connecting MI-BCI research with practical IoT deployment. Our code is available at https://github.com/ZikaiVan/NexusNet.

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the nonorthogonal multiple access (NOMA) uplink system for low-earth orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multisatellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments.

In motor imagery (MI) decoding, it still remains challenging to excavate enough contextual information of MI in different brain regions and to bridge the cross-session variance in feature distributions. In light of these issues, our study presents an innovative Bi-Stream Adaptation Network (BSAN) to bolster network efficacy, aiming to improve MI-based brain-computer interface (BCI) robustness across sessions. Our framework consists of the Bi-attention module, feature extractor, classifier, and Bi-discriminator. Precisely, we devise the Bi-attention module to reveal granular context information of MI with performing multi-scale convolutions asymptotically. Then, after features extraction, Bi-discriminator is involved to align the features from different MI sessions such that a uniform and accurate representation of neural patterns is achieved. By such a workflow, the proposed BSAN allows for the effective fusion of context coherence and session-invariance within the network architecture, therefore diminishing the reliance of redundant MI trials for MI-BCI re-calibration. To empirically substantiate BSAN, comprehensive experiments are conducted based on two public MI datasets. With average accuracies of 78.97% and 83.79% on two public datasets, and an inference time of 2.99 ms on CPU-only devices, it is believed that our approach has the potential to accelerate the practical deployment of MI-BCI.

Next-generation Internet of Things (IoT) technology is vital for sixth-Generation (6G) communication systems, driving exponential growth in spectrum resource demand. Spectrum sensing, essential for identifying unused spectrum, and Non-Orthogonal Multiple Access (NOMA), which allows efficient frequency band sharing among users, can significantly improve spectrum efficiency. However, current sensing methods do not fully support NOMA, resulting in suboptimal performance. Motivated by such a circumstance, we propose a feature-based spectrum sensing method for uplink communication in power-domain NOMA IoT scenarios with multi-user interference, aiming to maximize both static and dynamic spectrum efficiency. Firstly, we introduce the notion of orbits to represent non-fully occupied spectral holes. Then we elaborate on the sensing criterion as well as workflow and design the two-stage spectrum sensing framework, which proposes the orbit estimation sensing algorithm in the stage 1 and accurate sensing threshold in the stage 2, to mitigate false alarm fluctuations. The closed-form solution for the estimation threshold and accurate threshold configuration are derived thereafter. Simulation results show that our proposed multi-orbit sensing method has stable performance and achieves average 30% system throughput gains compared to the latest NOMA techniques at 5 dB.

With the rapid development of space technology, the role of satellite communications has become progressively significant. Non-terrestrial communication is deemed a critical scenario in the sixth generation (6G) communication systems, which showcases seamless connectivity, minimal geographic constraints and substantial communication capacity. Simultaneously, satellites and terminals spring up, spatial density increases, which further emphasizes the scarcity of spectrum resources. Consequently, to improve spectrum utilization for non-terrestrial communication is a significant concern. Spectrum sensing, which allows dynamic resource reuse, plays an important role in 6G. However, high mobility in non-terrestrial scenarios poses great challenges, such as fast time-varying channels, Doppler effect, etc., which seriously affect sensing accuracy and cannot well support optimal spectrum utilization. Motivated by such circumstances, this paper proposes an elastic sensing method for the downlink non-terrestrial communication scenario. Firstly, we design the system architecture and sensing workflow. To overcome the negative effects raised by high mobility, we propose the elastic sensing criterion and multi-area dividing scheme for the sensing zone. Thresholds affected by the elastic sensing are derived for different areas. Finally, the numerical results show that from -10 dB to -5 dB, the proposed method can improve the total performance by an average of 28.3% while stabilizing the false alarm probability around 0.1 typical level and demonstrating higher constancy compared with traditional technologies from −10 dB to −5 dB.

The upcoming sixth-generation (6G) of ubiquitous connectivity communications systems is driven by the next-generation of Internet of Things (IoT) technology. Accordingly, the demand for spectrum resources is growing exponentially. Spectrum sensing, which dynamically explores spectrum holes, is expected to be crucial in the 6G era. In the meantime, Non-Orthogonal Multi-Access (NOMA), an efficient means of reusing resources, can enable multiple users to share the same frequency band stably. The combination of both technologies holds promise for more effective use of spectrum resources in future commu-nications. In this paper, we present a spectrum sensing method for the uplink communication scenario in NOMA with multi-user interference, which aims to make effective use of both static and dynamic gain on spectrum efficiency. Firstly, the sensing criterion and workflow are outlined. The closed-form solution for the sensing threshold configuration is derived thereafter. The simulation results demonstrate the feasibility of the proposed approach, which can increase the system throughput up to 19.7%, when compared to NOMA without spectrum sensing.

In the context of the large-scale deployment of 5G base stations, atmospheric ducts cause remote interference in time division duplex systems. Addressing the impact of remote interference on communication systems necessitates timely prediction and emergency mitigation of atmospheric ducts. In this paper, a genetic programming-based multiclass remote interference prediction network model is proposed. Firstly, the proposed model can directly learn to make predictions from extensive databases without relying on any assumptions. Secondly, it presents a genetic programming strategy capable of automatically adjusting the model's structure, thereby enhancing the prediction accuracy of various interference classes. Numerical results demonstrate that the multiclass remote interference prediction network (MRIPNet) outperforms state-of-the-art interference prediction models when tested on real-world datasets. Further-more, MRIPNet excels in accurately predicting a small number of severe interference, which help operators promptly execute interference avoidance measures.

As a vital enabler for ubiquitous connectivity in space-air-ground integrated networks, the satellite-terrestrial communication system has been envisioned to be a crucial complement to terrestrial networks because of its superior capability of providing wide coverage. However, there are many practical limitations that degrade system performance, including on-board power constraints, severe path loss, high delay, and Doppler frequency shift. Reconfigurable intelligent surface (RIS), an attractive candidate technology for future networks, has been expected to be a promising solution to tackle these challenges. In this paper, we propose an RIS-assisted multi-satellite cooperative downlink transmission scheme, where an RIS is deployed near the terrestrial receiver to enhance the communication via joint design of beamforming vectors at satellites and phase shift optimization at RIS. Specifically, we first optimize the channel gain of the proposed transmission scheme under the continuous-time propagation model. Then, we formulate the problem as a maximization optimization of the received signal power during the duration of multi-satellite communication service, and we apply an alternating optimization (AO) algorithm to solve the non-convex problem. To reduce computational complexity, we further propose a low-complexity optimization algorithm under line-of-sight (LoS) transmission conditions and a scalable distributed optimization algorithm without the need to share the multi-satellite individual channel state information. Simulation results validate the superiority of our proposed transmission scheme and demonstrate that the low-complexity algorithm and the distributed algorithm can achieve system performance comparable to that of the AO algorithm under LoS channel conditions.

