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.

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.

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.

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.

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.

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%.

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.

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 this work, we develop a contract theory framework to tackle the allocations of heterogeneous baseband processing units (BBUs) in cloud radio access network. We first model a monopoly market by viewing the BBUs as a kind of resource. The infrastructure provider (InP), as the monopolist, owns all the heterogeneous BBUs of different processing abilities and maintaining costs, and leases them to multiple mobile network operators (MNOs) to gain profit. At the same time, the MNOs intend to rent reasonable amount of BBUs to provide services to their mobile clients. Then we propose a contract theory framework, in which contract items are optimized to maximize the InP’s utility, while maintain the welfare of the MNOs. We design the optimal contracts with complete and asymmetric information on the MNOs. Our contract design achieves the near optimum solution to heterogeneous computational resource allocation even under the information asymmetric case. Our derivations indicate that the optimal contracts with asymmetric information achieve a lower utility for the InP than the ones with complete information and the utility reduction is higher when the BBUs are heterogeneous rather than homogeneous. Numerical results demonstrate that, the InP having heterogeneous BBUs can achieve a higher utility relative to having homogeneous BBUs, which is more profitable and realistic for the InP. Moreover, we regard Stackelberg game theoretic approach as a comparison, and show that our method is more realistic.

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.

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.

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.

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.

One of focuses in green communication studies is the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems. Although the massive MIMO technology can improve the spectral efficiency (SE) of cellular networks by configuring a large number of antennas at base stations (BSs), the energy consumption of radio frequency (RF) chains increases dramatically. The increment of energy consumption is caused by the increase of RF chain number to match the antenna number in massive MIMO communication systems. To overcome this problem, a generalized spatial modulation (GSM) solution is presented to simultaneously reduce the number of RF chains and maintain the SE of massive MIMO communication systems. A EE model is proposed to estimate the transmission and computation power of massive MIMO communication systems with GSM. Simulation results demonstrate that the EE of massive MIMO communication systems with GSM outperforms the massive MIMO communication systems without GSM. Besides, the computation power consumed by massive MIMO communication systems with GSM is effectively reduced.

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.

As a promising wireless network virtualization technology, virtualized base station (BS) has been proposed to tackle the problem of low-efficient utilization of BS's computing resources, e.g., baseband processing units (BPU). In this paper, we design a novel scheme to achieve the efficient BPU allocation based on a contract-theoretic approach. To achieve this, we consider the BPUs as a kind of trading resources. We establish a monopoly market, where the infrastructure provider (InP) is the monopolist owning all the BPUs, and multiple mobile network operators (MNOs) intend to rent BPUs from the InP for processing their baseband signals. In such a market, the InP offers a set of quantity-price contract items to the MNOs based on statistical information of their types, and at the same time, the MNOs are stimulated to accept the offers for the purpose of making profit. We propose the optimal contract design to maximize the InP's profit, as well as develop an incentive mechanism to guarantee each MNO choosing a proper contract item. Numerical results validate the effectiveness of our incentive mechanism for BPU resource allocation.

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.

Surrogate model based design space exploration (DSE) techniques have been widely used in finding the Pareto-optimal design points of embedded processor architectures. However, existing such methods lack of sound modeling of the uncertainty of the surrogate models, which greatly limits the searching scope. In this paper we propose an expected hypervolume improvement (EHVI) algorithm which models the uncertainty of an adaptive component selection and smoothing operator (ACOSSO) surrogate model by means of constructing a Gaussian random distribution and searches the Pareto points by taking an EHVI criterion. Experimental results prove the effectiveness of the proposed algorithm through comparing with two existing DSE algorithms.

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.

Processor architects usually design uniprocessor or chip multiprocessor (CMP) by using a platform-based approach. One of the major challenges in this approach is to explore the exponential-size design space composed of many tunable and interacting architectural parameters. An exhaustive search of the design space is prohibitive because of the expensive run-time of simulations. So an efficient design space exploration (DSE) strategy that can fast find the multi-objective architectural configurations (points in design space) in terms of system metrics like performance and energy is needed. In this paper, we propose an accurate and efficient adaptive component selection and smoothing operator (ACOSSO) metamodel assisted NSGA-II (MA-NSGA-II) multi-objective optimization (MOO) technique for processor DSE. We show the effectiveness of our methodology by comparing with linear regression (LR), restrict cubic splines (RCS), natural cubic splines (NCS) and artificial neural network (ANN) metamodeling techniques for processor design metrics prediction and architecture optimization. The experimental results show that, the proposed methodology achieves higher prediction accuracy and better architecture optimization results.

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.

