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

In this paper, a novel network architecture that deploys the multi-functional reconfigurable intelligent surface (MFRIS) in low-Earth orbit (LEO) is proposed. Unlike traditional RIS with only signal reflection capability, the MF-RIS can reflect, refract, and amplify signals, as well as harvest energy from wireless signals. Given the high energy demands in shadow regions where solar energy is unavailable, MF-RIS is deployed on LEO to enhance signal coverage and improve energy efficiency (EE). To address this, we formulate a long-term EE optimization problem by determining the optimal parameters of MF-RIS, including amplification, phase-shifts, energy harvesting ratios, and LEO transmit beamforming. To address the complex non-convex and non-linear problem, a federated learning enhanced multi-agent deep deterministic policy gradient (FEMAD) scheme is designed. Multi-agent deep deterministic policy gradient (DDPG) of each agent can provide the optimal action policy from its interaction with environments, whereas federated learning enables the hidden information exchange among multi-agents. In numerical results, we can observe significant EE improvements compared to the other benchmarks, including centralized deep reinforcement learning as well as distributed multi-agent DDPG. Additionally, the proposed LEO-MF-RIS architecture has demonstrated its effectiveness, achieving the highest EE performance compared to the scenarios of fixed/no energy harvesting in MF-RIS, traditional reflection-only RIS, and deployment without RISs/MF-RISs.

Infrastructure-assisted autonomous driving has emerged as a pivotal technology to overcome the challenges posed by occlusions and limited fields of view for individual vehicles. Vehicles can fuse perception information from the infrastructure with their own in real-time, thereby enhancing their perception ability. However, our real-world experiments demonstrate that such an approach could introduce artifacts such as ghost objects, resulting in unsafe and unreliable planning outcomes. Besides, the system integration complexity and communication overhead are typically considerable, posing challenges to practical deployment. Therefore, we propose VI-Planning, an innovative infrastructure-assisted system that effectively optimizes autonomous vehicle planning in real time. The core idea of VI-Planning is to leverage the scene-level future occupancy grid maps constructed by the infrastructure as future drivable area references to directly optimize planning outcomes of autonomous vehicles. Since VI-Planning operates only at the autonomous vehicle's final output stage, without modifying the vehicle's underlying system architecture, it can be plug-and-play for most autonomous driving systems, whether they are modular or end-to-end architectures. Moreover, VI-Planning employs a novel bitwise encoding mechanism to efficiently compress these maps, enabling practical transmission. We implement VI-Planning end-to-end on a real-world testbed. The results of closed-loop and open-loop experiments indicate that VI-Planning can achieve real-time planning optimization (62.54 ms on average) and 817 × data transmission efficiency compared to the state-of-the-art baseline. A video demo of VI-Planning on our real-world testbed is available at: https://youtu.be/DXl5BhDEvFQ.

We propose VI-Planning, an innovative system that leverages the scene-level future occupancy grid maps predicted by the infrastructure as future drivable area references to directly optimize the planning trajectories of autonomous vehicles in real time. Since VI-Planning operates only at the autonomous vehicle's final output stage, without modifying the underlying system architecture, it can be plug-and-play for most autonomous driving systems. Moreover, VI-Planning employs a novel bitwise encoding mechanism to efficiently compress these maps, enabling practical transmission. The experimental results demonstrate that VI-Planning can achieve real-time planning optimization with extremely low bandwidth consumption, significantly enhancing the driving safety of autonomous systems. The source code and video demonstration of VI-Planning are available on GitHub: https://github.com/YANG-Deep/VI-Planning.

We propose VI-Planning, an innovative system that leverages the scene-level future occupancy grid maps predicted by the infrastructure as future drivable area references to directly optimize the planning trajectories of autonomous vehicles in real time. Since VI-Planning operates only at the autonomous vehicle's final output stage, without modifying the underlying system architecture, it can be plug-and-play for most autonomous driving systems. Moreover, VI-Planning employs a novel bitwise encoding mechanism to efficiently compress these maps, enabling practical transmission. The experimental results demonstrate that VI-Planning can achieve real-time planning optimization with extremely low bandwidth consumption, significantly enhancing the driving safety of autonomous systems. The source code and video demonstration of VI-Planning are available on GitHub: https://github.com/YANG-Deep/VI-Planning.

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

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

LiDAR-based localization plays a critical role in autonomous driving and robotic navigation. However, traditional methods rely heavily on constructing high-precision point cloud maps, which is both time-consuming and labor-intensive. To address this, we propose an innovative localization approach that eliminates the need for point cloud maps by leveraging LiDAR and position-encoded landmarks. Our method encodes positional information into the shape of specially designed landmarks, strategically deployed in the environment. Subsequently, we fully leverage the advantages of LiDAR in accurately measuring distances and capturing the spatial structures of objects to detect and recognize the landmarks in the environment. By decoding the positional information embedded in the landmarks, precise vehicle localization is achieved. To overcome the limited information capacity of individual landmarks due to LiDAR’s reduced accuracy at long distances, we integrate multiple landmarks in a collaborative manner. By combining their encoded information and spatial relationships, we achieve high-precision localization without relying on point cloud maps. Experiments in CARLA and Autoware.AI simulators validate the effectiveness of our approach, offering a novel solution for LiDAR-based localization.