With the global promotion of fifth-generation (5G) communications, researches for sixth generation (6G) communications are being globally launched from various perspectives. Industrial Internet of Things (IIoT), which is the most representative application that reflects the ubiquitous connectivity characteristics of 6G, has attracted much attention. To explore for a feasible perspective in promoting ubiquitous connectivity and improving IIoT abilities, in this article, we seek solutions to incorporate two promising techniques, i.e., nonorthogonal multiple access (NOMA) and statistical signal transmission (SST). To accomplish such a motivation, we conceive the feasible transceiver architecture for the union of NOMA-SST technique, and elaborate workflow and detection procedures for both uplink and downlink modes. A dedicated resilient window strategy is designed thereafter, which largely strengthen weaker NOMA users’ SST performance without depressing stronger users. The proposed technique is finally testified through both simulation experiments and practical applications. Numerical results verify that NOMA-SST technique has satisfactory detection performance in common IIoT environments. It is also manifested that the proposed technique can refine various aspects, including recall rate and sensing accuracy of monitoring services, successful handling rate of emergency situations, etc., for practical IIoT applications. Such advantages are promising for practice.

Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and applies in another different but related domain (target domain), and is therefore introduced into the BCIs to figure out the inter-subject variances of electroencephalography (EEG) signals. In this article, a novel transfer learning method is proposed to preserve the Riemannian locality of data structure in both the source and target domains and simultaneously realize the joint distribution adaptation of both domains to enhance the effectiveness of transfer learning. Specifically, a Riemannian graph is first defined and constructed based on the Riemannian distance to represent the Riemannian geometry information. To simultaneously align the marginal and conditional distribution of source and target domains and preserve the Riemannian locality of data structure in both domains, the Riemannian graph is embedded in the joint distribution adaptation (JDA) framework and forms the proposed Riemannian locality preserving-based transfer learning (RLPTL). To validate the effect of the proposed method, it is compared with several existing methods on two open motor imagery datasets, and both multi-source domains (MSD) and single-source domains (SSD) experiments are considered. Experimental results show that the proposed method achieves the highest accuracies in MSD and SSD experiments on three datasets and outperforms eight baseline methods, which demonstrates that the proposed method creates a feasible and efficient way to realize transfer learning.

Achieving seamless connectivity is one of the key visions for sixth-generation (6G) wireless communication systems. Satellite communication (SatCom) system, especially the multi-satellite cooperative communication, has become an indispensable part of space-air-ground integrated communication network due to its superior capability in providing wide area coverage. However, the performance of satellite-terrestrial communication is severely limited by high path loss. Reconfigurable Intelligent Surface (RIS) has been considered as a potential enabling technology in 6G to improve the service quality of various communication systems. In this paper, a RIS-aided multi-beam cooperative transmission scheme is proposed to boost the system performance of satellite-terrestrial communication. By optimizing the RIS reflection phase dynamically, the system capacity is considerably improved during the continuous service time of dual satellites. Based on the simulation results, we also point out the impact of the discrete phase optimization and RIS's inclination angle on the system performance considering the deployment of RIS in three-dimensional (3D) space.

With the rapid development of mobile communication and Internet of Things, the scarcity of spectrum resources is becoming increasingly severe. As a supplement to terrestrial networks, satellite communication can alleviate the pressure of the terrestrial spectrum. Meanwhile, dynamic spectrum utilization plays an important role in alleviating the scarcity of spectrum resources. To integrate the advantages, we propose the multi-level sliced sensing architecture and design the workflow combining satellites and spectrum sensing. The sensing threshold settings are deduced thereafter. According to the theoretical derivation above, the challenge of false alarm floating caused by high mobility of satellites is addressed by studying the relationship among false alarm probability velocity, Doppler frequency and slice number. Numerical results show that under highly dynamic channel conditions, the proposed method can improve detection probability by up to 54% over existing technique while stabilizing the equivalent limitation of Pf.

With the rapid increase of the demand for the Internet of Things (IoT), spectrum resources have incremental challenges. Nonorthogonal multiple access (NOMA) and spectrum sensing (SS) are considered key candidate technologies for next-generation wireless communications to improve spectrum utilization. Nevertheless, using both technologies at the same time makes the system more complex and brings new challenges to user differentiation. In order to make better use of these advantages, we creatively propose a feature detection-based SS method for NOMA systems. To better distinguish the relationship between the presence or absence of signals from different NOMA users, we employ feature detection to obtain the feature values of each user. We propose workflows and transceiver architectures combining the two technologies. Based on the relationship among users’ priorities, power, and transmission in common scenarios, we design a downlink mode and two uplink modes and deduce the threshold settings of the corresponding modes. Meanwhile, we also customarily propose enhanced algorithms, to have a marked increase in the performance for the proposed method in various modes. Experimental results illustrate that the proposed technique is feasible and has prominent detection performance and satisfying throughput performance.

Non-orthogonal multiple access (NOMA) is a promising technology for future mobile networks, which can achieve improved connectivity and spectral efficiency. However, current adaptive modulation and coding (AMC) schemes may not be preferable for NOMA systems due to the error propagation. To address this problem, an error-compensated AMC framework for uplink NOMA systems is introduced in this paper. We first demonstrate that the conventional AMC method cannot satisfy the target block error rate (BLER) of NOMA users. Then we develop the error-compensated AMC algorithm. Numerical results verify the propositions in this work, and show that the proposed error-compensated AMC algorithm outperforms the benchmark in terms of the system sum throughput.