Ultra dense cellular networks (UDNs) are concerned in this paper, which employ Macro-Diversity- Coordinated Multipoint (MD-CoMP) to deal with the serious inter-cell interference. Considering the constraints of inter-cell resource allocation posed by CoMP and the difference of load in each cell, a joint clustering and inter-cell resource allocation is necessary. To achieve a feasible solution, the joint optimization is approximated by two sub-problems, i.e. clustering based on load information using game theory and inter-cell resource allocation based graph-coloring algorithms. Thus a two-step joint clustering and scheduling (TS-JCS) scheme is proposed. Simulation results illustrate that TS-JCS has the capability to jointly consider clustering with the requirements on resources. Compared to No-CoMP and the comparing scheme without consideration of resource allocation, TS-JCS can significantly improve the system performance, demonstrating the necessity and superiority of joint CoMP clustering and inter-cell resource allocation.

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.

Vector Processing is an efficient way to exploit data-parallel with wide data operations, which is widely employed by wireless baseband signal processing. However, wide data operations also cost much more power and lead to high power consumption especially for vector register file (VRF). To resolve this issue, firstly, we profiled the read/write characteristics of LTE baseband signal algorithms to VRF and found three facts exist: a lot of copy or movement operations are sourced from vector load/store, the occasions for concurrent access to all VRF read/write ports are little and a large number of short-lived values exists. Based on these observations, a buffer-aided VRF is presented to exploit these characteristics for low power. The experiment results show: The proposed buffer-aided VRF can effectively reduce the vector copy and movement cost comparing with general register file architectures and also achieve better power saving effects than similar buffering technologies. A reduction of 40.9% power dispassion over centralized VRF can be reached in register file level with negligible performance loss.

High-order QAM modulation schemes are recommended in various communication systems. However, soft demodulating a high-order QAM constellation is complicated when applying traditional methods. In this paper, a novel de-mapper is proposed based on the fact that the clipped LLR can also offer sufficient soft-information, meanwhile the theoretical LLR curves are approximately linear in this clipped range. Hence a first-order linear function is designed to approximate the theoretical value. Simulation results and complexity analysis demonstrate that the proposed de-mapper outperforms traditional approaches in terms of BER performance, and also keeps low complexity.

In this paper, we study a full-duplex wireless-powered communication network (FD-WPCN), which consists of one full-duplex (FD) hybrid access-point (H-AP) and one FD user. The H-AP and user are both equipped with two antennas, one for downlink wireless energy transfer (WET) from the H-AP to user and the other for uplink wireless information transfer (WIT) from the user to H-AP, where WET and WIT are performed simultaneously through the same frequency band. We consider the scenario that the role of each antenna (i.e., transmission or reception) is not predefined and propose an antenna pair selection (APS) scheme to improve the performance by optimally configuring the transmit and receive antennas at each node. The closed-form expressions for outage probability and probability density function (PDF) of the received signal-to-noise ratio (SNR) at the H-AP are derived. Based on the PDF, we then calculate the closed-form expressions of ergodic capacity, SNR moments and symbol error rate (SER). Finally, we verify the analytical results through Monte Carlo simulations.

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.

A distributed antenna system (DAS) architecture is believed to be able to enhance capacity performance of Cloud Radio Access Networks (C-RAN), especially for users near the cell boundary who experience low Signal-Noise-Ratio (SNR). However, the problem of finding the analytical bounds on the capacity of DAS with the rising number of antennas in low SNR rigime has not been fully studied. In this paper, we investigate a case in C-RAN of multiple transmitting base stations and a single receiving user under low SNR conditions. We derive closed-form upper and lower bounds in efficiently computable expressions for differential capacity (DCAP) using the moment generating function (MGF) of SNR. Bounds accuracy is evaluated and compared to results in current literature. Numerical results corroborate our analysis and the analytic bounds on DCAP is tight in the low SNR regime. Furthermore, The upper bound approximates better compared with the one obtained in [1] under two different channel models. These lower and upper bounds provide more accurate capacity measures which can be used in the evaluation of DAS performance and C-RAN design.

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 presents an ASP (application specific processor) with 512-bit SIMD (Single Instruction Multiple Data) and 192-bit VLIW (Very Long Instruction Word) architecture optimized for wireless baseband processing. It employs optimized architecture and address generation unit to accelerate the kernel algorithms. Based on the ASP, a multi-core baseband processor is developed which can work at 2×2 MIMO and 20 MHz physical bandwidth configuration for LTE inner receiver and meet requirements of Category 3 User Equipment (CAT3 UE). Furthermore, a silicon implementation of the baseband processor with 130nm CMOS technology is presented. Experimental results show that the baseband processor provides 100 GOPS computing ability at 117.6MHz.