A mixed-criticality (MC) system is a computational platform shared by tasks with two or more safety-critical levels. An important research topic related to MC systems is designing scheduling algorithms that can satisfy the computation requirements of tasks with different criticality levels. Numerous studies have focused on this topic, but only a few have considered parallel tasks. To address the research gap, we propose a dual-criticality scheduling algorithm based on federated scheduling for parallel tasks with Directed Acyclic Graph (DAG) structures. We particularly focus on the task set in which each task has a deadline longer than its release period. To the best of our knowledge, our work is the first that does not assume the constrained-or implicit-deadline in the MC DAG task model. In addition to simulation experiments, we demonstrate that our algorithm has a capacity augmentation bound of 4, providing a quantitative worst-case performance guarantee for our algorithm.

Low Earth orbit (LEO) has a significant potential to provide ubiquitous global coverage with high capacity data services in sixth generation (6G) wireless networks. Due to the denser deployment of LEO satellites, it becomes mandatory to mitigate the interference induced by LEO beams. The high mobility of LEOs further stirs up a complex interference scenario different from conventional terrestrial networks. Therefore, we conceive a multi-LEO constellation that incorporates multi-beamforming and handover by using only the information of signal-to-interference-plus-noise ratio (SINR) and beam indexes. In this paper, we propose a joint LEO handover and fast beam switching (HOBS) algorithm that performs handover, beam search, and beam/power resource allocation. Our goal is to maximize energy efficiency (EE) while satisfying the SINR requirement of each user. We evaluate our proposed HOBS scheme in terms of different user densities, time frame sizes, and beam-sweeping schemes. Benefiting from a comparatively smaller beam search space, HOBS is capable of providing lower latency, as well as higher SINR and EE compared to conventional exhaustive beam search and fixed power control.

This work attempts to minimize power consumption for analog beamforming with non-ideal power amplifier (PA) models under the constraints of a per-PA maximum power and a user target rate in low earth orbit (LEO) satellite communications. We propose a majorization-minimization (MM) scheme, utilize a surrogate function to transform the problem into a second-order cone programming (SOCP) one, and derive an interference-free analog precoder. Simulation results show that the proposed scheme converges rapidly and tends to either pull the PAs on the antenna array to the maximum power level or directly turn off the PAs instead of staying in the middle power levels. This observation together with the proposed scheme may be used to simplify the PA design in practice, since the middle power levels are seldom visited and there is no necessity to design a great number of middle power stages.

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

This work proposes beamforming solutions to minimize system bit error rate (BER) for low earth orbit (LEO) satellite communications with non-orthogonal multiple access (NOMA). Designing beamformers to minimize BER in NOMA schemes is a nonlinear problem. We resort to the interior-point method to solve this problem, which can significantly improve the BER performance. To reduce computational complexity, we further propose another beamforming solution based on the projected gradient descent (PGD) scheme, which not only achieves comparable performance as the interior-point method but also greatly reduces the computational complexity. We evaluate the proposed beamforming schemes in a designed LEO satellite scenario. Simulation shows that the proposed system significantly outperforms conventional schemes in terms of BER.

This paper introduces a novel mathematical model for Molecular Communication (MC) systems, utilizing First Arrival Position (FAP) as a fundamental mode of information transmission. We address two critical challenges: the characterization of FAP density and the establishment of capacity bounds for channels with vertically-drifted FAP. Our method relate macroscopic Partial Differential Equation (PDE) models to microscopic Stochastic Differential Equation (SDE) models, resulting in a precise expression that links FAP density with elliptic-type Green’s function. This formula is distinguished by its wide applicability across any spatial dimensions, any drift directions, and various receiver geometries. We demonstrate the practicality of our model through case studies: 2D and 3D planar receivers. The accuracy of our formula is also validated by particle-based simulations. Advancing further, the explicit FAP density forms enable us to establish closed-form upper and lower bounds for the capacity of vertically-drifted FAP channels under a second-moment constraint, significantly advancing the understanding of FAP channels in MC systems.

Low-Earth orbit (LEO) satellites have been prosperously deployed for various Earth observation missions due to its capability of collecting a large amount of image or sensor data. However, traditionally, the data training process is performed in the terrestrial cloud server, which leads to a high transmission overhead. With the recent development of LEO, it is more imperative to provide ultra-dense LEO constellation with enhanced on-board computation capability. Benefited from it, we have proposed a collaborative federated learning for low Earth orbit (FELLO). We allocate the entire process on LEOs with low payload inter-satellite transmissions, whilst the low-delay terrestrial gateway server (GS) only takes care for initial signal controlling. The GS initially selects an LEO server, whereas its LEO clients are all determined by clustering mechanism and communication capability through the optical inter-satellite links (ISLs). The re-clustering of changing LEO server will be executed once with low communication quality of FELLO. In the simulations, we have numerically analyzed the proposed FELLO under practical Walker-based LEO constellation configurations along with MNIST training dataset for classification mission. The proposed FELLO outperforms the conventional centralized and distributed architectures with higher classification accuracy as well as comparably lower latency of joint communication and computing.