This paper proposes solutions to channel prediction with atmospheric duct based on feedforward neural network (FNN) modeling. Specifically, FNN-based model is to produce accurate prediction by directly learning from large database rather than depending on any assumption. The prediction accuracy of the model applied to Sub-6 GHz and 28 GHz bands attain 88.72% and 94.77%, respectively. Besides, the paper also validates the difference of prediction performance of networks by comparisons of artificial neural network (ANN) and FNN-based networks. The results show that when the bands get higher, FNN-based framework would enhance the prediction accuracy while ANN-based framework would bring it down. It is demonstrated that the proposed FNN-based network obtain accuracy gain over 30% than ANN-based framework.

Recently, reconfigurable intelligent surface (RIS) is emerging as an enabling technology to further enhance the performance of wireless communication systems. RIS is actually a metasurface which is consist of a large number of reflection elements. Each of the elements is capable of re-scatter the incident wave by an independent reflection coefficient. Signals can be redirected to the receiver by delicately assigning reflection coefficients to the elements. Thus a better receiving condition can be realized, and system performance can be enhanced. Existing studies already proved the superiority of general RIS-aided systems, e.g., single-user and multi-user, unicast and multicast, orthogonal and non-orthogonal multiple access. However, most of them considered ideal reflection elements. They assumed that full reflection can be realized at any values of phase-shift, which however is unrealistic due to energy dissipation in reflection elements. To circumvent this problem, in this paper we consider a practical phase shift model where reflection amplitude and phase shift are coupled. Specifically, under this practical model we try to maximize the achievable rate of an RIS-aided MIMO system. An alternating optimization based algorithm is proposed to solve the corresponding optimization problem. Reflection coefficients at the RIS and transmit covariance matrix at the transmitter are optimized alternately with the others being fixed. Simulation results are presented to manifest the validity of the proposed scheme.

Non-orthogonal multiple access (NOMA) as a promising technology, can achieve enhanced connectivity and spectral efficiency. However, the asymmetric channels in uplink NOMA systems can bring distinct uncertainty of the bit error rate (BER) and throughput performances to the users. To address this problem, an asymmetric adaptive modulation (AAM) framework for uplink NOMA systems is introduced in this paper. We first derive the closed-form BER expressions for uplink NOMA systems, then we propose a principle for reducing the performance uncertainty. Finally, we develop an AAM algorithm for uplink NOMA systems by studying the closed-form BER expressions. Numerical results demonstrate the correctness of the BER expressions we derived, and show that our AAM algorithm outperforms the benchmark in terms of the system sum throughput.

To further enhance the performance of traditional wireless multicasting, in this paper a novel multicast system is proposed based on non-orthogonal multiple access (NOMA) and device-to-device relay (D2D-relay). In the new system, all destination users are sliced into multiple tiers according to their different levels of channel states, and the multicast data are also divided correspondingly and then disseminated through power domain NOMA transmission. After executing NOMA decoding independently and locally, the destination users can share multicast data through D2D links. The joint tier slicing and power control problem is considered in this paper to minimize the overall system power consumption in the new system while satisfying that all users can successfully receive all multicast data. Extensive simulations are conducted and the superiority of the proposed new multicast system over traditional multicast system and another benchmark multicast system based on orthogonal multiple access (OMA) and D2D-relay is verified.

Statistical signal transmission (SST) is one of rising technologies, which can deliver extra data through regulated periodic statistical features of ordinary signals without additional bandwidth cost. Existing SST technologies always work on orthogonal multiple access (OMA)-based systems. Nonetheless, non-orthogonal multiple access (NOMA)-based systems have now become a prevailing trend for future communications; while researches on whether SST can work upon NOMA-based systems is still lacking in literature. Motivated by this circumstance, in this paper we probe into ways of integrating SST and NOMA systems. To accomplish such a target, we successively propose the transceiver architecture, analyze the feasibility, and design a dedicated transmission strategy for NOMA-based SST system. Simulation results demonstrate that SST can work well in NOMA systems. Meanwhile, it is also revealed that detection accuracy in SST domain can be further enhanced under dedicated transmission strategy, while it does not affect detection performance in frequency domain.

Spectrum sensing is an important tool to resolve coexistence issue and optimize spectrum efficiency for Internet of things (IoT) systems. Nonetheless, IoT applications in fifth generation (5G) communications involve complicated scenarios with unstable channel conditions, which impedes spectrum sensing performance and will further hinder the spectrum efficiency of entire IoT systems. Motivated by such a circumstance, this paper proposes an Automatic Repeat Sensing (ARS) mechanism. The proposed mechanism exploits the difference degree of sensing results among multiple sensing antennas, and executes repeat-sensing if such a degree satisfies the given requirement. Accordingly, the negative impact of unstable channel conditions can be alleviated. Throughout the paper, we provide the concept and working principle for ARS, and derive the false-alarm probability under ARS mechanism. Numerical results manifest that ARS mechanism can markedly improve the sensing accuracy over other sensing mechanisms. Moreover, ARS mechanism has excellent flexibility and extensibility: its rules and configurations can be freely designed according to practical requirements.

Device-to-device (D2D) communication is one of the key technologies for the collaborative heterogeneous networks towards the fifth generation (5G) ecosystem. Among D2D techniques, D2D-relay, which combines the advantages of both D2D and relay technology, is a promising tool to enhance performance of communication systems. In this paper, we propose a stable and proportional fair user pairing algorithm for D2D-relay systems. The proposed algorithm adopts the concept in symmetric pairing theory, which is verified to be absolutely stable. Moreover, we also proved that the proposed algorithm is Pareto optimal in system performance. Numerical results demonstrate that the proposed algorithm distinctly outperforms the traditional user pairing methods in both energy efficiency (EE) and proportional fairness (PF) aspects, while maintaining a comparable throughput performance with traditional pairing methods, which is attractive for practical applications.

In this paper, we investigate the joint user pairing and resource allocation (UP-RA) problem in uplink multi-user MIMO or virtual MIMO (V-MIMO) systems. Due to the practical transmission constraints, solving such a joint optimization problem is NP-hard. Therefore, we study this problem from a novel perspective, and propose a universal and efficient heuristic algorithm. First, we formulate the UP-RA problem as a special many-to-one matching problem with constraints. Different from most existing researches, we consider a universal scheduling problem, instead of a particular one. Then, we modify the classical deferred acceptance (DA) algorithm which is designed for the common many-to-one matching problem, and develop a lowcomplex heuristic algorithm called adapted deferred acceptance (ADA) algorithm for the UP-RA problem. Numerical results demonstrate that the ADA algorithm achieves robust and better performance compared with other representative algorithms in many scenarios.