In LTE uplink, a DFT with 34 possible lengths is performed. The traditional method directly transforms the sequences with different lengths by using the mixed-radix FFT which is not appropriate to apply on the DSP processor only supplying radix-2 butterfly hardware especially. In this paper, we resample the arbitrary-length input sequence so that a 2n-point FFT can be applied by using the polyphase filter. Theoretical analysis demonstrates the equivalence of the proposed polyphase-filter-based FFT (PF-FFT) and the original DFT. Moreover, the PF-FFT also keeps low complexity and good fixed-point performance.

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.

Cellular relay networks prove to be a cost-effective approach that offers significant performance benefits in coverage extension, cell-edge throughput enhancement and increased spectral efficiency. Radio resource management for cellular relay networks, where the network topology is highly dynamic, is particularly challenging. The highly dynamic topology may be caused by an ever increasing number of users accessing Internet and other multimedia services using mobile devices carried by pedestrians, in a train or in vehicles. This paper studies the subcarrier allocation problem in highly dynamic cellular relay networks with an objective to maximize the overall throughput. This optimization problem is formulated and solved as an expectation maximization problem. Statistical multiplexing gain is explicitly explored to further improve the channel utilization and spectral efficiency. Numerical evaluations are performed which demonstrate that the proposed scheme offers higher system throughput and improved radio resource utilization compared with existing schemes.

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.

Both Multiple Input and Multiple Output (MIMO) and Smart Antenna (SA) have been widely accepted as promising schemes to improve the spectrum efficiency and coverage of mobile communication systems. This paper addresses the issue of Direction of Arrival (DOA) estimation in Virtual MIMO (VMIMO) systems which adopt SA simultaneously. First of all, we propose a VMIMO scheme for DOA estimation in which a set of User Equipments (UEs) are grouped together to simultaneously communicate with the Base Station (BS) on a given Resource Block (RB). In this scheme, the BS has multiple antennas and can estimate DOA of each UE in the group simultaneously. However, in practical environment because of reflection and refraction of signals, the received signals may be coherent. It is desirable that the DOA estimation in VMIMO could provide high resolution when the number of independent signals is unknown, which can not be achieved by existing algorithms. In order to solve this problem, we propose an Automatic Weighted Subspace Fitting (AWSF) algorithm that can detect the number of independent signals automatically and show accurate DOA estimation. Then with consideration of computational cost and sampling cost while using the AWSF algorithm, we propose a utility factor to determine the optimal number of UEs in VMIMO scheme, i.e., the order of VMIMO. Finally, the performance of the proposed AWSF algorithm and the efficiency of the utility factor criterion are demonstrated by simulations.

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.

With the rapid development of high-speed wireless communication technology, more novel and different wireless networks are emerging. The co-existence of multiple wireless networks has made the wireless network environment complex and heterogeneous. Traffic flow characteristics study and effective traffic prediction algorithm selection are very important for optimal allocation of network resources, network protocol design and improvement of service quality in this heterogeneous wireless network. This paper uses non-statistical method which is based on chaos theory to make analysis of heterogeneous wireless network traffic flow characteristics. On that basis, a traffic model which uses phase space reconstruction algorithm on multivariate traffic time series is proposed and used for traffic prediction. Simulation results show that the proposed traffic prediction model can achieve better prediction performance.

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.

In 3GPP Long Term Evolution (LTE) networks, the frequency reuse schemes such as fractional frequency reuse (FFR) and soft frequency reuse (SFR) are used to improve system capacity. The allocation of transmit power and subcarriers to each cell in these schemes are fixed prior to network deployment. This limits the potential performance of these frequency reuse schemes. In this paper, we propose to improve the capacity of SFR scheme by jointly optimizing subcarrier and power allocation in multi-cell LTE networks. An iterative algorithm that can adaptively vary the number of major subcarriers and adjust the transmit power for each cell according to wireless traffic loads is proposed. Simulation results show that the proposed algorithm outperforms the existing Reuse 1, FFR and static SFR schemes in both system throughput and cell edge user performance.

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.