Multi-sensor data fusion is crucial for modern autonomous systems to accurately perceive their surrounding environments and make intelligent decisions. However, as different sensor sources may have significant time disparity, it is necessary to synchronize their data before sending them to the fusion algorithm, in order to control such differences and get meaningful fusion results. This paper discusses the message synchronization policy in ROS, a popular framework for robotic systems. The ROS message synchronization policy has proven to be highly effective in reducing the time disparity, but it introduces a certain level of latency. Therefore, to use it for real-time systems, it is essential to establish an upper bound for the worst-case latency that may occur. Specifically, we analyze two key latency metrics of the ROS message synchronization policy, the passing latency and reaction latency, which are needed to analyze the end-to-end delay and reaction time on the system level. We conduct experiments under different settings to evaluate the precision of our proposed latency upper bounds against the maximum observed latency in real execution.

Vehicular fog-edge computing (VFEC) is a new paradigm that has significant advantages in expanding the resource capacity of mobile edge computing (MEC) servers in vehicular networks. This work studies an incentive-based vehicular fog-edge computational offloading, and resource allocation schemes for vehicular networks using 5G-NR V2X communications. Here, smart vehicles in a given area are assumed to have computationally intensive tasks which can offload to the roadside unit (RSU) with a MEC server. To expand the resource capacity of the MEC server, in this work, we propose an incentive-based vehicular fog-edge computational offloading, and resource allocation scheme to maximize the unified utility function of the system with delay and incentive satisfaction of the entities under deadline and incentive constraints. This results in the mixed integer non-linear programming problem that is solved using continuous relaxation and an alternating optimization between convex sub-problems. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

The development of vehicular-to-everything (V2X) communications has led to the emergence of collective perception service (CPS), a concept that seeks to expand the limited horizon of individual vehicles’ perception. Through CPS, vehicles exchange their detected object’s information namely with the neigh-boring vehicles through collective perception messages (CPMs). However, conventional CPS approaches are limited in their ability to enhance perception, as they only allow for the sharing of CPMs among one-hop neighbor peers. To overcome this limitation, we propose a CPS scheme that employs a relay mechanism using an append-and-forward approach to improve perception beyond one-hop neighbors. For the resultant framework, to maintain the desired object redundancy and information freshness, we proposed solutions in terms of redundancy mitigation and time synchronization. Our numerical results demonstrate significant improvements in perception capabilities, with minimal impact on channel overheads.

The development of autonomous driving has led to the proposal of vehicle-to-everything (V2X) to improve the reliability of autonomous driving systems through information sharing among vehicles and infrastructure. However, the high-speed mobility of autonomous vehicles and the dynamic surrounding environment make the V2X network unstable and unreliable. To address this issue, empirically studying the characteristics and predicting the signal intensity of the V2X network is crucial, which can provide more information for further optimizing communication strategies and enhancing driving safety. In this paper, we collect the real-world vehicle-to-infrastructure (V2I) performance under different driving scenarios and build the quantitative relationship between several environmental factors and the V2I performance. We also develop deep learning models to predict the V2I signal intensity based on external environmental conditions. Experimental results in real-world data demonstrate the effectiveness of our model on classification and regression tasks compared to other models. Our study aims to enhance the applications of V2X on autonomous driving and improve driving safety and traffic efficiency.

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accurate channel state information between LEOs and ground users is challenging. Therefore, we construct a framework with a two-way transmission under unknown channel information and no data collected at long-delay ground gateway. In this paper, we propose hierarchical multi-agent multi-armed bandit resource allocation for LEO constellation (mmRAL) by appropriately assigning available radio resources. LEOs are considered as collaborative multiple macro-agents attempting unknown trials of various actions of micro-agents of respective resources, asymptotically achieving suitable allocation with only throughput information. In simulations, we evaluate mmRAL in various cases of LEO deployment, serving numbers of users and LEOs, hardware cost and outage probability. Benefited by efficient and resilient allocation, the proposed mmRAL system is capable of operating in homogeneous or heterogeneous orbital planes or constellations, achieving the highest throughput performance compared to the existing benchmarks in open literature.

In this paper, we present a codebook design with deep reinforcement learning (DRL) for orthogonal frequency division multiplexing with all index modulation (OFDM-AIM). The prior AIM codebook design usually has poor error performance at low SNR, and the complexity of finding the optimal codebook through exhaustive search is NP-hard. Therefore, we use DRL to reduce complexity and achieve better error performance. We map the problem of designing codebooks to DRL and design the reward function and other elements. We use the deep recurrent Q-network to make it applicable to codebooks with longer codeword lengths. We analyze the relationship between the Hamming distance and Euclidean distance of the optimal codebook and derive the upper bound of the BER performance. The BER performance is close to the theoretical upper bound and outperform the classic AIM. Through numerical analysis, we find that increasing the number of subcarriers can improve the error performance under the same spectrum efficiency. However, prior exhaustive search codebook design methods are impractical due to the high complexity, which makes the learning approach attractive.

In this paper, a code-domain non-orthogonal multiple access (NOMA) scheme called “Subcarrier and Permutation Index Modulation Multiple Access” (SPIMMA) scheme is proposed over the Orthogonal Frequency Division Multiplexing-Subcarrier and Permutation Index Modulation (OFDM-SPIM) architecture. The Index Modulation Multiple Access (IMMA) which applies index modulation (IM) technique is a codebook-based NOMA scheme. It uses the active subcarriers to make index indices which can carry additional bits besides the modulation bits. The proposed scheme uses the concept of IMMA and improves it by transmitting additional bits through different modes. Index bits which deliver additional information are not only produced from the active and inactive subcarriers but also from the different modes permutation. Therefore, he performance of energy efficiency (EE) and spectral efficiency (SE) are improved. The rotation code is proposed in the SPIMMA to solve the problem of inter-user interference (IUI) in contention based multiple access. Due to the diversity gain from the rotation code, the simulation results show that the bit error rate (BER) of SPIMMA is improved with rotation code. In brief, we have designed a code-domain NOMA scheme that facilitates grant free random multiple access, which also shows better SE, EE than IMMA, OFDMA, and SCMA.