Virtual multiple‐input multiple‐output (V‐MIMO) is an advanced technology in uplink systems, and a well‐designed user pairing and resource allocation scheme in V‐MIMO systems can effectively exploit multi‐user diversity to yield a significant performance gain. Recently, main attention has been devoted to the crucial user pairing issue with consideration of three key metrics, i.e. spectrum efficiency, energy efficiency and fairness. In this study, the authors investigate the unified tradeoff among all the three metrics for V‐MIMO systems with user pairing and resource allocation. They first formulate the three key metrics in a united form and derive a straightforward unified criterion based on the user‐specific preference to depict system performances. Through discussing several preference‐based cases, they observe that a portion of spectrum efficiency is considered redundantly. Thus, a modified unified criterion is proposed to remove the partial redundancy provided by spectrum efficiency contribution to the objective function. Based on the two unified criteria, they propose a user pairing scheme in flat fading channels and a joint user pairing and resource allocation scheme in frequency‐selective fading channels to balance the tradeoff flexibly. Simulation results show that, compared with existing schemes, the proposed scheme achieves a dynamical tradeoff among spectrum efficiency, energy efficiency and fairness.

Virtual multiple-input multiple-output (V-MIMO) is an effective way to achieve a diversity gain in uplink channels. A well-designed user pairing scheme in V-MIMO systems can exploit multiuser diversity to yield a significant performance gain. This paper investigates joint user pairing and power allocation problems in V-MIMO systems to maximize energy efficiency (EE) while guaranteeing spectrum efficiency (SE) under a total power consumption constraint. We formulate such a joint problem as a complex nonlinear optimization problem, which can be solved in two steps in M ×K V-MIMO systems, i.e., power allocation with known user group and joint user pairing and power allocation. A multi-level water-filling method is used, and an iterative algorithm based on the outputs from the first step is employed in the second step. Finally, we propose a joint user pairing and power allocation scheme to find a user group that can achieve the maximum EE with power allocation. Simulation results and performance comparisons illustrate improved performance of the proposed scheme. Compared to the existing user pairing approaches, the proposed scheme achieves a significantly high EE gain while guaranteeing SE of a V-MIMO system.

Device-to-device (D2D) communication has become one important part of next-generation mobile networks particularly due to the booming of proximity-based services, e.g. the ProSe standardized in LTE. However, D2D communications may create strong interference to nearby users that are sharing the same spectrum. Thus, interference management in a D2D-enabled cooperative network is critical. In this paper, the time reuse problem and its distributed solution in a D2D-enabled cooperative network are investigated. Even though the total number of possible time reuse patterns increases exponentially with the quantity of the users, the authors first show that the optimal network performance can be achieved by only activating a limited number of time reuse patterns. The effects of the incentive mechanism on the reuse pattern selection are also investigated. Meanwhile, the set of active reuse patterns are determined efficiently with the Frank-Wolfe method. For a specific set of active time reuse patterns, the authors further show that the optimal resource allocation problem can be formulated as one consensus-building problem. Based on the alternating-direction method of multipliers, one low-complexity algorithm is proposed to determine the optimal resource allocations in a distributed fashion. Numerical simulations are carried out to corroborate our designs.

Virtual MIMO (V-MIMO) is an advanced technology in uplink systems, and a well-designed user pairing scheme in V-MIMO systems can effectively exploit multi-user diversity to yield a significant performance gain. Recently, main attention has been devoted to the crucial user pairing issue with consideration of three key metrics, i.e., spectrum efficiency, energy efficiency and fairness. In this paper, we investigate the unified tradeoff among all the three metrics for V-MIMO systems with user pairing. We first formulate the three key metrics in a united form and derive a straightforward unified criterion (SUC) based on the user-specific preference to depict system performances. Through discussing several preference-based cases of the SUC, we observe that a portion of spectrum efficiency is considered redundantly. Thus, a modified unified criterion is proposed to remove the partial redundancy provided by spectrum efficiency contribution to the objective function. Based on the two unified criteria, we propose a user pairing scheme to balance the tradeoff flexibly. Simulation results show that, compared with existing user pairing schemes, the proposed scheme achieves a dynamical tradeoff among spectrum efficiency, energy efficiency and fairness.

The D2D-Relay mechanism, which integrates device-to- device (D2D) communications and relay technology, is promising in system performance improvement. This paper focuses on studying what kind of benefits the D2D-Relay mechanism could bring into a wireless communication system during the uplink transmission. To comprehensively analyze the system performance, we use a metric, which consists of the spectral efficiency (SE) and the energy efficiency (EE), as an indicator. With this criteria in mind, we theoretically describe the influence from users' preferences to the SE and the EE in detail. Noting that user pairing is an important part of this mechanism, we evaluate the performance of the D2D- Relay mechanism with three typical pairing algorithms based on different criteria. The simulation results conform to our derivations and reveal the performance gain brought by applying the D2D-Relay mechanism. In addition, with different configurations of the number of the users and their preferences to the SE and the EE, the most appropriate algorithm is suggested for the practical application.

This paper investigates the trade-off between throughput and energy consumption in heterogeneous networks with dual connectivity and backhaul delay. The problem of traffic splitting between the main eNB and secondary eNBs is formulated as a multi-objective optimization problem. Based on some observations, assumptions, and subsequent simplifications, two algorithms are devised. One targets throughput optimization (TO) by transmission delay minimization. The other one does energy savings (ES) by not transmitting on the main eNB depending on the delay allowance defined in the QoS criterion of the transmitted data. The main eNB is in control of the data transfer to the secondary eNB. Simulations show that the TO algorithm performs slightly better than an earlier published algorithm in which the secondary eNBs are in control of the data transfer from the main eNB. The ES algorithm does save energy at the expense of a larger transmission delay.