Due to frequent handovers in broadband wireless communications in high-speed rail, communication interruption during handover could seriously degrade the experiences of passengers on the train. Aiming to reduce the interruption time, this paper proposes a seamless handover scheme based on a dual-layer and dual-link system architecture, where a Train Relay Station is employed to execute handover for all users in a train and two antennas are mounted at the front and rear of a train. In the proposed scheme, the front antenna executes handover while the rear antenna is still communicating with BS, so that the communication can keep non-interruptive throughout the handover. Moreover, bi-casting is adopted to eliminate the data forwarding delay between the serving BS and target BS. A complete handover protocol is designed and the performance of the proposed scheme is analyzed. It can be seen from analytical results that the handover failure probability decreases as cell overlap increases and the communication interruption probability decreases with the decrease of train handover location and the increase of cell overlap. The simulation results show that in the proposed scheme, the communication interruption probability is smaller than 1% when the handover location is properly selected and the system throughput is not affected by handover. In conclusion, both theoretical and simulation results show that the proposed scheme can efficiently perform seamless handover for high-speed rail with low implementation overhead.

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.

An efficient hardware-optimized Physical Random Access Channel (PRACH) baseband signal generation algorithm and its ASIC implementation in the LTE user equipment (UE) transmitter are presented in this paper. A simplified DFT of the Zadoff-Chu (ZC) sequence as well as a phase computation are applied to the prime size DFT of the PRACH preamble and the large size IDFT is accomplished by groups of smaller size IFFTs. The optimized algorithm achieves significantly lower computational complexity compared with the original algorithm in the LTE specification and better performance compared to another publication. The ASIC architecture is also designed to reduce the memory size and logic complexity, which achieves a low hardware cost in terms of the cell area. The proposed design was implemented in 65nm CMOS and it was demonstrated that this design can satisfy the timing requirements of the LTE specification.

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.

In this paper, a novel call admission control (CAC) scheme for multicast and unicast integrated services is proposed, which optimizes service provision capacity for both services. A resource sharing model is presented in which multiple classes services are dynamically admitted into the system. Several basic CAC rules for the above resource model are proposed first and improved mechanisms are investigated to further increase system performance. Subsequently, the call level QoS and system performance of the resulting CAC scheme are evaluated. Numeric results show that the proposed scheme can achieve optimal multiclass service performance while satisfying QoS constraints.

QoS provisioning in wireless mesh networks has been to known as a challenging issue. In a distributed scheduling based wireless mesh backhaul network, conventional connection-based QoS provision mechanism requires considerable amount of overhead for per-link QoS signaling, thus cannot support high speed real time traffic. In this paper,we propose a DiffServ-like QoS provision mechanism for IEEE 802.16 mesh networks with directional antennas to increase network capacity and enable real time traffic. We develop a distributed scheduling algorithm based on our proposed scheme. Simulation results demonstrate that directional antennas can achieve higher network capacity compared with omni-antennas, and our QoS scheme can effectively guarantee QoS requirements of real time traffic.

We consider the robust linear precoding (LP) and Tomlinson-Harashima precoding (THP) schemes for multiuser MIMO-OFDM downlink channels with limited feedback. Benefiting from the correlation of spatial channels, the mobile terminal compresses and feeds back the time-domain channel vectors instead of the corresponding frequency-domain vectors to substantially reduce the feedback signalling overhead. A compression and restoration method and a codebook design for channel state information at the transmitter (CSIT) feedback are proposed in the time domain. By treating the partial CSIT as a random quantity, we develop the robust precoders to combat the truncation and quantization errors introduced in the feedback procedure. In comparison with the non-robust designs, both the robust LP and THP have better bit-error rate performance especially in high signal-to-noise ratio region.

The throughput of conventional multicast transmission in wireless systems is limited by the user with the worst channel quality in the multicast service group. The subcarrier allocation for multicast services in multicarrier systems is a feasible solution to overcome the capacity limitation by exploiting the frequency diversity among subcarriers. However, most of the current subcarrier allocation algorithms are limited to unicast services. In this paper, we propose an optimal subcarrier allocation algorithm for multicast services with Quality of Services (QoS) guarantees. A low-complexity suboptimal algorithm is also proposed, which includes three steps: Conservative Allocation, Greedy Step and Iterative Enhancement. Simulation results show that the proposed algorithms significantly outperform the conventional multicast transmission scheme while at the same time guaranteeing the minimum data rates of all users. Moreover, simulation results also show that the performance difference between the optimal and suboptimal algorithms is small.

With its new features such as multi-homing and multi-streaming the Stream Control Transmission Protocol (SCTP) has become a promising candidate as a general-purpose transport layer protocol. Multi-homing in an SCTP association can make concurrent multi-path transfer an appealing candidate to satisfy the ever increasing user demands for bandwidth. Multiple streams provide an aggregation mechanism to accommodate heterogeneous objects, which belong to the same application but may require different QoS from the network. However, the current approach lacks an internal mechanism to support preferential treatment among its streams for concurrent multi-path transfer. In this paper, we introduce WM2-SCTP (Wireless Multi-path Multi-flow - Stream Control Transmission Protocol), a transport layer solution for concurrent multi-path transfer with parallel subflows. WM2-SCTP aims at exploiting SCTP's multi-homing and multi-streaming capability by grouping SCTP streams into subflows based on their required QoS and selecting best paths for each subflow to improve data transfer rates. The results show that under different scenarios WM2-SCTP, can effectively enhance transmission efficiency.

