Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation.To overcome these challenges, we introduce Spatio-Temporal Visual Odometry (STVO), a novel deep network architecture that effectively leverages inherent spatio-temporal cues to enhance the accuracy and consistency of multi-frame flow matching. With more accurate and consistent flow matching, STVO can achieve better pose estimation through the bundle adjustment (BA).Specifically, STVO introduces two innovative components: 1) the Temporal Propagation Module that utilizes multi-frame information to extract and propagate temporal cues across adjacent frames, maintaining temporal consistency; 2) the Spatial Activation Module that utilizes geometric priors from the depth maps to enhance spatial consistency while filtering out excessive noise and incorrect matches.Our STVO achieves state-of-the-art performance on TUM-RGBD, EuRoc MAV, ETH3D and KITTI Odometry benchmarks. Notably, it improves accuracy by 77.8% on ETH3D benchmark and 38.9% on KITTI Odometry benchmark over the previous best methods.

As the scale of large language model (LLM) training continues to expand, the underlying data center networks are subjected to increasing pressure. In the event of a network failure, significant time can be wasted on troubleshooting, leading to substantial economic losses. To detect network failures in a timely manner without affecting the efficiency of LLM training, this paper proposes MegaMon, a Transformer-based network traffic monitoring and analysis tool. MegaMon collects real-time traffic sequences from network interfaces and uses the AdaptivePatchTransformer model for time series prediction. By comparing the predicted sequences with actual sequences, it identifies anomalies to detect potential network failures. Testbed results show that MegaMon can accurately detect various network failures, has millisecond-level anomaly detection capabilities, and significantly reduces troubleshooting time, ensuring the overall efficiency and stability of LLM training.

In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in global map to identify moving objects. The surrounding points in global map are localized by voxel-location-based nearest neighbor search, without involving any massive computations. In addition, the proposed method is embeded into a LIO system (i.e., Dynamic-LIO), and achieves state-of-the-art performance on public datasets with extremlely low computational overhead (i.e., 1~9ms/sweep). We have released the source code of this work for the development of the community.

Accurate short-term traffic prediction is essential for improving the efficiency of data transmission in low Earth orbit (LEO) satellite networks. However, traffic values may be missing due to collector failures, transmission errors, and memory failures in complex space environments. Incomplete traffic time series prevent the efficient utilization of data, which can significantly reduce the traffic prediction accuracy. To overcome this problem, we propose a novel spatio-temporal correlation-based incomplete time-series traffic prediction (ITP-ST) model, which consists of two phases: reconstituting incomplete time series by missing data imputation and making traffic prediction based on the reconstructed time series. In the first phase, we propose a novel missing data imputation model based on the improved denoising autoencoder (IDAE-MDI). Specifically, we combine DAE with the Gramian angular summation field (GASF) to establish the temporal correlation between different time intervals and extract the structural patterns from the time series. Taking advantage of the unique spatio-temporal correlation of the LEO satellite network traffic, we focus on improving the missing data initialization method for DAE. In the second phase, we propose a traffic prediction model based on a multi-channel attention convolutional neural network (TP-CACNN) by combining the spatio-temporally correlated traffic of the LEO satellite network. Finally, to achieve the ideal structure of these models, we use the multi-verse optimizer (MVO) algorithm to select the optimal combination of model parameters. Experiments show that the ITP-ST model outperforms the baseline models in terms of traffic prediction accuracy at different data missing rates, which demonstrates the effectiveness of our proposed model.

Contextual features are important data sources for building citywide crowd mobility prediction models. However, the difficulty of applying context lies in the unknown generalizability of contextual features (e.g., weather, holiday, and points of interests) and context modeling techniques across different scenarios. In this paper, we present a unified analytic framework and a large-scale benchmark for evaluating context generalizability. The benchmark includes crowd mobility data, contextual data, and advanced prediction models. We conduct comprehensive experiments in several crowd mobility prediction tasks such as bike flow, metro passenger flow, and electric vehicle charging demand. Our results reveal several important observations: (1) Using more contextual features may not always result in better prediction with existing context modeling techniques; in particular, the combination of holiday and temporal position can provide more generalizable beneficial information than other contextual feature combinations. (2) In context modeling techniques, using a gated unit to incorporate raw contextual features into the deep prediction model has good generalizability. Besides, we offer several suggestions about incorporating contextual factors for building crowd mobility prediction applications. From our findings, we call for future research efforts devoted to developing new context modeling solutions.

Multi-access edge computing (MEC) assisted satellite-terrestrial integrated network (STIN) is promising in offering ubiquitous network coverage and swift computing services for Earth observation-based applications. Given the constraints of limited onboard resources and the substantial energy costs associated with complex computations and transmissions, the imperative of minimizing energy consumption emerges as a pivotal challenge in MEC-assisted STIN. This paper investigates the energy minimization problem, subject to the resource and latency constraints of terrestrial base stations and Low-Earth-Orbit satellites. To solve the formulated NP-hard mixed-integer nonlinear programming problem efficiently, we first conduct problem analysis to decompose the original problem into two coupling sub-problems. Then we propose a genetic algorithm based-joint computing and communication resource allocation (GA-CCRA) scheme to solve the sub-problems. Our simulation results show the superiority and effectiveness of GA-CCRA in terms of energy efficiency in the MEC-assisted STIN.

As high-performance computing clusters (HPCC) scale up and single-node computing power improves, the bottle-neck of large-scale models training is shifting from computation to network communication. Datacenter Quantized Congestion Notification (DCQCN), as the most widely used congestion control algorithm in lossless Remote Direct Memory Access (RDMA) networks today, provides HPCC with low-latency, high-bandwidth, and high-quality network services. However, DCQCN has more than ten adjustable parameters on RDMA-enabled network inter-face cards (RNICs) and switches, and currently, relying on expert experience to adjust these parameters in engineering is inefficient. Additionally, even minor increases in single-iteration time during training can lead to significant resource wastage. Therefore, after conducting an in-depth analysis of different heuristic search algorithms and loss functions, we propose Megatuner—an offline DCQCN parameters tuner tailored for the traffic pattern of large-scale models. We conducted traffic simulations on the collective communications at the foundation of large-scale models, testing both on simulation platforms and in real environments. The results show that parameters tuned by Megatuner significantly outperform those set based on expert experience. We also addresses the issue of bandwidth degradation in real environments, ensuring long-term optimization of network performance.