Heterogeneous network (HetNet) plays an important role in the upcoming 5G communications. A HetNet in futuristic communications is envisaged to provide full access data services, such that users will always have the best service with their available radio resources. To realize a full access HetNet, statistical signal transmission (SST) can be an important enabling technique. SST exploits statistical features of ordinary signals as a data transmission scheme to carry information data. Due to its inherent unique properties, SST signal is more robust than ordinary signals in conventional quasi-static channels. However, detection of SST signals in fast time-varying channels will face challenges. Motivated by this fact, this paper aims to analyze the impact of fast time-varying channels to SST, and reveals negative effects of burst of deep fading channel-state (BDFC) and feature dissipation with out-of-phase (FDOP). Accordingly, we design an automatic re-transmission scheme and a fragmental feature capturing method to mitigate the aforementioned issues, respectively. Based on these studies, we propose a channel condition aware detection scheme for SST, aiming to enhance SST detection performance in fast time-varying channels. Numerical results show that with the proposed scheme, a maximum gain of more than 10 dB can be obtained under either BDFC or FDOP condition. Furthermore, it is also demonstrated that the proposed scheme is robust with imperfect channel coherence time information, which is attractive for practical applications.

In this paper, we investigate the user pairing scheme in virtual multiple-input multiple-output (V- MIMO) systems, which can significantly improve system performance and spectral efficiency (SE) using multiuser diversity. Considering the development of green communication, we want to find the upper bound of energy efficiency (EE) under the constraint of SE. Firstly, the optimal transmit power allocation for the optimal EE under the constraint of SE is formulated as a non-convex optimization problem and a novel projective constraint analysis (PCA) is devised for it. Then, we reveal the maximum EE and the corresponding transmit power allocation in the global solution space. According to the location relationship between the maximum EE and the constraint of SE, we propose a spatial approach based on PCA to pursue the upper bound of EE with two different cases. Finally, a novel PCA-based user pairing scheme achieving the upper bound of EE under the constraint of SE is proposed. Numerical results illustrate the impact of different system parameters for the upper bound of EE and prove that the proposed PCA-based iteration algorithm has much lower complexity than the global search approach.

Due to the scarcity of spectrum resources, spectrum sensing techniques are vital for future wireless communication systems. Compared with energy detection methods (ED), feature detection (FD) methods exploit statistical periodicity of transmission signals and have better performance under low signal-to-noise ratio (SNR) conditions. However, for fast time-varying fading channels, the detection accuracy of FD methods will be severely affected when the channel coherence time is shorter than the sensing observation window. In this paper, we analyze the negative impact of fast time-varying channels on the sensing performance of traditional FD sensing methods, and discover the effect of channel-state diversity. Based on this finding, we propose a novel FD sensing algorithm, named “Spectrum Sensing with Channel-state Diversity (SSCD)”. The SSCD algorithm can effectively capture and accumulate the targeted statistical features of primary users from different phases, whereby it is suitable for sensing issues under fast time-varying channels. Simulation results show that for a given probability of false-alarm, the proposed SSCD algorithm significantly outperforms traditional FD with fixed window (FDFW): a detection probability enhancement of 60% is observed under the scenario of sensing window L = 10, coherence time Tc= 2× OFDM symbol length, and SNR= -10 dB. Furthermore, this performance gain is expected to further increase with a faster channel varying rate.

Abstract The two‐way relay (TWR) protocols are efficient in providing appreciable throughput gains in wireless networks through the use of network coding to combine packets from multiple channels. The key determinant factor in driving the throughput improvement is the degree of simultaneity achieved in the relay scheme. In this paper, we propose a new TWR protocol named interference cancellation TWR (IC‐TWR), which combines network coding, spatial diversity, and IC techniques to arrive at high degree of simultaneity and in the meanwhile to relax the requirement on channel state information as compared with TWR schemes based on amplify‐and‐forward. Numerical analysis shows that the proposed IC‐TWR is uniformly advantageous over the traditional decode‐and‐forward scheme in terms of system throughput and end‐to‐end delay. The proposed scheme may be useful for system designers of high‐speed multimedia applications in wireless mobile networks, wireless cellular networks, wireless sensor networks, and so on. Copyright © 2015 John Wiley & Sons, Ltd.

Based on orthogonal frequency division multiplexing (OFDM) systems, an additional individual data transmission can be established in statistical spectrum domain, which can be adopted for specific applications. However, the performance of existing detection methods for this kind of transmission will be severely restricted when the channel coefficients are dynamic within each observation window. In this paper, we propose an adaptive detection method for statistical signal transmission. This method has adequate adaptability for different channel varying rates, while maintaining low complexity. Simulation results show that the proposed method achieves significant performance gain compared to the original method under time varying channels.

User‐specific network controlled device‐to‐device (D2D) communication is a promising method to improve the system performance of the future cellular networks. It is a challenging task to realise the optimal link adaptation for D2D reliable multicast because of the nature of wireless radio channels. In this study, network coding is employed to enhance D2D multicast by introducing the characteristic that different information network‐coded into a single multicast packet is destined to different receivers. A user‐specific bit mapping algorithm is then proposed to make different information having a different equivalent coding rate before network‐coded. A corresponding user‐specific link adaptation scheme is proposed to adaptively choose an optimal modulation and coding scheme (MCS) for D2D multicast. Furthermore, the receiver detection procedure is then derived to recover the expected information for different users. As a result, different users obtain their respective information from a single network‐coded multicast packet with different equivalent MCS according to the respective channel quality from the transmitter to them. Numerical results show that the user‐specific link adaptation scheme can improve the capacity performance of network controlled D2D multicast. The throughput gain varies from 13 to 45% in different channel quality scenarios.

This paper describes a device triggering optimization technique for controlling system overload when deploying massive Machine Type Communication (MTC) devices in 3GPP-based cellular networks. Triggering a large number of MTC devices can dramatically overload the underlying transport network system and incur congestion in both the Radio Access Network (RAN) and the Evolved Packet Core (EPC). The proposed solution aims at controlling the rate of device trigger requests that MTC servers can generate in order to reduce the system overload. For this purpose, we propose that the Mobility Management Entity (MME), or an alike core network node, computes the device trigger rate that alleviates congestion, and communicates this value to the MTC-Interworking Function (MTC-IWF) element that enforces MTC traffic control, via admission control or data aggregation, on the device trigger request rate received from the different MTC servers. The proposed solution is evaluated through computer simulations and encouraging results are obtained.

