Integrated sensing and communication (ISAC) can enhance artificial intelligence-generated content (AIGC) networks by providing efficient sensing and transmission. Existing AIGC services usually assume that the accuracy of the generated content can be ensured, given accurate input data (e.g., pose image) and command (i.e., prompt), thus only the content generation quality (CGQ) is concerned. However, it is not applicable in ISAC-based AIGC networks, where content generation is based on inaccurate sensed data. Moreover, the AIGC model itself introduces generation errors, which depend on the number of generating steps (i.e., computing resources). Thus, to assess the quality of experience (QoE) of ISAC-based AIGC services, this paper proposes a content accuracy and quality aware service assessment metric (CAQA). Since allocating more resources to sensing and generating improves content accuracy but may reduce communication quality, and vice versa, this sensing-generating (computing)-communication three-dimensional resource tradeoff must be optimized to maximize the average CAQA (AvgCAQA) across all users with AIGC (CAQA-AIGC). This problem is NP-hard, with a large solution space that grows exponentially with the number of users. To solve the CAQA-AIGC problem with low complexity, a standard linear programming (LP) guided deep reinforcement learning (DRL) algorithm with an action filter (LPDRL-F) is proposed. Through the LP-guided approach and the action filter, LPDRL-F can transform the original three-dimensional solution space to two dimensions, reducing complexity while improving the learning performance of DRL. Simulations show that compared to existing DRL and generative diffusion model (GDM) algorithms without LP, LPDRL-F converges faster and finds better resource allocation solutions, thus improving AvgCAQA by more than 10%. With LPDRL-F, CAQA-AIGC can achieve an improvement in AvgCAQA of more than 50% compared to existing schemes focusing solely on CGQ.

Mobile Edge Computing (MEC) is promising to enable low delay services with which users can offload computing intensive and delay sensitive tasks to the edge. Considering a multi-cell MEC (MC-MEC) network without sufficient resources to serve all users, user selection and non-orthogonal multiple access (NOMA) should be introduced. Then, to maximize the delay-aware average user service satisfaction degree (DA-AveUSD), user selection and resource allocation are jointly optimized (DA-JUSRA), which is modeled as a mixed integer nonlinear programming (MINLP) problem and proven to be NP-hard. To solve this problem, it is decomposed into two independent subproblems, i.e., the power allocation (PA) problem and the user selection, subchannel scheduling and computing resource allocation (USC) problem. Next, a convex evolutionary alternating optimization (CEAO) algorithm is proposed, which alternately applies the convex optimization method and the Karush-Kuhn-Tucker (KKT)-embedding enhanced elite genetic algorithm KKT-embedding E2GA to solve the PA and the USC problem, respectively. Simulations show that compared to the optimal exhaustive search algorithm, the proposed CEAO algorithm converges rapidly within a few iterations, with a gap in DA-AveUSD of less than 1% to the optimum performance. Next, compared to existing user selection schemes, DA-JUSRA with CEAO can enhance DA-AveUSD by more than 50% and yield a higher optimal load.

In integrated sensing and communication (ISAC) enabled unmanned aerial vehicle (UAV) systems, based on sensed information such as user positions, UAV scheduling could be optimized to enhance the communication performance. However, sensing errors are inevitable, leading to a performance degradation. This paper proposes a sensing-error-aware (SEA) multi-UAV scheduling scheme (SEA-scheduling). First, the impact of the sensing errors on communication performance is analyzed, and a SEA communication rate is derived. Then, targeting to maximize this SEA rate, multi-UAV collaborative scheduling is jointly optimized with sensing resource allocation. The problem is solved by decomposing into two subproblems, i.e., a joint UAV position schedule, user association and bandwidth allocation optimization subproblem (PUB) and a sensing resource optimization subproblem (SRO), which can be solved iteratively. A generative diffusion(GD)-driven multi-agent reinforcement learning (GD-MADRL) algorithm is proposed to solve PUB, and a classical simulated annealing (SA) algorithm is adopted to solve SRO. The main idea of GD-MADRL is to introduce the GD model in MADRL to generate training data with sensing errors, enhancing the robustness of generated UAV scheduling strategies. Simulation results demonstrate that when there are sensing errors, the proposed SEA-scheduling scheme improves the communication rate by up to 30% compared to existing sensing-error-unaware schemes.

In this paper, we present a novel orthogonal Doppler frequency modulation (ODFM) to meet the challenging requirements of high-mobility communications. Firstly, we revisit the basic concepts of multicarrier modulations (MCMs) and discuss the capability to explore both time and frequency diversity in doubly selective channels for the most popular MCMs. We then illustrate how ODFM maps information symbols into Doppler-frequency domain and converts them into time-delay domain for transmission, where we show ODFM has the potential to exploit both delay space (frequency) and Doppler space (time) diversity. Specifically, we propose two types of ODFM schemes using the conventional time-frequency domain orthogonal pulses (TFOP-ODFM) and the novel delay-Doppler domain orthogonal pulses (DDOP-ODFM). We give exact channel input-output relations (IORs) in the Doppler-frequency domain of these two ODFM schemes and show their properties in efficient channels, implementation complexity, and others. Subsequently, we simulate the performance of these ODFM schemes with other MCMs, such as orthogonal frequency division multiplexing (OFDM), and orthogonal time frequency space (OTFS) modulation. In particular, we investigate the performance of the TFOP-ODFM in the physical downlink shared channel of the Five-Generation New Radio (5G NR) link, where TFOP-ODFM show better performance than OFDM and OTFS.

Ultra-reliable and low-latency communication (uRLLC) is an important feature for the fifth-generation mobile network and beyond ($\mathbf{5 G} \boldsymbol{/} \mathbf{B 5 G}$). It has stringent requirements for ultra-high reliability (e.g., 1e-5) and extremely low user plane latency (e.g., 1ms). For cell edge user equipments (UEs) with severe inter-cell interference, uRLLC availability, i.e., defined as the probability of satisfying both reliability and latency requirements simultaneously, tends to be low. Under the constraint of 5G new radio (NR) protocols, in order to improve the availability of uRLLC, the bottleneck mainly lies in the guarantee of transmission reliability. Coordinated multi-point (CoMP) transmission is an enabling technique to improve the uRLLC availability via spatial diversity. This paper mainly focuses on the availability analysis of CoMP-enabled uRLLC with NR protocols. The uRLLC availability can be equivalent to a full probability model with the conditional probability of reliability given a latency constraint. Firstly, considering the NR frame structure and the procedure of the user plane processing with hybrid automatic repeat request (HARQ), a maximum transmission count is obtained given the latency requirement. Then, the system uRLLC availability is derived with the maximum transmission count for typical and edge UEs using stochastic geometry (SG). Finally, the analytical results are validated by Monte Carlo simulations. Moreover, the results show that in a single transmission, the system uRLLC availability with CoMP can be improved by 39.20 % for typical UEs and 322.44% for edge UEs compared with No-CoMP.

The maximum distance separable (MDS) coding based caching is effective to reduce the delivery delay of content in low earth orbit (LEO) satellite networks. However, due to the severe packet loss of satellite links and the variations among them, the existing distance aware delivery node selection methods may lead to significant packet loss and delivery delay. To solve this problem, a packet loss aware delivery node selection method is proposed in this paper. First, the delivery delay of coded sub-contents is analyzed by taking packet loss into account. And the delivery node selection problem is formulated as a delivery delay minimization problem. Then, the problem is divided into multiple sub-problems according to the time slot, where each sub-problem is a single-constraint knapsack problem. Based on the Edmonds theorem, the greedy algorithm is used to obtain the optimal solution. Finally, simulations are carried out on a walkerdelta constellation to verify the performance of the proposed method. And the results show that the user perceived packet loss and the packet retransmission induced delivery delay can be reduced by 83% and 42.8%, respectively, when compared with the existing distance aware delivery node selection method.

In this paper, we focus on the sensing performance for communication-centric integration of sensing and communication (ISAC) systems, which utilize communication signals for sensing. Specifically, we mainly focus on the sensing capabilities of four representative modulation schemes, corresponding to four fundamental channel representations: single-carrier (SC), orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS) modulation and orthogonal Doppler frequency modulation (ODFM) in time-delay, time-frequency, delay-Doppler, and Doppler-frequency domains, respectively. We compute the autocorrelations of these modulation signals and give a general Kronecker-based framework to derive their delay profiles and sidelobe levels. Considering the randomness of communication signals, we derive the expected mainlobe and sidelobe levels for these modulation schemes and define intra-slot and inter-slot transforms for symbols within and across time slots. Theoretical results reveal that the intra-slot transform of modulation schemes determines delay profiles, and waveforms defined in the frequency domain achieve superior ranging performance compared to those in the delay domain. Additionally, we investigate the impact of constellation mapping on sensing performance, demonstrating that frequency-domain modulations achieve optimal ranging performance when using constant-modulus mapping constellations. The simulation results verify the theoretical analysis and provide a sensing performance comparison across these modulation schemes in terms of detection probability Pdand root mean square error (RMSE).

With the growing diversity and density of mobile nodes, indoor wireless channels are becoming increasingly complex. Existing channel modeling methods struggle to balance accuracy and adaptability, which calls for low-complexity models capable of capturing dynamic indoor environments efficiently. This paper proposes a novel propagation graph (PG) framework that models the channel effects of dynamic objects indoors by designing a scattering-aware point cloud neural network (SPointNet). The proposed PG framework explicitly incorporates reflection, transmission, and diffuse scattering into channel modeling, and employs a physics-aware scatterer discretization and classification strategy, which reduces the complexity of the conducted graph. Then, SPointNet enables fast estimation of scattering coefficients, allowing the model to bypass exhaustive analysis of material properties. Finally, we conduct channel measurements in real indoor environments to validate the proposed approach. Experimental results show that the proposed model accurately models channel responses while significantly reducing modeling time compared to traditional PG-based methods.

The deployment of object detection tasks on embedded or mobile platforms has become increasingly prevalent, driven by the heightened demand across various scenarios. However, for object recognition tasks such as gesture recognition, the use of overly complex network models presents a formidable obstacle in achieving real-time detection tasks, and the majority of lightweight convolution menthods based on depth-separated convolution lack accuracy. In this paper, we propose a lightweight and highly accurate convolutional neural network for gesture recognition on terminal device (GRTD-Net), specially designed for devices with scarce computing power and tight hardware resources. In GRTD-Net, we proposed the convolution method R2SGConv that masterfully harmonizes model size and accuracy, elegantly achieving a delicate balance between efficiency and lightweight design. Moreover, we propose a neck network paradigm with good feature fusion capability to compensate for the accuracy degradation due to the use of lightweight convolutional modules in neck networks. Experimental results show that the proposed GRTD-Net model improves the mAP0.5and mAP0.95by 0.8% and 2.2%, reduces model parameters by 35.2%, increases FPS by 20.7%, and reduces the inference latency by 2.9 ms, compared with the popular YOLOv5 algorithm on the dataset Gesture. We successfully deployed GRTD-Net on ARM devices and proved its practicality in constrained environments.

In mobile edge computing (MEC) enabled Industrial Internet of Things (MEC-IIoT), task offloading and resource allocation are always jointly optimized to achieve the best energy efficiency of IIoT terminals, which is important for MEC-IIoT. Existing research mainly focused on the instant energy consumption for the current task, ignoring the fact that the energy consumption is long term since energy is also required for subsequent tasks. Excessive energy consumption by the current task will affect the execution of subsequent tasks, leading to a degraded quality of service (QoS). Meanwhile, to solve the joint optimization problem, existing multi-agent deep reinforcement learning (MADRL) based methods face challenges like slow convergence. To tackle these problems, this paper proposes a long-term energy control enabled joint optimization scheme for the task offloading and resource allocation (LTE-JTORA). The main idea is to maximize the terminal energy efficiency while enabling the terminal to complete as many tasks as possible, so that the long-term energy control is realized. Then, an energy efficiency (EE) reward based collaborative MADRL (EE-CMADRL) is proposed to solve the NP-hard optimization problem. Different to MADRL where multiple agents work independently, EE-CMADRL is based on the Centralized Training Distributed Execution (CTDE) framework, where multiple agents can work collaboratively to train one EE reward enabled critic network. With more diverse data from multiple actor networks, EE-CMADRL can converge fast. Simulation results show that, compared to existing MADRL algorithms, the proposed EE-CMADRL improves the convergence speed by 71%. Compared to existing schemes with instant energy control, with the same time and energy constraints, the proposed LTE-JTORA scheme increases the number of completed tasks by 57%, and improves energy efficiency by 68%.

With the growing demand for high spectral efficiency in large-scale ad hoc networks, traditional half-duplex communication fails to meet the requirements. Although in-band full-duplex (IBFD) communication improves spectral efficiency, there is serious mutual interference (MI) in high-density node environments. This paper proposes a novel Frequency Hopping and Duplex Mode Selection (FH-DMS) method for MI suppression. By leveraging stochastic geometry theory, the throughput of the IBFD network with FH-DMS is derived. Furthermore, the optimization of the number of frequency hopping points and the FD mode selection probability is analyzed to maximize throughput. Simulation results show that the proposed method reduces MI intensity nearly tenfold and increases throughput by approximately ten times, particularly in high-density, self-interference-limited high-quality communication scenarios.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation enhances transmission in high-mobility scenarios but also increases detection complexity, especially when fractional Doppler shifts are present. To tackle these issues, we propose a low-complexity sparse pre-conditioned conjugate gradient-linear minimum mean square error (SPCG-LMMSE) detector, which can obtain the LMMSE solution without needing matrix inversion. Based on the sparsity and quasi-banded structure of the ODDM time-domain equalization matrix, the algorithm utilizes an improved conjugate gradient method to approach the optimal solution quickly. Simulation results demonstrate that the detector can significantly reduce complexity while maintaining excellent performance, with additional gains in multipath and high-order modulation scenarios.