The power allocation problem is addressed for time division duplex (TDD) LTE-A uplink systems in this paper. Due to the IDFT de-spreading in LTE-A uplink, the channel frequency responses in an IDFT de-spreading block will be tangled together. After analyzing the equivalent signal to interference plus noise ratio (SINR) in the time domain, a Truncated Squared norm of channel equalization Coefficients based Power Allocation (TSCPA) method is proposed to improve the final SINR performance after the IDFT de-spreading block. The proposed TSC-PA algorithm is verified for the clustered DFT-s-OFDM system in eigen-model block diagonalization multi-user MIMO uplink environment by simulations. The results demonstrate that the proposed TSC-PA algorithm can further improve the system block error rate (BLER) performance by selecting a proper truncation threshold.

In this paper, a novel radio admission control (RAC) scheme is proposed for handling multiclass services in Long Term Evolution (LTE) systems. An objective function of maximizing the number of admitted users is proposed to evaluate the system capacity. To solve the optimization problem, we present a combined complete sharing (CS) and virtual partitioning (VP) resource allocation model and develop a service degradation scheme in case of resource limitations in the proposed RAC scheme. Call blocking probability, system resource utilization and system capacity are used as performance metrics and are evaluated by using a K-dimensional Markov Chain model. Numerical results show that an optimal proportion of resource deployment for different service groups can be identified to maximize system capacity while at the same time maintaining quality of service (QoS) constraints of all admitted users.

In order to improve the efficiency of paging management, various paging strategies have been proposed for tracking single mobile user in the wireless cellular network. However, most of the schemes have ignored the important problem of efficient search for multiple mobile users under delay and bandwidth constraints. Given the condition that the search is over only after all the users in the group are found, this problem is defined as the Conference Call Search (CCS) problem. As opposed to the single user tracking, for which one can always reduce the expected search cost, for a multicast search the dependency between the delay and the search cost is NP-hard. In this paper, we propose a non-optimal search method that yields a low search delay as well as a low search cost. The 2D Markov walk is used as the mobility model which describes a broad class of mobility patterns. The Weighted Bipartite Graph Matching is used to allocate paging bandwidth which keeps a total maximum location probability at each paging round. Experimental results show that our paging scheme also achieves a low paging cost and delay compared to other schemes proposed in the literature.

Location prediction is one of the key technologies of active mobility management in the next generation of mobile communication systems. Most of known location prediction models only take parts of predictive factors into account, which leads to a low prediction success ratio of these models. The motivation of this paper is to design a location prediction model considering multiple predictive factors to improve the prediction success ratio and improve the efficiency of the model. In this paper, a location prediction model based on Bayesian Network theory is proposed. The proposed model can effectively solve multi-factor location prediction. Firstly, the relative predictive factors are coded in the Bayesian Network node, and location prediction results can be calculated based on cell topology information integrated in the model structure. A factors distribution mechanism is designed to solve the problem when the nodes cannot obtain location prediction information directly. Subsequently, the method of calculating location prediction results for each cell is presented. The simulation results indicate that the proposed location prediction model is effective in improving accuracy of location prediction and the stability of the model is better than that in comparative schemes.

Network selection is an important issue in the next generation of heterogeneous wireless network and well studied for individual flows. However, researches of network selection for multi-flows at network side are seldom touched upon but also important from the global view of optimizing usage of network resources. In this paper, a weighted bipartite graph based network selection scheme for multi-flows is proposed, which adopt secondary exponential smoothing method to perform network resource prediction and assign flows to networks based on Matching Degree(MD). The network selection scheme is modeled as weighted bipartite graph with objectives of maximizing Matching Degrees and access ratio as well as the constraint of guaranteeing no network overloaded. Weighted Bipartite Graph Algorithm (WBGA) is designed to achieve the goals. Simulation results show that WBGA demonstrates best performance compared to other schemes, with regard to average MD, access ratio and utilization ratio of network resources.