Accurate short-term traffic forecasting is essential to improve the efficiency of data transmission in Low Earth Orbit (LEO) satellite networks. Due to collector failures, transmission errors and memory failures in the complex space environment, the traffic value absence phenomenon may occur. However, in-complete traffic time series can significantly reduce the accuracy of traffic forecasting. To overcome this problem, in this work, we propose a novel Spatio- Temporal Multi-view based Short-term Traffic Forecasting (STMV-STF) model for incomplete time series to improve the accuracy of traffic prediction by combining the unique spatio-temporal correlation of satellite network traffic. Specifically, this model utilizes the time-lagged pearson correlation equation to select the$k$most correlated time series to impute the missing values from a spatial view. Meanwhile, this model utilizes a proposed gated recurrent unit (GRU) with a memory decay gate (MG-GRU) to impute the missing values from a temporal view. Finally, we aggregate the missing values imputed from these two views and design the training process of this model to enable online imputation of missing traffic values and real-time output of predicted traffic values. Experiments on real traffic datasets show that the STMV-STF model achieves 19.86% to 33.46% error reduction under different missing rate conditions compared to the baseline model in terms of root mean square error (RMSE) metric.

With the rapid development of Internet of Things (IoT) in recent years, the security of IoT becomes more and more prominent. To protect data privacy and device availability, identity authentication technology has been applied to the IoT environment. However, although scholars have designed a variety of authentication protocols for the IoT environment, the resource costs of these protocols are still expensive for resource-constrained devices. In particular, when the frequent upgrade of computer hardware has led to a gradual increase in the key length of the cryptosystem, IoT devices are hard to keep up with the upgrade speed because of the balance between cost and tasks. Although the storage space has increased in recent years, the computational and communication capabilities are still limited by some physical factors, such as energy, power, and communication link bandwidth. To improve the situation, this article proposes an anonymous authentication protocol suitable for IoT devices with current hardware performance, which is based on elliptic curve and signcryption techniques. Compared to other protocols, our protocol further reduces the communication and computing cost of the client devices while ensuring security requirements. Thus, we formally prove its theoretical security under the random oracle model and use simulation experiments to verify that the protocol can effectively reduce the resource requirements.

Fog computing is a promising way to deal with computation-extensive tasks by deploying distributed nodes at the network edge. However, the resources of fog nodes may not be enough when many users need to be served simultaneously. Therefore, task offloading between a deviceto-device (D2D) pair is introduced in existing works, which ignores the computing resources of other ubiquitous users. In this paper, we study the partial task offloading in D2D-assisted fog computing networks, where the task is collaboratively executed by multiple D2D users or a Fog-Access Point (F-AP). Firstly, the social relationship based on task offloading is introduced for user incentives. Secondly, aiming to maximize the total revenue, an optimization problem is formulated with the joint consideration of offloading strategy, computation resource allocation and D2D user selection. Next, to solve this non-linear and non-convex problem, a potential game is formulated and the existence of a Nash equilibrium (NE) is proved. Then, we propose a joint optimization algorithm to obtain the NE, where computation resource allocation problem is solved by Lagrange Multiplier Method while D2D users are selected through greedy strategy. Finally, simulations show the effectiveness of the proposed scheme.

The prosperity of data-enabled mobile devices and wireless-enabled data applications in today’s mobile ecosystem has fostered the rapid development of wireless content caching, which has recently been considered as an efficient way to mitigate the contradiction between huge amount of multimedia data traffic and limited wireless communication rate. In this paper, a software-defined network (SDN) caching system is proposed where the cache of every mobile device (MD) is divided into two partitions for caching files in different levels of popularity by different cache manners. The requested files of MDs can be obtained locally, through device to device (D2D) cooperation or cellular network. The proposed D2D cooperative caching strategy is optimized by maximizing the cache hit probability (CHPro) and we analyze the effect of the power consumption expectation (PCExp) on the strategy in the SDN based network. Simulation about the influence of cache splitting coefficient on CHPro and PCExp under different conditions, as well as the impact of file popularity order on caching probability when the cache splitting coefficient gets the optimal value reminds us to optimize CHPro and PCExp comprehensively, and we propose a method to solve the comprehensive optimization.

In a dense urban area, conventional cellular V2X communications require frequent and heavy resource allocation, which can lead to processing congestion and large delay. In this paper, we propose a semi- persistent resource allocation scheme using the least minimum mean square error (LMMSE) traffic prediction in a two-tier network. The two-tier network architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocate dynamic resource upon real-time requests from vehicles through RSUs. We formulate an optimization problem for minimizing the total bandwidth under latency constraints and provide an optimal solution to the problem. Simulation is conducted for both artificially generated and real-world data, and the results validate the effectiveness of the proposed semi- persistent scheme.

Mobile edge computing (MEC) has been recognized as an effective technology to augment computing capabilities to user equipments (UEs). Although some existing works have been done on MEC, but only few works focus on the area of full duplex (FD) enabled Heterogeneous Networks (HetNets), which is an important scenario in future 5G networks. In this paper, we integrate FD technology in a MEC enabled HetNets. Firstly, a maximization optimization problem of users' revenues is formulated, in which user association, computation offloading strategy policy, uplink FD transmission power allocation, and computation resource scheduling are all considered. Secondly, due to the original optimization problem is NP-hard and non-convex, we transform it into two sub problems, named user allocation optimization problem (UAOP) and resource allocation optimization problem (RAOP). Moreover, RAOP is proved as a convex problem and its optimal resource allocation solution is found by Lagrangian multiplier method. Finally, a greedy-based optimization algorithm (GBOA) is proposed to solve the original problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.

