Text-to-3D generation has made significant progress in terms of fidelity and geometric consistency. However, current methods still struggle to generate complex attributes from the text prompts. We thus present RainbowDreamer, a three-stage framework that builds up the semantic controls to generate attribute-consistent 3D Gaussian Splattings (3DGS). In detail, in the first stage, we optimize a 3DGS for geometry reference by removing the attribute prompts optimized by a hybrid stable diffusion and multi-view diffusion models with a view-dependent rescaling strategy. Then, utilizing the geometry of this reference 3DGS, we optimize their color only via a diffusion-based full prompt lifting, where the attention restriction between two stages is utilized to measure the semantic consistency. Finally, we further introduce a refinement stage for the overall quality of the 3D assets via Bootstrapped Score Distillation. We evaluate the generated 3D assets from multiple aspects using both CLIP similarity and complex vision language model understanding abilities. Results demonstrate that RainbowDreamer achieves state-of-the-art performance in both quantitative and qualitative evaluations.

Aero-engines, as pivotal components in aviation, demand robust prognostics and health management (PHM) to mitigate degradation risks and optimize maintenance amid complex operational stresses. Conventional scheduled strategies often yield inefficiencies, prompting the integration of digital twins (DTs) for enhanced predictive capabilities. This study introduces a comprehensive DT-based aero-engine health management framework, stratified into physical, data, model, and application layers, augmented by a Unity-driven 3D visualization module for immersive virtual-real interactions. The physical layer captures real-time sensor data from turbofan components, feeding into the data layer for preprocessing, fusion, and lifecycle storage. The model layer employs a novel physically decoupled multi- branch long short-term memory (LSTM) network with attention fusion, segmenting sensor features (e.g., temperature, pressure) into specialized branches to discern degradation channels while dynamically weighting salient contributions. Outputs manifest in the application layer via interactive dashboards for RUL fore- casting, anomaly alerts, and maintenance simulations. Validated on the N-CMAPSS DS01 dataset, the model attains an R2 of 0.9376 and RMSE of 5.76 cycles, outperforming single-branch LSTM (R2 = 0.7306) and Transformer (R2 = 0.7810) baselines by leveraging physical priors for superior trend capture and interpretability. Unity integration enables real-time 3D mapping of predictions, fostering user-centric diagnostics. This framework advances PHM by harmonizing deep learning with DTs, boosting accuracy, usability, and decision-making in civil aviation. Limitations in multi-condition scenarios underscore avenues for future uncertainty modeling and lightweight adaptations. Note to Practitioners This digital twin (DT) framework advances aero-engine prognostics and health management (PHM) by delivering interpretable remaining useful life (RUL) predictions to combat inefficiencies in traditional scheduled maintenance, which costs aviation billions in downtime and overhauls. Structured across physical, data, model, and application layers, it processes real- time turbofan sensor data (e.g., temperature, pressure) to support proactive decision-making. The core multi-branch LSTM with attention fusion decouples features by physics (e.g., temperature branches), dynamically weighting degradation signals, such as turbine exhaust, for superior accuracy and transparency. On the N-C MAPSS DS01 dataset, it reaches R2 = 0.9376 and RMSE = 5.76 cycles, surpassing the single-branch LSTM (R2 = 0.7306) and Transformer (R2 = 0.7810) baselines by 20–28%. Benefits include 10-15% longer on-the-wing times and reduced inspections. Unity 3D visualization maps RUL onto interactive models, enabling engineers to identify anomalies, simulate maintenance, and receive alerts, bridging AI to actionable insights without requiring expertise. Deployable via edge cloud for avionics, it scales to variable flight sizes; future work will add uncertainty modeling for noisy data. This empowers predictive stewardship, boosting safety and economics.

Federated learning (FL) in resource-constrained wireless networks faces the challenge of long training delays. In this work, we explore delay-efficient FL by leveraging device-to-device (D2D) communications to accelerate the uploading of local models. We formulate a joint problem of upload mode selection and bandwidth allocation, which is a mixed-integer nonlinear programming (MINLP) problem and difficult to solve directly. To address this, we propose a low-complexity two-step algorithm: the first step determines the upload modes for edge devices, while the second step optimally allocates the bandwidth. Simulation results show that our algorithm outperforms baseline schemes, with delay reduction becoming more pronounced as the number of edge devices increases.

On unlicensed spectrum, the beam scanning in directional listen before talk (LBT) channel access is similar to the beam training procedure adopted for channel estimation before data transmission. Therefore, repeating these operations not only increases signalling overhead but also wastes limited resources. To address this issue, this paper proposes an efficient data transmission strategy that combines the two similar aforementioned steps. Considering the limited capacity of each beam, the optimal beam pairing problem between the transmitter and the receiver is formulated as a matching game problem and solved by means of the Gale-Shapley algorithm. Finally, the results validate the effectiveness of the proposed mechanism in reducing the complexity of beam search, minimizing the signalling overhead, and optimizing the system capacity.

With their high mobility and ease of deployment, unmanned aerial vehicle (UAV)-assisted communication systems have emerged as a prominent area of academic research and a cornerstone technology for Sixth-Generation (6G) mobile communication networks. This paper investigates a multi-UAV downlink wireless communication system in which users exhibit random movement on the ground. To maximize the sum-rate of all users over the observation period, we propose a joint optimization framework that integrates user association, UAV 3D trajectory design, and power allocation, while addressing channel estimation across different timescales. In the long timescale, we model the UAV-user connections as a graph and utilize a graph neural network to jointly optimize user association and UAV trajectories. In the short timescale, we deploy a deep unfolding network for efficient channel estimation and power allocation. Simulation results validate the effectiveness of the proposed approach, showcasing significant performance improvements.

In decentralized learning systems over wireless device-to-device (D2D) networks, training latency is a key metric that needs to be minimized by link selection and resource allocation, thereby accelerating model training. However, it may cause large computational complexity in general. To tackle the challenge, this paper proposes a graph neural network (GNN)-based algorithm to minimize the training latency. Under modeling the D2D network as a graph, the link selection and resource allocation can be efficiently obtained based on the local computing power and link quality. By the constraint on the network connectivity, the training latency can be significantly reduced while guaranteeing accuracy with a low complexity. The simulation results demonstrate that the GNN-based approach outperforms traditional approaches, offering superior scalability and robustness in heterogeneous large-scale D2D networks.

Autonomous driving technology has achieved remarkable advancements, offering substantial potential to revolutionize traffic safety and smart mobility. However, when faced with rare scenarios (weather, accident scenes, and lighting), autonomous driving systems can still only play a limited role due to insufficient learning in these rare situations. To address this challenge, we propose a novel approach that leverages low-rank adaptation (LoRA) and Mixture of Experts (MoEs) technologies to enhance the performance of pretrained autonomous driving models in handling rare situations. Specifically, we first use LoRA to fine tune the pretrained model of autonomous driving to focus on capturing knowledge related to rare scenarios and enhance the model’s ability to handle rare situations. Furthermore, we introduce MoEs and propose local, global, and hybrid adaptive solutions to overcome LoRA’s fixed intrinsic rank limitation. These approaches enable adaptive adjustment of LoRA’s rank, and improve the model’s performance from both local and global perspectives. Finally, we design detailed algorithms for different adaptation schemes. Extensive experiments demonstrate that our proposed solutions not only effectively improve the performance of the autonomous driving perception model in rare scenarios but also maintain lower inference latency compared to baseline methods.

Massive multiple-input multiple-output (MIMO) arrays have emerged as pivotal technology for 5G wireless communication systems, finding widespread implementation and deployment. However, despite their significant potential, the performance gains achieved in practical environments do not consistently scale with the accompanying rise in hardware costs. To address this issue, we delve into the design of rectangular array structures for massive MIMO with varying parameters. The core idea is to optimize the array structure to suit diverse propagation characteristics. Our approach treats the massive MIMO array as a spatial sampling system. A 2-D Fourier transform concerning the elevation and azimuth steering factors is employed to derive the angular spectrum of incoming signals at the base station. Building on spatial spectrum analysis, we unveil the relationship between antenna array parameters, such as the number of antennas and the vertical/horizontal antenna spacings, and the spatial resolution and spectral aliasing inherent to the massive MIMO system. Furthermore, we investigate how array structure parameters impact channel capacity in multiuser scenarios and propose effective strategies for enhancing capacity while mitigating aliasing through parameter adjustments. Finally, we present numerical results that validate the effectiveness of our proposed approaches. The outcomes of this study establish a solid foundation for optimizing the design, deployment, and spatial resource allocation of practical massive MIMO systems.

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming (RABF). Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose an RABF method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance.