This paper studies the use of supervised learning on the channel delay spreads to exploit variable Guard Interval (GI) for Orthogonal Frequency Division Multiple Access (OFDMA) based systems. In a multi-user OFDMA system, the cyclic prefix (CP) as External Guard Interval (eGI) applies fixed length among users for synchronization within the cell. However, the excessive length of eGI leads to spectral inefficiency for near users. The DFT-s-OFDM scheme allows flexible length internal guard interval (iGI) and improves the SE of the systems by stagnating the total symbol duration. This paper studies the use of supervised learning technique to determine the length of iGI for each users. The training database is generated based on normalized delay and received power for Tapped Delay Line (TDL) models defined in 3GPP 5G NR technical release. The machine learning classification technique is used to classify the different channel delay spreads which can later be used to determine the length of iGI to the users. The numerical simulation shows that the spectral efficiency can be improved over the CP based OFDM system up to 27%.

The emerging Internet of Vehicles (IoV) is expected to transform the everyday vehicular experience in the near future. However, IoV will also bring unprecedented challenges unseen in traditional wireless communications systems. Fortunately, the emergence of Open Radio Access Network (O-RAN) technology provides an intelligent base station (BS) solution for IoV networks. In this paper, we propose an edge intelligent framework in compliance with O-RAN architecture. A vehicle traffic flow prediction mechanism is performed in the developed prototype to verify the applicability and feasibility. Through the experiments with a real-world dataset, some insights and guidelines are provided in terms of operational efficiency and applicability. To the best of our knowledge, this work is the first attempt to study the edge intelligence for O-RAN based IoV networks.

In this paper, the concept of opportunistic bits (OBs) is developed in short-packet communications and investigated from a finite blocklength perspective. In the OB-based transmission, the data unit of a packet is divided into two parts: OBs and conventional bits (CBs). The OBs are not physically transmitted but used to indicate the index of the time slot (TS) when the packet containing CBs is transmitted. The loading of a bulk of OB-based packets into multiple TSs can be modelled as a Repeated Balls-into-Bins process with a multi-queue storage. If the bulk is not large enough, certain combination(s) of OBs will not appear, which leaves certain TS(s) empty and hence reduces the TS load efficiency. To evaluate the OB-based transmission performance, we formulate its maximal payload rate and TS load efficiency. With the aid of these two formulations, the energy gain, the goodput, and the latency of OB-based short-packet communications are derived and obtained in analytical forms. For achieving further insights, illustrative numerical results on the resource utilisation efficiency and the performance not only substantiate the advantages of the OB-based transmission over the conventional but also provide useful tools and specifications for its design in massive short-packet communications.

In this paper, we present a codebook design for OFDM with all index modulation (OFDM-AIM). The OFDM-AIM scheme encodes all the messages with index bit mapping, which delivers better diversity gain than the conventional index modulation. The error probability of the scheme in terms of Hamming distance and Euclidean distance of the codebook is devised. We show that the Hamming distance of codebook affect the diversity gain of error probability directly and we have developed a design procedure to build the codebook with different minimum Hamming distance requirements. We have also reworked the complex OFDM-AIM into two real index modulations on the in-phase and the quadrature channels called OFDM with in-phase/quadrature all index modulation (OFDM-I/Q-AIM), which further improves the spectral efficiency. Both the theoretical and simulation results show that the proposed codebooks for the OFDM-AIM and the OFDM-I/Q-AIM outperform prior arts of index modulated OFDM schemes in diversity gain over frequency-selective Rayleigh fading channel.

This paper studies the out-of-subband emission (OSBE) and inter-symbol interference (ISI) problems for the cyclic prefix (CP) free DFT-s-OFDM system. Solving the OSBE problem, the waveform techniques such as precoding, windowing and filtering have been studied. However, most waveforms using windowing and filtering suffer from ISI. The ISI results in degradation of detection error performance, especially in a CP free DFT-s-OFDM system. In this paper, we study a joint optimization algorithm to design the spectral precoder and tail precoder iteratively. The spectral precoder supresses the OSBE and the tail precoder deals with the ISI. However, the spectral precoder and the tail precoder are not independent to each other. This work considers iterative design of spectral and tail precoders to reducie the OSBE and to mitigate ISI jointly with constraints on energy, spectrum emission mask (SEM) defined in 5G NR, and guaranteed spectral efficiency. In the joint optimization process, the decomposition techniques are also used to reduce the computation complexity. The numerical simulations have been conducted which shows that the proposed approach achieves better error performance than the prior arts of works in CP free DFT-s-OFDM systems.