Mobile Edge Computing (MEC) is regarded as an enabling technology to provide computing capacity closer to the mobile equipments (MEs). Due to the MEs' mobility, the execution results of offloaded tasks can't be transmitted back to MEs if they are out of the coverage of the small cell network (SCN). Therefore, the migration is needed, which causes the extra cost. In this paper, a novel mobility-aware offloading and migration scheme with MEC is proposed to maximize the total revenue of MEs. Firstly, with the analysis of MEs' mobility, the problem of maximizing the total revenue of MEs is formulated, which integrates the offloading decision and resource allocation to reduce the probability of migration. Secondly, the problem is formulated as a mixed integer non-linear programming (MINLP) problem, so a SUb-optimal algorithm named genetic algorithm based allocation algorithm (GAAA) is proposed to solve this NP-hard problem. Finally, extensive simulations demonstrate that GAAA can effectively improve the total revenue of MEs in contrast with other baseline algorithms.

This paper presents a downlink multi-user network model based on sparse code multiple access (SCMA), where two phases cooperative transmission strategy is introduced to reduce the average outage probability. Motivated by the fact that the reliability is an important indicator in the next generation mobile communication system. Two strategies are considered: in the direct transmission strategy, the communication between the base station and the users is completed in one phase, where the relay is inexistent. In the two phases cooperative transmission strategy, any number of relays at fixed positions are placed to improve the performance of the system. To verify the performance of cooperative transmission and direct transmission, we derive the exact expressions of average outage probabilities for these two strategies. The simulation and analytical results are presented to estimate the performances of the proposed network model. These results show that the two phases cooperative scheme can effectively reduce the outage probability in SCMA network, especially in the high signal-to-noise ratio scenario.

As a candidate non-orthogonal multiple access technology for 5G, sparse code multiple access (SCMA) can effectively improve spectrum efficiency. In this paper, we first study the layered multicast scheme in SCMA network, its system capacity is no longer limited by the worst channel quality user in the multicast group. Specifically, we formulate a network energy efficiency (EE) maximization problem subject to quality-of-service (QoS) requirements, codebook assignment, and power allocation. In order to reduce the computational complexity, we then propose a sub-optimization algorithm by separating codebook assignment and power allocation. Finally, the simulation results verify the superiority of the proposed algorithm in layered multicast based on SCMA network in terms of the network EE.

Sparse code multiple access (SCMA) is considered as a promising technique for improving spectral efficiency. In this paper, we apply SCMA to wireless multicast communication with the purpose of taking the advantage of SCMA to expand multicast capacity. Particularly, the scheme of layered multicast based on SCMA network is studied. In this scheme, we first formulate the capacity of the system, which is no longer constrained by the user with the worst channel quality in the multicast group. Then, resource allocation for the system is optimized with the objective of maximizing the capacity of the system. In order to reduce the computational complexity, a suboptimal algorithm is proposed by separating the power allocation and codebook assignment. Finally, simulation results are presented to estimate the performance of our algorithm in layered multicast based on SCMA network and orthogonal frequency division multiple access (OFDMA) network.

Device-to-device (D2D) communication has been regarded as an effective technique for enhancing ultra dense network (UDN) system. However, the energy costs of mobile devices have become a great challenge due to the limited battery lifetime. In this paper, we investigate a two-tier heterogeneous cellular network where the macro tier and small cell tier operate according to a dynamic timedivision duplex (TDD). Based on dynamic TDD which can adjust UL and DL time configurations to accommodate to the traffic asymmetry, we propose a mode selection scheme jointing time allocation, power control to minimize the energy consumption of the mobile devices and the whole network. Simulation results show that the energy consumption can be decreased effectively in D2D mode, and D2D communication area accounts for a large part both in the macro cell and small cell.

Non-orthogonal Multiple Access(NOMA), as a key technology of spectral efficiency improvement in future 5G networks, has attracted a lot of attention. This paper proposes a joint transmission scheme for two multicast groups based on NOMA. Firstly, to guarantee rigorous channel difference to perform Serial Interference Cancellation(SIC), a Maximum and Minimum Group Method (MMGM) is proposed to divide each multicast group (MG) into three subgroups (the best, the middle and the worst subgroups), which makes the maximum and minimum of one MG's channel gains to be the other MG's two cutoff points. Secondly, according to the relationship between the two MGs' channel gains sets, two transmission modes are proposed. In different modes, BS respectively transmits to two different subgroup pairs (SPs) on the orthogonal frequency bands. In one mode, the one SP is consisted by a MG's best subgroup and the other MG's middle and worst subgroups. In the other mode, one MG's best and middle subgroups and the other MG's worst subgroup constitute the first SP. The second SP in each mode is made up of remaining subgroups. Finally, BS chooses the mode which can achieve higher spectral efficiency to complete data transmission with power domain superposition. Simulation results show that the scheme can improve spectral efficiency with higher average service ratio for users.

Concurrent multipath transfer (CMT) can achieve bandwidth aggregation by employing multiple TCP subflows in a connection. However, constrained by the transport layer protocols such as Multipath TCP, the throughput cannot increase linearly with bandwidth, which means bandwidth resources cannot be fully utilized. To improve the spectral efficiency, in this paper we propose a bandwidth scheduling scheme by allocating appropriate spectral resources for each link. Firstly, the concept of validity bandwidth (VBW) is employed to estimate the required size of bandwidth based on Mathis Model. Secondly, the Kalman Filter is applied to smooth the value of VBW caused by dynamical changes of network status. The simulation results demonstrate the effectiveness of our proposed scheme in achieving a tradeoff between throughput and spectral efficiency. With path loss rate varies, the spectral efficiency improves by 92.6%. while the corresponding throughput suffers a decline by 17.4%. When delay changes, the biggest gap occurs at 100ms with 14.1% decline in throughput and 89.5% growth in spectral efficiency.

ln this paper, a distributed beamforming algorithm is proposed for downlink multicell network when the intra‐cell local channel state informations (CSIs) are available at each base station (BS). The proposed algorithm includes three parts, firstly, an algorithm is proposed to estimate the direction vectors of the inter‐cell local channels. The algorithm above utilizes a set of random orthogonal bases and the known training formula to get all basis vectors of orthogonal complement space of the inter‐cell local channel vector. Secondly, a proposition based on singular value decomposition of all local channel vectors is presented for the distributed beamforming causing minimum intra‐cell interference (InCI) and inter‐cell interference (ICI) toward other users. Thirdly, with only intra‐cell local CSIs at BS, a training algorithm is proposed to obtain the amplitudes of the whole local channel vectors. This training algorithm exploits the unit matrix and unit transmit power to perform fixed steps of training process, and the amplitude of inter‐cell local channel vector is estimated by the observations of BS or user. Finally, simulation results demonstrate that the proposed algorithm can achieve higher user rate while offering more advantages to suppress InCI and ICI than the traditional ones.