While the average delay is traditionally an importance metric for system performance, the emerging technologies have given birth to a variety of critical applications, for which the deterministic delay guarantee is highly desired. When optimizing the average delay objective is embraced with satisfying the deterministic delay constraint, it leads to complicated coupling and brings new challenges to resource allocation. In this paper, we propose a renewal framework for multi-user power control and subband allocation to improve the comprehensive delay performance. The average delay objective is optimized under the Markov decision process (MDP) problem, while the deterministic delay constraint is satisfied through Lyapunov optimization with virtual queues. Due to the conflict of the inter-slot influence in MDP and the i.i.d. state requirement in Lyapunov approach, we exploit the recurrent property of queue states and construct a renewal system. By solving the equivalent infinite-horizon MDP in the renewal framework, we propose a resource allocation algorithm, which is proved to be asymptotically optimal. Finally, the simulation results demonstrate that the proposed scheme meets the deterministic delay constraint and achieves better average delay performance than existing baselines.

We consider the problem of reducing the learning latency of layerwise federated learning through joint device selection and bandwidth allocation. Specifically, we examine practical scenarios with heterogeneous devices with varying system parameters (e.g., CPU frequency, transmit power, etc.) and energy budgets. We formulate a long-term optimization problem, which is difficult to solve even with perfect channel state information. To address the issue, we employ Lyapunov theory to transform the problem into a series of online optimization problems, each of which can be efficiently solved using an alternating optimization-based method. Simulation results show that our scheduling scheme surpasses baseline schemes not only in terms of reducing the learning latency but also in reducing the energy deficit.

Deep mutual learning (DML) is one of the emerging technologies for mobile intelligent applications that has attracted much attention in recent years. To effectively deploy DML at a large scale, in this paper, we propose a novel hierarchical asynchronous deep mutual learning (HADML) system that enables devices to collaborate in model training without the exchange of local datasets. To further improve the learning efficiency, the average energy cost for model exchanging is minimized by jointly optimizing the link scheduling and communication resource allocation. To efficiently solve this problem, the graph neural network and deep unfolding network are employed to obtain the link scheduling and resource allocation, respectively. Finally, the simulation results demonstrate that our proposed algorithm can achieve a balance between knowledge sharing and communication energy consumption.

Unmanned Aerial Vehicle (UAV) technologies have garnered significant attention, particularly in the context of UAV-assisted wireless networks, which are seen as a pivotal component in the development of Sixth-Generation (6G) mobile communication systems. In this research, we delve into the realm of UAV-assisted wireless communication networks, where a single UAV efficiently caters to numerous random mobile users on the ground. Our focus lies in optimizing user movement tracking, beamforming, and UAV trajectory to maximize the data transmission rates for users within a specified time frame, all while adhering to stringent power constraints and the UAV's limited flight range. We harness the power of deep reinforcement learning (DRL) to monitor mobile users and predict the ever-changing channel state information. As beamforming and UAV trajectory adjustments operate on different timescales, we introduce a dual-layer deep unfolding network to fine-tune the transmit beamformer and UAV trajectory simultaneously. The outcomes of our simulations demonstrate the effectiveness and commendable performance of the proposed scheme.

The digital twin technology facilitates the application of Metaverse in autonomous driving. Particularly, this paper focuses on investigating an unmanned aerial vehicle (UAV)-aided vehicular Metaverse. In specific, the moving vehicles in physical world collect the real-time traffic data, which is synchronized through the UAV to the virtual world to help the autonomous vehicle (AV) simulation. For such a physical-virtual synchro-nization process, we define the age of incorrect information (AOII) to measure the traffic data freshness. Accounting for the randomness in the physical world, we jointly optimize the UAV trajectory, the vehicle scheduling and the semantic extraction of collected data under the Markov decision process (MDP) framework. Our objective is to minimize the expected long-term system AOII. Without the statistical knowledge of physical-world randomness, we propose to leverage a proximal policy optimization based deep reinforcement learning algorithm to solve the optimal control policy to the MDP formulation. We conduct numerical experiments to verify the accuracy of the theoretical analysis, and the results demonstrate the performance gains from our proposed algorithm.

We address the minimum-delay beam scheduling problem leveraging reflections for switched beamforming systems. The objective is to efficiently disseminate a data file from a transmitter to a set of nodes via multiple predetermined beams with arbitrary overlapping patterns. The problem is formulated as a challenging mixed integer nonlinear programming (MINLP) and then decomposed into a set of subproblems. The subproblems are still difficult to solve due to their NP-hardness. We propose a heuristic algorithm for the subproblems, based on which two heuristic algorithms with different computational complexities are developed for the original problem. Simulation results high-light the significant reduction in dissemination delay achieved by the proposed algorithms compared to baseline approaches without leveraging reflections.

In the Internet of Things (IoT) ecosystem, optimizing processing capabilities of devices through Wireless Powered Mobile Edge Computing (WP-MEC) is crucial. This research addresses the challenge of efficiently scheduling task offloading from devices to an edge server, which is vital for enhancing system performance. Prior studies often overlook the necessity for rapid adaptation to changing wireless conditions, resulting in suboptimal offloading strategies. Our work introduces the Intelligent Online Computation Offloading (IOCO) algorithm, leveraging Deep Neural Networks (DNNs) to make informed, real-time offloading decisions based on previous experiences. This approach not only optimizes the allocation of wireless and computing resources but also incorporates novel quantization and sampling methods to improve robustness and adaptability. Simulation results demonstrate that IOCO can achieve near-optimal efficiency swiftly and adapt effectively to significant resource changes, highlighting its practicality in dynamic WP-MEC environments.

We jointly consider full-duplex operation and network coding in two-hop relay networks to enhance the throughput of the block transmission of packets over erasure channels. Two coded transmission schemes, termed Fewest Broadcast Packet First (FBPF) and Buffer Contents-based Coded Transmission (BCCT), are proposed, where random linear network coding is employed at the Base Station (BS) and the Relay Station (RS), respectively. Both schemes do not rely on users’ Channel State Information (CSI), buffer status, channel parameters, etc., and hence are practically viable. We derive closed-form upper bounds on the throughput of both schemes. We prove that both schemes achieve the optimal throughput when the BS-to-RS channel is perfect. Through extensive simulations, we demonstrate that both schemes incur substantially higher throughput than the traditional uncoded Automatic Repeat-reQuest (ARQ) scheme and perform close to a general upper bound on the system throughput. Furthermore, even with imperfect Self-Interference Cancellation (SIC) at the full-duplex RS, our schemes are shown to be superior to state-of-the-art coded transmission schemes designed for half-duplex relay networks, given that the impact of imperfect SIC on the BS-to-RS channel quality is not high.

Ultra-reliable low-latency communication (URLLC) is an indispensable element towards supporting various latency-sensitive and reliability-critical applications. To optimize the average delay while satisfying the deterministic delay constraint, initial transmission and retransmission should be handled with different priorities due to the differentiated urgency, which creates complex interdependency and brings new technical challenges to delay-oriented optimization. In this paper, we propose a retransmission-aware adaptive modulation and coding (RAMC) scheme to improve the delay performance in URLLC scenarios. Specifically, we first establish a cascaded queue system, including an initial transmission queue and a retransmission queue. The deterministic delay constraint is satisfied through Lyapunov optimization, where we transform the Lyapunov drift-plus-penalty problem into an infinite horizon Markov decision process (MDP) by constructing a renewal system with sampling to overcome the challenge brought by queue coupling. Next, we propose the delay-optimal RAMC scheme by solving the associated Bellman equation by improved reinforcement learning, which is proved to be asymptotically optimal. Finally, the superiority of the proposed RAMC scheme is verified through simulations.

Deep mutual learning (DML) is one of the most high-profile technologies emerging in the field of machine learning during the past few years. DML has the potential of exchanging knowledge on the premise of ensuring data privacy, while retaining the characteristics of local models. In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), which allows neighbor mobile devices to learn from each other via bidirectional device-to-device links over unlicensed spectrum (D2D-U). On this basis, we formulate a non-convex optimization problem for the one-to-one pairing scenario with the goal of minimizing the average communication energy cost for sharing knowledge. We further propose a two-layer iterative algorithm that includes the outer layer based on the enumeration method and the inner layer based on the sum-of-ratios optimization, aiming to find the optimal pairing scheme between devices and obtain the global optimal communication resource allocation scheme, respectively. The numerical results validate the effectiveness of the proposed algorithm in improving the DML performance.

We study efficient federated learning (FL) using random pruning in resource-constrained edge intelligence networks. We propose an edge device selection strategy to identify appropriate edge devices for participating in FL at the beginning of each training iteration. We then formulate an optimization problem that jointly optimizes the pruning ratio, CPU frequency, uplink power, and bandwidth allocation for the selected edge devices. Since the optimization problem is non-convex and challenging to solve directly, we decompose it into three subproblems and propose efficient algorithms or closed-form solutions for each subproblem. Based on the solutions to the subproblems, an alternating optimization algorithm is constructed to solve the original problem. Simulation results demonstrate that our scheme outperforms baseline schemes in terms of both learning accuracy and energy consumption.