The multiplexing of Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) results in a loss of the eMBB transmission rate. Existing multiplexing resource allocation schemes focus on enhancing the eMBB data rate, ignoring the Quality of Experience (QoE) of URLLC users and degrading the profit of the operator. Considering all users' QoE, we define the operator profit as the weighted difference between the average QoE and the operator cost which integrates the eMBB transmission rate loss and the successful gain of the URLLC service. This paper focuses on the cost aware eMBB-URLLC multiplexing resource allocation (CAMRA) to maximize the operator profit. With our proposed event-driven Actor-Critic (EDAC) Deep Reinforcement Learning (DRL) algorithm, CAMRA scheme can enhance the operator profit by more than 30% compared to the Rate- Aware strategy,

Orthogonal time-frequency space (OTFS) modulation has emerged as a promising solution for reliable communication in high-mobility scenarios, addressing the limitations of traditional orthogonal frequency-division multiplexing (OFDM) systems. However, existing OTFS detection methods, including linear, nonlinear, and AI-based detectors, struggle with either high computational complexity or suboptimal performance. To overcome these limitations, we propose a low-complexity graph attention network-based OTFS detector (GAT-OTFS) tailored for the reduced cyclic prefix (RCP) OTFS scenario. Our approach leverages the sparsity of the DD domain equivalent channel to reduce complexity by precisely constructing a sparse graph for GNN-based detection. By introducing an attention mechanism, the GAT-OTFS effectively captures the correlation between different channel paths, enhancing detection accuracy. Simulation results demonstrate that the GAT-OTFS offers substantial performance improvements over existing detectors, with reduced complexity, making it a viable solution for future high-mobility communication systems.

Environment-dependent applications, such as high-definition maps, that rely on environmental information as input data deserve further research to reduce the amount of transmission data. The cooperation of crowndsensing and local preprocessing is a potential solution to sense and preprocess the environmental data in real-time. However, the sensing and computing capabilities of different participants are heterogeneous, which may lead to significant differences in the total amount of transmission data (TATD). Therefore, a novel communication-efficient participant selection strategy is proposed, incorporating heterogeneous sensing, communication, and computing resources. The matching process between target sensing subregions and participants, along with preprocessing task allocation, is jointly optimized to minimize the TATD. A heuristic algorithm with low complexity is developed to solve the optimization problem. Performance evaluations show that the proposed mechanism can reduce the TATD by up to 62.8% compared with benchmark mechanisms.

Task-oriented communications with semantic encoders are promising to enhance the communication efficiency, by selecting and transmitting valuable data according to task requirements/queries. However, existing semantic encoders lack the capability to track the changing in queries, leading to biased data selection. This paper proposes a query-aware semantic encoder, i.e., Query-Data Cross (QDC) encoder for task-oriented communications. By consistently focusing on data features that are most relevant to the current query at the transmitter, QDC can adapt to changing queries. Based on the dynamic semantic relevance obtained by QDC, a relevance-based data selection and bandwidth allocation optimization (RDSBA) problem is formulated, considering a multi-device task-oriented communication system, where devices should transmit valuable data with high relevance to the queries broadcasted by the base station (BS). RDSBA aims to maximize the data profit of all devices, which is defined as the difference between the relevance of data selected for the BS and the cost of obtaining the data. Then, a DRL-based data selection and bandwidth allocation (DRL-DB) algorithm is proposed to solve the NP-hard optimization problem. Simulation results demonstrate that QDC can smartly track the changing in queries and achieve an accuracy of at least 85% in relevance evaluation, more than 8% higher than existing schemes. Based on the relevance provided by QDC, the proposed RDSBA scheme with DRL-DB can increase the data profit by at least 18%, comparing to existing schemes.

Although resource sharing and mixed numerology among slices are promising for improving wireless resource utilization, these techniques can compromise isolation performance and cause serious inter numerology interference (INI). Therefore, this paper studies wireless resource management in a mixed numerology-based sliced 6G space–terrestrial integrated radio access network (STI-RAN) with the aim of reducing INI and guaranteeing isolation performance while decreasing interference from Doppler frequency shifts caused by the high-speed movement of low-orbit satellites. First, an isolation performance indicator is defined to evaluate different isolation performances, and a universal spectral distance model is formulated to rewrite the INI power model. Next, the dynamic wireless resource management problem is formulated in a discrete form, yielding a scheme called Flex-$\mu$, which is designed to reduce the INI and Doppler frequency shifts, guarantee isolation performance, and enhance the SINR. Finally, an intelligent multi-characteristic matrix coding-based social group optimization (MultiMatrix-SGO) algorithm is designed to solve the proposed NP-hard discrete optimization problem. Compared with existing schemes, the system utility is efficiently increased by up to 58.32%, the SINR can converge to 38.44 dB, and the isolation performance is guaranteed while the INI and Doppler frequency shifts are reduced.

In low earth orbit satellite (LEO-Sat) communication systems based on orthogonal time frequency space (OTFS) modulation, existing channel estimation methods usually use high-power pilots, leading to a high peak-to-average power ratio (PAPR). In this paper, we propose a comb-type pilot based frame structure by inserting multiple identical Zadoff-Chu (ZC) sequences along the Doppler dimension. The ZC sequences are arranged at equal intervals in the delay dimension. The power of pilots is consistent with the power of data symbols, which can achieve low PAPR at the transmitter. At the receiver, based on the analyzed power distribution of the correlation function, we derive the power threshold to identify the received pilots. Then, an iterative ZC-sequences based channel estimation algorithm is proposed aided by data detection and interference cancellation. Through iterations, the data interference can be alleviated and estimation accuracy can be improved. Simulation results demonstrate that the proposed scheme can achieve superior channel estimation performance with low PAPR in the time domain.

Decentralized coded caching (DCC) is promising to relieve the load pressure of the networks (i.e., reducing the delivery rate) by creating multicast opportunities for a group of users. However, DCC suffers from high complexity (i.e., exponential of user number) because multicast opportunities are obtained by traversing all the possible user subsets. Considering individual user preferences, this article proposes a low-complexity user-pair-based modified DCC scheme (UP-MDCC). It only traverses user subsets with two users (i.e., user-pair) to generate coded subpackages, while obtaining similar delivery rate performance as DCC. To achieve this, we first update the traditional DCC to modified DCC (MDCC). Different to DCC which caches all N contents uniformly, MDCC can choose the favorite$N_{s}$contents to cache, such that more caching capacity can be allocated to the contents with higher requesting probability. Then, given MDCC, the relationship between the delivery rate and the number of cached contents is derived. It is revealed that when the favorite contents are entirely cached for each user, more than 99% of the delivery rate gain is generated by the user subsets containing only two users. Therefore, a low-complexity UP-MDCC can be proposed by limiting MDCC to traverse only the user subsets containing two users. Moreover, to find the optimal user-pairs to provide the minimum delivery rate for UP-MDCC, a graph-partitioning-based user-pairing strategy (GP-UPS) is proposed. Simulations verify that with GP-UPS, UP-MDCC can achieve similar delivery rate as that of uncoded placement absolutely fair (UPAF) caching and MDCC with a gap of less than 1%. Moreover, compared to DCC, the number of user subsets traversed is significantly reduced in UP-MDCC and the complexity is reduced from exponential to square of user number.

Collaborative mobile edge computing enables resource-constrained edge facilities to work cooperatively for computation-intensive tasks. However, as the number of tasks demanded by various applications increases, resource competition is inevitable in edge facilities. Existing works tackle the resource competition problem with a first come first served (FCFS) scheme, which is blind to different delay requirements among tasks. This may result in tasks with higher delay requirements waiting a long time for service, thereby reducing overall service quality. This paper proposes a prioritized queuing scheme with task dependency (PQTD), which allows high-prioritized sub-tasks with higher delay requirements to jump into the queue ahead of low-prioritized sub-tasks with lower delay requirements. To describe the complicated delay change caused by queue-jumping, a joint DAG-queue delay (JDQD) model is proposed, which analyzes the chain reaction of delay changes caused by the processing queue on the server and the task dependency. With JDQD, a multi-task assignment optimization problem is formulated to maximize the average satisfaction degree (AvgSatD), which is defined according to the priorities of the tasks and their delay requirements. Then, a tree-based algorithm is proposed to solve the NP-hard optimization problem, i.e., Monte Carlo Tree Search (MCTS). Simulation results demonstrate the effectiveness of the PQTD queuing scheme and tree search mechanism of MCTS. Overall, PQTD + MCTS can increase AvgSatD by at least 45.8% with an acceptable complexity.

Federated Learning (FL) empowers wireless intelligent applications, by leveraging distributed data of edge clients for training without compromising privacy. Client selection is inevitable in FL, since clients have diversified learning capabilities arising from heterogeneous computing and communication resources. Existing methods like fair-selection and dropping-straggler are either inefficient or unfair (resulting in a less effective trained model). Therefore, we propose FedAoI, an Age-of-Information (AoI) based client selection policy. FedAoI ensures fairness by allowing all clients, including stragglers, to submit their model updates while maintaining high training efficiency by keeping round completion times short. This trade-off is achieved by minimizing Peak-AoI (PAoI), the interval between a client's consecutive participations. An optimization problem is formulated by minimizing the Expected-Weighted-Sum-of-PAoI. This NP-hard problem is addressed with a two-step sub-optimal algorithm, PriorS. It first calculates client priority in a round using Lyapunov optimization and then selects the highest-priority clients through G-FPFC (Greedy minimization of the round weighted-sum-of-PAoI with First-Priority-First-Considered). Simulation results demonstrate that, compared to fair-selection, FedAoI improves average efficiency by 83.8% and achieves an average model accuracy of 97.3% (or at the cost of averaging 2.7% degradation in model accuracy). Compared to dropping-straggler, FedAoI reduces the average model accuracy degradation from 9.5% to 2.7%.

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services' satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services' satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

Digital Twin technology is a valuable approach for modeling and simulating complex network systems. It is used to comprehensive analysis of network performance, protocols and configurations. The ns-3 simulator has the capability to simulate real-world network environments, supporting the implementation of Network Digital Twins. However, the current ns-3 simulator pays more attention to simulating network protocol algorithm, while ignoring the network packet processing in device node. It can not simulate packet processing based on OS. In this paper we propose a new architecture based on ns-3 for simulating OS packet processing mechanism, called NS3 - Modular Packet Processing Simulation Architecture (NS3-MPPSA). In NS3-MPPSA, various hardware resources involved in packet processing are modeled. Packet processing mechanisms can be implemented by selecting and combining processing units flexibly. The obtained results indicate that NS3-MPPSA enhances the capabilities of ns-3, enabling accurate and realistic simulation of packet processing. Thus, it provides better support for the implementation of Network Digital Twins.

In this paper, we study a secure communication scheme assisted by multiple distributed friendly users over fading channels. To defeat a malicious user, friendly users transmit artificial noise continuously regardless of whether the transmitter is sending signals or not. The idle nodes in wireless networks can be selected as friendly users to assist in achieving secure communications. Due to the different positions of friendly users relative to the malicious user and receiver, the assisting abilities of friendly users in secure communications are different. To explore the potential of friendly users, a metric factor is proposed in this paper to evaluate the assisting abilities of friendly users. Based on the evaluating results, an optimum user selection and power allocation scheme is designed to maximize the secure throughput under the power limit and secure constraint. Compared with the benchmark scheme, the proposed secure communication scheme is easy to implement. Moreover, according to the numerical results, the proposed scheme can improve the secure throughput by more than 16% compared with the benchmark scheme when the secure constraint is larger than 0.5.

The Doppler frequency offset which caused by the high dynamic characteristics of LEO satellites increases the difficulty of signal recovery at the receiving terminal. Aiming at the Doppler frequency offset problem of LEO satellite communication systems, this paper proposes a Doppler frequency offset pre-compensation algorithm based on Multimodal Long-Short Term Memory (MLSTM-DPC). By judging the difference between the current ephemeris data and the current time, select single or multiple LSTM models to predict the orbital parameters. The predicted orbit parameters and traditional algorithms are used to extrapolate the orbit. Finally, the value of Doppler frequency offset pre-compensation is predicted. The simulation results show that the effective frequency offset ratio of MLSTM-DPC algorithm is 36.39% higher than that of comparison algorithm, and the computational time is significantly reduced. In a certain range, if the TLE data is farther from the reference time, the advantage of using MLSTM-DPC Doppler frequency offset precompensation algorithm for precompensation is more obvious. The algorithm provides a reliable pre-compensation mechanism for terminal fast synchronous access.