In this paper, the resource sharing problem of device-to-device (D2D) communications underlying evolved multimedia broadcast multicast service (EMBMS) system is investigated, and a novel round- robin resource sharing algorithm (RRSA) is proposed. To avoid the interference between D2D user equipments (DUEs) and cellular user equipments (CUEs) without consuming more frequency resources, in this study, we assume that DUEs do not share frequency with CUEs in the same single frequency network (SFN), but reuse frequency with CUEs in other SFNs. The contribution is threefold: 1) D2D communication is introduced to the SFN in EMBMS system for the first time, 2) the system performance is significantly improved without additional frequency resource costing, and 3) the QoS requirement of high level user is ensured. Simulation results show that the proposed resource sharing algorithm can significantly reduce the outage probability of D2D receivers and enhance the throughput of system.

In this paper, an iterative algorithm for decentralized beamforming with internal penalty function is proposed. This algorithm utilizes the property that a stationary point corresponding to local maximum of the objective function is found when gradient is zero. The convergence of the proposed algorithm is proved. The simulation results show that the proposed algorithm is more robust against intra-cell and inter-cell interference than the traditional ones. Especially the proposed algorithm has higher rates as the number of users increases.

In this paper, we study the performance of fixed decode-and-forward (DF) two-way relay cooperative networks with multi-antenna sources employing limited feedback beamforming (LFBF) for signal transmission. The closed-form expressions for the overall outage probability and sum symbol error rate (SSER) are derived using the moment generation function (MGF) of the signal- to-noise-ratio (SNR) at the relay. Subject to total power constraint, we also explore an optimal power allocation scheme between sources and relay to minimize the system outage probability and symbol error rate. Simulations are given to verify the correctness of the theoretical derivations. Results show that the proposed LFBF scheme can achieve considerable tradeoff between performance and complexity.

In this paper, two novel joint relay-and-antenna strategies are proposed and analyzed for multi- relay two-way multiple-input multiple-output (MIMO) decode-forward (DF) networks, namely, joint OR-TAS(Opportunistic Relaying -Transmit Antenna Selection) and joint SC-TAS (Selection Cooperation-Transmit Antenna Selection). For each joint selection scheme, the best transmit antenna at the two sources, a best relay, and a single best antenna at the selected relay are jointly determined in an optimum sense. The performance of these strategies is quantified by deriving the overall outage probability. For OR- TAS, we derive its outage probability directly, while for SC-TAS, we obtain its outage probability by proving that it is identical to that of OR-TAS. Also, the diversity orders is derived by presenting the upper bound and the lower bound of the overall outage probability, and the results show that the proposed selection strategies achieve the full diversity order. Finally, simulations are given to verify the correctness of the theoretical analysis.

In this study, an adaptive user selection of opportunistic multicast scheduling system (OMS‐AUS) scheme is proposed to improve the system throughput performance of opportunistic multicast scheduling system. Different from the previous literatures, the user selection ratio would no longer be a constant, but would vary as the number of users that have not received multicast bits changes, which can exploit the system transmission ability better. The objective is to obtain the optimal values of user selection fractions. (i) For the case without instantaneous channel state information (CSI), a channel gain distribution‐based OMS‐AUS is proposed. Firstly, a system throughput matrix (STM) is formulated to imitate the user grouping process. Secondly, the value of each element in STM is derived according to the channel gain distribution. Finally, by fully searching the elements in STM, the optimal solution is obtained. To reduce the computational complexity, a suboptimal heuristic searching scheme is also proposed. (ii) For the case with instantaneous CSI, an improved approach based on both the channel gain distribution and instantaneous CSIs is proposed. Simulation results show that both of the two proposed approaches can obtain better performance compared to the existing schemes.

Device-to-Device (D2D) communications make high-speed multicast services possible since the multicast receivers with poor downlink channel conditions can be retransmitted by devices nearby via D2D links. In this paper, we consider how to efficiently use D2D communications to help enhance the quality of wireless multicast services in cellular networks. To achieve this, a dynamic D2D retransmission scheme with maximized utility is proposed, which can adaptively select the retransmission algorithm according to the state of the network load. Through both analysis and simulations, we show that our algorithms achieve a significant gain in terms of utility, and reduce the burden of the base station (BS).

This letter addresses the problem of feedback load reduction for opportunistic multicast scheduling (OMS) in OFDM-based systems. We investigate two scenarios, multicast service with large group size and multicast service with small group size, and derive two practical schemes respectively. The superiorities of the proposed schemes are: 1) no precise knowledge of the wireless fading channel is required, and 2) feedback load can be significantly reduced while the throughput performance remains comparable with the case of full feedback.

Users will handover between spot-beam frequently when Low Earth Orbit (LEO) satellite moves round the earth, so proper channel reservation strategy should be applied to reduce call dropping probability. A channel status-based reservation strategy (CSRS) is proposed for multimedia LEO satellite network in this paper. Considering that the channel locking time is very long in the Guaranteed Handover (GH) algorithm, the proposed method sets reservation time adaptively for different users according to the status of cell so as to utilize the resource more effectively. Simulation results show that the proposed strategy can reserve channel adaptively for different users and reduce handoff call dropping probability and new call blocking probability, while also improve resource utilization.