In this paper, we design a novel system named as decentralized mutual learning over unlicensed spectrum (DML-U), where edge devices are allowed to learn from each other via bidirectional device-to-device communications over unlicensed spectrum. We further formulate a non-convex optimization problem to minimize energy consumption and accelerate knowledge sharing with constrained power, bandwidth and transmission latency. Under this context, we propose a two-layer iterative algorithm, which contains an enumeration-based outer layer for the pairing scheme and a sum-of-ratios-based inner layer for obtaining a globally optimal allocation of communication resources. Simulation results verify that our obtained algorithm converges fast and finds efficiently the balance between knowledge sharing and communication energy consumption.

The integration of both sensing and communication functions is a crucial feature for future communication systems. This paper considers a novel scenario where a radar covers multiple small-cell base stations (BSs) which operate in different spectra. We propose a co-existence system of multiple-input multiple-output (MIMO) radar and multiple-input single-output (MISO) communication systems. We aim to minimize the system transmit power while maintaining the performance of both radar and communication. Due to the complexity of the original problem, we traverse the BS selection and transform the subproblem into a more tractable one by introducing auxiliary variables and propose a penalty dual decomposition (PDD)-based algorithm to solve it. In the inner loop, we propose a concave-convex procedure (CCCP)-based algorithm to deal with the optimization problem, and the block coordinate descent (BCD) algorithm is utilized to update the variables. In the outer loop, we update the penalty term or Lagrange multipliers. Finally, numerical simulations validate the superiority of our proposed algorithm over benchmark algorithms.

Mobile-edge computing (MEC) brings the potential to address the ever increasing computation demands from the mobile users (MUs). In addition to local processing, the resource-constrained MUs in an MEC system can also offload computation to the nearby servers for remote execution. With the explosive growth of mobile devices, computation offloading faces the challenge of spectrum congestion, which, in turn, deteriorates the overall quality of computation experience. This article, hence, investigates computation task scheduling in a heterogeneous cellular and WiFi MEC system. Such a system provides both licensed and unlicensed spectrum opportunities. Due to the sharing of communication and computation resources as well as the uncertainties, we formulate the problem of computation task scheduling among the competing MUs in a stationary heterogeneous edge computing system as a noncooperative stochastic game. We propose an approximation-based multiagent Markov decision process without the global system state observations, under which a multiagent proximal policy optimization (PPO) algorithm is derived to solve the corresponding Nash equilibrium. When expanding to a nonstationary heterogeneous edge computing system, the obtained algorithm suffers from the slow convergence due to constrained adaptability. Accordingly, we explore meta-learning and propose a multiagent meta-PPO algorithm, which rapidly adapts the control policy learning to the nonstationarity. Numerical experiments demonstrate performance gains from our proposed algorithms.

This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

In this paper, a novel decentralized mutual learning (DML) network is designed, where each mobile device can share knowledge with its neighbour devices via bidirectional device-to-device (D2D) communication. We subdivide and discuss mutual learning scenarios, and investigate the user association and resource allocation problems for the one-to-many scenario. With constraints on power, bandwidth and communication latency, we formulate a non-convex optimization problem to minimize the average communication energy consumption for sharing new knowledge. On the basis, a two-layer iterative algorithm is proposed, which consists of an outer layer algorithm based on particle swarm optimisation (PSO) for searching a suitable user association strategy and an inner layer algorithm based on sum-of-ratios optimization for achieving a globally optimal allocation of communication resource. Numerical results are presented to verify the fast convergence and the effectiveness of the proposed algorithm in terms of a trade-off between energy consumption and knowledge sharing efficiency.

Massive data drives the development of machine learning (ML) for a long time. However, at present, data is starting to hinder ML's development. The first reason is that the privacy of data is increasingly valued by the public. Therefore, Federated Learning (FL) has emerged, which realizes model training through distributed computing and centralized aggregation. Second, due to the popularity of FL, edge devices need to store all data, which may quickly occupy the entire storage space of edge devices, resulting in fatal errors. To address these challenges, we proposed a storage-aware joint user scheduling and spectrum allocation algorithm, named FedSUS, to reduce the storage stress of each device and guarantee traditional FL metrics, i.e., learning accuracy and training latency. First, a probabilistic framework is adopted for user scheduling. Second, we introduce a data influence evaluation method to FL and analyze its convergence. Based on this, two problems are formulated to tradeoff the storage resource, the influence of data, and the learning latency and to minimize the transmission latency, respectively. Then, the closed-form results to the above problems are both developed. Finally, FedSUS is validated by using a popular convolutional neural network (CNN) and datasets (CIFAR-10). And numerical results demonstrate that our algorithm can effectively reduce the local data size while keeping (even improving) the learning accuracy as compared with baseline.

In point cloud video streaming systems, the field of view (FoV) prediction is critical for selecting the tiles, the objective of which is to optimize the expected long-term quality-of-experience (QoE) from the perspective of a user. On one hand, a satisfactory QoE accounts for not only the playback quality but also the playback smoothness. On the other hand, the large data volume of a selected tile requires the transmission to be adaptive to the system uncertainties. This paper applies a Markov decision process to formulate the problem of tile selection across the infinite discrete time horizon. In particular, a system state includes the FoV information, which is predicted from the Transformer. To alleviate the dependence on system uncertainty statistics, a deep reinforcement learning approach is derived for solving the optimal control policy. Under different settings, we conduct experiments based on the real throughput and head-mounted display data. The results show that compared to the existing baselines, our proposed prediction-control approach achieves a higher FoV prediction accuracy, better playback quality as well as smoothness, and hence a better average QoE for the user.

We consider the minimum-delay multicast scheduling problem for switched beamforming systems. A salient characteristic of mmWave links, reflection, is considered, which enables opportunistic reduction of data dissemination delay. We formulate the problem as a mixed integer nonlinear programming, which is difficult to solve directly. Instead, we decompose the problem into a set of subproblems, by allocating a fixed path to each receiver for data reception. The optimal solution to each subproblem has a contiguous structure, and hence can be computed using a dynamic programming-based approach. We propose an optimal algorithm for the original problem based on the solutions to the subproblems. By simulation we show the outperformance of our algorithm over an optimal multicast scheduling policy without leveraging reflections and a broadcast baseline scheme.

We consider the channel-aware scheduling (CAS) problem for block transmission of packets in two-hop full-duplex relay networks with multiple users. At each time slot, the full-duplex relay station (RS) can fetch a network-coded packet from the macro base station (BS), and schedule a previously received packet for broadcasting to the users over time-varying channels. Our goal is to maximize the broadcast throughput. Since the associated Markov decision programming problem turns out to be intractable as the size of the problem increases, we propose a CAS scheme which is simple to implement and also achieves near-optimal performance. We provide a closed-form expression of the throughput of our scheme when the BS-to-RS channel is perfect, and prove that our scheme is optimal for one-user systems. Finally, numerical results demonstrate that our scheme performs close to an upper bound of the system and outperforms other transmission schemes.

The massive onsite data produced by the Internet of Things (IoT) can bring valuable information and immense potentials, thus empowering a new wave of emerging applications. However, with the rapid increase of onsite IoT data streams, it has become extremely challenging to develop a scalable computing platform and provide a comprehensive workflow for processing IoT data streams with lower latency and more intelligence. To this end, we present a Kubernetes-based scalable fog computing platform (KFIML), integrating big data streaming processing with machine learning (ML)-based applications. We also provide a comprehensive IoT data processing workflow, including data access and transfer, big data processing, online ML, long-term storage, and monitoring. The platform is feasibly validated on a clustered testbed, which comprises a master node, IoT broker servers, worker nodes, and a local database server. By leveraging the lightweight orchestration system, namely Kubernetes, we can readily scale and manage containerized software frameworks on our testbed. The big data processing layer utilizes the advanced data flow frameworks such as Apache Flink, to support both streaming processing and statistical analysis with low latency. In addition, the specified long short-term memory (LSTM)-based ML pipelines are employed on the online ML layer, to enable the real-time predictive analysis of IoT data streams. The experiments on a real-world smart grid use case demonstrate that the container-based KFIML platform can be well-scaled with Kubernetes to efficiently perform big data processing increased onsite IoT data streams with lower latency and conduct ML-based applications.

In recent years, wirelessfederated learning(FL) has been proposed to support the mobile intelligent applications over the wireless network, which protects the data privacy and security by exchanging the parameter between mobile devices and thebase station(BS). However, the learning latency increases with the neural network scale due to the limited local computing power and communication bandwidth. To tackle this issue, we introduce model pruning for wireless FL to reduce the neural network scale. Device selection is also considered to further improve the learning performance. By removing the stragglers with low computing power or bad channel condition, the model aggregation loss caused by model pruning can be alleviated and the communication overhead can be effectively reduced. We analyze the convergence rate and learning latency of the proposed model pruning method and formulate an optimization problem to maximize the convergence rate under the given learning latency budget via jointly optimizing the pruning ratio, device selection, and wireless resource allocation. By solving the problem, the closed-form solutions of pruning ratio and wireless resource allocation are derived and the threshold-based device selection strategy is developed. Finally, extensive experiments are carried out to demonstrate that the proposed model pruning algorithm outperforms other existing schemes.