In this paper, we present a multi-user detection technique using successive Gaussian approximation based belief propagation (SGA-BP) detection with linear pre-filtering for the new radio multiple access (NR-MA) uplink communications. The NR-MA system represents the codebook-based, the sequence-based schemes, or the interleaver/scrambler-based non-orthogonal multiple access (NOMA) schemes. The SGABP detection approximates the interference-plus-noise term as a Gaussian random variable and performs iterative SGA-BP, where messages are updated successively during each iteration. By doing so, messages can be delivered efficiently and accurately with low computational complexity. For validation of the approach, two of the NR-MA schemes, i.e. the codebook based NOMA and the sequence based NOMA, are studied to evaluate the performance of SGA-BP detection. Simulation results show that SGA-BP detection achieves better diversity gain and error floor in BER performance than the GA-BP detection or the MMSESIC detection, especially when the number of antennas grows. We also evaluate the cases of high overloading of users, and verify the potential of SGA-BP detection with linear pre-filtering.

This paper studies the design of signature code multiple access (SIGMA) to support code domain non-orthogonal multiple access (NoMA). In a NoMA system, the users apply the spreading codes for multiple access that shares the same time-frequency resource element simultaneously. Consequently, multiple access interference (MAI) occurs which degrades the error performance and limits the number of connectivities. In SIGMA, each user is assigned the codewords from the signature codebook to generate the complex spreading code. In this work, the SIGMA scheme constructs the signature codebook with minimum distance maximization to improve the error performance. Numerical simulation has been conducted to evaluate the error performance. The simulation results show that the codebook minimum distance affects the error performance. The error performances of prior arts such as the multi-user shared access (MUSA) and the sparse code multiple access (SCMA) are compared with the SIGMA by using the maximum likelihood (ML) detection.

In this paper, the use of index modulation (IM) technique for non-orthogonal multiple access (NOMA) is presented, called the Index Modulation multiple access (IMMA). From the concept of index modulation, IMMA technique uses the active subcarriers and inactive subcarriers to make index indices. The index indices can carry bits in addition to the QAM modulation bits, which enables IMMA to have better energy efficiency (EE). Also, IMMA can choose the active and inactive subcarrier combinations to achieve different spectral efficiency (SE) and energy efficiency (EE) for flexibility. In IMMA, each user chooses the active codeword according to the index bits, and shares the same time-frequency resources to do multiple access. By combining index modulation and multiple access, IMMA can improve the SE and EE relative to other multiple access schemes. The SE and EE between different multiple access schemes are compared. Moreover, the IMMA average bit error rate (ABER) performance using maximum likelihood (ML) detection and the ABER error bound are devised. Our simulation results show that IMMA always has better EE than orthogonal frequency division multiple access (OFDMA). Better SE can also be achieved. Furthermore, IMMA always has better SE and EE relative to sparse code multiple access (SCMA).

This paper presents the use of channel degree-of-freedom (DoF) as criterion for handoff in the co-channel cellular network to improve the system capacity and quality of service (QoS). In cellular networks, the popular received signal strength indicator (RSSI) based handoff scheme does not consider the inter-cell interference (ICI) in the network. However, the overall system capacity performance is subject to the ICI. The sum rate based handoff scheme takes the interference into account, however, the complexity is quite high. In this paper, by adopting interference alignment technique, we present a DoF based handoff scheme for the co-channel multiple-input multiple-output (MIMO) network. We have developed a tree based handoff scheme with the DoF as the decision criterion. We find that the proposed scheme is especially beneficial to the edge users as compared to the RSSI scheme. Furthermore, we have developed a joint approach that combines both RSSI and DoF criterions in the handoff scheme to adapt to the near far channels in the network. The proposed scheme is compared with the RSSI-only and the sum rate based handoff schemes. The simulation results show that the interference aware handoff scheme is beneficial to system capacity, especially for the cell edge users.

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

This paper presents an Sparsity Update Subspace Pursuit (SUSP) algorithm for compressed sparse signal reconstruction with unknown sparsity. From practical point of view, the sparsity information is usually unavailable in many applications. In particular, the compressed spectrum sensing application is considered in this paper . The proposed SUSP algorithm begins with sparsity estimation and iteratively updates the sparsity based on the the residual value with subspace pursuit approach. A termination criterion is developed to facilitate the convergence of the sparse update iteration. Moreover, a tail biting rule is devised to refine the reconstruction. Consequently, the sparse signal is recovered and the reconstruction performance is improved. The recovery rate performance is numerically evaluated for both the known sparsity and the unknown sparsity cases. For each case, the recovery mean square errors are also presented for the noisy noisy environment. The results show that the resulting performance outperforms the popular methods using the maximum pursuit or the subspace pursuit based approaches.

This paper studies the fairness optimization of dynamic multiuser multicarrier allocation in the cellular downlink of MIMO orthogonal frequency division multiple access (OFDMA) systems. The varying capacity demands of different users motivate the fairness problem. In the resource allocation approaches that maximizing the sum rate or minimizing the total power often leads to poor fairness among users. The allocation is prone to starvation situation for the users with deep fading subchannels. Hence, this work considers the fairness issue and proposes to maximize the minimum rate surplus, where the rate surplus is defined as the difference between the demand data rate and the resulting allocated data rate. The fairness is inverse proportional to the gap of the maximum rate surplus to the minimum rate surplus among all users. In this work, the design of the precoding and decoding matrices for the MIMO structure is also developed. To solve the optimization problem, an iterative algorithm is proposed to optimize the subcarrier assignment with low complexity. Simulation results on multiuser MIMO environment show that the proposed algorithm strikes the balance between sum rate and fairness. Comparing with the state-of-the-art works, the proposed algorithm shows an advantage in keeping the sum rate while the fairness is significantly improved.