Resource management is important for wireless networks. However, most model-driven resource allocation algorithms are limited by computational complexity and suboptimal spectral efficiency. This paper focuses on learning-based resource management to improve spectral efficiency in multicellular networks with imperfect channel state information (CSI). Considering imperfect CSI, the joint subchannel selection and power allocation problem can be formulated as a probability-constrained non-convex optimization problem. By means of parameter transformation, the non-convex optimization problem with probabilistic constraints is first transformed into a non-probabilistic optimization problem. Then, to solve this problem, a dual-module network based on dueling deep Q-network and deep deterministic policy gradient algorithm is proposed to maximize spectral efficiency. Simulation results show that the proposed dual-module network outperforms model-driven optimization algorithms such as fractional programming and existing deep reinforcement learning algorithms in terms of spectral efficiency.

Dynamic changes in network topology bring challenges to the management of mega low earth orbit (mega-LEO) systems. End-to-end network topology prediction is one of the key technologies to meet the challenges. At present, the graph theory-based prediction method can predict periodic changing links such as inter-satellite links (ISL) and satellite-ground links (GSL), but it cannot support the prediction of aperiodic user links. Moreover, when the scale of network nodes grows, the memory consumption and calculation time also increase rapidly, and not applicable in LEO mega-constellation networks with more than 10,000 nodes, such as Starlink satellite networks. To address these problems, we propose a prediction method based on graph convolutional neural network (GCN) and gated recursive unit (GRU). The key point of our method is to predict the end-to-end link changes of LEO mega-constellation, while reducing memory consumption and computing time. Simulation results show that the proposed method can achieve the topology prediction accuracy of more than 85% and reduce the memory consumption and computation time by more than 25% and 18.1%, respectively.

Federated learning (FL) over mobile networks has attracted intensive attention recently. User mobility is a fundamental feature of mobile networks, which leads to dynamic network topology and wireless connectivity losses. As such, user mobility is usually considered a “trouble maker” and a great challenge to FL over mobile networks. Interestingly, we found that small user mobility can positively contribute to improving FL performance. This is because the total dataset size and the data diversity that the FL can utilize are increased by user mobility. Based on this observation, we aim to tame and exploit mobility instead of treating it as a hostile “trouble maker”. To this end, we first investigate how the FL performance changes with user mobility theoretically by jointly taking into account the positive and negative aspects of mobility. Specifically, a closed-form expression to quantify the impact of mobility on the FL loss is derived, which explains when negative or positive aspects of mobility dominate the FL performance. Next, a joint FL and communication optimization problem is formulated based on theoretical analyses to minimize the FL loss function by optimizing wireless resource allocation. Finally, we propose a two-step optimization algorithm to solve the formulated problem. The simulation results verify the theoretical analyses. It is also shown that the proposed method can significantly enhance learning performance considering users with high mobility. When the average velocity is larger than 150 km/h, the proposed method achieves more than 80% accuracy in the MNIST dataset, while the existing methods may fail during training.

Deploying 1-bit analog-to-digital converters (ADCs) in massive multiple-input multiple-output (MIMO) systems is promising to reduce the energy consumption. However, serious quantization errors limit the feasibility of high order quadrature amplitude modulation (QAM). This paper focuses on the performance analysis of 1-bit ADC massive MIMO systems with high order QAM. Firstly, we theoretically analyze the relationship between the quantization error and various quantization parameters. We reveal that there exists an overlapping of constellations at high signal to noise ratio (SNR) with symmetric quantization. This is because the amplitude information in the detected signal at high SNR is lost which is multiplied by the zero quantization threshold in the symmetric quantization. To overcome this problem, asymmetric quantization should be considered. Based on the analysis of the quantization error, we propose two approaches to recover the amplitude information. One is a near unbiased detection (NUD) by scaling the traditional linear detector and optimizing the quantization threshold. The other is a sub-array detection based on non-uniform quantization (SAD-NQ) to correct the deviation of the detection result by averaging over sub-arrays. Simulation results show that the proposed approaches can significantly improve the detection performance of high order QAM signal in the 1-bit ADC massive MIMO systems.

High throughput satellites (HTS), which can provide wide coverage, are recognized as a promising extension and supplement to achieve global coverage for the space-air-ground integrated network (SAGIN). Different to current literature on seamless coverage of HTS which considers that the shapes of multi-spot beam coverage regions are regular, this paper considers a practical HTS system, in which the multi-spot beams with irregular coverage regions are discussed, and the limitations of spot beam resource and coverage overlapping are taken into account. In order to achieve efficient seamless coverage of HTS, a grid division based multi-spot beam footprint planning (GD-MBFP) scheme is proposed with a given maximum coverage overlap ratio, aiming to minimize the number of spot beams. Meanwhile, for GD-MBFP scheme, a grid division based singlespot beam coverage calculation (GD-SBCC) method is proposed to obtain the irregular coverage regions of spot beams accurately. Numerical results validate the effectiveness of the proposed GD-MBFP scheme. Given a maximum coverage overlap ratio of ηmax=30%, the number of spot beams achieving seamless coverage with the proposed GD-MBFP scheme is only 37.9% of that achieving seamless coverage with cellular beam footprint planning (CBFP) scheme. Meanwhile, the average SINR of UTs with the proposed GD-MBFP scheme is 1.58 times that with CBFP scheme. The average throughput of spot beam with the proposed GD-MBFP scheme is improved by 30.4% than that with CBFP scheme.

Sloped terrain is common in practice. This paper proposes a lateral controller for controlling the autonomous ground vehicle (AGV) to track the reference path on sloped terrain. The impact of sloped terrain on AGV’s motion is analyzed. The lateral disturbance from gravity is modeled, and a feedforward compensator is designed to counteract the effect of the lateral disturbance. A lateral controller consisting of a feedforward compensator and a feedback controller based on linear time-varying model predictive control (LTV-MPC) is proposed. Simulations are carried out to evaluate the control performance. The controller proposed in this paper is compared with an LTV-MPC-based feedback controller which models the lateral disturbance from gravity. It is shown that the controller proposed in this paper can achieve better control performance.

In highly dynamic LEO satellite communication systems, the grade of service (QoS) is severely degraded by the frequent handover. Existing handover schemes focusing on the overall system QoS cannot ensure single user’s QoS, which is not desirable for important users, such as emergency communication users. Exploiting the fact that the movement of LEO satellites are periodic and predictable, this paper proposes a channel reservation based load aware (CRLA) handover algorithm. On one hand, channel can be reserved for important users to ensure their QoS. On the other hand, the load status of each satellite is considered in CRLA handover of the whole system. Simulation result shows that CRLA can reduce handover failure rates while ensure the load balance of the system. Comparing to existing schemes, the CRLA handover algorithm can reduce the handover failure rate by 20% and improve the QoS by 16%.

Recently, automatic modulation recognition (AMR), i.e., identifying the modulation modes of signals using deep learning (DL) has received much attention. This paper proposes an AMR method based on DL (i.e., SG-NET), including a novel DL architecture (i.e., GuResNet) based on the deep residual network (ResNet) and a SNR-aware mechanism, which can effectively extract signal characteristics to achieve a better recognition accuracy. Specifically, we design a deep residual network model that mainly consists of six novel Residual Units to abstract effective signal features and prevent over-fitting. Then, to further improve the recognition performance in low SNR scenarios (i.e., the SNR is lower than 0dB), we train different parameters in the GuResNets based on the aware SNR. That is, we firstly use original signals with high and low SNRs to train a general GusResNet. When SNR is larger than 0dB, we directly adopt the general GuResNet for AMR. While, in low SNR scenarios, we exploit the Legendre method to extract signal features and then re-train the parameters in the GuResNets under different SNR conditions to improve the recognition accuracy further. The simulations demonstrate that our proposed SG-NET can obtain nearly 30% accuracy gain when the SNR is lower than 0dB, and 10% improvement when SNR is larger than 0dB compared with existing schemes.

Communication-enabled Cooperative Adaptive Cruise Control (CACC) is an important technology to improve the safety and traffic capacity of vehicle platoons. However, most existing CACC research considers a conventional communication delay and fixed network topology. When the network is attacked, the stability of the system will be affected. In this paper, a CACC system considering dynamic network topologies and communication delay jitters is proposed. A multi-vehicle look ahead network topology of CACC is designed to investigate the relationship between communication delay and the optimal network topology. Control parameters are obtained by solving Algebraic Riccati equations. Moreover, the relationship between the minimum inter-vehicle spacing and the communication delay is analyzed and derived under different network topologies. Finally, the performance of the proposed CACC system with adaptive network topologies is evaluated by simulations. Simulation results demonstrate that the proposed CACC system outperforms the H infinity synthesis-based controller considering the fixed network topologies. The robustness against communication failure of the proposed system, such as the DeGrading of Service (DGoS) attack, can be significantly improved, which can reduce the minimum safety headway buffer and further support better mobility of vehicles.

High-definition map (HD Map) for autonomous driving brings huge pressure on networks due to its bandwidth-greedy, computing-intensive, and latency-sensitive characteristics. Data collection, transmission, and processing for HD Map update should cooperate to meet these requirements. In this paper, crowd-sensing which exploits the sensing ability of autonomous vehicles is adopted for real-time data collection. Vehicular distributed computing is adopted to improve the computing capability and reduce the transmission of massive raw environmental data. And a crowd-sensing assisted vehicular distributed computing (CS-VDC) mechanism is proposed based on the convergence of sensing, communication, and computation. In addition, considering the differences in sensing range and computing capability of different vehicles, the selection of crowd-sensing nodes and task allocation are jointly optimized to further minimize the communication load. A heuristic algorithm is developed to solve the optimization problem. The performance of the proposed mechanism is evaluated and CS-VDC can always achieve the minimum missing update ratio and amount of equivalent transmission data regardless of the parameter configuration. Especially, the amount of equivalent transmission data under the proposed CS-VDC can be reduced by 37% compared with the nearest node selection mechanism.

In 5G mMTC system, massive user equipment (UE) access network simultaneously by contending a common RACH resource. It is thus of significant importance for network to estimate the time-of-arrival (ToA) of each UE efficiently based on preamble allocated for Physical Random Access Channel(PRACH) so that UE can achieve uplink synchronization with network reliably. In this paper, we focus on improving the accuracy and performance of ToA estimation. Considering the phase ambiguity problem due to varied propagation delay defined as ToA during detection of preamble signals generated by UE, we propose a ToA estimation technique to compensate phase transition with a set of pre-determined phase corrector vectors(PCV) associated with all possible ToA values. Furthermore, we optimize PCVs by build a reduced and essential PCV candidate as a smaller search space for more efficient phase recover and ToA estimation. Simulation results show that the performance of proposed method and its variants can fulfill the requirements of 3GPP standard with lower computational complexity.

In the fifth/sixth generation (5G/6G) communication networks, network slices are formed where resources and infrastructures are shared among slices. Therefore, it is important to share the scarce wireless resource among slices efficiently and to achieve quality of service (QoS) requirement and the resource isolation of each slice. Most of the researches allocate slices wireless resource based on the QoS requirements of the slices, such as transmitting rate, where the resource isolation is ignored. Therefore, this paper designs the wireless resource allocation in sliced networks, where the QoS requirements and the resource isolation requirements are both achieved. To this end, one isolation indice is defined for each slice considering the performance of both the QoS requirements and the resource isolation requirements for the mobile terminals (MTs) served by this slice. And wireless resource is assigned to the slices based on their resource assignment parameters, which are adjusted according to the isolation indices periodically. To design the adjustment scheme, two optimization problems are formulated to minimize the system utility by setting the slices' resource assignment parameters properly. By the optimization problem, the system tries to assign the slices minimal wireless resource they require to meet the QoS and the resource isolation requirements. To solve these problems, two heuristic algorithms are proposed where the resource assignment parameters are proportional or inversely proportional to the isolation indices and updated periodically. By simulating, it is proved that the proposed wireless resource management based on isolation indices in sliced networks can assign the slices wireless resource effectively.

With the explosive growth of computation-intensive and latency-sensitive vehicular applications, limited on-board computing resources can hardly satisfy these heterogeneous requirements and task offloading becomes a potential solution. However, task offloading in vehicular networks may face the dilemma of unaffordable uploading time caused by the huge amount of uploading traffic. Therefore, considering the applications which use the environmental data as their input, the sensing abilities of serving nodes (SNs) are exploited and a traffic-aware task offloading (TATO) mechanism based on convergence of communication and sensing is proposed. In the TATO mechanism, a task vehicle can adaptively upload the input data to some SNs and transmit the computation instructions to others which use the environmental data sensed by themselves as input. The objective is to minimize the overall response time (ORT) by jointly optimizing the task and wireless bandwidth ratios. Next, a binary search and feasibility check (BSFC) algorithm is designed to solve the optimization problem. Simulation results demonstrate the effectiveness of the proposed BSFC algorithm and show that the TATO mechanism always outperforms the benchmark mechanisms (i.e., communication-based offloading and sensing-based offloading) in terms of the ORT. Specifically, when the task offloading traffic is huge and the wireless transmission capability becomes a bottleneck, TATO can reduce the ORT by 42.8% compared with that of the communication-based offloading.