In multiuser MIMO systems, the Channel Inversion Regularization (CIR) precoding outperforms Zero-Forcing (ZF) in the case of a small number of users and low SNR. However, unlike the zero-interference ZF, the optimal power allocation issue using CIR is a nonconvex optimization problem which will become more intractable with nonconvex QoS constraints. In this paper we focus on the challenging QoS-aware optimal power allocation problem, aiming to maximize the system sum rate and guarantee the users' minimum data rates. As a result, an "Iterative Geometric Programming" (IGP) strategy is proposed which transforms the underlying problem to a series of tractable Geometric Programming (GP) problems through an iterative convex approximation. Extensive simulations have been conducted and the results indicate that IGP is quite suitable to tackle the problem, which can achieve a good balance between the system sum rate and the individual QoS requirements.

Significant throughput gains and system fairness can be obtained by employing scheduling schemes based on precoding techniques. However, QoS guarantee requirements are seldom taken into account. In this paper, we propose a downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) systems with limited feedback. The proposed algorithm combines stream selection with multi-user packet scheduling. To maximize the overall capacity and reduce co-channel interference, streams are selected in accordance with preceding matrices. In multi-user packet scheduling, the base station first determines the SDMA region size for the primary streams. In addition, packet scheduling for the secondary streams is performed to completely exploit spatial multiplexing gains. Numerical results show that, at the cost of slightly lower system fairness, the proposed algorithm can achieve higher spectrum efficiency, and have a noticeable improvement in guaranteeing QoS requirements in terms of data rates and delay.

In MIMO systems, spatial multiplexing is a powerful technique for increasing channel capacity by transmitting multiple data streams in the same channel simultaneously. Moreover, additional performance can be extracted in the presence of channel state information (CSI) at the transmitter. However, channel estimation error usually exists in practical systems and leads to imperfect CSI. As a result, the system performance is degraded. Fortunately power allocation can mitigate the problem effectively. In this paper, the power allocation problem is investigated in the case of imperfect CSI with accurate system model. A greedy power allocation (GPA) algorithm with adaptive modulation scheme is proposed to maximize the system data rate while satisfying each data stream's bit error rate requirement. Simulation results show that GPA can reduce the effects of imperfect CSI and obtain better performance than other traditional algorithms, e.g. waterfilling and equal power allocation algorithms.

In multiuser MIMO systems, the achievable system data rate as well as quality of service (QoS) of individual users is limited by the inter-user interference (IUI). The interference balancing issue via power allocation can be usually formulated as a nonconvex optimization problem which will become more intractable with nonconvex QoS constraints. In this paper we focus on the challenging QoS-aware optimal power allocation problem, aiming to maximize the system sum rate and guarantee the users' minimum data rates. As a result, a "Polynomial Approximations based on Single Condensation" (PASC) strategy is proposed which transforms the underlying problem to a series of tractable Geometric Programming (GP) problems through single condensation scheme. Extensive simulations have been conducted and the results indicate that PASC is quite suitable to tackle the problem, which can achieve a good balance between the system sum rate and the individual QoS requirements.

Various paging strategies have been proposed to improve the efficiency of paging management. However, most of the schemes ignore how to obtain location predictions or are too complex for real systems. In this paper, we propose a new adaptive paging scheme designed according to the mobility pattern and location probabilities. The Bayesian network is used as the location prediction model which describes a broad class of mobility patterns. The probability distribution of a mobile terminal's location is derived on the condition that the incoming calls form a Poisson process and the cell holding time has an exponential probability distribution. The paging strategy is implemented using a heuristic algorithm which can adaptively change according to the given mobility pattern and traffic parameters. The running time of the strategy is only ¿(N) where N describes the number of cells in a Tracking Area. Experimental results show that the adaptive paging scheme also achieves a low paging cost compared to other schemes proposed in the literature.

Although significant throughput gains and system fairness can be obtained by slot-based scheduling algorithms, the corresponding complexities are considerable. In this paper, we propose a region-based downlink scheduling algorithm for QoS-guaranteed services in multi-user multiple-input multiple-output (MIMO) precoding systems. In contrast with conventional slot- based scheduling, region-based scheduling allocates bandwidth based on resource regions consisting of several slots. The proposed algorithm combines QoS-urgent-degree and the proportional fairness to select candidate connections, and determines the size of each SDMA region. In addition, multi-user scheduling is performed in all SDMA regions to completely exploit spatial multiplexing gains. Simulation results show that, at the cost of slightly lower spectrum efficiency and data rate guarantee, the proposed region-based scheduling algorithm has much lower complexity, while at the same time it can achieve higher performances in system fairness and delay guarantee.

The concept of Logical Service Provision Number (LSPN) is proposed to evaluate the service provision capacity in multicast and unicast integrated multiclass service cellular networks. The tradeoff between call blocking probability and LSPN is investigated and an objective function of maximizing LSPN is proposed. To solve the optimal problem, the system is modeled as a 2K-dimensional Markov process with following features. The multiclass service system can provide multiple service classes using either multicast traffic or unicast traffic and each service class has call level QoS requirements. A static channel allocation model is adopted as the multicast traffic integration scheme. By solving the objective function, an optimal proportion of multicast traffic to unicast traffic can be identified to maximize the service provision capacity while satisfying QoS constraints.