This paper addresses a joint radio and computational resources allocation problem for mobile edge computing (MEC) networks. To alleviate the shortage of licensed spectrum resources, computing tasks can be offloaded to the MEC server through not only the licensed channels but also the unlicensed channels, where the adaptive duty-cycle-muting (DCM) mechanism is employed at the user terminals (UTs) to guarantee the fair coexistence with the WiFi networks. Moreover, Stackelberg game formulation is used to build up a decentralized radio and computational resources allocation framework, where the MEC server is modeled as a leader to set the price of the licensed spectrum, while UTs as followers compete for the radio and computational resources as a non-cooperative game. The objective of each UT is to minimize the long-term energy consumption with the guarantee of task buffer stability. Accordingly, we develop a distributed algorithm to achieve the equilibrium solution for the formulated Stackelberg game. Numerical results are presented to demonstrate that the proposed scheme is effective with respect to the reduction on energy consumption of UTs with limited signaling overheads.

Mobile edge computing (MEC) is a promising paradigm for alleviating the computation burden of resource-constrained mobile devices. Nevertheless, the majority of existing efforts concentrate on offloading computations from mobile de-vices to an edge computing server through the cellular networks only. With the development of wireless connectivity technologies, WiFi networks over unlicensed spectrum provide a “green” (i.e., cost-efficient and economical) alternative for computation offloading. In this paper, we investigate the problem of computation offloading in a heterogeneous WiFi and cellular MEC system, where both the WiFi and the cellular networks are possible for offloading the arriving computation tasks at a mobile user (MU). The objective of an MU is to minimize the long-term cost, which can be described as a single-agent Markov decision process (MDP) by accounting for the inherent system dynamics in the MU mobility, sporadic computation task arrivals and wireless connectivity variations. To solve the optimal strategy for the formulated MDP with a high-dimensional state space but without the statistical knowledge of system dynamics, we resort to a model-free deep reinforcement learning algorithm. Numerical experiments verify that the proposed algorithm is able to significantly reduce the average computation offloading cost compared with other baselines.

In this paper, a distributed power and spectrum allocation scheme is proposed to maximize the system energy efficiency (EE) for unlicensed device-to-device (D2D-U) networks. A non-convex optimization problem is formulated while considering the co-channel interference on licensed bands as the global constraint. To deal with the non-convex situation, the object function is converted into an equivalent convex one. Then, a distributed algorithm is developed to solve the optimization problem in which each D2D-U pair can find the optimal allocation strategy independently. The signaling overheads are analyzed and numerical results are presented to show that the proposed scheme is capable of achieving the optimal EE while confining the cochannel interference and guaranteeing the fair coexistence.

Unlike the conventional device-to-device (D2D) networks, the unlicensed D2D (D2D-U) pairs can not only reuse the licensed channels with the base station (BS) but also share the unlicensed channels with the WiFi stations. One challenge arises from the fact that the co-channel interference on licensed channels and the collision probability on unlicensed channels may cause extra power consumption at the terminals. Accordingly, we first propose a channel access method for the D2D-U pairs on unlicensed channels. Then, a decentralized joint spectrum and power allocation scheme is designed to minimize the power consumption at D2D-U pairs. Different from the existing distributed schemes, the proposed scheme can guarantee the global minimization of power consumption across the D2D-U pairs. Simulation results validate the theoretical analysis and verify the performance from the proposed scheme.

By enabling direct communications between nearby user equipment (UE), device‐to‐device (D2D) communication has become one of the key technologies in 5th generation (5G) mobile networks. D2D communication brings new communication opportunities for mobile devices, especially in a highly dense network. In this paper, D2D communication in the unlicensed spectrum, namely, D2D‐Unlicensed (D2D‐U), is discussed. The use of unlicensed frequency bands can ease the shortage of spectrum resources and improve network performance. However, the D2D‐U in 5G has significant effects on the network performance of existing unlicensed networks sharing the same frequency bands, such as Wi‐Fi and Bluetooth. Therefore, it is necessary to design a fair coexistence scheme for D2D‐U. To understand the coexistence problem, in this paper, we first formulate the network performance of D2D‐U and Wi‐Fi under two different coexistence schemes, namely, listen before talk (LBT) and duty cycle mechanism (DCM). Then, we use computer simulations to investigate a mode selection scheme that switches between these two schemes and point out the best possible solution for the coexistence between D2D‐U and Wi‐Fi.

Although the millimeter wave (mmWave) massive multiple‐input and multiple‐output (MIMO) system can potentially boost the network capacity for future communications, the pilot overhead of the system in practice will greatly increase, which causes a significant decrease in system performance. In this paper, we propose a novel grouping‐based channel estimation and tracking approach to reduce the pilot overhead and computational complexity while improving the estimation accuracy. Specifically, we design a low‐complexity iterative channel estimation and tracking algorithm by fully exploiting the sparsity of mmWave massive MIMO channels, where the signal eigenvectors are estimated and tracked based on the received signals at the base station (BS). With the recovered signal eigenvectors, the celebrated multiple‐signal classification (MUSIC) algorithm can be employed to estimate the direction of arrival (DoA) angles and the path amplitude for the user terminals (UTs). To improve the estimation accuracy and accelerate the tracking speed, we develop a closed‐form solution for updating the step‐size in the proposed iterative algorithm. Furthermore, a grouping method is proposed to reduce the number of sharing pilots in the scenario of multiple UTs to shorten the pilot overhead. The computational complexity of the proposed algorithm is analyzed. Simulation results are provided to verify the effectiveness of the proposed schemes in terms of the estimation accuracy, tracking speed, and overhead reduction.

We consider the full-duplex operation with network coding in two-hop relay networks to enhance the throughput for block transmission of packets. We propose a low-complexity transmission scheme, which does not rely on channel state information (CSI), and hence can be easily implemented in practical systems. We derive a closed-form upper bound on the asymptotic throughput of the proposed scheme, and show that the derived upper bound is tighter than a general upper bound on the throughput of any transmission scheme even with perfect CSI. Simulation results show that, the proposed scheme actually performs close to the general upper bound, and in most cases it substantially outperforms the traditional uncoded Automatic Repeat-reQuest scheme which relies heavily on the ACK/NAK feedback for packet retransmission.

In wireless federated learning system, wireless communication and local computation have a significant impact on the learning latency due to the limited bandwidth and computing power of mobile devices. To reduce the learning latency, local stochastic gradient methods and gradient compression can be applied, which however would decrease the convergence rate. To tackle such issues, in this paper, the trade-off between the convergence rate and the learning latency is taken into account. We first formulate an optimization problem to maximize the convergence rate under the given training latency constraint via jointly optimizing the batchsize, compression ratio, and spectrum allocation. Then, by decomposing the problem into two subproblems, an adaptive algorithm is proposed to obtain the optimal solution. The results show that batchsize and compression ratio should be selected according to the computing power and channel state information of the devices to improve the convergence rate. Finally, experimental results are presented to verify the effectiveness of the proposed algorithm.

Device-to-Device (D2D) communication, which enables direct connection between the nearby user equipments (UEs), is one of the key technologies in 5G network. In this paper, D2D communication on the unlicensed spectrum, namely, D2D-U is discussed. D2D system performance can be efficiently enhanced by utilizing the unlicensed band. However, it has a huge impact on the performance of other unlicensed networks. Therefore, the fairness of the coexistence scheme is a key problem for D2D-U. To solve the coexistence problem, two access schemes called Listen Before Talk (LBT) and Duty Cycle Mechanism (DCM) have been discussed extensively. D2D users choose the transmission mode according to transmission environments. In this paper, we discuss the performance of these two modes. Furthermore, the problem of how to access the unlicensed band with combination of these two modes is also discussed.

Green communications in dense small cell network (DSCNs) has attracted much attention. Energy saving (ES) and energy efficiency (EE) are two main goals and they are usually optimized separately. In this paper, taking into account the heterogeneity and cooperation opportunities among small cells, EE and ES are jointly optimized through subframe configuration and power allocation in the DSCNs. To quantize the heterogeneity, we define an EE preference function. Accordingly, a multi-objective optimization problem is formulated while considering the EE and ES optimization simultaneously. Due to the coupling of EE and ES, obtaining the solution is non-trivial. A heterogeneity-based ES and EE (HESEE) optimization algorithm is proposed, where the sleep mechanism is adopted via the subframe configuration. Particularly, the concave-convex procedure (CCCP) method is applied to solve the non-concave sum-of-ratios optimization for system EE. Simulation results show that the proposed HESEE algorithm can optimize the EE of small cell base stations (SBSs) according to their EE preference weights. In addition, compared with the base scheme, the HESEE algorithm can save energy by over 35.2% while improving system EE by up to 23%.