Massive multiple-input multiple-output (MIMO) systems with 1-bit analog-to-digital converters (ADCs) are promising to reduce the energy consumption. However, the serious quantization error caused by 1-bit ADCs will potentially limit the feasibility of high order modulations. This paper focuses on the analysis of the high order phase-shift keying (PSK) signal transmission in the 1-bit ADC massive MIMO system. Firstly, assuming ideal channel estimation and a single mobile station (MS), we theoretically prove that with an asymptotically large number of antennas at the base station, PSK signals with arbitrary modulation order can be recovered in the 1-bit ADC massive MIMO system. Secondly, we analyze the impact of pilot based channel estimation on the recovery of the high order PSK signals, which leads to a periodic asymptotic detection phase error (ADPE) at high signal to noise ratio (SNR). Furthermore, we also propose to optimize the pilot sequence for minimizing the cumulative absolute ADPE. Finally, the analysis is extended to the multi-MS case and the performance with different pilot patterns is discussed. Simulation results validate our analysis and show that using our proposed optimized pilot sequence can significantly improve the detection performance for both single-MS and multi-MS 1-bit ADC massive MIMO systems.

In the fifth generation of mobile cellular network (5G), based on the allocating of communicating and computing resources, multiple slices are formed to serve various use cases with different quality of service (QoS) requirements. Since the communicating and computing resources are limited, it is important to share them among slices efficiently. However, the requirements on communicating and computing resources are usually coupled together considering mobile edge computing (MEC) in sliced 5G networks. To this end, this paper proposes the tandem queues to represent and analyze the coupling relationship between communicating and computing resources for slices. Then, based the derived relationship, the communicating and computing resources are allocated among slices when there is no burst computing task and there is burst computing task, which are formulated by two optimal problems. For the optimal problem without considering the burst computing task, the coupling relationship of the resources is introduced to the objective function to share the resources among the slices efficiently. Then considering the burst computing task, the probability that the enough resources are required by the slices is added to the constraint conditions. And two algorithms are designed to solve the two problems. By simulating, it can be seen that the system utility can be improved since more slices can be served by the system with the proposed algorithms. Therefore, the efficiency of managing communicating and computing resources jointly is proved.

Packet-based fronthaul (FH) transport networks are promising for future centralized radio access networks (C-RANs), which support statistical multiplexing via flow scheduling. With stringent requirements on FH delay, the FH network is time sensitive. Targeting to minimize the maximum FH delay of all packets, this paper investigates the optimized flow scheduling in the packet-based FH network. Due to the high complexity of optimal solutions, a heuristic higher rate flow scheduled later (HRSL) scheme is proposed to achieve good performance with a low complexity. The main idea is to schedule the higher-rated flow with a lower priority, because packets in the higher-rated flow have a larger solution space to be scheduled with a smaller delay. It is proved that using HRSL, each packet must have a transmission position and no packet will be discarded during scheduling. Moreover, the asymptotic delay of HRSL is derived when the number of flows is sufficiently large. We show that the delay performance of HRSL approaches to the optimal one and yields a reduction of up to 92.6% compared to existing schemes. The properties of HRSL are also verified via simulations. If the required delay is larger than the asymptotic delay performance, the FH network with HRSL can always deliver packets in time, no matter how many flows are scheduled and what the rates of flows are.

In massive multiple-input multiple-output (MIMO) systems with time shifted pilot (TSP) schemes, the inter-group interference caused by the pilot contamination can be eliminated when the number of base station (BS) antennas M approaches infinity. However, M is finite in practice and the effectiveness of the TSP is limited by channel estimation errors. In this paper, it is analytically shown that the mean square channel estimation error (MSCEE) of the TSP is dominated by the inter-group data interference. To reduce the MSCEE in the finite antenna massive MIMO systems, an interference cancellation based channel estimation for the TSP (IC-TSP) is proposed, where the dominant inter-group data interference is canceled based on BS cooperation. To show the advantage of the IC-TSP, the additional overhead of IC-TSP is evaluated by considering different M and the coherence time of BS-BS channels. Furthermore, the impact of sectorization and compressed sensing based BS-BS channel estimation are also discussed. We show that when 128 M 2048, with the inter-group data interference from the nearest two cell layers being canceled, the IC-TSP achieves a spectral efficiency gain of more than 1.2 bps/Hz over the TSP.

The surging Deep Neural Network (DNN) based applications are becoming increasingly popular in mobile computing. However, they impose significant challenges for mobile computing, as DNN tasks lead to much more computation complexity and data volume compared with traditional tasks. To alleviate this, mobile edge computing (MEC) provides a feasible approach through task partitioning and offloading. In this paper, we investigate a DNN based MEC scheme considering multiple mobile devices and one MEC server. To facilitate task partitioning, we first develop a processing delay prediction mechanism for typical DNN tasks. To achieve the minimal processing delay as well as to release the computing burden of mobile devices, a mixed integer linear programming (MILP) based DNN task partitioning and offloading mechanism is presented. Evaluations show that our mechanism can achieve up to 90.5% and 69.5% processing delay reduction compared with MEC server only and mobile device only schemes respectively.

Mobile edge computing (MEC)-enabled vehicular networks can improve the quality of service (QoS) of vehicular networks, such as the round-trip time (RTT) and transmission control protocol (TCP) throughput. However, the high mobility of vehicles requires frequent service migrations among MEC servers to maintain the QoS. Frequent service migrations incur prohibitive migration cost. To achieve the tradeoff between the QoS and migration cost, this paper proposes a novel dynamic service migration scheme, which considers the effect of velocities of vehicles. The main idea is that the QoS and migration cost are modeled as the function of velocity, and then they are jointly considered economically. The system captures incomes from vehicles according to their QoS. The cost (expenditure) consists of the migration cost and service cost for the use of computing, communication and memory resources to provide service. The system utility is defined as the difference between incomes and costs. A novel deep reinforcement learning algorithm, i.e., deep Q-learning is employed to maximize the system utility, by designing dynamic service migration scheme. Simulation results show that compared with existing migration schemes, the proposed dynamic scheme can increase the system utility with various velocities. The system utility gain increases as the velocity increases, which reaches about 2 times when the velocity is larger than 30m/s. Moreover, it improves the QoS of vehicles with the higher mobility, where the RTT can be decreased by 2 times, and the TCP throughput can be increased by 1 time.

Since low delay is required in most healthcare user cases, data computing are performed by mobile edge computing (MEC) deployed in Radio Access Networks (RANs). Therefore, MEC computing resource must be allocated among terminals properly. In existing researches, MEC computing resource is allocated dynamically or statically. However, dynamic schemes suffer from high complexity and static schemes will lead to poor service quality or low resource utilization. To achieve tradeoff between complexity and efficiency, this paper designs a semi-dynamic scheme for MEC computing resource allocation, called SD-CRA. As its main idea, MEC computing resource is firstly assigned to access point (AP) which is then shared by its users dynamically. And computing resource allocated to AP is updated periodically. However, updating period in SDCRA lasts longer than that in dynamic schemes since AP’s computing tasks, which equals sum of its users’ computing tasks, varies much slower than user’s. To design SD-CRA, we consider wireless transmitting adequately since computing tasks need be transmitted to MEC wirelessly. With this consideration, one minimal problem of MEC computing resource is formulated. To solve the formulated problem, research scenarios are first divided into two categories based on wireless channel capacity. In ideal scenario of unlimited wireless channel capacity, algebraic expression of MEC computing resource allocation is provided with its applicable condition. And in scenario of limited wireless channel capacity, two optimal problems are then designed considering the tradeoff between minimal MEC computing resource and maximal wireless throughput, which are solved by proposed algorithms. Performances of complexity and efficiency of proposed algorithms are expressed by simulation results.

Aiming at the load balancing problem for uneven traffic demands among different beams in a broadband multibeam satellite network (BMSN), we present a mathematical analysis and propose a load balancing algorithm to improve the efficiency of system resource allocation. In contrast to existing studies, our research focuses on the heterogeneous real-time services, including both VoIP and MPEG traffic flows. Subject to the delay constraint of real-time traffic, we introduce a QoS guaranteed resource scheduling framework for each beam. Based on this framework, we simulate the load imbalance scenario through the Poisson random point process. Then, we present joint adjacent beams and hopping beams load balancing algorithm to maintain the better match between the system resource and uneven traffic demands for BMSN over the satellite lifetime. The key point of our algorithm is intended to reduce the packet loss ratio (PLR) for each user and improve the BMSN's throughput. Simulation results show that the proposed load balancing algorithm can effectively reduce the average packet loss ratio of all beams by 71.25% and improve the average throughput of all beams by 13.97%.

Next generation fronthaul interface (NGFI) is a promising fronthaul (FH) interface for the future centralized radio access network (C-RAN), which is packet-based and supports statistical multiplexing via flow scheduling at the node in the FH network. However, FH delay may be increased by flow scheduling. Targeting to minimize the maximum FH delay, this paper investigates the optimized flow scheduling in NGFI. Due to the high complexity of optimal solution, an intuitive low rate scheduled first (LRSF) scheme is proposed to achieve the good performance with low complexity. The main idea is to schedule the flow of lower rate with higher priority, so that the maximum FH delay of packets in the flow with lower priority can be reduced. Simulation results show that the delay performance of LRSF approaches to the optimal one and outperforms existing schemes.

As an important component of resource management, admission control is vital to prevent the wireless network from congestion and ensure the quality of service (QoS). Mobile edge computing (MEC), which provides computing resources at the edge of radio access networks (RAN), is able to better support new mobile services. Therefore, enhanced admission control policies should be designed for mobile cellular networks based on MEC. In this paper, a novel MEC-assisted admission control mechanism is proposed from the perspective of convergence of communication and computation. In this mechanism, MEC computing resources are leveraged to pre-process the transmission content. The purpose is to reduce the consumption of wireless bandwidth and increase the number of accepted services. Next, the admission control process is modeled as a Markov decision process (MDP) with objective to maximize the long-term expected average effective throughput. In consideration of the large state space, a simulation-based optimization algorithm of MDP is adopted to obtain the optimal policy. Simulation results show that our proposed admission control mechanism achieves higher effective throughput than that without MEC computing resources. And the probability of accepted services can also be improved significantly. Furthermore, the optimal amount of MEC computing resources can be acquired according to the system traffic statistics.

The rolling bearing fault diagnosis with vibration data is critical to the reliability and the safety of rotating machinery. According to the non-stationary characteristics and the simple logical structure characteristics of rolling bearing vibration data, a rolling bearing fault diagnosis method based on modified bidirectional long short-term memory (Bi-LSTM) neural network is put forward in this paper. Firstly, original vibration data are decomposed into time-frequency feature with the combination of Daubechies 10 wavelet packet transform and Symlets 8 wavelet packet transform. Then, we design bidirectional long-term memory (Bi-LTM) neural network, the Bi-LTM neural network only uses long-term memory to process rolling bearing feature data and get the result of fault diagnosis. In order to enhance functionality of the Bi-LTM internal activation function, the Bi-LTM internal function uses softsign. We evaluate our models on a standard dataset. Moreover, given the analytical results, compared to Bi-LSTM, the proposed Bi-LTM method further reduces the rolling bearing fault diagnosis error rate by 6 times. Numerical and simulation results verify that the rolling bearing fault diagnosis method based on the proposed method is justified.

In centralized radio access network (C-RAN), the statistical multiplexing gain (SMG) of computing resource can be achieved. This paper focuses on analyzing the SMG considering the quantization granularity of computing resource which indicates the degree of resource sharing in practical C-RAN. Firstly, based on a spatial-temporal traffic model for multiple cells, the quantized model of computing resource is set up. Then, giving a system service threshold and defining the SMG as the ratio of the amount of computing resources deployed in distributed radio access network (D-RAN) and CRAN, the asymptotic SMG with quantized computing resource is derived. The impacts of the service threshold, the granularity of quantized computing resource and the average traffic load on the asymptotic SMG are analyzed. In the special case that the granularity of quantized computing resource is infinitely small, i.e., the computing resources are not quantized, the asymptotic SMG only depends on the service threshold and the fluctuation of spatial traffic load. Simulations are carried out to verify the correctness of the derivation of the asymptotic SMG with quantized computing resource. Simulation results show that when the service threshold gets higher, the asymptotic SMG increases. Moreover, the asymptotic SMG decreases obviously when the ratio of the granularity of quantized computing resource to the average traffic load increases over 1%. In addition, compared to a C-RAN with dramatic traffic load fluctuation in spatial domain, a moderate fluctuation can bring a higher asymptotic SMG.

The mobile edge computing (MEC)-enabled cellular network provides a promising paradigm for emerging services with intensive computation and low delay requirement. However, when multiple computation-intensive and delay-sensitive services are required concurrently, how to minimize the delay and monetary cost on mobile devices (MDs) under the limited computation resource and communication resource remains a challenging issue. In order to solve the issue, we formulate computation offloading, communication resource and computation resource allocation as an optimization problem in MEC-enabled cellular network. The problem is non-convex. Hence, we formulate it as a multi-objective computation offloading and resource allocation (MCORA) game and prove the existence of Nash equilibrium (NE). To obtain the NE, we design a MCORA algorithm taking the uplink, downlink spectrum resource, computation resource and offloading strategy into consideration. Simulation results show the MCORA algorithm can minimize system cost of all MDs. Moreover, in comparison to the existing algorithms, the proposed algorithm can achieve better performance in delay and system cost.