Multimedia Broadcast/Multicast Service (MBMS) uses point-to-multipoint transmission mode, which can effectively improve the frequency utilization efficiency of the mobile communication system and becomes a hot research field of mobile communication. However, as the frequency selectivity of the wireless channel, the throughput of the MBMS system is always limited by the user with the worst channel quality. In order to overcome the frequency selective fading, resource allocation methods is widely studied in the research. In this paper, the improved resource allocation algorithm based on user QoS is proposed. Firstly, the subcarriers are allocated according the users' urgency degree guaranteeing the QoS of all users, Secondly, the remaining subcarriers are allocated by the throughput maximization rule, Finally, the system throughput is further improved by the bit loading step with greedy algorithm. Simulation result shows that the proposed resource allocation scheme cannot effectively guarantee the QoS of all users in the group, but also can improve the system throughput as much as possible.

In this paper, the fairness problem of opportunistic multicast scheduling (OMS) is investigated and a parity-based OMS is proposed. In conventional multicast system, the system throughput is limited by the users with poor channel state information (CSI). In the proposed OMS, to improve the system throughput performance, the data reception of each user is taken into consideration by the base station (BS). Each user is assigned two aspects of priority, one of them is based on the system throughput, and the other is based on the data reception of users. For the users with poor channel condition, fewer packets would be received than the users with fine channel conditions. Thus, by raising the priority level of the user with worse channel condition, the performance of fairness among users can be improvement. Simulation results show that the proposed OMS can improve the system throughput performance while ensuring the fairness performance among users.

A wireless resources allocation method for Device-to-Multi-Device (D2MD) communication under the control of base station is proposed. In the premise of ensuring quality of services (QoS) of all D2MD groups, the method can allocate wireless resources for D2MD groups through three steps: mode selection, spectrum allocation and power control. Simulation results show that the method can select the high-spectrum-efficiency mode which reuses the spectrum of cellular users for most D2MD communications, and does not affect cellular communications, the interference to cellular uplink of proposed suboptimal LCH algorithm which allocates cellular spectrums for D2MD groups to reuse can achieve the same level as the optimal algorithm, in addition, the calculating complexity is lower therefore shortening the response time of the base station.

Satellite network has played an important role in providing multicast services for its ability of wide coverage. The performance of satellite multicast can be improved if multi-layer Satellites are used in the architecture. In this paper, combining LEO satellites' advantages on transmitting real-time information with GEO's ability of big computing, a GEO/LEO Double-Layer Multicast Routing Algorithm (DLMRA) is introduced. Using the logical location concept to calculate the first added destination, take the permanent of link into account, our algorithm aims to minimize cost of multicast trees, under the condition of the available bandwidth bound and multicast tree delay bound. Simulation results demonstrate that the performance benefits of the proposed DLMRA in terms of the tree delay, the bandwidth cost by comparison with the other algorithms proposed before.

Device-to-Device (D2D) multicast underlaying a cellular infrastructure has recently investigated. In this paper, we propose a new interference management scheme for D2D multicast to increase the overall capacity and improve resource utilization. The proposed scheme includes two steps. First, in order to mitigate the interference from D2D multicast transmission to cellular networks (CNs), a dynamic power control scheme is proposed that can determine the upper bound of D2D transmitter power based on the location of Base Station (BS) and areas of adjacent cells from the coverage area of D2D multicast group. Next, an interference limited area control scheme that reduces the interference from CNs to each D2D multicast receiver is proposed. The proposed scheme does not allow the coexistence of cellular equipments (CUEs) located in the interference limited area to reuse the same resources as the D2D multicast group. From the simulation results, it is confirmed that the proposed schemes improve the performance of the hybrid system compared to the conventional ways.

In this letter, under the assumption of multiple antennas are available at the relay nodes, we provide a detailed analysis about the performance of opportunistic multi-antenna relay selection (OMRS) scheme in Decode-and-Forward (DF) relay networks over independent, non-identical, Nakagami-m fading channels. By applying the moment-generating function (MGF) of the total signal-to-noise ratio (SNR) at the destination, the closed-form expressions of outage probability and error performance for binary phase shift keying (BPSK) modulation of OMRS scheme are derived. Simulations results show fine consistency with theoretical analysis.

In this paper, new transmission schemes are proposed to deal with the CCI (co-channel interference) in wireless relay transmission. We first introduce an optimal decoding process to fully utilize the information forwarded by relay. Then based on conventional transmission schemes, two modified transmission schemes and a novel adaptive scheme are proposed. Furthermore, we derive the outage probabilities and throughput for all the proposed schemes in closed form. Numerical simulations are presented to validate the analytical results and it shows that the adaptive scheme improves the spectral efficiency significantly compared to others in most condition.

Two Opportunistic Multi-antenna Relay Selection (OMRS) strategies are proposed and analyzed in Decode-and-Forward (DF) relay networks for cooperative multicast using space time codes, assuming multiple antennas are available at the relay nodes. These two strategies select the best relay based on (i) maximizing the average SNR and (ii) maximizing the minimum SNR respectively. The closed-form expressions of multicast outage probability for the two strategies are studied over Rayleigh fading channel. Simulations are provided to verify the correctness of the theoretical analysis. Our multicast outage probability results reveal that OMRS based on maximizing the minimum SNR is outage-optimal among multi-antenna relay selection schemes and approaches the space time codes scheme. Moreover, it can be found that compared with previous single-antenna Opportunistic Relaying (OR) scheme, OMRS brings remarkable performance improvement obtained from maximum ratio combining (MRC) and space-time coding, which proves that multiple antennas at the relays could provide more array gain and diversity gain.

To reduce the feedback load of OFDM-based wireless multicast systems, an opportunistic feedback scheme (OFS) is proposed. OFS aims to only feed the key channel gains that can significantly affect the system throughput back to the base station (BS). By mathematical derivation, the key channel gains are proved to belong to a channel gain interval, which can be estimated by a proposed statistical method. Simulation results show that OFS can significantly reduce the feedback load of OFDM-based multicast system, while remaining 99% multicast throughput performance compared with the full feedback scheme. Besides, the channel fading type of the BS-user link is not required, which would be more realistic.