In this paper, inspired by the minority game (MG), we propose a distributed user association mechanism for the heterogenous networks (HetNets) on unlicensed bands. Our proposal aims to achieve load balance under different resource contention schemes between the LTE-unlicensed (LTE-U) and Wi-Fi networks in a fully distributed fashion. To formulate the user association problem as MG, we first prove that there exists a unique cut-off value in the single-AP scenario for both listen-before-talk and duty cycle muting schemes. Meanwhile, both the pure strategy and the mixed strategy are developed and the Nash equilibria are achieved. We further extend our analysis into the scenario with multiple Wi-Fi access points and prove the existence and uniqueness of the cut-off value set. Numerical results show that the proposed MG-based user association algorithm can achieve load balance and fine spectrum utilization without channel state information (CSI). Some inspiring results are also highlighted through the numerical simulation. The proposed distributed mechanisms not only handle the LTE-U/Wi-Fi selection, but also give fascinating insights into user association and resource allocation in other scenarios of heterogenous networks.

In this paper, a fast adaptive Principal Component Analysis (PCA) based scheme is proposed to estimate and track the channel state information (CSI) of two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular array (URA). First, the signals received online at the base station (BS) are used to estimate and track the principal components. Then, based on the estimated signal eigenvectors, a 2D unitary estimating signal parameters via rotational invariance technique (ESPRIT) algorithm is introduced to jointly estimate and track the channel coefficients which include the direction of arrival (DoA) of elevation and azimuth angles and the channel gain corresponding to each resolvable path of the channel. In order to improve the tracking speed, an optimal step size is derived which can accelerate the convergence speed of channel tracking significantly. Since the 2D unitary ESPRIT algorithm is applied, the proposed method can reduce computational complexity by converting the complex data matrix to the real one. Simulation results are provided to verify the estimation and tracking accuracy of the proposed scheme.

In licensed assisted access (LAA) based small cell networks (SCNs), the small base station (SBS) can reuse the uplink licensed bands with the macro cell while sharing the unlicensed bands with the Wi-Fi networks to improve its throughput. To mitigate the severe co-channel interference to the macro cell and guarantee the harmonious coexistence with the Wi-Fi networks, the spectrum and power should be jointly allocated at the SBSs. Moreover, to overcome the overwhelming signaling overheads introduced by the traditional centralized scheme and adapt to the variable radio environments, an adaptive decentralized scheme is necessary. Therefore, in this paper, an adaptive semi-distributed scheme is proposed to jointly allocate the power and spectrum on both licensed and unlicensed bands, which can achieve the global optimal spectrum efficiency (SE) of the SCNs. The proposed scheme can enable the SBSs to work independently and adaptively without sharing the whole information of the SBSs, but requires some Lagrangian parameters exchange via the coordination of the macro base station (MBS). Theoretical analysis and numerical results are presented to show that the proposed scheme is capable of achieving the optimal SE on both licensed and unlicensed bands adaptively while confining the co-channel interference to the MBS and guaranteeing the fair coexistence with the Wi-Fi network.

The long term evolution-unlicensed (LTE-U) has been introduced by the third Generation Partnership Project (3GPP) to meet the continued insufficient capacity challenges. As multi-radio access technology (RAT) HetNets gradually become the mainstream, RAT coordination and user association issues arise from the extensions of cellular networks to unlicensed bands, for fair and harmonious coexistence with other RATs such as WiFi. In this work, we propose for the first time a distributed method of RAT selection and user association based on minority game, where users self-organize to achieve a balanced state between the cellular and WiFi networks while seeking their own interests. This distributive method only requires minimal external information from the environment and low computational complexity. Numerical results demonstrate the effectiveness of our distributed method. Furthermore, our work could be extended to handle distributed RAT coordination and user association issues in other scenarios.

Principle component analysis (PCA) can be used to estimate the eigenvalues and eigenvectors of high dimensional data set with low complexity. By using this instinct feature, an adaptive PCA based channel parameter estimation and tracking method is proposed for two dimensional (2D) massive multiple-input and multiple-output (M-MIMO) systems with uniform rectangular arrays (URAs) of antennas in this paper. The online received data is used to estimate and track the channel parameters (including the direction of arrival angles and the path gain). Since the adaptive PCA method is used, the proposed scheme has low complexity and high accuracy for estimation and tracking which are verified via the numerical simulations.

In the paper, an inverse discrete Fourier transform-based Vandermonde-subspace frequency division multiplexing (IDFT-VFDM) waveform is proposed for the new radio (NR) supplementary uplink (SUL) to share the same time and frequency resources with the frequency division duplex (FDD) based long-term evolution (LTE) network. To avoid the co-channel interference to the LTE user equipment (UE) uplink transmission, the interference channel state information (CSI) is necessary for the NR UE to design the interference-free precoder. Since the operating band used for NR SUL corresponds to LTE FDD mode, the channel reciprocity condition in the time division duplex (TDD) mode is no longer held. To deal with it, the reciprocity on the channel related parameters for each path, i.e. amplitude, initial phase, propagation distance, angle of arrival, angle of departure, is exploited to estimate the uplink CSI from the NR UE to the LTE base station (BS) via the downlink CSI. Accordingly, the uplink waveform is designed for the NR UE to guarantee the absence of interference towards the LTE BS with the knowledge of uplink CSI. Numerical results are presented to validate the accuracy of the CSI estimation and the merit of the IDFT-VFDM as a potential waveform to achieve the LTE FDD-NR SUL co-existence.

Facing the challenges brought by the surge in the demand for mobile data traffic and increasingly scarce spectrum resources, two well-known channel access mechanisms named as duty-cycle muting (DCM) and listen- before-talk (LBT) have been proposed. In this article, we propose a novel adaptive hybrid channel access scheme which takes advantages of both mechanisms. Based on the WiFi traffic and the available licensed spectrum resource, our proposal can adaptively adjust the important parameters, such as the back-off window size and the duty-cycle time fraction, while ensuring fair and harmonious network coexistence between the WiFi and LTE-U systems. It can realize the flexible handoff between the DCM and LBT mechanisms to meet the requirements of different markets as well. Moreover, joint transmission power and spectrum resource allocation is also studied to improve the spectral efficiency on both licensed and unlicensed bands. The effectiveness of the proposed scheme is finally validated by numerical simulations.

In this paper, a low complexity channel parameter estimation method is proposed for two-dimensional (2D) uniform rectangular array (URA) massive multiple-input and multiple-output (MIMO) systems. Instead of assuming independent fading between different transmit-receive antenna pairs, a physical channel which models the realistic scattering environment via the angles and gains associated with different propagation paths is studied. A novel 2D Fourier transform (FT) based on elevation and azimuth steering factors is designed to derive the spatial spectrum distribution of received signals at base-station (BS). Accordingly, the received data matrices are used to estimate the channel parameters, which includes the direction of arrival (DOA) angles and the channel gains respective to each resolvable path. Since the channel coefficients are estimated from the DOA perspective and the proposed 2D FT can be realized by Fast-Fourier-Transform (FFT), the computational complexity is reduced significantly. Simulation results are provided to verify the accuracy and the complexity of the proposed scheme.

In the paper, we investigate the energy efficiency (EE) tradeoff between licensed and unlicensed bands for an LTE unlicensed (LTE-U) small cell system, where the small base station (SBS) can use both licensed and unlicensed bands to serve users. The tradeoff between the EE on licensed and unlicensed bands is first analyzed to reveal this interaction when the SBS reuses the licensed bands with a macro base station (MBS) and shares unlicensed bands with multiple Wi-Fi access points (AP)s. Accordingly, an algorithm is proposed to find the complete Pareto optimal solution set for the tradeoff problem via weighted Tchebycheff method. Then, numerical results are presented to validate the analysis and demonstrate the performance of the proposed scheme.

In this paper, we leverage multi-antenna transmit beamforming techniques in order to enable spatial reuse for coexisting LTE and Wi-Fi systems in unlicensed spectrum. For the cellular small cell base stations equipped with multiple transmit antennas and operating in the unlicensed spectrum, some spatial degrees of freedom (DoF)s are dedicated to serving small cell user terminals (SUEs) and others are employed to mitigate interference to the co-existing co-channel Wi-Fi users by applying a linear multi-user precoding technique, such as zero-forcing transmit beamforming (ZFBF). Through careful allocation of spatial DoFs, enhanced spatial reuse of unlicensed spectrum resources can be achieved, thereby improving spectrum efficiency on unlicensed bands. However, due to inherent channel state information (CSI) estimation and feedback errors, ZFBF cannot completely alleviate detrimental co-channel interference effects. After analysing the so-called intra radio technology (intra-RAT) interference among SUEs, i.e., the residual interference caused by imperfect CSI used in ZFBF, and the inter-RAT interference experienced by the Wi-Fi users, we derive the throughput of the co-existing LTE and Wi-Fi systems, respectively. Based on the derived throughput, spatial DoF and power can be optimally allocated to balance the throughput between the small cell and Wi-Fi systems in different scenarios. Our theoretical analysis and proposed schemes are further confirmed with exhaustive numerical simulation results.