In ultra-dense cellular networks (UDNs) with user/control plane (U/C) splitting, frequent handovers in user planes are unavoidable. This seriously degrades MS's transmission control protocol (TCP) throughput. This paper proposes a prediction-based user plane handover scheme to improve the TCP throughput in UDNs. Firstly, based on algorithms used in recommender systems, a mobility prediction algorithm called content-based collaborative hybrid filters (CCHF) is proposed to predict the target small base station (SBS). When the mobile station (MS) moves into the cell-edge of the source SBS, it can set up connections to the predicted target SBS and the source SBS simultaneously. An accurate prediction and a simultaneous connection can enhance the signal to interference and noise ratio (SINR) at cell-edge and reduce the handover interruption ratio (HIR). Thus packet loss can be reduced and the MS's TCP throughput will be improved. Simulations are carried out to verify the effectiveness of the proposed CCHF-handover. It is shown that using CCHF, the prediction accuracy of random trajectory can be improved by more than 100% compared with existing prediction algorithm. Moreover, the CCHF-handover improves the average TCP throughput significantly by more than 3 times compared with that of existing handover schemes.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.

The performance of cloud radio access networks (Cloud-RAN) is constrained by the limited fronthaul capacity. Physical layer caching is an effective technique to reduce the requirement on fronthaul capacity in peak time, which depends on the cache-hit ratio and the wireless transmission performance. Considering a dynamic points selection (DPS) based coordinated multi-point (CoMP) scheme to enhance the wireless transmission performance, this paper proposes an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement in cloud-RAN. First of all, given the content caching probabilities, the average outage probability of cache-based services is derived. Based on the derivation, the relationship between the wireless transmission performance and the fronthaul capacity requirement is established. Then, an optimization problem is formulated to find an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement, which can be solved using the interior point method. Simulation results show that using CoMP transmissions and optimizing the caching probabilities, the proposed scheme can reduce the fronthaul capacity requirement efficiently. Compared to the existing scheme which does not consider the effect of the wireless transmission performance on the fronthaul capacity requirement, the fronthaul capacity requirement can be reduced by 36\%. Moreover, the proposed scheme can also improve the cache service probability.

This paper concerns a heterogeneous ultra dense network (HUDN) with regularly deployed macrocells. Aiming to combat both cross-tier and co-tier inter-cell interference (ICI), this paper proposes a user-centric and adaptive interference aware coordinated multipoint transmission (IA-CoMP) scheme, which takes the serving or master base station (BS) and main interfering BSs decided by a signal strength threshold as cooperative nodes. The coverage performance of IA-CoMP is analyzed. The main idea is to divide MSs in HUDN into three types according to different main interference they suffer. Then the whole coverage can be obtained as the sum of the coverage of each type of MSs. A lower bound of MSs not using CoMP is derived for HUDN with IA-CoMP, which demonstrates that using IA-CoMP, unnecessary CoMP can be avoided and the overall system overhead can be reduced compared with that of the existing CoMP schemes with fixed size for all MSs. Simulations are carried out to verify the analysis and compare the coverage performance of IA-CoMP with that of cross-tier CoMP (CT-CoMP). It is shown that the proposed IA-CoMP scheme can provide a much better coverage performance especially in HUDN with higher small cell densities.

Computing resource allocation algorithms are very important in centralized radio access networks to reduce the power consumption and construction cost of base stations. Most of the current research cannot make the best use of computing resources because they take whole processing tasks of one BS as the allocation object which is too large for a processor. Therefore, an Energy Aware Dynamic Resource Allocation (EADRA) algorithm is proposed in this paper, which takes a whole BS or the sub-task of a BS as the allocation object dynamically to achieve the target that the processing ability of computing resources can be fully utilized. Simulation results show that the EADRA effectively decreases the number of allocated processor cores when compared with existing algorithms. In addition, EADRA reduces the power consumption more and more significantly compared to that of the existing algorithms when the number of Virtual BSs (VBSs) is gradually increased.

This paper focuses on time shifted pilot (TSP) based time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems. Since the performance of TSP with finite number of base station (BS) antennas is limited by poor channel estimation, the impact of channel estimation error is firstly analyzed, which demonstrates that the signal to interference plus noise ratios (SINR) of both downlink and uplink are inversely proportional to the covariance coefficient of the channel estimation error. Next, a successive interference cancellation based TSP (SIC-TSP) scheme is proposed where the downlink data contamination on the uplink pilot is cancelled out by utilizing the shared downlink data, precoding vectors and the estimated channels among BSs. To obtain accurate estimation for the channels among BSs, SIC-TSP employs a special time slot at the beginning of a frame, where each BS will transmit orthogonal pilots. Simulations are conducted to obtain the optimal uplink pilot power, and it is shown that the proposed SIC-TSP can reduce the normalized square channel estimation error by 11.3 dB, achieving a gain of 27% on both the downlink and uplink spectral efficiency compared with the existing TSP scheme.

Node mobility is one of the essential features of the ad-hoc communication network, especially on the battlefield, where node movement is determined by both movement characteristics and combat factors. This paper proposes a motion simulation framework with a set of novel movement models. The movement simulation framework is consisted of three systems, i.e., the threat and adjustment assessment system, the casualty simulation and statistic system, and the motion library system. The first and second systems are responsible for outputting the combat factors and the fire simulation results to the third system. Based on the input information including the movement characteristics and the combat factors, the motion library system as the core part of the simulation framework is composed of the proposed motion models with considering the tightly coupled relationship of the inputs. And according to the designed models, the waypoints are chosen by the proposed algorithm of the minimal firing force problem, and nodes adjust their locations. Apart from that, from the simulation results, the performance of the tight coupling motion models are proven in the aspects of path selection, combat formation and casualty statistics.

Computing resource allocation in Baseband Unit (BBU) Pool is a challenging research topic for a Centralized Radio Access Network (C-RAN) when the resource allocation algorithm has to heed thermal characteristics of the computing resources (Processors) of BBU along with the optimization of power consumption and computing resource usage. Since the network load acts as a heat source for the processor this thermal phenomenon of a processor has to be addressed properly to optimize its power consumption and total computing resource usage efficiency (CRUE). This paper presents an optimal solution for dynamic computing resource allocation considering two models (Thermal and Computing Resource Usage) of a computing unit in the BBU. We use Lagrange multiplier with Kuhn-Tucker condition to get an optimal solution. The proposed algorithm is assessed in terms of CRUE and power consumption. The simulation results show that the TADCRA remarkably outperforms the conventional method and Load Diversity based Allocation (LDA) method respectively by 66% and 45% in CRUE and 50% and 95% in power consumption difference. Moreover, the CRUE is consistent over varying network load.

The tsunami of video services brings huge pressure on communication systems, especially on some bandwidth-limited scenarios like wireless networks. Represented by H.264, current video compression approaches can save the transmission bandwidth by reducing the intra- and inter-frame redundancy. However, the huge amount of video content redundancy (VCR) in the transmission is still existing. In this paper, we propose a novel Content-Slimming System (CSS) framework based on the convergence of Computing, Communication and Cache to avoid the transmission of VCRs. The main idea of CSS is to detect VCRs, generate VCR models and clip them from the original frames by Computing, then transmit the necessary video content and semantic difference description by Communication, finally reconstruct the full video based on the received video content, semantic difference description and VCR models stored by Cache. Moreover, we also investigate the Video Monitoring application based on the CSS framework (CSS-VM) in which the background of monitoring frames is modeled, detected and sheared to reduce the transmission bandwidth consumption. The simulation results show that the bandwidth consumption of CSS-VM can be reduced at least by half compared to H.264, while the video quality and visual experience of CSS-VM are even better.

Due to the irregular topology of ultra dense cellular network (UDN), base station (BS)-centric methods are usually with low spectrum efficiency and not flexible for quality-of-service (QoS) guaranteeing. This paper investigates the QoS-aware interference coordination in UDN from a user-centric way (UCQA-IC). Targeting at enhancing the spectrum efficiency and user experience with controlled inter-cell-interference (ICI), the basic idea of UCQA-IC is to guarantee a desired signal- to-interference-plus-noise-ratio (SINR) for each mobile station (MS) by avoiding the major ICI and allocate resource with priorities to each MS according to its QoS requirements. The problem can be solved with iterative resource allocation based on graph-coloring algorithms. Simulations are carried out to verify the effectiveness of the UCQA-IC. It is firstly demonstrated that in UDN, the average number of interference BSs for an MS is much less than that for a BS, so it is favorable to use user-centric interference coordination in UDN. Then, it is verified that the proposed UCQA-IC outperforms the existing interference coordination algorithm with improved spectrum efficiency and enhanced user experience. In detail, the spectrum efficiency of UCQA-IC can be about three times as high as that of the comparing scheme. Moreover, using UCQA-IC, the proportion of MSs with their QoS satisfied can be about two times as much as that using the comparing scheme.

Due to the high relative velocity between mobile terminals and satellites, the broadband mobile LEO (Low Earth Orbit) satellite communication signals suffer a severe Doppler shift. Therefore, Doppler shift estimation would be one of the most important techniques to improve the performance of satellite communication systems. In this paper, a MAP Doppler estimator is proposed, exploiting a predictable Doppler characterization of the circular orbit LEO satellite and an OFDM (Orthogonal Frequency Division Multiplex) broadband signal structure with a CP (Cyclic Prefix). Simulations show that the proposed MAP algorithm outperforms the Doppler characterization estimator and CP-based estimator in both AWGN and satellite channels and robust to the velocity of terminals.

Switching off some Base Stations (BSs) with low traffic is a promising approach to save energy in cellular networks. However, it should be noted that frequently switching BSs on/off will result in extra energy consumption, hardware booting delay, bad quality of services (QoS) and so on. Therefore, we propose a Centralized Sleeping Scheme (CSS) to consider the performance of BS state stability which is defined as the number of BS on/off state transitions. In the CSS, a bi-objective optimization problem is formulated to minimize the energy consumption and improve the BS state stability. A fast exhaustive algorithm (CSS-E) to obtain near- optimal solutions and a modified Particle Swarm Optimization (CSS-PSO) algorithm with low complexity are proposed to solve the bi-objective optimization problem. Simulation results show that the CSS-E and CSS-PSO can significantly outperform the traditional traffic aware sleeping scheme in the number of BSs state transitions while the performance difference in energy saving is small.

As a key component of intelligent transport system (ITS), vehicular communication network (VCN) is expected to reduce traffic accidents by providing more information to drivers via wireless communication. However, it remains unknown how VCN affects traffic accident probabilities. Given a highway scenario where vicious rear-end collision accidents happen, this paper investigates the impact of VCN on the rear-end collision probability. Considering a three-vehicle chain on highway and assuming that VCN conveys braking messages, vehicle behaviors are analyzed. Based on these analyses, the average collision probability of the concerned vehicle can be derived as a function of the communication success probability and driving parameters such as reaction time and deceleration. The analyses show that the average distance of adjacent vehicles is critical to reduce the probability of collision, especially when there is no VCN. When VCN is introduced, the collision probability could be reduced in two ways. One is that the reaction time of the vehicle is decreased and the other is that VCN could provide more information to the vehicle so that it can take precautions to avoid collision. Moreover, due to the relatively short distance of interest (i.e., less than 500 meters) and favorable wireless channel conditions on highway, the communication between vehicles always success and the collision probability with non-ideal communication can be reasonably approximated by that with ideal communication. These analytical results are verified by numerical computation and simulations. It is also shown that given a transmit power of 10dBm, aided by VCN, the rear-end collision probability of the concerned vehicle on highway can be significantly reduced by 70% compared to that without communications.

Load balancing (LB) is important in heterogeneous networks (HetNet). This paper investigates effective cell association schemes for LB. Considering the centralized cellular network architecture with Super Base Stations (SBS), a centralized cell association scheme is proposed, where the calculations are all performed in the SBS and information exchange within the SBS is convenient. There is also no need for the users to feed back information. Thus the signaling overhead is reduced a lot. Moreover, to alleviate the sensitiveness to load changes, the load fairness index (LFI) is defined. The proposed scheme is not performed until the LFI is lower than a given threshold. It is verified by simulations that the proposed centralized cell association scheme can adjust the cell-specific bias according to the cell load and provide better geometry mean rate (GMR) and outage probability (OP) over the comparing schemes. Moreover, with the introduction of LFI, the number of LB performed can be effectively reduced with a lower LFI threshold, however, the system performance such as GMR and OP also degrades. Therefore, an appropriate LFI threshold is needed to balance the system performance and complexity.

Device-to-device (D2D) cooperation can improve both the system performance and Quality of Services (QoS) of users with bad channel qualities. However, cooperation consumes valuable power of a terminal to help others and thus is not preferred by the terminal. So it is important to design schemes to stimulate selfish terminals to cooperate. Defining energy efficiency as incentive parameters, this paper proposes a novel resource allocation scheme to encourage users to relay data for others with the reward of transmitting resource including time and power. Since the optimal algorithm is highly complicated, the proposed energy efficient incentive resource allocation scheme is a suboptimal solution which is composed of three steps. Firstly, D2D relay users are selected for others in bad channel condition. Secondly, the two-user case resource allocation problem is formulated to simplify the original problem in cellular networks. Finally, a two-dimensional search method is designed to solve the two-user case problem based on monotonicity analysis. Simulation results demonstrate that the proposed scheme can stimulate users to implement D2D relay to achieve better performance in energy efficiency and throughput. It is also shown that the performance of the proposed scheme is close to that of the optimal one.