In the IEEE 802.16e OFDMA systems, the data mapping algorithm maps the data to the appropriate rectangular regions in the two-dimensional matrix of time and frequency domain. Each region is described by an Information Element (IE) which is used for signaling and occupies a slot. The IEs as well as vacant slots in the allocated rectangular region result in a substantial amount of overhead. In order to minimize the overhead so as to increase system throughput, the paper proposes a "Mapping with Appropriate Truncation and Sort" (MATS) algorithm. Extensive simulations are conducted in terms of mapping efficiency, mapping cost and system throughput to evaluate the performance of MATS. The results show that compared with Raster, MATS can increase the mapping efficiency by up to 2.4% and reduce the mapping cost by up to 80% and 37% for constant bit rate traffic and variable bit rate traffic, respectively. Moreover, system throughput is increased by more than 3% in the 10 MHz bandwidth network. Consequently, MATS can substantially reduce the overhead and achieve high system throughput.

In this paper, a low-complexity SDMA-based greedy resource allocation (SGRA) algorithm is proposed for the uplink of IEEE 802.16 MIMO-OFDMA systems taking into account co-channel interference. The objective of SGRA is to allocate resources in the space-time-frequency domain in order to maximize system throughput while guaranteeing QoS requirements of real time services. By performing efficient interference management, SGRA can be carried out in two phases. In the first phase, greedy resource allocation, primarily involving uplink scheduling and subchannel allocation across the MAC and PHY layers, is performed in the time-frequency domain. In the second phase, the resource allocation is extended to the space-time-frequency domain. Simulation results show that SGRA can improve system throughput while at the same time guaranteeing the delay and minimum data rate requirements of users.

In this paper, a singular value decomposition based signature matrix inversion (SVD-SI) scheme is proposed for downlink in multiuser MIMO systems, which is more efficient and able to reduce the feedback overhead by limited feedback. The wireless channels are decomposed into several eigenmodes with the right singular vectors as the spatial signatures, which are quantized and fed back to the base station. Then, the operating users are selected according to their signatures, and the multiuser interference is eliminated by the signature matrix inversion scheme. Finally, we characterize the performance of SVD-SI under limited feedback, and give the recommended feedback rate under different scenarios. Numerical results show that the proposed scheme achieves significant throughput improvement while reducing the feedback overhead.

Single frequency networks (SFN) broadcast is an efficient method to provide broadcast services in cellular networks. How to select broadcast services by the SFN operation to trade off between the occupied bandwidth and the SFN performance including spectrum efficiency and broadcast service continuity is a new problem. To the best of our knowledge no solutions have been proposed to solve this problem so far in the literature. We define the problem of SFN broadcast services selection as a knapsack problem and solve it to minimize the occupied bandwidth while at the same time guaranteeing the SFN performance. Based on the solution, several SFN broadcast services selection algorithms are proposed which vary in the reselection policy. Numerical results show that our proposed algorithms are applicable to different cases with different system requirements and in particular, that the semi-dynamic-SFN broadcast services selection algorithm is an efficient solution in general.

In this paper we address a new problem that has not been addressed in the past: how to improve energy efficiency for both unicast and multicast services without violating QoS requirements of mobile stations in 802.16e wireless networks. We propose a scheduling set based integrated scheduling (SSBIS) algorithm to solve the problem. SSBIS partitions all the mobile stations into multicast Scheduling Sets and a unicast Scheduling Set on the principle of minimizing mobile stations' energy consumptions by making use of the multicast transmission scheme and it adopts different scheduling policies based on the attributes of the Scheduling Sets to improve energy efficiency of the whole system. Numerical results show that SSBIS can result in a significant overall energy saving while at the same time guaranteeing the minimum data rates of mobile stations.

In this paper, a practical eigenmode based scheduling algorithm with double loops (ESDL) is proposed for multi-user MIMO/OFDM systems. The wireless channels are decomposed into several eigenmodes, and the eigenmode based power allocation is formulated into an optimization problem, whose object is to maximize the system capacity while guaranteeing users' QoS requirements. By replacing the QoS constraints with penalty functions, the problem is reformulated to a penalty problem with a given penalty factor, which is solved by a greedy power allocation (GPA) scheme as the inner loop. The penalty factor is then updated in the outer loop according to QoS constraints. Numerical results show that compared with other algorithms proposed in the literature, ESDL can result in significant improvement in channel efficiency, while at the same time guaranteeing minimum data rates of users.