In this paper, we study how to reuse the unlicensed spectrum in LTE-U systems while guaranteeing harmonious coexistence between the LTE-U and Wi-Fi systems. For a small cell with multiple antennas at the base station (SBS), some spatial degrees of freedom (DoFs) are used to serve small cell users (SUEs) while the rest are employed to mitigate the interference to the Wi-Fi users by applying zero-forcing beamforming (ZFBF). As a result, the LTE-U and Wi-Fi throughput can be balanced by carefully allocating the spatial DoFs. Due to the channel state information (CSI) estimation and feedback errors, ZFBF cannot eliminate the interference completely. We first analyze the residual interference among SUEs, called intra-RAT interference, and the interference to the Wi-Fi users, called inter-RAT interference after ZFBF, due to imperfect CSI. Based on the analysis, we derive the throughputs of the small cell and the Wi-Fi systems, respectively. Accordingly, a spatial DoF allocation scheme is proposed to balance the throughput between the small cell and the Wi-Fi systems. Our theoretical analysis and the proposed scheme are verified by simulation results.

Small cell base stations (SBSs) have been deployed in heterogeneous networks to improve the spectrum efficiency on licensed channels by reusing the spectrum resource of the macro base station (MBS). To relief the shortage on the licensed spectrum resources, licensed-assisted access (LAA) has been introduced to LTE small cell systems to share the unlicensed channel with the Wi-Fi users. In this paper, we investigate the fundamental tradeoff between the collision probability (CP) to the Wi-Fi users and the co-channel interference (CI) power to the MBS in such a heterogeneous LAA system. A multi-objective resource allocation problem is first formulated while guaranteeing the quality-of-service (QoS) of small cell users (SUEs). Then, the double waterfilling-line power allocation on the licensed and unlicensed channels is developed to analyze the CI-CP tradeoff and the weighted Tchebycheff method is applied to convert the multi-objective optimization problem into a single objective optimization problem. To find the complete set of Pareto optimal solutions to the tradeoff problem, a novel feasibility method is proposed. Based on the simulation results, the proposed joint resource allocation algorithm can achieve a flexible CI-CP tradeoff according to the QoS of SUEs in LAA systems.

To improve system capacity, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to use unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate joint licensed and unlicensed RB allocation to maximize the EE of each small cell base station (SBS) in a multi-SBS scenario, taking into account fair resource sharing between LTE and WiFi networks. The complete Pareto optimal EE set can be obtained by the weighted Tchebycheff method. We also develop an algorithm to provide fair EE among different SBSs based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithms.

Small cell deployment in heterogeneous networks, whereby small cell base stations (SBS) are deployed alongside traditional macro-cell base stations, is a proven solution for enhancing spatial frequency reuse across licensed spectrum in long-term evolution (LTE) networks. In order to mitigate the shortage of licensed spectrum resources, licensed-assisted access (LAA) has been introduced to allow LTE SBSs to share the unlicensed channel with WiFi nodes. As such, a complex yet interesting optimization problem results from the joint utilization of licensed and unlicensed spectrum resources by the SBSs to meet the quality-of-service (QoS) requirements of small cell users (SUEs). In this paper, we highlight the fundamental tradeoff induced by the SBSs between the amount of co-channel interference (CI) resulting from the reuse of licensed spectrum resources and the collision probability (CP) imposed on the co-existing WiFi nodes due to the sharing of unlicensed spectrum resources in such a coexisting LTE LAA-WiFi heterogeneous network deployment. We find that this fundamental tradeoff can be analyzed by developing a power allocation rule with double water-filling lines and the complete set of Pareto optimal solution can be achieved by the weighted Tchebycheff method. Our simulation results show that the proposed joint resource allocation algorithm can achieve a flexible and suitable tradeoff between the licensed spectrum CI and the WiFi CP according to the QoS requirements of SUEs in LTE LAA networks.

Driven by the demand for more radio spectrum resources, mobile operators are looking to exploit the unlicensed spectrum as a complement to the licensed spectrum. LTE-unlicensed (LTE-U), also referred to as licensed-assisted access by the third generation partnership project, is an extension of the LTE standard operating on the unlicensed spectrum. To realize LTE-U, its coexistence with Wi-Fi systems is the main challenge and must be addressed. In this paper, a listen-before-talk access mechanism featuring an adaptive distributed control function protocol is adopted for the small base stations (SBSs), whereby the backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and the Wi-Fi traffic load to satisfy the quality-of-service requirements of small cell users and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBS to limit collision with Wi-Fi traffic. Extensive simulation results show that the proposed schemes achieve fair and harmonious coexistence between LTE-U small cells and the surrounding Wi-Fi service sets and substantially outperform baseline non-adaptive channel access mechanisms in the unlicensed spectrum.

In this paper, an adaptive channel access mechanism is proposed to optimize the performance of licensed-assisted access (LAA) long-term evolution (LTE) small cell networks through joint allocation of licensed and unlicensed spectrum resources all the while ensuring a fair coexistence with Wi-Fi service sets on the unlicensed spectrum. A listen-before- talk (LBT) access mechanism featuring an adaptive distributed control function (DCF) protocol is adopted for the small cell base stations (SBSs), whereby the minimum backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and Wi-Fi traffic load to satisfy the quality-of-service (QoS) requirements of small cell users (SUs) and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBSs to limit collision with Wi-Fi traffic. Extensive numerical results are presented to demonstrate the effectiveness of the proposed schemes.

Multi-stream aggregation (MSA) allows users to receive data from multiple base stations simultaneously to increase their data rates. In this paper, we propose a joint user association and resource allocation algorithm for MSA systems to achieve energy efficiency (EE) balance among different base stations. The problem is formulated as a non-convex combinatorial sum-of-ratios optimization problem, which is very hard to solve directly. We first relax the combinatorial variables and then transform the problem into a convex optimization problem by the sum-of-ratios algorithm and the successive convex approximation (SCA) method. Based on this, a near-optimal algorithm is developed. Simulation results show that the proposed algorithm can achieve a good performance with a fast convergence speed.

A framework on decentralized interference coordination based on the pricing mechanism is developed for device-to-device (D2D) communication underlying cellular systems to guarantee quality of service (QoS) of both cellular users (CUs) and D2D links. We aim at coordinating two types of interference: inter-layer interference from D2D pairs to CUs and intra-layer interference among D2D pairs. The former is mitigated by the base station through setting a price on the channel being reused by D2D pairs while the latter is solved by a game-theoretic approach, in which the D2D pairs compete for the spectrum until a Nash Equilibrium (NE) is achieved. Finally, numerical results verify that the proposed distributed scheme is effective for the interference coordination and its performance is close to the centralized scheme.

Licensed-assisted access (LAA) has been developed to improve LTE system capacity by using unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate how to jointly allocate licensed and unlicensed RBs to achieve EE fairness among small cell base stations (SBSs), based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithm.

When sharing the unlicensed band with Wi-Fi users in unlicensed LTE (U-LTE) systems, the most critical issue is how to guarantee the harmonious coexistence between the two systems. On the other hand, when sharing the licensed band with the micro base station (MBS) in the small cell base station (SBSs), the co-channel interference needs to be properly coordinated. To address these two issues, power control, licensed and unlicensed spectrum allocation are jointly considered at the SBS to maximize the spectrum efficiency while guaranteeing the QoS of small cell users (SUs) and at the same time providing fair resource sharing with Wi-Fi users. The convex optimization method is applied to design the optimal scheme. Then, numerical results are provided to verify the proposed scheme and demonstrate the tradeoff between the Wi-Fi performance and the achievable spectrum efficiency at the SBS.

Device-to-device (D2D) communication underlaying cellular network has recently been proposed as a featured technique for future LTE systems. In this paper, we consider the scenario that D2D users can share the resources with multiple cellular users and investigate the joint reuse partner selection and power allocation strategy to maximize the overall system throughput. A heuristic suboptimal algorithm is developed, which is based on the reuse partner selection criterion obtained by utilizing the Lagrange relaxation method. Numerical results show that the system throughput will be improved dramatically by enabling D2D users to share resources with multiple cellular users.