With the rapid increasing demand on the data traffic and energy efficiency, centralized radio access network architecture is proposed for next generation communication. Similar to the VM (Virtual Machine) running on the cloud computer servers, V-BS (Virtual Base Station) is proposed. Problem exists since characteristics of V-BS PHY layer tasks are different from traditional computing tasks in the cloud-computing, such as time constraint, data dependency and heterogeneous mapping. This paper proposed a new virtualization method based on ASIP (Application Specified Instruction Set Processor) architecture, with consideration of both scheduling efficiency and resource utilizing. Experiments are made to evaluate the validation of the new virtualization method.

In this paper, we discuss MIMO transceiver design under a new type of power constraint named mixed power constraints. Mixed power constraint is referred to the power model in which for a given set of antennas, several subsets are constrained by sum power constraints while the other antennas are subject to individual power constraints. It includes sum power constraint and per-antenna power constraints as its special cases and it can strike a balance between complexity and performance. In our work, we try to solve the optimization problem in an analytical way instead of relying on some famous software packages e.g., CVX or SeDuMi. Firstly, the specific formula of the optimal signal covariance matrix has been derived. Based on the structure, a low complexity non-iterative solution is given in our work, which can be interpreted as a matrix version water-filling solution. This solution has a much clear engineering meaning and is suitable for practical implementations even for massive MIMO systems. Finally, simulation results demonstrate the accuracy of our theoretical results.

A parallelized generation of Zadoff-Chu (ZC) and the Discrete Fourier Transform of Zadoff-Chu (ZC-DFT) sequences is proposed. In this algorithm, the sampling operation is completely eliminated for the ZC-DFT sequences generation. Implemented on a vector Digital Signal Processor (DSP), the proposed algorithm makes an efficient use of the parallel DSP structure and achieves a high computing speed, owing to the decomposition of the root index. Since only a few “seed sequences” are required, the proposed algorithm obtains an extremely low memory requirement and a high precision.

The centralized radio access cellular network architecture with Super BS (CSBS) has been proposed to reduce high construction cost and energy consumption. In CSBS, the computing resource is centralized and can be flexibly allocated to different virtual BSs (VBS). In the general purpose platforms, the protocol processing of multiple VBS can be carried out in a single processor due to its high processing capability. However, using this organization, it is difficult to guarantee the real-time protocol processing in TDD systems. This paper firstly analyzes the requirement of real-time processing of TDD protocols. Then, a real-time guaranteed TDD protocol processing mechanism, dynamic adaptive organization mechanism (DAOM), is proposed, whose main idea is to carry out downlink protocol processing consecutively. Simulation results show that DAOM can guarantee the real-time protocol processing and keep a high computing resource efficiency at the same time.

In this paper, three iterative algorithms (which are denoted by IA-Orth, IA-Power and IA-Rate algorithms in this paper) based on interference alignment (IA) are proposed to make further efforts to consider the useful signal subspace to improve the sum rate in the wireless multi-input multi-output(MIMO) interference networks. The proposed algorithms consider not only minimizing the spatial distance between different interference subspaces but also desired signal subspace from different aspects to further optimize the sum-rate performance. The optimization problems could be performed on the Grassmann manifold since the true domain of IA is over the Grassmann manifold. Moreover, considering different interference power levels at each receiver and IA feasibility constraints in practical environment, our previously proposed IA-Rate algorithm is modified to be adapted to a broader range of applications. It is on the basis of clustering and properly dealing with inter-cluster interference. A series of simulations show that our algorithms are superior to the conventional IA algorithms on the achievable sum rate and provide a very efficient way to realize IA in practical wireless MIMO interference channel environment.

In this paper, we present a sparse channel estimation method for orthogonal frequency division multiplexing (OFDM) based two-way relay networks (TWRN). Conventional channel estimation methods, such as least squares (LS), have been proposed to obtain channel state information (CSI) at the cost of the training resource, which reduce spectrum efficiency. However, physical measurements have verified that the wireless channels tend to exhibit sparse structures in high-dimensional spaces, e.g., delay spread, Doppler spread and space spread. With the development of compressive sensing (CS), a novel compressive channel estimation method which is called adaptive compressive matching pursuit (ACMP) algorithm is proposed by using the sparse constraint between the terminal nodes and the relay node in the TWRN. Simulation results confirm that ACMP channel estimation method provides significant improvement in mean square error (MSE) performance compared to the conventional channel estimation methods.

In order to reduce high construction cost and energy consumption of distributed base stations in cellular networks, the centralized radio access Cellular network infrastructure with a Super BS(CSBS) architecture has been proposed in which the centralized processing resource can be flexibly organized. However, there is sill the problem of low processing resource efficiency in protocol processing. In this paper, based on a protocol processing resource management framework, distributed load diversity based processing resource allocation (D-LDA) as well as the improved hybrid load diversity based processing resource allocation(H-LDA) are proposed, which take load discrepancy into consideration in the situation that a large number of BSs are supported. Simulation results show that D-LDA and H-LDA algorithm significantly outperforms the conventional method respectively by more than 30% and 55% in the processing resource efficiency.

Interference mitigation is a major challenge in deploying a two-tier small cell network, where small cells are deployed underlaying a central macrocell and share the same spectrum with the macrocell. In this paper, we develop a new decentralized power control solution for interference mitigation in a two-tier small cell network, from a game theoretic perspective. We aim to maximize the number of small cell user (SU) transmissions that can be admitted in the network while satisfying the signal-to- interference-noise ratio (SINR) constraints of both transmitting SUs and the macrocell user (MU). We formulate the problem of power control for SUs as a game with common utility. The Nash equilibria of the game are investigated. We then propose a learning automata based distributed discrete power control algorithm with which the SUs can learn from their action-reward histories and adjust their transmit powers towards a NE point. Simulation results show the proposed algorithm achieves higher number of SU transmissions that can be admitted compared with existing schemes in the literature.

Wireless multicast is a spectrum efficient method for group-data transmission. This paper focuses on energy efficient two-stage cooperative multicast transmissions, aiming to minimize the total transmission power while ensuring a practical coverage ratio. To focus on the relationship between the base station (BS) power at the first stage (PBS,C) and the total power consumption, a selective combining based on average received signal strength (SCA) is assumed at the receiver and the user density is supposed to be sufficiently high. Then a mobile relay (MR) arrangement based on sector ring structures is proposed for the second stage transmission, followed by an analytical derivation of the optimal PBS,C conditioned on a desired coverage ratio. In addition, further approximations are exploited to provide a simple theoretical estimation for the optimal PBS,C, whose effectiveness is verified by numerical results. It is shown that compared to the conventional one-stage multicast transmission, the proposed two-stage cooperative scheme can reduce the total power consumption and the BS power consumption by more than 40% and 80%, respectively. Although the proposed scheme is obtained based on SCA, when the user density is higher than 2 × 10-4, a coverage ratio of 95% can be guaranteed by using a practical CP combining with the proposed scheme. Moreover, the effectiveness of the proposed MR arrangement is verified by simulations where it outperforms other three arrangements. It is also shown that targeting at minimizing the total transmission power with guaranteed coverage, the proposed scheme significantly outperforms the existing two-stage scheme in terms of energy consumption and efficiency.

Based on random network (RN) topologies generated from Poisson point processes (PPP), this paper investigates the performance of macrodiversity coordinated multipoint transmission (MD-CoMP) in dense cellular networks. First, the signal-to-interference-plus-noise ratio (SINR) outage probability is analyzed for a typical mobile station (MS) and for the global network. Next, a user-centric adaptive clustering method is described, which is designed to maximize each MS's normalized outage capacity (goodput). Simulation are carried out and show that MD-CoMP could significantly improve both the RN and regular hexagonal network (HN) coverage performance by increasing the tenth percentile of the SINR by 12 dB if each MS uses a CoMP cluster of size four. It is also shown that MD-CoMP is more beneficial for the RN since 78 MSs in the RN would choose CoMP to optimize their normalized goodput, whereas this number is 58 in the HN. Moreover, 58 MSs in the RN have their normalized goodput doubled compared with that with no CoMP, whereas this number is 36 in the HN. The impact of predefined clustering schemes is also evaluated, to show the importance of using a fully adaptive clustering to overcome cluster-edge issues, where the MSs' performance is poor due to the limited choices of BSs.

This paper addresses the most basic and crucial problem in smart antenna, i.e., the estimation of DOA (Direction-of-Arrival) finding. The performance of smart antenna system greatly depends on the resolution of DOA. MUSIC (MUiltiple SIgnal Classification) and ESPRIT (Estimation of Signal Paramter via Rotational Invariance Technique) are the most classic two algorithms for DOA finding in real systems. However, these two algorithms cannot handle coherent signals directly which happens for example in multipath propagation and the performance will be greatly deteriorated if the pre-processing technique such as spatial smoothing is used. Therefore, the system employing these two algorithms usually works in the condition of Line-of-Sight (LOS), e.g., in suburb circumstance. WSF (Weighted Subspace Fitting) algorithm is a more superior technique which has much higher resolution and can handle coherent signals without any pre-processing. However, conventional WSF needs to know the independent number of signals, otherwise its performance will be deteriorated. In this paper, we propose a modified WSF algorithm for DOA. The proposed modified WSF can detect the independent number of signals automatically and show much higher resolution compared to conventional WSF and MUSIC.

Small cell cluster is a new paradigm to extend the usage of small cells from residential environment to large indoor or outdoor areas. However, coverage optimization is a challenge due to the ad-hoc deployment and plug-and-play feature of small cells. This paper considers decentralized self-optimization network (SON) architecture of small cell cluster and proposes distributed coverage optimization algorithm using game theory (DGT). A non-cooperate game is modeled to tune the Tx power of each small cell with a net utility function considering both gain of throughput and punishment of interference. Nash Equilibrium (NE) is proved to be existed in the game and a power update scheme is proposed which converges to the NE. Simulation results show that DGT can significantly improve throughput as well as coverage ratio with only several iterations. Compared with centralized algorithm such as modified particle swarm optimization (MPSO) and simulated annealing (SA), DGT algorithm reaches higher network throughput, uses less iteration and keeps considerable coverage ratio.

Recently, cognitive pilot channel (CPC) has been proposed to assist spectrum awareness and network selection in the heterogeneous network and dynamic spectrum access mechanism. The CPC channel can provide the necessary wireless environment information for the terminals and avoid long time- and energy-consumption for spectrum scanning. In previous researches, two different CPC information delivery modes have been proposed, i.e., broadcast CPC and on-demand CPC. In order to overcome the defects of these two approaches, Feng et al. proposed a multicast CPC method to optimize the delay performance and information scheduling as an evolution of the on-demand mode. However, in their mode, the waiting time of each mesh is fixed never mind the user distribution is high or not. In this paper, we analyze the relationship between waiting time and user distribution in different scenarios. And according to the difference among their user distribution, we propose setting an optimal waiting time for each mesh. While in actual systems, the mesh number usually is very large. Due to the computational complexity, it is difficult to get the optimal waiting time of each mesh. So we propose a clustering scheme as a suboptimal solution. This suboptimal solution especially fits for the case of centralized distribution scenario. Simulation results will be shown to demonstrate the performance of the optimal and suboptimal solutions.

Due to the cooperation diversity, Coordinated MultiPoint (CoMP) transmission may potentially improve energy efficiency of cellular networks. The main purpose of this paper is to analyze this potential. By employing CoMP, there are two schemes to improve energy efficiency; one is to decrease the transmission power of base stations (CDTP) without changing cells' size and re-deploying base stations (BSs); another one is to reduce the number of BSs by increasing coverage area (CICA). In this paper, the coverage gain brought by CoMP technology given the guaranteed coverage ratio performance is firstly performed. Based on that, the energy efficiency gain of CDTP and CICA is investigated. It showed that comparing to the scheme of CDTP which is nearly no potential to improve energy efficiency, CICA can improve energy efficiency by more than 15%. This result could provide references for operators when carrying out new cellular deployments.

This paper focuses on the energy efficiency of two-stage cooperative multicast with CPC (CP combining). Aiming to provide a coverage of 95%, cooperative multicast with SC (selective combining) has been analytically investigated and a power saving of 43% can be achieved compared with conventional one-stage multicast [9]. However, CP combining which is more practical can provide a stronger received signal than SC and thus is expected to be able to provide further energy saving. Assuming high user density, analysis is carried out on the coverage performance of OFDM based two-stage cooperative multicast with CPC, which is decided by the BS (base station) transmission power at the 1st stage and the number and locations of MRs (mobile relays) at the 2nd stage. Given a fixed coverage, it is difficult to obtain the BS transmission power and MR scheme that provide the optimal total power consumption. Therefore, two sub-optimal schemes are proposed, i.e., BS coordination and MR coordination, which are based on the schemes for cooperative multicast with SC [9] and reduce the BS transmission power and the number of MRs, respectively. Numerical results show that with BS and MR coordination, cooperative multicast with CPC can save 9% and 17% compared to that with SC, respectively. Moreover, MR coordination provides more uniform coverage than BS coordination. Simulations are carried out to verify the numerical results. It is shown that MR coordination provides better coverage than BS coordination for all investigated user densities and the performance gap increases as the density decrease.