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

This paper presents an inductive intrinsic parasitic feedback technique to enhance the circuit bandwidth of the electro-absorption/Mach-Zehnder optical modulator driver with high-voltage swing-driving capability. The modulator consists of a series-shunt inductor peaking predriver stage and a multicascode postdriver stage. The postdriver stage integrates the proposed inductive intrinsic parasitic feedback network, the interstage series inductor-peaking scheme, and the auxiliary source degeneration structure. The chip is fabricated in 0.13- μm mixed-signal 1P8M standard CMOS process with a die size of 900×800 μm2. The operation data rate of this design is measured up to 20-Gb/s with 3.7 VPP S.E. output amplitude swing, driving 50-Ω resistive load with input signal less than 250 mV. The measured rise/fall time of the output electrical eye diagram is less than 20 ps and the total power consumption is 0.9 W with 1.2/4.0 V dual supplies.

This study explores the convergence property of the interference leakage minimization algorithm (ILMA) proposed by S. W. Peters and R. W. Heath Jr. Among the cooperative interference alignment (IA) schemes, we specialize the ILMA for its better feasibility in real applications than others. However, ILMA cannot converge to a global minimum because of its unitary constraints. As an iterative algorithm, no guarantee of global convergence means the tendency towards system instability. Up to the present, most efforts on cooperative IA are focused on pursuing high sum rate or low error floor. None of existing works looks into the convergence issue. In this paper, we explore the global convergence of ILMA. Specifically, we devise a constraint relaxation version of ILMA, named rILMA. Except that rILMA inherits the benefits from ILMA, mathematical proofs given in this paper also show that rILMA converges to a global minimum. This exploration directly results in the improvement of performance. Numerical simulations show that rILMA outperforms Max-SINR and ILMA in sum rate performance.

In this paper, we study in depth the alternating minimization algorithm (AMA) proposed by S. W. Peters and R. W. Heath Jr. Among iterative interference alignment (IIA) schemes, AMA is considered more feasible for real applications than other IIA schemes. However, no research has investigated how AMA is affected by some real system characteristics. Motivated by this, we look into the effects when taking into account the initial conditions and the final conditions of AMA in the MIMO interference networks. We found the sum rate performance of AMA can be improved by well exploiting its initial conditions. On the other hand, by considering the interference leakage residue (ILR) which is the final condition of AMA, we derived a more realistic formulation which fast calculates the smallest possible codebook for precoders.

This paper presents an injection-locking clock and data recovery circuit (CDR) for serial link receivers. A transformer-coupled injection-locking scheme with all passive components is proposed to lock the quadrature voltage controlled oscillator (QVCO) to align the received data. The quad-rate CDR successfully regenerates the serial 100 Gb/s PRBS 231-1 data into 4 parallel data streams at 25 Gb/s. The fabricated chip occupies 1.92 mm2in 65 nm standard CMOS process with recovered data peak-to-peak jitter of 0.84ps and consumes 130 mW power with 1.0-V supply.

A load-balanced Birkhoff-von Neumann (LB-BvN) 4 × 4 switch fabric IC is proposed for feedback-based switch systems. This is fabricated in 0.13- μm CMOS technology and the chip area is 1.380 × 1.080 mm2. The overall data rate of the LB-BvN 4 × 4 switch fabric IC is up to 32 Gb/s (8 Gb/s/channel) with only 0.8 ns propagation delay. The LB-BvN switch is highly recommended for constructing the next-generation terabit switch. In a feedback-based switch system, the long propagation delay of the switch module reduces the system throughput significantly. In this paper, we present a scalable LB-BvN 4 × 4 switch fabric IC directly in the high-speed domain. By observing the deterministic switching pattern of the N×N LB-BvN switch, we present a low-complexity pattern generator that reduces the PG complexity from O(N3) to O(1). This technique reduces the propagation delay of the switch module from 30 to 0.8 ns, and also provides 80% area saving and 85% power saving compared to serializer-deserializer interfaces. The proposed LB-BvN 4 × 4 switch fabric IC is suitable for feedback-based switch systems to solve the throughput degradation problem.

This paper proposes Multiplexer-Flip-Flops (MUX-FFs) to be a high-throughput and low-cost solution for serial link transmitters. We also propose Multiplexer-Latches (MUX-Latches) that possess the logic function of combinational circuits and storing capacity of sequential circuits. Adopting the pipeline with MUX-FFs, which are composed of cascaded latches and MUX-Latches, many latch gates for sequencing can be removed. Analysis shows that an 8-to-1 serializer in the pipeline topology with MUX-FFs reduces 52% gate-count compared to the traditional pipeline topology. To verify the function of the proposed design, a chips is implemented with the proposed 8-to-1 serializer with MUX-FFs in 90 nm CMOS technology. The measured results show that the proposed serializer with MUX-FFs are bit-error-free (with BER-12), operating at up to 12 Gbit/s.