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, the authors introduce a resource allocation algorithm by exploiting layered coding combined with erasure correction coding for multicast services in the downlink of orthogonal frequency‐division multiple access‐based multi‐antenna system. To reduce the feedback overhead of uplink, the authors design a novel transmission scheme with limited feedback. Then, the joint subcarrier and power allocation problem for the data of base layer and enhancement layers are formulated, which is shown to be non‐deterministic polynomial‐ hard. Hence, in order to reduce the computational complexity, they propose a three‐phase suboptimal algorithm. The algorithm is designed to maximise the system throughput, whereas at the same time guarantee the quality of services (QoS) requirements of all multicast groups. It is composed of precoding scheme, proportional fairness subcarrier allocation algorithm and modified water‐filling power allocation algorithm with QoS guarantees (MWF‐Q). To further decrease the complexity of MWF‐Q, a power allocation algorithm with increased fixed power allocation algorithm with QoS guarantees is introduced. Simulation results show that the proposed algorithms based on limited feedback scheme significantly outperform CMS and any other existing algorithm with full feedback. Moreover, the proposed scheme can efficiently reduce 50% of the full feedback overhead.

This paper proposes a new opportunistic decode-and-forward (DF) relaying with beamforming in multiple-input and multiple-output (MIMO) multiple relay networks. In the proposed scheme, only one relay which correctly decodes the signal from the source and has the best channel to the destination is selected. To examine the benefits of our proposed scheme in two-wave with diffuse power (TWDP) fading channels, we first derive a closed-form expression for the exact outage probability. We then derive a tight lower bound on the exact result to allow for a convenient and accurate approximation. Furthermore, we derive the asymptotic outage probability to gain valuable insights into the network behavior in the high signal-to-noise ratio (SNR) regime. We demonstrate that the diversity order is equal to the product of two parameters: 1) the number of relays and 2) the minimum number of antennas at the source and the destination. Notably, this diversity order is independent of TWDP fading parameters. The accuracy of our analytical results is validated via Monte Carlo simulations.

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, we consider an opportunistic multicast scheduling (OMS) based on layered coding. A novel subcarrier and bit allocation algorithm is exploited for targeting the maximum throughput (MT) of a whole multicast group while at the same time guaranteeing the quality of services (QoS) requirements of all users. In this paper, we propose an optimal resource allocation algorithm in the downlink of OFDMA-based wireless multicast group. A two-phase suboptimal algorithm is proposed to reduce the computational complexity. Simulation results show that the performance gap between the optimal algorithm and the proposed suboptimal algorithm is quite small. The proposed algorithm significantly outperforms CMS. Moreover, it obtains more throughput than another existing algorithm.

This paper propose an optimal resource allocation algorithm by exploiting opportunistic multicast scheduling (OMS) scheme using layered coding combined with erasure correction coding for multicast services in the downlink of OFDMA-based single frequency networks (SFN). In this algorithm, we exploit unequal error protection (UEP) Reed-Solomon (RS) coding to compensate for possible data packet loss of base layer and enhancement layers which are obtained from fine-granularity-scalability (FGS) video coding. In order to reduce the feedback load, we only use the knowledge of average channel state information (CSI) for the users. The proposed algorithm maximizes the enhancement layers throughput while guaranteeing the minimum data rate of the base layer in each multicast group. A two-phase suboptimal algorithm is proposed to reduce the computational complexity. Simulation results show that the performance of the algorithm with UEP RS coding is more than the algorithm without UEP RS coding. Moreover, the proposed algorithms significantly outperform conventional multicast scheme (CMS).

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, we consider a resource allocation algorithm based on layered coding, when a limited feedback scheme is considered. A novel subcarrier and power algorithm is exploited for targeting the maximum throughput (MT) of enhancement layers while at the same time guaranteeing the quality of services (QoS) requirements of all users. In this paper, a three-step subcarrier allocation algorithm and two power allocation algorithms are proposed. Simulation results show that the proposed algorithm significantly outperforms CMS. Moreover, it obtains more throughput than another existing algorithm.

This paper propose an optimal resource allocation algorithm by exploiting layered coding combined with erasure correction coding for multicast services in the downlink of OFDMA-based single frequency networks (SFN). In this algorithm, we exploit unequal error protection (UEP) Reed-Solomon (RS) coding to compensate for possible data packet loss of base layer and enhancement layers which are obtained from fine-granularity-scalability (FGS) video coding. In order to reduce the feedback load, BSs only utilize the average channel state information (CSI) of the users. The proposed algorithm maximizes the enhancement layers throughput while guaranteeing the minimum data rate of the base layer in each multicast group. A two-phase suboptimal algorithm is proposed to reduce the computational complexity. Simulation results show that the performance of the algorithm with UEP RS coding is much better than the algorithm without. Moreover, the proposed algorithms significantly outperform conventional multicast scheme (CMS).

In this paper, resource allocation problem for multicast service in OFDM-based wireless cellular networks is investigated and a coding-based opportunistic scheduling scheme is proposed. In the proposed scheme, according to frequency resource and time resource, layered coding and erasure-correction coding are adopted, respectively. Layered coding divides the data into base layer and enhancement layer, which transmits the data in different rates to maximize the system capacity; erasure-correction coding compensates for the possible packet loss due to instantaneous channel information. To complete subcarrier allocation and bit loading for the coding-based scheme, a practical algorithm is designed. As only the users' mean channel gains are required, the proposed scheme reduces the overheads required for instantaneous channel information and is suitable for a fast time-varying fading environment. Numerical results show that the coding-based scheme can get significant performance improvement compared with the scheme without coding.

In the context of two-wave with diffuse power (TWDP) fading scenarios, this paper analyzes the outage probability and symbol error rate (SER) of single relay cooperative networks using amplify-and-forward (AF) relaying. Each channel in the network is assumed to be independently and non-identically distributed. The statistical result of the upper bound on the received signal-to-noise ratio (SNR) is derived. We derive the closed-form asymptotic expressions to approximate the outage probability and SER in the high SNR regime, respectively. Based on the asymptotic results, the diversity order and coding gain of the cooperative network is analyzed. Simulation results show that these asymptotic results are tight particularly in the high SNR regime for cooperative networks in different fading scenarios.