3GPP Long Term Evolution (LTE) and its enhancement has become the main candidate of 4G standards. Due to the requirements on high data rate and low latency for broadband wireless communication systems, LTE air-interface protocol stack must be designed and implemented with high data processing efficiency. Following a general description of the LTE user equipment (TIE), this paper analyzes the main challenges in the link-layer protocol stack design and implementation, including the data processing efficiency, synchronization, flexibility and portability. A reference design of LTE TIE link-layer protocol stack software is then presented with several design methodologies. A memory access optimization and light-weighted thread model are proposed to improve the data processing efficiency, while a hardware triggered synchronization mechanism is exploited to maintain synchronous between the link-layer protocol stack and the physical layer. Finally, the platform flexibility and portability is achieved by employing interface abstraction and adapters. Extensive system level testing shows that the proposed LTE TIE protocol stack software achieves high data rate and low data processing latency in a hardware resource restricted platform, which can be further extended to commercial LTE TIE products.

The articles in this special issue focus on the technology and applications supported by virtual multiple antennas, or VMIMOs. The impetus for this has been spurred by the strong desire to understand VMIMO, which is a rapidly growing research area. VMIMO is believed to be a key technology for beyond 4th generation mobile communications technologies (B4G). It enables one to make use of all the neighboring terminals and amortize the cost of multiple antennas; hence, a large MIMO channel can be created to increase capacity significantly as well as improve error rate performance. Nevertheless, fundamental roadblocks need to be addressed in order to take full advantage of VMIMO.

In this paper, the performance of hybrid automatic repeat request (HARQ) technique in an ad hoc network is analyzed. Unlike most prior works on the analysis of HARQ, both time-correlated interference and feedback errors are taken into account in the analysis. Based on the theory of point processes, outage probability after the nth retransmission, delay limited throughput and mean transmission time are derived in closed forms. The analytical results are verified by simulations and the impacts of various parameters on the network performance are investigated in detail. It is found that the outage probability obeys the inverse-2/α power law over the number of retransmissions, where α is the path loss exponent. Furthermore, it is demonstrated that the feedback error significantly degrades the delay limited throughput when the ad hoc network becomes dense, while the increment of mean transmission time due to the feedback error is a concave function of the intensity of the network.

To achieve higher throughput and guarantee Quality of Service (QoS), we analyze the queue model with adaptive modulation and coding (AMC) and truncated automatic repeat request (ARQ) protocol and develop a cross-layer scheme, where the feedback delay of ACK/NAK from receiver is considered. When the delay of ACK/NAK feedback is included in the queue model, some transmitted packets would be in the state of waiting for ACK/NAK feedback. We drop the packets in this state firstly to reduce the packet loss rate (PLR) and the queue delay. Moreover, we obtain the closed-form analytical expression of performance metrics such as throughput, average packet delay and PLR based on the stationary state probability of the queue state. The simulation is carried out to verify the validity of the proposed queue model and packet dropping policy.

Coverage optimization is a main challenge for femtocell clusters which are considered to be a promising solution to provide seamless cellular coverage for large indoor areas. Although particle swarm optimization (PSO) can be employed to solve the coverage optimization of femtocell clusters, it reduces to single particle swarm optimization (SPSO) when only one femtocell cluster is considered and can only find the local optimum solution. In this paper, a modified PSO (MPSO) algorithm is proposed, which employs a heuristic power control scheme to guide the algorithm to search for the global optimum solution. Simulation results show that MPSO significantly outperforms SPSO and fixed power scheme with low complexity. Moreover, MPSO can converge rapidly and is suitable for online coverage optimization.

Handover triggered probability is the probability of handover triggered before the train arrives at a specific position. A low handover triggered probability at the cell edge will result in serious communication interruptions and call drops. This paper analyzes the impact of overlapped area and user measurement report period on the performance of handover triggered probability. Moreover, an adaptive handover trigger scheme is proposed to guarantee a high handover triggered probability by configuring overlapped area and measurement report period adaptively according to the train speed. Numerical and simulation results are provided. It is shown that analytical results are close to the simulations. Moreover, using the proposed scheme, the handover triggered probability at cell edge approaches to 99.8% while the number of base stations is reduced by 17.6% compared to GSM-R systems. Finally, for high speed trains whose velocity is below 540km/h, it is verified that signal strength measurement report period can be configured to 200ms, just the same as that of long term evolution (LTE) systems.

In order to maintain sufficient mobile communication network capacity for value added services, 3GPP has recently introduced an architecture called ”SIPTO”(Selected IP Traffic Offload) to offload selected mobile IP traffic from the core network. This brings new challenge to on line traffic classification scheme needed to figure out the traffic which should be offloaded in real time. Although traffic classification methodologies have already been investigated in wired IP network, they are not applicable in mobile environment where high bit error rates (BER) and temporary disconnections are observed due to hostile wireless channel conditions. This paper proposes a maximum entropy based IP-traffic classification scheme (METCS) to address on line traffic classification problems in mobile communication networks. METCS extracts the application layer payload pattern using randomly arrived packets instead of the first few sequential packets of an application flow. Simulation results show that METCS outperforms existing methods by offering average 5%-8% improvement in classification accuracy with about 60% time.

This study focuses on the resource allocation of multicast services in distributed antenna systems (DAS). Firstly, the capacity limitation of conventional multicast transmission is analysed in DAS. Then a resource allocation algorithm is proposed for multicast services to improve the system throughput of DAS with quality of service (QoS) guarantees. A low-complexity suboptimal algorithm is also presented, which includes three steps: conservative allocation, greedy step and iterative enhancement. Simulation results show that the proposed algorithms significantly outperform conventional multicast transmission in throughput while guaranteeing the minimum data rates of all users as well. Moreover, the performance of the suboptimal algorithm is close to that of the optimal one and the throughput loss is negligible. Meanwhile, it is also shown that the proposed algorithms can exceed the upper bound of conventional multicast scheme when the user number is larger, which indicates the proposed algorithms can overcome the capacity limitation of conventional multicast transmission in DAS.

The 15 papers in this special issue are divided into four categories: challenges in broadband wireless communications; physical layer techniques; radio resource management techniques; and field measurement and channel modeling.

The two-stage cooperative multicast transmission is a promising scheme to combat the channel fading of different users in a multicast group, where the users successfully receiving data at the 1ststage can serve as relays to forward the data to the rest of users at the 2ndstage. Both the system throughput and total energy consumption increase with the number of relays. To obtain a balance between the system throughput and energy consumption, this paper proposes an energy efficient cooperative multicast transmission scheme with power control. In the proposed scheme, the number of relays used at the 2ndstage is limited by constraining the total power consumption. Then, the preferred relays are chosen according to a minimum-SNR (Signal-to-Noise Ratio) criterion. It is shown by simulations that compared to previous cooperative multicast transmission, the proposed scheme achieves a flexible tradeoff between the system throughput and energy consumption by controlling the total available power. Moreover, it can always achieve better energy efficiency than the existing cooperative and conventional multicast schemes in terms of throughput per energy consumption.

This paper focuses on the resource allocation of multicast services in Distributed Antenna Systems (DAS). First of all, the capacity limitation of conventional multicast ransmission in DAS is analyzed. Then a resource allocation algorithm for multicast services is proposed to improve the system throughput of DAS with Quality of Services (QoS) guarantees. The proposed algorithm includes three steps: Power Assignment, Conservative Allocation and Greedy Step. Simulation results show that the proposed algorithm significantly outperforms the conventional multicast transmission methods in throughput while guaranteeing the minimum data rates of all users as well. Moreover, simulation results also show that the proposed algorithm can exceed the capacity upper bound of conventional multicast scheme when the user number is larger than 50, which indicates the proposed algorithm can overcome the capacity limitation of conventional multicast transmission in DAS.

This paper analytically investigates the impact of low pass filter (LPF) mismatch in direct conversion receivers, which is a main source of frequency selective (FS) I/Q imbalance. Assuming a practical Elliptic LPF realized by active RC (resistor and capacitor) architecture, the LPF mismatch is mainly resulted from the non-ideal RCs in the circuit. The relationships between the imperfect RCs and the LPF mismatch and also the I/Q imbalance are analyzed using a first order Taylor expansion approximation. Close-form expressions are derived for the LPF mismatch variance and front end image leakage ratio (ILR), which are further verified by simulations. It is shown that for RC matching inaccuracies and tolerances less than 5%, the LPF mismatch (or the FS I/Q imbalance) is dominated by the RC matching inaccuracy, while the influence of RC tolerances is negligible. Moreover, simulations are carried out to investigate the impact of I/Q imbalance on the BER performance. It is shown that for both orthogonal frequency division multiplexing (OFDM) and single-carrier-frequency division multiple access (SC-FDMA), the bit error rate (BER) is seriously degraded by FS I/Q imbalance and an error floor will occur. In addition, the BER of OFDM is even worse than that of SC-FDMA, due to the poor image attenuation near the cutting off frequency. Therefore, compensation for the FS I/Q imbalance is always recommended, especially for OFDM systems.

For orthogonal frequency division multiplexing (OFDM) systems in multipath channels, the detection of the first path is desirable to avoid inter-symbol-interference (ISI). However, the conventional peak detection of the cyclic prefix (CP) correlation curve can detect the strongest path but not the first path, while the largest slope detection is sensitive to background noise. This paper presents a novel coarse timing algorithm using double peak detection of CP correlation, which is robust to noise and simple to implement in practice. Considering practical hardware impairments like sampling frequency offset (SFO), the performance of the proposed method is verified by simulations.

This paper investigates the performance of clustered base station (BS) coordination with limited feedback and channel state information (CSI) delay. Given imperfect CSI caused by the limited feedback and channel delay, the expression of data rate per cell is derived. Moreover, compared to the rate with perfect CSI, a rate loss upper bound is obtained. A optimal feedback bits expression are derived to minimize the rate loss caused by limited feedback and channel delay. The numerical results show that the rate loss reduces when the number of feedback bits increases and the channel delay decreases. Meanwhile, the rate loss value reaches to a fixed value when feedback bits increase gradually. On the other hand, when the channel delay increases gradually, the rate loss can be improved little by the increasing of feedback bits. Lastly, as SNR increases, the system performance with the imperfect CSI improves more slowly than that with the perfect CSI.

This paper investigates the performance of multicast services in distributed antenna systems (DAS), which is limited by the mobile user with the worst channel condition. Compared to conventional centralized antenna systems (CAS), DAS provides shorter transmission distance and thus improves channel conditions. Focusing on the effect of path loss and rayleigh fading, the single user and ergodic throughputs for multicast services in DAS and CAS are analyzed, and verified by simulations. It is shown that given various path loss parameters, transmission powers and number of users, DAS is always preferred to CAS for multicast services. The superiority of DAS over CAS increases with path loss parameters and the number of users. Moreover, the total transmission power should be high enough to exploit the spectrum efficiency of multicast services in both DAS and CAS. Finally, to achieve the same performance, the transmission power needed in DAS is 5dB less than that in CAS, testifying DAS as a power efficient cellular structure.

Using conventional resource allocation algorithms in OFDM systems, each user can employ different Modulation and Coding Scheme (MCS) on allocated subcarriers to achieve good throughput. However, in the downlink transmission of LTE systems, the minimum allocation unit for one user is Scheduling Block (SB) and all SB assigned to one user must adopt the same MCS. Therefore, the application of conventional resource allocation algorithms in LTE results in degraded performance since MCS must be chosen according to the worst SB. To solve this problem, a QoS guaranteed resource block allocation algorithm is proposed for LTE systems, which takes into account both the constraint on MCS and the requirement of Quality of Services (QoS). The proposed algorithm firstly estimates the number of SB required by each user and then allocates SBs to users according to their priorities. Simulation results show that compared to conventional schemes, the proposed algorithm can achieve high throughput as well as significant improvement in guaranteeing users' QoS requirements.

It is a great challenge to design an efficient handover scheme of broadband wireless communication systems for high-speed rail to reduce the communication interruption time during handover. A seamless handover scheme based on the dual-layer and dual-link system architecture is proposed in this paper. In the scheme, two antennas are mounted at the front and rear of a train, and one of them executes handover while the other is still communicating with BS. Therefore, the communication will not be interrupted throughout the handover. Furthermore, bi-casting is adopted to eliminate the data forwarding delay between the serving BS and target BS. In order to reduce handover implementation overhead, a Train Relay Station is employed to represent all the users in a train to execute handover in the proposed scheme. The performance of the proposed scheme is analyzed in terms of handover probability, handover failure probability and communication interruption probability. Simulation and theoretical results show that the proposed scheme can efficiently support seamless handover for high-speed trains with low implementation overhead.