In this paper, a low propagation delay, low power, and area-efficient 4×4 load-balanced switch circuit for feedback-based system is presented. In this periodic and deterministic switch, only two DFFs are used to implement a pattern generator which is a O(N3) hardware complexity in traditional matching algorithm based N×N switch. For packet reordering, a feedback path is established in series of symmetric patterns. As comparing with commercial switch systems, we implement a 4×4 switch IC directly in high speed domain without the use of SERDES interfaces to achieve low propagation delay and high scalability. In CML output buffer, PMOS active load and active back-end termination are introduced. A stacked current source and symmetric topology in CML-DFF are adopted. From our results, this work efficiently deducted 28ns propagation delay, 80% area and 80% power introduced by the SERDES interface. The throughput rate is up to 32Gbps (8Gbps/Ch).

To exploit the multiplexing and the diversity gains in a multiple-input multiple-output (MIMO) wireless system, the singular value decomposition (SVD) is typically used to decompose the MIMO channel into parallel eigen-modes. However, the dynamics of the frequency selective channel fading variations often make the decomposed subchannels ill-conditioned and lead to unsatisfied performance in terms of bit error rate (BER). Recently, two improved SVD-based decomposition schemes, i.e. the geometric mean decomposition (GMD) and the uniform channel decomposition (UCD), are developed which try to achieve uniform subchannel gains. Unfortunately, both of them require even higher complexity than that of SVD with power allocation. In this paper, we propose a decomposition method, called LDLHwhich has moderate subchannel gains uniformity while exhibits the lowest complexity compared with that of aforementioned SVD-based schemes. Simulations are also given to show that the proposed LDLHscheme is more robust than all other schemes at higher SNR as channel estimation errors become severe.

This paper presents a 10.0-11.5 Gb/s full-rate phase and frequency detector integrated with the clock recovery circuit (CRC) for application in optical receivers. A rotational phase and frequency detector (RPFD) without external reference clock is proposed to train the conventional bang-bang phase detector (BBPD) to capture the clock frequency. The proposed RPFD shows 1.5 GHz capture range and works in the high speed data rate of 10 Gb/s. Only one closed loop is used to track the internal clock. The acquisition time for the clock frequency to adjust from 11.5 to 10 GHz is 160 ns. The fabricated chip occupies 0.7 mm in 90 nm CMOS process with -108.8 dBc/Hz at 1-MHz offset and consumes 52 mW power with 1.0-V supply.

In this paper, a low propagation delay, low power, and area-efficient 4×4 load-balanced switch circuit for feedback-based system is presented. In this periodic and deterministic switch, only two DFFs are used to implement a pattern generator which is a O(N3) hardware complexity in traditional matching algorithm based N×N switch. For packet reordering, a feedback path is established in series of symmetric patterns. As comparing with commercial switch systems, we implement a 4×4 switch IC directly in high speed domain without the use of SERDES interfaces to achieve low propagation delay and high scalability. In CML output buffer, PMOS active load and active back-end termination are introduced. A stacked current source and symmetric topology in CML-DFF are adopted. From our results, this work efficiently deducted 28ns propagation delay, 80% area and 80% power introduced by the SERDES interface. The throughput rate is up to 32Gbps (8Gbps/Ch).

This paper proposes a packet-based verification platform with serial link interface for emulating the hardware of the heterogeneous IPs before tape out. With the serial link interface Serializer/Deserializer (SerDes) added between IPs, significant amount of pin counts can be reduced in the platform. An adapter is inserted between IP and SerDes to convert parallel bus into packets and handle the handshaking. Under our proposed adapter architecture and handshaking scheme, the limitation on the number of the master adapter is eliminated compared with Bus-based Advanced High-performance Bus (AHB) architecture. Simulation results show the data transfer through our proposed architecture works correctly without the limitation on the number of masters. With the proposed adapter and SerDes architecture, the number of required signals in the interconnect is reduced from 79 to two for the AHB bus.

In this paper, a novel multiplexer-flip-flop (MUX-FF) topology using the current mode logic (CML) is presented. A CML multiplexer-latch (MUX-latch) is proposed by combining a multiplexer and the loopback storage part of a latch into a single module so that the buffer part of a latch can be removed. A MUX-FF is implemented by cascading two stages of MUX-latches. The output of a MUX-FF is edge-triggered, so it is insensitive to input noise. All the paths from inputs to the output are symmetric. Power and area can be reduced due to the removal of DFFs. Simulation results show that a MUX-FF can achieve a similar frequency as a conventional tree-type MUX by saving 56% of area and 72% of power consumption.

The performance of a space-time block coded orthogonal frequency-division multiplexing (STBC-OFDM) system often relies on the assumption of quasi-static channels. For the time-varying multipath channel, co-channel interference (CCI) and inter-carrier interference (ICI) occur and performance degrades seriously. In this paper, we propose an iterative interference cancellation scheme to reduce both CCI and ICI jointly. In particular, a list successive interference cancellation (List-SIC) algorithm is presented to obtain a candidate list to obtain soft information used in the CCI cancellation. Furthermore, an ICI cancellation algorithm that uses prior List-SIC information from the preceding iteration is proposed. The simulation results show that the proposed scheme achieves near maximum likelihood (ML) error performance within only two or three iterations in fast fading channels.

In classical MIMO systems, singular value decomposition (SVD) is adopted as a common way to decompose the MIMO channel into parallel subchannels. However, in addition to its high complexity, it is also sensitive to the ill-conditioning of the channel matrix. In this paper, we propose a channel decomposition strategy called LDLHdecomposition for low complexity MIMO precoder design. We show that the computation complexity of LDLHachieves one degree of magnitude lower than that of SVD, as well as some SVD-based decomposition scheme such as geometric mean decomposition (GMD) and uniform channel decomposition (UCD). In addition, we also show that LDLH-based precoder requires less quantization effort and feedback bandwidth.