In this paper a multicast concept for Device-to-Device (D2D) communication underlaying a cellular infrastructure has been investigated. To improve system throughput and increase resource utilization, a novel interference coordination scheme is proposed. The proposed scheme includes two steps. First, in order to mitigate the interference from D2D transmission to cellular communication, we propose an efficient power control scheme to obtain an upper bound of D2D transmitter power. Next, based on the upper bound, two resource block (RB) allocation rules aiming to control the interference from cellular networks (CNs) to receivers in D2D multicast group are proposed. Simulation results show that by applying the proposed scheme, the performance of the hybrid system can be significantly improved compared to the conventional ways.

Recently, XOR-based network coding has been demonstrated as an effective approach to improve the network performance for wireless broadcast. In this way, the source XOR multiple lost packets from different receivers into one packet and retransmits it, then multiple receivers are able to recover their lost packets, which can effectively reduce the number of retransmissions and utilize wireless bandwidth. However, to find the optimal XOR coding set that minimizes the total number of transmissions has been proved as NP-hard and of high complexity. In this paper, we proposed a new wireless broadcast retransmission scheme based on linear random network coding, in which the source code combines all lost packets to a single one by linear network coding for retransmission, and receivers are able to decode the original packets by method of Gaussian elimination when they receive enough coded-packets. Simulation results are given to demonstrate superior performance of our algorithm over previously proposed works.

Since Hopfield and Tank applied their neural networks to Travelling Salesman Problem (TSP), this classical NP-hard (non-deterministic polynomial) problem has been intensively studied in the field of artificial neurocomputing. Lipo Wang et al. proposed an efficient approach named noisy chaotic neural network (NCNN), which has been proved to be a powerful tool to solve combinatorial optimization problems in their literatures. However, its exact parameters choice is exquisitely sensitive and complicated under different scenarios. In order to improve the convergence performance, the characteristics of its parameters are investigated in detail again in this paper. We focus on further researching the effects parameters have on the performance of NCNN. Through a large quantity of analyses and numerical simulations, we present the modified scheme with a new parameter set which gives 1) less steep sigmoid function, 2) stronger synaptic weights, and 3) higher initial temperature for annealing. Simulation results show that the modified scheme has much faster convergence speed with a small amount of accuracy loss, compared with the original NCNN which used a traditional parameter set.

This paper considers cooperative networks in independent and non-identical two-wave with diffuse power (TWDP) fading channels using opportunistic amplify-and-forward (AF) relaying. We derive new closed-form expressions for the outage probability and symbol error rate (SER) in the high signal-to-noise ratio (SNR) regime. Such expressions can be applied to opportunistic AF relaying networks consisting of an arbitrary number of relays, and in different channel fading scenarios. By analyzing the asymptotic results, we analyze the diversity order and the coding gain of opportunistic AF relaying networks.

This paper analyzes the performance of cooperative relay networks using adaptive decode-and-forward (DF) relaying in two-wave with diffuse power (TWDP) fading channels. Among the multiple parallel relays that participate in transmission, only the relays that successfully decode the source message retransmit the signals to the destination. At the destination, the direct signals from the source and the indirect signals via the relays are combined using maximal-ratio combining (MRC). We first derive a new closed-form expression for the outage probability, which encompasses the Rayleigh fading and Rician fading as special cases. We then derive a tight lower bound on the outage probability to reduce computational complexity. The correctness of our derivation is validated through Monte Carlo simulations. It is shown that increasing the number of relays decreases the outage probability at high signal-to-noise ratio (SNR), but increases the outage probability at low to medium SNR.

In this paper, we investigate resource allocation issue in OFDM-based wireless multicast system and joint subcarrier and power allocation scheme is proposed. The optimal solution of the combinable allocation has high computational complexity, so the solution is divided into two steps. In the first step, the subcarriers are allocated to the multicast services, and all the users that have subscribed to one service share the same subcarriers. In order to increase the system capacity, a subcarrier cooperation and user grouping method is proposed. To further lower the computational complexity, a simplified method is derived. In the second step, the water-filling method is adopted to get further capacity improvement. The simulation results show that the proposed scheme performs better than the existing schemes, and the proposed simplified scheme can get a similar performance. Besides, the resource allocation schemes with multi-dimension water-filling allocation notably outperform the schemes with equal power allocation.

In this paper, we consider a multi-antenna wireless multicast system with multiple multicast groups. The problem of resource allocation with downlink precoding for the multicast systems is studied, assuming the channel state information between transmit and all the receivers is available at the transmitter. Specifically, we formulate an optimization problem to maximize the system throughput given a set of available resources (power and subcarriers). A three-step algorithm is proposed, in which the multicast precoding process, subcarrier assignment and power allocation is operated separately to reduce the computational complexity. In two extreme cases, namely, low and high SNR regimes, the low-complexity allocation algorithm is further simplified. Simulation results also show that the system throughput is significantly improved by using our proposed algorithms both in multicast system and in unicast system.

In this paper, we propose an asynchronous multi-channel MAC (AM-MAC) protocol for cooperative network, which keeps nodes aware of all the state information and introduce cooperative transmission into asynchronous multi-channel environment. This Multi-channel MAC design adds a helper-confirm phase in channel negotiation process and also different information tables and learning methods. As we analyzed the properly designed MAC frames and handshake schemes make it perfect in data channel selection and helper confirmation. Simulations have shown that the network throughput of AM-MAC is much higher than none-cooperative multi-channel MAC protocol when causing less dropped packets.

In the context of two-wave with diffuse power (TWDP) fading scenarios, this paper analyzes the outage probability of cooperative relay networks. The decode-and-forward (DF) cooperative network and independent and identically distributed (i.i.d) fading channels are assumed in this paper. Specifically, a novel closed-form expression is derived for the outage probability of DF cooperative relay networks over TWDP fading channels. Monte Carlo simulations confirm the presented mathematical analysis. Our derived results apply to general operating scenarios with distinct TWDP fading parameters and arbitrary number of relays.