Low-pass-filter (LPF) mismatch is a main source of frequency-selective (FS) I/Q imbalance in direct-conversion receivers. Using a first-order Taylor expansion approximation, the relationship between the non-ideal resistors and capacitors (RCs) in the circuit and the LPF mismatch is analyzed. Close-form expressions are derived for the expectation of LPF mismatch and front end image-leakage-ratio (ILR), and further verified by simulations. It is shown that for RC matching inaccuracies and tolerances less than 5%, the LPF mismatch is dominated by the matching inaccuracy, while the influence of tolerances is negligible. FS I/Q imbalance caused by LPF mismatch varies significantly across the transmission bandwidth, which is extremely serious near the cutoff frequency. Therefore, compensation for FS I/Q imbalance is always recommended, especially near the cutoff frequency.

This paper investigates effective SINR mapping (ESM) methods for link abstraction (or channel quality evaluation) used in system level performance simulation. Among all existing ESM methods, the mean mutual information per-bit (MMIB) ESM has been shown to be superior in terms of high accurate block error rate (BLER) prediction and supporting multiple modulation schemes within one data block. However, complicated multi-Gaussian distribution approximation is needed in MMIB-ESM when high modulation schemes such as 16QAM and 64QAM are considered. This paper proposes a new generalized bit-wise E-ESM (BE-ESM), which is shown to have similar performance as MMIB-ESM but with a very simple structure. Furthermore, it is also shown that BE-ESM and MMIB-ESM can be equivalent to each other in certain extents.

This letter proposes an analytical approach to evaluate the performance of MIMO-OFCDM systems with multicode transmission. Assuming zero-forcing successive interference cancellation (ZF-SIC) in the space domain and MMSE detection in the frequency domain, it is shown that at each step of SIC, the error events on multiple code channels are correlated to each other, which make the performance evaluation difficult due to the involvement of a complicated multivariate probability. By approximating the multivariate probability by a series of two-variate probabilities, the proposed analytical approach takes the correlation into account and provides accurate performance estimations. The analytical results are verified by simulations and shown to be more accurate than those where no correlation is considered.

This paper investigates the performance of orthogonal frequency and code division multiplexing (OFCDM) systems with multiple input multiple output (MIMO) multiplexing (MIMO-OFCDM), where multi-code transmission is employed to increase the data rate. An analytical approach is presented to evaluate the performance of the system using joint space-frequency detection with zero-forcing successive interference cancellation (ZF-SIC) in the space domain and minimum mean square error (MMSE) detection in the frequency domain. It is shown that at each step of SIC, the decision errors on multiple code channels are correlated to each other. By taking the correlation between the errors into account, the proposed analytical approach provides a precise estimation on the performance of MIMO-OFCDM. Simulations demonstrate the accuracy of the proposed analytical approach.

This paper investigates the orthogonal frequency and code division multiplexing (OFCDM) systems with multiple input multiple output multiplexing (MIMO-OFCDM) and multicode transmission. Combining the iterative detection in the space domain and the hybrid multi-code interference (MDI) cancellation and minimum mean square error (MMSE) detection in the frequency domain, a joint iterative detection is proposed, which enables space and frequency diversity gains to be jointly exploited. Moreover, using a two-dimensional (2-D) averaging channel estimation algorithm, a close form expression is derived for the optimal power allocation between the pilot and all data channels that achieves the best system performance. It is shown that the optimal power ratio mainly depends on the channel estimation algorithm, the number of transmit antennas as well as the number of pilot and data symbols in a packet, but is not sensitive to the changes in signal-to-noise ratio (SNR) and diversity gains. Simulations are conducted to verify the derived optimal power ratio and study the performance of the proposed joint detection algorithm. It is shown that considerable improvement can be obtained when the number of loops in the joint iterative detection increases. Moreover, the system performance is enhanced significantly when the frequency domain spreading factor, NF, increases.

To provide high speed mobile services, orthogonal multi-code transmission is employed in the orthogonal frequency and code division multiplexing (OFCDM) system. However, in a wireless frequency selective channel either with or without Doppler shift, code orthogonality is distorted and multi-code interference (MCI) is caused. The variance of the MCI is dependent on which codes to be used. This paper investigates the code assignment in the OFCDM system, aiming to mitigate the MCI. The code assignment can be explored in terms of the code distance defined in this paper. Generally, the codes with large code distances to each other should be assigned with high priority. Moreover, to avoid large interference from the powerful code-multiplexed pilot channel, the time spreading code adjacent to the pilot code in the code tree (Adachi et al., 1998) is suggested not be used when the time domain spreading factor is large. The performance of the proposed non-sequential code assignment scheme is analytically evaluated. It has been shown that the system performance can be improved significantly by the non-sequential scheme under various channel conditions, especially when a low to medium system load is assumed.

As a wireless access technique for future broadband mobile communications, orthogonal frequency and code division multiplexing (OFCDM) systems employ multi-code transmission to provide high speed mobile services. Since the multiple code channels are no longer orthogonal in the broadband wireless channel, multi-code interference (MCI) is caused. The variance of the MCI is dependent on which codes to be used. This paper investigates the code assignment in the multi-code OFCDM system, aiming to mitigate the MCI at the receiver. Since two-dimensional (2-D) spreading is employed in the OFCDM system, the code assignment is carried out in time and frequency domains, separately. In order to avoid large interference from the powerful code- multiplexed pilot channel, the time spreading code adjacent to the pilot code in the code tree is suggested not be used when the time domain spreading factor is large. The performance of the proposed code assignment scheme is analytically evaluated. It has been shown that the system performance can be improved significantly by the proposed scheme under various channel conditions, especially when a low to medium system load is assumed.

This paper presents an improved derivative method to provide good symbol synchronization in multi-path channels for orthogonal frequency division multiplexing (OFDM) systems. As the conventional derivative method (Willians et al., 2005), the improved derivative method can reduce the inter-symbol interference (ISI) caused by multi-path channels. Moreover, the proposed method is robust against the frequency offset and the filter length which are very selective to the conventional derivative method. Simulation results confirm that the improved derivative algorithm outperforms the maximum likelihood algorithm (ML) (Van De Veek et al., 1997) and the conventional derivative method.

This paper investigates the performance of turbo-coded orthogonal frequency and code division multiplexing (OFCDM) systems with soft multi-code interference (MCI) cancellation. In order to regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft decision functions are proposed, i.e., LLR-soft-decision and Gaussian-soft-decision functions. By means of computer simulations, it is shown that in a highly frequency selective channel, soft MCI cancellation and MMSE detection can significantly improve the performance of turbo-coded OFCDM systems. Two iterations in turbo decoding are sufficient for both hard and soft decision functions. The proposed soft decision functions outperform the hard decision function with various channel conditions and system parameters.

In this paper, the performance of turbo-coded orthogonal frequency and code division multiplexing (OFCDM) systems is investigated with soft multi-code interference (MCI) cancellation. To regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft decision functions are proposed, called LLR-soft-decision and Gaussian-soft-decision functions. By means of computer simulations, the performance of soft MCI cancellation is studied extensively and compared to that of hard ones. It is shown that in a highly frequency selective channel, the proposed soft decision functions outperform the hard decision function with various channel conditions and system parameters. Furthermore, the Gaussian-soft-decision function provides better performance than the LLR-soft-decision function

In this paper, the performance of turbo-coded orthogonal frequency and code-division multiplexing (OFCDM) systems is investigated with soft multicode interference (MCI) cancellation and minimum mean-square error (MMSE) detection for downlink transmission in future high-speed wireless communications. To regenerate the soft interference signal, the conventional turbo decoding algorithm must be modified to provide log-likelihood ratio (LLR) values for all coded bits. Based on the LLR outputs of turbo decoder, two soft-decision functions are proposed, called LLR-soft-decision and Gaussian-soft-decision functions. The Gaussian assumptions used for deriving these two soft functions are verified by simulation results, and simple methods are proposed to estimate parameters used in the soft functions in practical systems. By means of computer simulations, the performance of soft MCI cancellation is studied extensively and compared with that of hard ones. It is shown that in a highly frequency-selective channel, soft MCI cancellation and MMSE detection can significantly improve the performance of turbo-coded OFCDM systems. Two iterations in turbo decoding are sufficient for both hard and soft-decision functions. The proposed soft-decision functions outperform the hard-decision function with various channel conditions and system parameters, such as the channel correlation, the quality of channel estimation, the number of iterations in turbo decoding and the frequency-domain spreading factor (N/sub F/). Furthermore, the Gaussian-soft-decision function provides better performance than the LLR-soft-decision function. Finally, although frequency diversity gain is saturated for large channel correlation when N/sub F/ is large as in , the gain increases further with increasing N/sub F/ for small channel correlation even when N/sub F/ is large.

Orthogonal frequency and code-division multiplexing (OFCDM) systems have been introduced for high-speed data transmission in future wireless mobile communications. In this paper, a hybrid multicode interference cancellation (MCI) and minimum mean-square error (MMSE) detection scheme is presented for the turbo-coded OFCDM systems. Channel estimation based on a code-multiplexed pilot channel is employed. The weights of the hybrid detection are derived theoretically and an effective method to generate the weights in practical applications is proposed. By means of computer simulation, the effects of system parameters on the performance are studied extensively. It is shown that the hybrid detection outperforms pure MMSE detection in various channel conditions, especially for high-level modulation schemes. To carry out interference regeneration for the hybrid detection, the conventional turbo decoding algorithm which only decodes systematic bits should be extended to decode parity bits as well. Moreover, two iterations in turbo decoding are sufficient to provide good performance for the system with the multistage hybrid detection. Finally, given time-domain spreading factor N/sub T/, the system performance improves with frequency-domain spreading factor N/sub F/. However, the frequency diversity gain is saturated when N/sub F/ is large (i.e., N/sub F//spl ges/16).

Hybrid automatic-repeat-request (ARQ) is a flexible and efficient technique for data transmissions. In hybrid ARQ, subpacket schemes are more attractive for systems with burst errors than complete packet schemes. Although subpacket schemes were proposed in ARQ systems, optimum subpacket transmission is more effective to maximize throughput in a dynamic channel. Since convolutional codes have properties of burst errors in decoding, the optimum subpacket can be applied to convolutional codes. This paper investigates the performance of subpacket transmission for convolutionally coded systems. An efficient method is proposed to estimate the optimum number of subpackets, and adaptive subpacket schemes, i.e., schemes that enable a system to employ different optimum numbers of subpackets under various conditions, are suggested to achieve the maximum throughput of the system. Numerical and simulation results show that the adaptive subpacket scheme is very effective for the convolutionally coded hybrid ARQ system, and it can provide higher throughput, smaller delay,and lower dropping rate than complete packet schemes. Moreover, the adaptive subpacket scheme can be flexibly used with packet combining techniques to further improve the system throughput.

The orthogonal frequency and code division multiplexing (OFCDM) system with 2-D spreading (time- and frequency-domain spreading) is becoming a promising candidate for future broadband wireless communication systems. OFCDM is more attractive than orthogonal frequency-division multiplexing (OFDM) both by introducing frequency-domain spreading for frequency diversity provision and time-domain spreading for flexible data rate provision. To provide high-speed mobile services, multicode transmission is employed in conjunction with OFCDM. In a Gaussian or flat-fading channel, multicode channels are orthogonal. However, in a realistic wireless channel, the orthogonality no longer maintains. Thus, multicode interference (MCI) is caused. This paper focuses on the investigation of the effect of Doppler shift on the downlink transmission of high-speed mobile OFCDM systems. A practical channel estimation algorithm based on a code-multiplexed pilot channel is employed to track the variations of fading channels. Hybrid MCI cancellation and minimum mean-square error (MMSE) detection proposed by the authors is employed as an efficient way to eliminate the MCI in the frequency domain. The system performance is analytically studied with imperfect channel estimation to show how it is affected by parameters such as the window size in the channel estimation, Doppler shift, the number of stages of the hybrid detection, the power ratio of pilot to data channels, spreading factor, and so on

The broadband orthogonal frequency and code division multiplexing (OFCDM) system with two-dimensional spreading (time and frequency domain spreading) is becoming a very attractive technique for high-rate data transmission in future wireless communication systems. In this paper, a quasianalytical study is presented on the downlink performance of the OFCDM system with hybrid multi-code interference (MCI) cancellation and minimum mean square error (MMSE) detection. The weights of MMSE are derived and updated stage by stage of MCI cancellation. The effects of channel estimation errors and sub-carrier correlation are also studied. It is shown that the hybrid detection scheme performs much better than pure MMSE when good channel estimation is guaranteed. The power ratio between the pilot channel and all data channels should be set to 0.25, which is a near optimum value for the two-dimensional spreading system with time domain spreading factor (N/sub T/) of 4 and 8. On the other hand, in a slow fading channel, a large value of the channel estimation window size /spl gamma/N/sub T/, where /spl gamma/ is an odd integer, is expected. However, /spl gamma/=3 is large enough for the system with N/sub T/=8 while /spl gamma/=5 is more desirable for the system with N/sub T/=4. Although performance of the hybrid detection degrades in the presence of the sub-carrier correlation, the hybrid detection still works well even the correlation coefficient is as high as 0.7. Finally, given N/sub T/, although performance improves when the frequency domain spreading factor (N/sub F/) increases, the frequency diversity gain is almost saturated for a large value of N/sub F/ (i.e., N/sub F/ /spl ges/ 32).