In this study, a distributed biasing scheme is designed to achieve load balancing for heterogeneous networks. Based on the limited backhaul capacity and user distribution in the system, each small cell base station adaptively and distributively changes its cell range by setting the bias value, to effectively utilise the wireless resource and achieve load balancing as well. The Q ‐learning algorithm is adopted to design the biasing scheme in each small cell base station. The tradeoff between the backhaul resource utilisation and the quality‐of‐service of users is considered in the reward function of the Q ‐learning model. To examine the performance of the distributed scheme, a centralised scheme aiming at maximising the backhaul resource utilisation is also proposed for comparison, whose performance lower bound is derived. Numerical results show that the proposed distributed scheme can effectively utilise the backhaul resource for load balancing, and achieve a close performance to the centralised one.

In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co‐channel interference for small‐cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality‐of‐service cannot be satisfied because of the severe co‐channel interference from other users. Since the general bargaining problem is a non‐linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low‐complexity bargaining model which only takes into account the strongest co‐channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low‐complexity bargaining solution can achieve a close performance to the genetic algorithm‐based solution.

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.

In this paper, joint energy-efficient resource allocation for both the base station and users is studied for time division duplex (TDD) systems with carrier aggregation (CA). We aim at balancing the energy efficiency (EE) between downlink and uplink, as well as the EEs among individual users, by joint bandwidth and power allocation on each carrier component (CC). We formulate the optimization problem into maximizing the weighted summation of EEs for the base station and different users, where the weights are used to reflect the levels of importance. The objective function of the problem is a sum of several fractional functions, therefore, nonlinear sum-of-ratios programming needs to be used to solve it, which has not been exploited in resource allocation problems yet. Specifically, a novel transformation is performed to formulate an equivalent but better tractable problem, based on which we develop an iterative algorithm to find the global optimum of the considered problem. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm in terms of EE balancing.

In this paper, we propose a novel energy-efficient resource allocation method to simultaneously improve both downlink and uplink energy efficiency (EE) for time division duplex (TDD) systems with carrier aggregation (CA). We aim at EE tradeoff between downlink and uplink by optimizing the power and bandwidth allocation on each carrier component (CC) for each user. The objective function is a sum of several fractional functions, therefore, a novel nonlinear sum-of-ratios programming technique is used to solve it. We first transform the problem into an equivalent and better tractable one and then propose an iterative algorithm to find the global optimum solution. Numerical results show that our method can converge with an acceptable number of iterations and achieve flexible EE tradeoff between downlink and uplink.

Sleep mode control is essential to the energy efficiency of small cell networks. However, frequently switching on/off small cell base stations (SBSs) may cause the degradation to the quality‐of‐service of their users and the increase of network operational cost as well. In this study, the authors propose a novel dual‐threshold‐based sleep mode control strategy for small cell networks. The motivation of using dual‐thresholds to control the sleep mode is to minimise the network energy consumption while avoiding the frequent mode transitions of SBSs at the same time. They utilise the Markov chain method to analyse the performance of the proposed strategy. Optimisation problems are formulated to achieve the optimal dual‐thresholds for two different scenarios: the homogeneous threshold scenario in which uniform dual‐thresholds are applied to all SBSs and the heterogeneous threshold scenario where different dual‐thresholds are assigned to SBSs. For the homogeneous threshold scenario, they develop an optimal solution which is based on exhaustive searching. A reinforcement learning‐based algorithm and a heuristic algorithm are proposed for the heterogeneous threshold scenario, respectively. Simulation results are presented to demonstrate the performance of the author's proposed algorithms.

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

In this paper, we analyze the ergodic capacity of a dual-hop amplify-and-forward relaying system, where the relay is equipped with multiple antennas and subject to co-channel interference and the additive white Gaussian noise. Specifically, we consider three heuristic precoding schemes, where the relay first applies the: 1) maximal-ratio combining (MRC); 2) zero-forcing (ZF); and 3) minimum mean-squared error (MMSE) principle to combine the signal from the source, and then steers the transformed signal toward the destination with the maximum ratio transmission (MRT) technique. For the MRC/MRT and MMSE/MRT schemes, we present new tight analytical upper and lower bounds for the ergodic capacity, while for the ZF/MRT scheme, we derive a new exact analytical ergodic capacity expression. Moreover, we make a comparison among all three schemes, and our results reveal that, in terms of the ergodic capacity performance, the MMSE/MRT scheme always has the best performance and the ZF/MRT scheme is slightly inferior, while the MRC/MRT scheme is always the worst one. Finally, the asymptotic behavior of ergodic capacity for the three proposed schemes are characterized in large N scenario, where N is the number of relay antennas. Our results reveal that, in the large N regime, both the ZF/MRT and MMSE/MRT schemes have perfect interference cancellation capability, which is not possible with the MRC/MRT scheme.

In this paper, the interference mitigation problem in device-to-device (D2D) communication underlaying cellular networks using fractional frequency reuse (FFR) is studied. For such system, we propose a location based channel reusing scheme in which the D2D users in the inner region have a chance to reuse the channel resources of cellular users in the outer region of neighboring cells, and the D2D users in the outer region could reuse the channel resources of particular cellular users in the inner region of the same cell. Two novel concepts are introduced, namely the accessible region and the reusable region. To protect the outage probabilities of both cellular links and D2D links, only the D2D users in the accessible region could reuse the channel resources of cellular users in the reusable region. We present a specific method to calculate the boundary of the accessible region, as well as the reusable region. Simulation results demonstrate that the proposed scheme could effectively alleviate the interference between both users.

Two‐path successive relaying (TPSR) is an effective way to reduce the multiplex loss induced by the half‐duplex operation of the relay node in a conventional relay network. One crucial issue in TPSR network is that the listening relay always suffers inevitable inter‐relay interference (IRI), which degrades detection performance at the destination. In this study, a concatenated channel‐and‐network coding approach is proposed to solve the problem. In particular, a highly flexible channel code, namely, rateless code, is employed at the source to provide resilience to the residual IRI and reduce the retransmissions, which might break the system steady state of successive relaying. Then recognising the special interference structure, physical‐layer network coding is incorporated into the forwarding scheme of the relay nodes to exploit network diversity and improve system efficiency. By extrinsic information transfer analysis, the minimum number of required code symbols for successful data recovery are calculated, and degree distribution of the rateless code is optimised.

In this paper, we propose a three-stage rateless coded protocol for a half-duplex time-division two-way relay system, where two terminals send messages to each other through a relay between them. In the protocol, each terminal takes one of the first two stages respectively to encode its message using rateless code and broadcast the result until the relay acknowledges successful decoding. During the third stage, the relay combines and re-encodes both messages with a joint network-channel coding scheme based on rateless coding which provides incremental redundancy. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. The degree profiles of the specific rateless codes, i.e., Raptor codes, implemented at both terminals and the relay, are jointly optimized for both the AWGN channel and the Rayleigh block fading channel through solving a set of linear programming problems. Simulation results show that, the system throughput as well as the error rate achieved by the optimized degree profiles always outperforms those achieved by the conventional degree profile optimized for Binary Erasure Channel (BEC) and the previous network coding scheme with rateless codes.

In this paper, we design a joint channel-network coding scheme based on rateless code for the three-stage two-way relay system, where two terminals send messages to each other through a relay between them. Each terminal takes one of the first two stages to encode its message using a Raptor Code and then broadcasts the result into the air, respectively. In the third stage, upon successfully decoding the corresponding messages, the relay node re-encodes them with the new Raptor Codes, and then XORs the outputs and broadcasts the result to both terminals. Together with the packets received directly in previous stages, each terminal then retrieves the desired message using an iterative decoder. Here, the degree profiles of the Raptor Codes used at each node are jointly optimized through solving a set of linear programming problems. Simulations show that, the system throughput achieved by the optimized degree profiles always outperforms the one with conventional degree profile optimized for binary erasure channel (BEC) and the conventional network coding scheme with rateless coding.

Device-to-Device (D2D) communication as an underlaying cellular network empowers rich multimedia application, improves local communication, and enables local services, which would also bring out interference between cellular users and D2D terminals. In this paper, we study the challenges of the interference management in a hybrid network that D2D communication reuses uplink resource of cellular networks. We first introduce an interference coordination strategy for D2D communication. Then we design an optimal channel reusing selection algorithm for single cell scenario which is based on Hungarian algorithm. We also propose a heuristic algorithm to reduce the computational complexity. Our simulation results show that the heuristic algorithm has a close performance as the optimal algorithm with a significant decreasing of computational complexity, and both of the proposed algorithms could improve the system performance.

Self-organizing network (SON) is considered as a driving technology for the deployment of next generation radio access networks. This paper addresses the problem of load balancing (LB) for multi-hop cellular network (MCN) with fixed relays such as LTE-A network in the context of SON. The designed SON algorithm, namely RSPC-RL, is based on two ideas: relay node reference signal power control (RSPC) and multi-agent reinforcement learning (RL). In the proposed RSPC-RL algorithm, the relay node is modeled as an agent that learns an optimal policy of reference signal power control from its interaction with environment to balance the load distribution of the network through dynamically changing its coverage area. Numerical results show the significant performance gain brought about by the proposed algorithm RSPC-RL.