In the paper, a novel 2.56/3.2Gb/s full-rate phase detector is developed for integration with clock data recovery (CDR) circuit in high speed SerDes applications. An extended phase detector (EPD) circuit is proposed to replace the full-rate Hogge architecture. To incorporate a LC-tank voltage control oscillator with cross-coupled pair and a differential charge pump with common mode feedback. The digitally assisted lock detector (DALD) circuitry provides a timing decision for switching dual loops between phase or frequency detector in the 6MR circuits. The CDR circuit is fabricated in a 0.18 mum 1P6M standard CMOS process in an area of 0.8times1.0 mm2. This CDR chip exhibits a low jitter performance of 2.12 ps RMS in the recovered clock and a BER is 3.5 times 10-9 with PRBS of 231-1 sequence. The power consumption is 136 mW with a 1.8 V supply at 3.2 Gb/s.

A 10-Gb/s current mode logic (CML) input/output (I/O) circuit for backplane interconnect is fabricated in 0.18-mu m 1P6M CMOS process. Comparing with conventional I/O circuit, this work consists of input equalizer, limiting amplifier with active-load inductive peaking, duty cycle correction and CML output buffer. To enhance circuit bandwidth for 10-GB/s operation, several techniques include active load inductive peaking and active feedback with current buffer in Cherry-Hooper topology. With these techniques, it reduces 30%-65% of the chip area comparing with on-chip inductor peaking method. This design also passes the interoperability test with switch fabric successfully. It provides 600- mVppdifferential voltage swing in driving 50-Omega output loads, 40-dB input dynamic range, 40-dB voltage gain, and 8-mV input sensitivity. The total power consumption is only 85 mW in 1.8-V supply and the chip feature die size is 700 mum times 400 mum.

In this paper, we present a 7 Gbps/Ch quad SerDes integrated with a 4times4 load-balanced switch fabric circuit for high speed networking applications. To achieve high-speed and low area, we propose an area-saving RF model device for the SerDes design. The area-saving RF model has almost the same speed and jitter performance with the RF model but only consumes one half of the area. In our hybrid design of the SerDes architecture, the area-saving RF model mixed with the baseband model can reduce 75% of area compared with the design using only the conventional RF model. The grounded coplanar waveguide (GCPW) type transmission line is also employed to reduce the clock tree skew for the quad SerDes to within 1 ps. The total area is 3 mm times 2.48 mm, including the switch fabric, the quad SerDes interface, and a LC-PLL. In our results, each input/output port of the 4x4 switch fabric can achieve 7 Gbps data rate, and the overall throughout is 28 Gbps.

For the first time, we implemented a reconfigurable load-balanced TDM switch IC with SERDES interface circuits for high speed networking applications. An N times N TDM switch could be constructed recursively from the TDM switch IC to achieve switching capacity of hundred gigabits per second or higher. The TDM switch IC contained a digital 8 times 8 TDM switch core with 8B10B CODECs and analog SERDES I/O interfaces. In the I/O interfaces, eight 2.56/3.2Gbps dual-mode 16/20:1 SERDES with CML buffers were developed. The 16/20:1 instead of 8/10:1 serializer and deserializer were used to reduce the required operating frequency in the switch core by half. New half-rate architectures and all static CMOS gates were used in the 16/20:1 serializer and deserializer for the low power consumption. A wide-band CML I/O buffer with our patented PMOS active load scheme was developed. All implementation were based on the 0.18 mum CMOS technology. Our implementation showed a 20 Gbps switching capacity for the 8 times 8 TDM switch IC.

For the first time, a scalable load balanced Birkhoff-von Neumann TDM switch IC with SERDES interface circuits for high speed networking applications was implemented. AnyN×NBirkhoff-von Neumann TDM switch could be constructed recursively from the designed TDM switch IC to achieve switching capacity of hundred gigabits per second or higher. The TDM switch IC contained a digital 8×8 TDM switch core with 8B10B codecs and analog SERDES I/O interfaces. In the I/O interfaces, eight 2.56/3.2Gbps dual-mode 16/20:1 SERDES with CML buffers were developed. The 16/20:1 instead of 8/10:1 serializer and deserializer were used to reduce the required operating frequency in the switch core by half. New half-rate architectures and all static CMOS gates were used in the 16/20:1 serializer and deserializer for the low power consumption. A wide-band CML I/O buffer with our patented PMOS active load scheme was developed. All the implementations were based on the 0.18 μm CMOS technology. Test results showed a 20 Gbps switching capacity for the 8×8 TDM switch IC.

In this paper, we present an adaptive channel estimation scheme for IEEE 802.16-2004 Wireless Metropolitan Area Network (a.k.a. WiMAX) in the case that the channel impulse response is longer than the cyclic prefix (CP). The scheme is combined with time domain impulse response shortening technique and frequency domain channel equalization. Simulation results show that the shortening algorithm gives the shortening SNR (SSNR) of effective channel is at least 10 times better than the original channel impulse response. The performance of proposed method can satisfy the BER requirement of IEEE 802.16-2004 standard in the non-line-of-sight environment with the highest data rate (20NMz) transmission. We have also found that the better strategy to design a shortening filter is to over-constraint the desired length to much shorter than CP.