The energy consumption in radio access network is expected to increase significantly as the network expends to fulfill the explosively growing data traffics. However, when evaluating the energy saving mechanisms, the impact on other performance metrics such as spectral efficiency, should also be taken into account. In this paper, based on the multiple component carrier (CC) feature specified in Long Term Evolution-Advanced (LTE-A) systems, an energy-efficient coordinated scheduling mechanism is proposed to reduce the energy consumption in cellular networks by dynamically switching off CCs and base stations (BS) according to load variations, with special attention on the switching off order and BS transmit power adjustment to maintain service continuity of downlink users. Both the energy and spectral efficiency of the system under the proposed scheduling mechanism are analyzed. Simulation results show that when data traffics are below a quarter of the system capacity, with the proposed scheme more than half of the network power consumption can be saved, while the requirements for user data rate, service continuity, network coverage and spectral efficiency are guaranteed.

The Cyclic Delay Diversity (CDD)-induced cyclostationary signature is considered to be a robust and cost-efficient scheme for self-coordination of Cognitive Radio Network (CRN). However, the performance of network coordination relies on the reliable detection of such cyclostationary signatures. In this paper, we deduce an exact covariance matrix to characterize the statistics of cyclostationary signature. Based on the covariance matrix, we propose an Asymptotically Optimal Likelihood (AOL) detector for the test of the CDD-induced cyclostationary signature. In addition, an Asymptotically Maximum Likelihood Probability (AMLP) criterion is provided to solve the multiple signatures identification issue. Comprehensive simulations verify that the proposed detector provides superior performance in detection probability and observation duration, compared with the existing Constant False Alarm Rate (CFAR) detector.

Employing user pairing schemes in uplink virtual multiple-input multiple-output (V-MIMO) systems can significantly improve system capacity owing to multi-user diversity. Most of the existing user pairing algorithms were developed under the assumption that a scheduler always possesses the perfect channel state information (CSI). In this paper, taking into account the effects of channel estimation error (CEE), the authors propose a robust user pairing algorithm based on a closed-form average sum rate under CEE. In addition, the authors further propose an adaptive user selection scheme as an effort to maximise the sum rates owing to the rate constraint under CEE. The gain of the proposed method is shown in simulation results.

Fourth generation standards will provide interoperability between different wireless access technologies. This will be partly enabled by Vertical Handoff (VHO) which ensures a continuation of service when the access technology is switched, e.g. from a Wireless Local Area Network (WLAN) to a Cellular Network (CN) and vice versa. In this paper, we propose a VHO decision algorithm based on scalar Kalman filtering. Criteria such as probability of false handoff, number and position of handoffs are used to evaluate and compare our work with existing handoff algorithms based on filtering techniques. Our algorithm is shown to limit by 40 percent, in a worst case scenario, the probability of false handoff when compared to other HO algorithms.

Cyclostationarity based sensing methods are appealing for OFDM signals under low SNR areas. However, such methods are sensitive to doubly-selective fading channels. In this paper we develop a new sensing method by exploiting the cyclostationarity incurred by the spread of autocorrelation due to the Doppler effect in doubly-selective fading channels. First, we model the doubly-selective fading channel with basis expansion model (BEM). Then we analyze the relationship between cyclostationary statistics of transmitted and received OFDM signals, hence derive the cyclostationary statistics of the received signal. Based on the new cyclostationary signatures of the received OFDM signal spread by the Doppler effect, more suitable cyclic frequencies can be chosen to detect cyclostationary features when signal experiences doubly-selective fading. Simulation results demonstrate that the proposed sensing method provides substantial improvement on detection performance.

High energy consumption at mobile devices is a critical issue for end users to access and enjoy high data-rate multimedia applications and services in Wireless Local Area Networks (WLANs). This paper develops an analytical framework for analyzing and comparing the energy efficiency performance of IEEE 802.11n Medium Access Control (MAC) protocol and a Busy Tone Based Cooperative MAC Protocol(namely BTAC) in multi-rate WLANs. Our proposed analytical model considers the impact of dynamic radio channel conditions and multi-rate transmission scenarios. Analytical and simulation results show that BTAC can achieve up to 50% energy saving, comparing to the IEEE 802.11n MAC protocol, under different radio channel conditions, network sizes and traffic loads.

The man-induced cyclostationary signatures can provide a robust mechanism for the self-coordination of cognitive radio networks. However, such artificial signatures incur signaling overhead and come at the bandwidth cost. In this paper, we show intrinsic cyclostationary signatures in the Orthogonal Frequency Division Multiplexing (OFDM) system with Cyclic Delay Diversity (CDD). The standard conformable CDD technique is initially motivated by the objective for exploiting spatial diversity. Significantly, the underlying periodicity of CDD can simultaneously induce advantageous cyclostationary signatures without any signaling overhead. The lag-indices of the CDD- induced signatures are uniquely determined by the assigned amount of cyclic delay. Consequently each CDD-OFDM system can be identified by a pre-assigned cyclic delay. The signed system can be easily and robustly recognized through cyclostationary detection. Furthermore, the CDD-OFDM systems still preserve the cyclic-prefix induced cyclostationarity as primitive OFDM. By exploiting the overall cyclostationarity, we present a desirable cyclostationarity detector with asymptotical constant false alarm rate for spectrum sensing. Comprehensive simulations are also given to show the performance improvement.

The carrier frequency offset (CFO) could destroy the orthogonality of OFDM subcarriers and induce the inter-carrier interference (ICI) spread. However, the generalized multi-carrier (GMC) system reflects different interference characters in the presence of CFO. In this paper, we exactly analyze the CFO effect on GMC, which multiplexes several single band single carrier based frequency division multiple access (SC-FDMA) streams in frequency domain, under the scenario of additive white Gaussian noise (AWGN) channels. A closed-form expression of signal to interference plus noise ratio (SINR) is derived for the GMC systems in the presence of CFO. Furthermore, the bit error rate (BER) performance analysis is also performed in comparison with the traditional OFDM system for both uncoded and coded environments. Analysis and simulation results show that the feature procedure of discrete Fourier transform (DFT) spread may condense the CFO-induced interference in the first symbol of each subband, resulting in less interference in other symbols. Such condensing procedure can provide robustness against the CFO effect, which also lies on the subband bandwidth for GMC systems. In contrast to the conventional OFDM system, GMC exhibits more robustness when each subband width is designed for 4 times larger than the subcarrier spacing.

In this paper, we propose a new medium access control (MAC) protocol, called cooperative access with relay's data (CARD) for multi-rate wireless local area networks (WLANs). CARD allows remote nodes to transmit their information at a higher data rate to access point (AP) by using intermediate nodes as relays. Particularly, under CARD, a relay node sends its own data packet after forwarding a packet from the original source node, thus to improve system throughput. Analytical and simulation results show that the CARD protocol can effectively improve system throughput and reduce service delay.