This paper proposes a new cross layer scheme (Fast RSVP) to reserve resources in mobile IPv6 networks. Through the cooperation of mobile IP and RSVP modules, Fast RSVP includes a number of mechanisms such as advanced resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions, path merge etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

The random access scheme is used for initial and periodic ranging in the IEEE 802.16 protocol. The number of contention slots in the uplink subframe decides the system performance. However, how many contention slots should be allocated for ranging is not standardized in the protocol, so it is still necessary to determine the optimal number of contention slots. In this paper, the exact equations of the optimal numbers of initial ranging slots and periodic ranging slots are derived. An optimal dynamic allocator is also proposed to optimize the allocation of the initial and periodic ranging regions in the uplink subframe. The simulation results show that good system performance can be achieved with the optimal dynamic allocator.

This paper proposes a new cross layer scheme (fast RSVP) to reserve resources for mobile IPv6. Through the cooperation of mobile IP and RSVP modules, fast RSVP includes a number of mechanisms such as advance resource reservation on neighbor tunnels, resource reservation on optimized routes, resource reservation for handover sessions (guard channel) etc. Network simulation results show that our scheme, compared with other traditional ways to reserve resources in mobile environments, has the following advantages: (1) it allows a mobile node to realize fast handover with QoS guarantees; (2) it avoids resource wasting caused by triangular routes and duplicate reservations; (3) it distinguishes different types of reservation requests, greatly reducing the handover session forced termination rate while maintaining high performance of the network.

In this paper, we propose a practically efficient Subcarrier Allocation scheme based on Lagrangian relaxation to solve the problem of subcarrier allocation in OFDMA wireless channels. The problem of subcarrier allocation is formulated into an Integer Programming (IP) problem, which is relaxed by replacing complicating constraints with Lagrange multipliers using Lagrangian Relaxation. A subgradient method is used to optimize the Lagrangian dual function and a heuristic is designed to obtain the feasible solution. Lagrangian Relaxation Subcarrier Allocation (LRSA) is proven to be of polynomial complexity and it provides bounds on the value of channel efficiency. Numerical results show that compared with other algorithms proposed in the literature, LRSA can result in a significant improvement in channel efficiency, while at the same time guaranteeing minimum data rates of users.

In this paper, we tackle the packet scheduling problem in IEEE 802.16e wireless metropolitan area network (WMAN), where the Sleep Mode is applied to save energy of mobile stations (MSs). Our objective is to design an energy efficient scheduling policy which works closely with the sleep mode mechanism so as to maximize battery lifetime in MSs. To the best of our knowledge no power saving scheduling algorithms based on sleep mode defined in IEEE 802.16e have been proposed so far in the literature. We propose a longest virtual burst first (LVBF) scheduling algorithm which schedules packets of MSs in a virtual burst mode where there is one primary MS and multiple secondary MSs sharing the wireless link resource. LVBF prolongs MSs' lifetime by reducing the average time when MSs stay in the idle state and the number of state transitions between the awake and sleep states. Simulation results show that, in comparison with the round robin scheduling scheme, LVBF can produce significant overall energy saving, while guaranteeing the QoS requirements of MSs in terms of their minimum data rates.

In this paper, the problem of scheduling multiple users sharing a time varying wireless channel is studied, in networks such as in 3G CDMA and IEEE 802.16. We propose a new generic wireless packet scheduling framework (WPSF), which takes into account not only the quality of service (QoS) requirements but also the wireless resource consumed. The framework is generic in the sense that it can be used with different resource constraints and QoS requirements depending on the traffic flow types. Subsequently, based on this framework a minimum rate and channel aware (MRCA) scheduling algorithm is presented. MRCA attempts to greedily enhance wireless channel efficiency by making use of multi-user channel quality diversity, while providing acceptable QoS in term of users' minimum rate constraints. Simulation results show the desirable properties identified in the algorithm.

We present an original end-to-end available bandwidth measurement method, called SMART (statistics measurement for avail-bw by random train). It resolves some of the problems common for many types of existing probing methods, e.g. the long latency and large probe traffic. Contrary to traditional estimates of available bandwidth, SMART is not a methodology based on packet dispersion in packet pair or packet train, but a completely new methodology in the light of probability and statistics. The fundamental idea is to send very small packets at random moment and calculate the proportion of minimal delay ion total test samples. To reach this purpose, we redefine the available bandwidth based on probability and statistics. We have evaluated our method in controlled and reproducible environment using NS2, and the simulations show our method is accurate, efficient, quick and non-intrusive.