Relay assisted transmission could efficiently enhance the performance of Device-to-Device (D2D) communication when D2D user equipments (UEs) are too far away from each other or the quality of D2D channel is not good enough for direct communication. The relay selection problem for D2D communication underlaying cellular network is studied in this paper. We proposed a distributed relay selection method for relay assisted D2D communication system. The method firstly coordinates the interference caused by the coexistence of D2D system and cellular network and eliminates improper relays correspondingly. Next, the best relay is chosen among the optional relays using a simple distributed method. Numerical results show that performance of the proposed method is close to the optimal (centralized) method.

In this paper, the queue dynamics of secondary users (SUs) in a multi-SU and multi-channel cognitive radio network is analyzed to obtain the expressions of quality of service (QoS) metrics. Specially, in the analysis, we take several lower-layer mechanisms and settings into account, including automatic repeat request (ARQ), finite-size buffer, adaptive modulation and coding (AMC) and non-ignorable spectrum sensing errors. By modeling the queue dynamics as a Markov chain, we derive the analytical expressions of queue length, packet dropping rate and packet collision rate. Based on these expressions, the QoS metrics including delay, packet loss rate and throughput are calculated further. Through simulation, our queueing analysis is verified and the QoS metrics are investigated.

This paper considers the power allocation problem in a relay-assisted Time-Division-Duplex-based multiple-cell system. In such a system, the achievable data rate of each user is not only coupled with those of others due to the inherent dynamic spatial frequency reuse therein, but also coupled between consecutive time slots due to the required two-hop transmission, which results in the so-called "spatial-coupling" and "time-coupling" nature, respectively. To address both of the two coupling effects in a relay assisted TDD cellular system, we formulate an optimal power allocation problem for the asynchronous scenario of the system where different cells work independently. We observe that the problem is non-convex and NP-hard, but can be converted to Geometric Programming (GP) problems and solved by interior-point methods. In case there is no central controller and/or massive information exchange among cells is prohibitive, game theory is employed to derive the distributed solutions. Finally, we validate the proposed algorithms by extensive simulations.

A novel Distributed Spectrum-Aware Clustering (DSAC) scheme is proposed in the context of Cognitive Radio Sensor Networks (CRSN). DSAC aims at forming energy efficient clusters in a self-organized fashion while restricting interference to Primary User (PU) systems. The spectrum-aware clustered structure is presented where the communications consist of intra- cluster aggregation and inter-cluster relaying. In order to save communication power, the optimal number of clusters is derived and the idea of groupwise constrained clustering is introduced to minimize intra-cluster distance under spectrum-aware constraint. In terms of practical implementation, DSAC demonstrates preferable scalability and stability because of its low complexity and quick convergence under dynamic PU activity. Finally, simulation results are given to validate the proposed scheme.

A novel concept of Joint Source and Channel Sensing (JSCS) is introduced in the context of Cognitive Radio Sensor Networks(CRSN). Every sensor node has two basic tasks: application-oriented source sensing and ambient-oriented channel sensing. The former is to collect the application-specific source information and deliver it to the access point within some limit of distortion, while the latter is to find the vacant channels and provide spectrum access opportunities for the sensed source information. With in-depth exploration, we find that these two tasks are actually interrelated when taking into account the energy constraints. The main focus of this paper is to minimize the total power consumed by these two tasks while bounding the distortion of the application-specific source information. Firstly, we present a specific slotted sensing and transmission scheme, and establish the multi-task power consumption model. Secondly, we jointly analyze the interplay between these two sensing tasks, and then propose a proper sensing and power allocation scheme to minimize the total power consumption. Finally, simulation results are given to validate the proposed scheme.

In this paper, we propose a new user scheduling scheme which can reduce the system feedback significantly for the MIMO downlink system in multiuser cellular networks. The proposed scheme first performs user scheduling according to the valuebook-based scalar feedback of all users' channel gains and then applies block diagonalization (BD) precoding according to the codebook-based matrix feedback of the selected users' channel matrices. A novel method to generate the valuebook is designed. The proposed user scheduling scheme can reduce both the multiuser interference and the feedback bits with low computational complexity. It can be observed from the simulation results that, in the interference-limited SNR regime, the proposed scheme can achieve a sum rate very close to the case when the coordinated base stations know the channel state information perfectly.

To improve the performance of a relay system with multiple channels, the following issues should be addressed. Namely, how to allocate the power at source and relay to subchannels, how to pair subchannels of the first and second hops, and which users should be scheduled to which subchannel pair. Considering these issues in the design of an optimal joint resource allocation scheme in orthogonal channels, in this paper we study a multi-user network with single regenerative relay node. A stochastic optimization problem to maximize system ergodic throughput with joint transmission power constraint and user average data rate is formulated. To satisfy user average data rate request, a scheme with a weighted factor associated to each user at each time slot is proposed. The stochastic approximation method is utilized to estimate this weighted factor and the proof of optimality is given. With the help of this weighted factor, the problem is converted into a deterministic optimization problem in each time slot and the Lagrange dual method can be employed to derive the optimal solution. Finally, the Stochastic Optimal Programming (SOP) is used to evaluate the performance by computer simulations.

In this paper we study the problem of relay-enhanced cell (REC) coverage for which relay location is optimized to maximize the achievable REC radius. The problem is investigated for both Rayleigh and Rician relay fading channels, under a pre-determined user's outage probability constraint. We propose a statistical approach to formulate the problem and develop an optimization algorithm for it. The analytical derivation is justified by numerical simulations.

In this paper, based on the effective capacity theory, we identify the maximal arrival rate of secondary user that an arbitrary ON/OFF primary channel can sustain, in the presence of sensing errors. We find that, the arrival rate of secondary user with statistical QoS requirement, is limited by both the effective capacity provided by primary channel, and the packet collision probability constraint of primary user. In general, the above two constraints result in unequal arrival rates, exhibiting different impacts on spectrum utilization. Based on this observation, two spectrum utilization approaches, i.e., η-probability random access and sensing parameter adjustment, respectively, are proposed to fully utilize the transmission opportunities, depending on whether the sensing parameters could be adjusted or not. In specific, the η-probability random access approach reserves more transmission opportunities for other secondary users, which increases network throughput, while sensing parameter adjustment approach yields improved arrival rate for specific secondary user. Performances of the proposed approaches are validated by numerical results.

In this paper, we will study the joint problem of power allocation, relay selection and subchannel pairing in OFDM based amplify-and-forward multiple relays system. The optimization problem of maximizing system capacity under joint power constraint at source and relays is firstly formulated. Then, based on Lagrangian dual method, an optimal algorithm to the problem is derived with high SNR assumption. Both computational complexity and dual gap are analyzed. Through simulations, we show that the performance of the proposed algorithm is perfectly matched with that of exhaustive search method and the computational complexity of the proposed algorithm is acceptable for practical implementation.

In this paper, the problem of the multi-user uplink scheduling in cognitive radio cellular network (CogCell) is investigated. The objective is to maximize the system throughput, while protecting the QoS of primary user (PU) from being affected by secondary user (SU). Here, PU's QoS is represented by its signal-to-interference-plus-noise (SINR) outage probability. It is equivalent to say that SU can increase its transmit power to enhance the system performance as long as PU's SINR outage probability does not exceed the predefined threshold. So the first scheduling algorithm is proposed to maximize the system throughput through utilizing the multi-user diversity. Different from the first algorithm which does not take the fairness among SUs into account, the second scheduling algorithm with considering proportional fairness among SUs is proposed. It is shown to provide a satisfactory tradeoff between maximizing the system throughput and achieving fairness among SUs. Finally, these proposed algorithms are validated through extensive simulations.

In the presence of multiple non-regenerative relays, we derived optimal joint power allocation, relay selection, and subchannel pairing schemes in orthogonal frequency division multiplexing (OFDM) based wireless networks. The Lagrange dual method was employed to design the optimal algorithm. First, the optimization problem was formulated for the single-relay system and the optimal centralized algorithm was presented by resolving the dual problem. Next, the optimal algorithm for a multi-relay system was proposed in a similar way. Compared with the exhaustive search method, the computational complexity of the proposed optimal algorithms was reduced from non-polynomial to polynomial time. Finally, the centralized algorithm was extended to the distributed algorithm, which was more feasible for the practical system. Simulation results verify our analysis.

Relay selection and subchannel pairing are important issues in OFDM based cooperative wireless systems to improve the system performance and reliability. Most of the relay selection schemes in modern literatures are centralized under the assumption that the overall channel condition is known at the source node, each relay node and the destination node which is impractical. In this paper, we will propose a low computational complexity distributed relay selection and subchannel pairing algorithms under limited channel state information. Through the computer simulation, we found that under the limited channel state information constraint, the proposed algorithm can achieve a much better system performance than the traditional relay selection and subchannel matching schemes and earn most of the system performance of the optimal one.