In this paper, we propose a null space-based precoding scheme for secondary transmission in a cognitive radio multiple-input multiple-output (CR-MIMO) network under the assumption that time-division-duplex is employed by the primary transmission. First, the secondary transmitter periodically senses the transmitted signals from the primary users and estimates the corresponding covariance matrix. Then, subspace techniques are utilized to estimate the noise subspace of this covariance matrix, and the dimension of the noise subspace is estimated using information theory criteria (AIC or MDL). Finally, the obtained null space is used as the precoding matrix for the secondary transmission, which effectively avoids the interference induced by the secondary transmission at the primary users. Moreover, the achievable capacity of the secondary MIMO channel by the proposed scheme is derived. Simulations are performed to show the efficacy of the proposed scheme.

The concept of cooperative communication has been proposed to improve link capacity, transmission reliability and network coverage in multiuser wireless communication networks. Different from conventional point-to-point and point-to-multipoint communications, cooperative communication allows multiple users or nodes in a wireless network to coordinate their packet transmissions and share each other's resources, thus achieving cooperative diversity. In this paper, we analyze the throughput performance of a cooperative MAC protocol called Cooperative Access with Relay's Data (CARD) under unsaturated traffic conditions in wireless local area networks (WLANs). A Markov chain model is developed to derive the analytical results which are verified by extensive computer simulations.

The aim of this paper is to provide an energy efficiency analysis of the Cooperative Access with Relay's Data for Multi-rate wireless local area networks (WLANs), named CARD protocol at the Medium Access Control (MAC) layer taking into account the impact of both transmission channel states and multi-rate transmission. Simulation results closely match the analytical results confirming the effectiveness of the proposed model.

In this paper, we analyze the throughput performance of a new access protocol, called Cooperative Access with Relay's Data (CARD) for multi-rate wireless local area networks (WLANs). CARD allows remote source nodes to transmit their information at a higher data rate to Access Point (AP) by using intermediate nodes as relays, particularly, a relay node sends its own data packet after forwarding a packet from the original source node, thus to improve system throughput. We also develop a Markov chain model to capture channel conditions. Our analysis considers both ideal and error-prone channel conditions. Analytical results are verified by computer simulations and show significance improvement in the system throughput.

Cooperative relay is an effective means to improve spectral efficiency in wireless networks. In this paper, we propose a cooperative relay network protocol based on adaptive cooperative coding (ACC), in which the level of cooperation (LoC) is made adaptive to follow the variation of channel propagation conditions. We derive the strict performance upper bound and asymptotic bound expressions for the ACC protocol. Moreover, we develop a sub-optimal power allocation algorithm based on the derived asymptotic bound to further improve the performance of the ACC protocol. The optimization is achieved in two scenarios based on different link channel qualities. The cooperative diversity gain and power allocation gain of the ACC protocol are evaluated and their numerical results are provided.

The cooperative transmission is an efficient way in the wireless network which benefits from the spacial diversity. In this paper, we introduce and analyze a novel three-node relay protocol with high transmit efficiency, termed as the Adaptive Cooperative Coding (ACC). This technique is implemented by transmitting the rate-compatible punctured convolutional code (RCPC code) sequence and its complementary punctured convolutional (CPC) sequence during the two-hop transmission. Compared with the conventional cooperative transmission, the ACC scheme remarkably improves the achievable data rate and is adjustable to variable channel conditions. Furthermore, we deduce the theoretical upper bound of the ACC protocol, from which the lowest packet error probability can be achieved by selecting a proper coding rate for the first hop.

In this letter, we study the user pairing algorithms for uplink virtual multiple-input multiple-output (V-MIMO) systems, and propose a new double proportional fair (D-PF) algorithm for user pairing of the uplink V-MIMO systems. The proposed D-PF algorithm uses the PF criterion to decide the first user, and a modified PF criterion is suggested to choose the pairing user. Simulation results show that the proposed D- PF algorithm outperforms the conventional determinant formula (DF) algorithm in terms of both throughput and fairness performances. Moreover, if compared with the conventional single PF (S-PF) algorithm, the proposed D-PF algorithm achieves a fairly good trade-off between the throughput and fairness, being able to trade with 5% throughput loss for 15% fairness enhancement.

Random access channel (RACH) is usually used for initial channel access, bandwidth request, etc. In this paper, we analyze the throughput and access delay performance of the RACH in an orthogonal frequency division multiple access (OFDMA) system. A closed-form expression of the RACH throughput is presented and its mean access delays under both binary exponential and uniform backoff policies have also been derived. Moreover, to further improve the utilization of RACH in an OFDMA system, we propose a novel message transmission scheme in a shared RACH. The message in the proposed scheme can be sent implicitly in the cyclic-shifted preambles which make the transmission much more reliable in a contention based RACH. Meanwhile, performance of the preamble detection has also been improved by the redundant information conveyed in messages. Finally, simulation results will demonstrate performance gains of the proposed scheme.

This paper, patent pending, put forwards a novel low complexity channel estimator for a special case of comb-type pilot pattern with pilot interval I = 2, which can combat the effect of residual timing offset on interpolation-based channel estimation. The channel estimation algorithm based on comb-type pilots is divided into pilot signal estimation and channel interpolation. The pilot subcarriers estimation can be based on least square (LS) or minimum mean square error (MMSE) algorithms which can achieve better performance than LS at the cost of high complexity. Furthermore MMSE need to know in advance the priori knowledge of noise variance and channel covariance. So considering engineering implementation, LS algorithm for pilot subcarriers estimation and linear interpolation for non-pilot subcarriers estimation are adopted in this paper. Then the effect of residual timing offset on such channel estimator is analyzed. Based on the unique characteristics of the special pilot pattern focused by this paper, a quite simple correction method is put forward. And simulation results show that the proposed method can effectively improve the system performance when existing residual timing offset.

Sphere decoding (SD) has been proposed as an efficient algorithm in digital communications. Its complexity is polynomial for both an infinite and finite lattice which makes the real-time implementation of a maximum-likelihood (ML) decoder feasible. The choice of an initial radius for the SD has a great impact on the trade-off between the performance and complexity. In this paper, we present a new initial radius selection scheme for the SD based on the constellation modulation type and the target bit error rate (BER). The proposed scheme accurately define an initial radius of the SD, which reduces the radius redundancy and ensures that at least one lattice point is inside a sphere. Complexity analysis shows that the proposed scheme has much lower computational complexity than that of the conventional one with zero decoding failure rates meanwhile ensures no performance loss.

A channel estimation method based on sample-shift of time-domain signal is proposed for OFDM systems. Through artificial cyclic sample-shift of time-domain signal, the equivalent coherent bandwidth of OFDM systems can be broadened. Hence, the channel estimation errors, caused by the simple linear interpolation in frequency-domain, can be reduced due to the increase of correlation between neighboring pilot sub-carriers. The theoretical analysis and simulation results show that equivalent channel coherent bandwidth can be broadened effectively and the channel estimation performance can be improved by sample-shift significantly, especially over high SNR range. When the value of cyclic sample-shift is given as the sampling-interval normalized RMS of channel, the near optimal performance can be achieved.

Channel quality indicator (CQI) is essential for the transmitter to select the proper modulation and coding scheme (MCS). However, the feedback of the CQI also decreases the spectrum efficiency. This paper proposes a reduced CQI feedback signaling for the multi-stream downlink multiple-input multiple-output (MIMO) system. In addition, power loading algorithms are applied to further increase the throughput of the system. The proposed scheme decreases the overhead of the CQI feedback remarkably, while achieves good throughput performance.

Orthogonal frequency division multiplex (OFDM) has become one of the most popular air-link technologies for future broadband wireless communications. To further improve its bandwidth efficiency and system performance, adaptive resource allocation and smart antenna techniques have been widely used in the OFDM system. However, the use of fully adaptive beamforming in an OFDM system significantly increases the complexity of the medium access control layer design and thus affects the implementation of adaptive resource allocation. A novel cross-layer adaptive resource allocation strategy with hybrid adaptive array and switched-beam smart antennas suitable for the OFDM systems has been proposed. With the help of different smart antennas schemes based on different users' quality of service requirements, the strategy effectively reduces the complexity of adaptive resource allocation in an OFDM system, while still maintaining a satisfactory system performance.

The adaptive transmission mode selection (ATMS) scheme increases the spectrum efficiency and the flexibility of radio resource management for the distributed wireless communication systems (DWCS). In this paper, different optimal regions are planned for the adaptively selected transmission modes, based on the received signal to noise ratio (SNR) from each access point (AP) and the required quality-of-service (QoS) of the mobile stations (MS).

A cumulant-based joint angle-frequency estimation (CB-JAFE) algorithm for angle-frequency parameter estimation of independent nonGaussian sources is proposed. The proposed CB-JAFE algorithm, which utilizes the fourth-order cumulants of the spatio-temporal data, can effectively suppress the spatially and temporally correlated Gaussian noise with unknown correlation structure. Simulation results are carried out to demonstrate the effectiveness of the proposed algorithm compared with the JAFE algorithm.

A DFT spread generalized multi-carrier (GMC) based frequency division multiple access (FDMA) scheme, i.e. DFT-S-GMC, is proposed for uplink transmission of wideband mobile communication systems. The scheme achieves spreading gains in the frequency-domain by discrete Fourier transform (DFT), and the frequency division multiplexing (FDM) and FDMA by inverse filter-bank transform (IFBT). By theoretical analysis and simulation results, it is shown that the proposed scheme has good peak-to-average power ratio (PAPR) performance and is more robust to the multi-access interference (MAI) than the existed main stream uplink schemes, e.g. OFDMA and DFT-S-OFDMA scheme

In order to increase the bandwidth efficiency and further improve the performance of orthogonal frequency division multiplex (OFDM) systems, adaptive resource allocation and smart antennas techniques are widely applied. However, the employment of fully adaptive beamforming in physical layer for OFDM systems significantly raises the issues in medium access control (MAC) layer, hence affects the implementation of adaptive resource allocation. From a cross-layer approach, we propose a new adaptive resource allocation strategy with hybrid smart antennas for OFDM systems. By applying different smart antennas schemes according to the different quality of service (QoS) requirement of different users, the new strategy reaches a good trade-off between the complexity and the performance of OFDM systems.

Abstract Digital beamforming (DBF) techniques are capable of improving the performance of communications systems significantly. However, if the transmitted signals are conflicted with strong interference especially in the direction of the transmitted beams, these directional jamming signals will severely degrade the system performance. In order to efficiently mitigate the interference of the directional jammers, in this contribution a beam‐hopping (BH) communications scheme is studied. In the BH communications scheme, only one pair of the beams is used for transmission and it hops from one to the next according to an assigned BH pattern. In this contribution, a range of expressions in terms of the average signal to interference plus noise ratio (SINR) performance have been derived, when both the uplink and downlink are considered. The average SINR performance of the BH scheme and that of the conventional single‐beam (SB) as well as multiple‐beam (MB) assisted beam‐processing schemes have been investigated. Our analysis and results show that the BH scheme is capable of efficiently combating the directional jamming, with the aid of utilizing the directional gain of the beams generated by both the transmitter and the receiver. Furthermore, the BH scheme is capable of reducing the intercept probability of the communications. Therefore, the BH scheme is suitable for communications when several distributed antenna arrays are available around a mobile. Copyright © 2005 John Wiley & Sons, Ltd.

Digital beamforming (DBF) techniques improve the performance of communications systems significantly. However, if the communications are strongly jammed in the direction of the transmitted beam, the directional jamming will severely degrade the performance of the systems. In order to solve this problem, the paper proposes a beam hopping (BH) communications scheme. The analysis results of the average SINK performance show that compared with the conventional single beam (SB) and multibeam (MB) communications scheme, the new BH communications scheme is capable of combating the directional jamming efficiently, while fully utilizes the directional gain of the beams of the transmitters and the receivers. Moreover, the BH scheme can reduce the intercept probability of the communications to some degree. Therefore, the BH scheme is a promising communications scheme with a good performance-cost trade-off.

We propose a switched-beam smart antennas scheme which is based on the 2-dimensional searching of beams and channels, and analyze the signal to interference (SIR) performance of the system uplink when employing the scheme. The proposed scheme has a better performance compared with the traditional scheme of 1-dimensional singular searching of the beams and channels. It accords with the development of smart antennas in future mobile communication systems.