This paper studies closed-form expression between detect probability and false alarm probability for centralized cooperative network in cognitive radio based on a fuzzy decision scheme. By introducing two thresholds and fuzzy logic values to represent the reliability of the decision results, a novel fuzzy decision scheme for cooperative spectrum sensing is proposed. Analysis and simulations show that the proposed scheme could improve the detect probability effectively under 'AND' and 'OR' fusion rules, and reduce the number of cooperating cognitive users which depends on Δλ.

In structured peer-to-peer (P2P) overlay networks, similar documents are randomly distributed over peers with their data identifiers consistently hashed, which makes complex search challenging. Current state-of-the-art complex query approaches in structured P2P systems are mainly based on inverted list intersection. When the identifiers are distributed among peers, a complex query may involve many peers and cause a large amount of network traffic. One solution of implementing efficient complex query is to organize documents on each peer using clustering. In this paper, we propose a clustering method, QBC, which is composed of pull mode and push mode. Pull mode uses historical queries to direct clustering in structured P2P overlay networks and push mode applies modified vector space model (VSM) to define document set on each peer in order to assist clustering. Experiments show that QBC can reduce the number of peers visited during complex search, hence both query response time and network traffic are decreased.

In this paper, we propose a location-aware relay selection (LARS) scheme based on geographical information in the distributed opportunistic relay (OR) communication system. The scheme aims to limit the contention among potential relays through dividing the whole relay selection region into some small nonoverlapping contending torus zones. If a relay lies in the specified contending zone and its local channels are better than the source-destination channel, it becomes a contending relay and will transmit a FLAG packet to signal the presence when its own timer expires. If no relay is selected in the previous contending zone, the current contending torus zone will be extended. The contention is an iterative course until the relay selection process completes. Performance analysis and simulation results show that the LARS scheme leads to a remarkable improvement in collision probability. Meanwhile the outage performance and consuming time of LARS scheme are closed to those of original OR scheme.

Some recent studies have focused on the performance of multi-antenna relay systems assuming that perfect channel state information at the transmitter(CSIT) or no CSIT of relay-destination link are available. However, there is still a lack of research on the performance of fixed relay networks with finite feedbacks. In this paper, we present the closed-form expression of outage probability for decode-and-forward (DF) multi-antenna relay networks with finite feedbacks over Nakagami-m fading channels, where relay retransmits decoded signal with transmit antenna selection or limited feedback beamforming. The moment generation function (MGF) of the combined signal-to-noise-ratio (SNR) at the destination is obtained, then the outage probabilities are derived by using numerical inversion of Laplace transform. Simulations are given to verify the correctness of the theoretical analysis.

To avoid inter-cell interference effectively and to improve the performance of cell-edge users, a novel cellular architecture, termed Cellular Architecture Based on Sector Relay (CASR), is proposed in this paper, and two frequency planning schemes based on CASR are investigated. In CASR, fixed relays equipped with directional antennas are located at the vertex of the hexagonal cell for sectorization, thus each relay can be shared by three cells. The two frequency planning schemes are designed for different situations, one of which focuses more on the performance of cell one-hop users and the other one focuses on the performance balance between one-hop users and two-hop users. Interference analysis and performance evaluation are implemented for both one-hop users and two-hop users. System-level simulation in multi-cell and multi-user environment shows that compared with the existing cellular relay cell architecture, the proposed CASR combined with the two frequency planning schemes yield higher cell spectral efficiency. Moreover, the performance of cell-edge users is significantly improved.

In this paper, we propose a novel adaptive cooperative medium access control (MAC) protocol, called ACoopMAC, that adopts new frame control formats and is completely backward compatible with the legacy IEEE 802.11 distributed coordination function (DCF). To adapt to dynamic channel variation and network topology, the sender selects either direct transmission or cooperative transmission adaptively based on the instantaneous channel measurements. An extended analytical model based on elementary conditional probability is adopted to evaluate throughput and average delay performance of our proposed protocol. Analytical and simulation results show that ACoopMAC can improve the throughput performance and substantially reduce system delay, compared to CoopMAC protocol.

In this paper, we investigate resource allocation issue in OFDMA-based decode-and-forward cooperative networks and propose joint subcarrier and power allocation schemes. The optimal solution of this combinable allocation has high computational complexity, so we divide our solution into two steps. The first step is to distribute subcarriers to relays and destination under the assumption of equal power distribution. Here, we propose Proportional Allocation (PA) strategy to achieve tradeoff between total throughput and fairness. To further improve the system performance, we introduce threshold into PA strategy, named Proportional Allocation with Threshold (PA-T), where subcarriers with bad performance are prevented from transmitting. Next, water-filling method is adopted to distribute the power to cooperative links in order to fully utilize the limited power. Simulation results show that system performance of the proposed schemes is significantly enhanced compared with an existing resource allocation scheme. Besides, the resource allocation schemes with water-filling method notably outperform schemes with equal power allocation.

This paper studies expression of outage probability and partner selection strategies for cooperative network based on IDMA superposition modulation. By changing existing outage gain matrix into a symmetric new one and applying Hungary and WLF algorithm, a centralized optimal and a distributed sub-optimal partner selection strategies are proposed for superposition modulation cooperative multiple access cellular network. Simulation results show that the two proposed partner selection strategies could reduce the average outage probability of the network effectively.

According to the comprehensive analysis of the traditional DV-Hop, we propose an Iterative Cooperation DV-Hop localization (ICDV-Hop) algorithm, which improves the localization performance through adopting hop count threshold, collinearity test and new beacon nomination. The proposed algorithm selects the optimal beacon nodes for high localization accuracy by employing the hop count threshold to limit the distances between nodes and using collinearity degree to restrict topology relations between nodes. When the localization error of the located node is under the error threshold, it is nominated as new beacon to help the remaining unknown nodes to re-locate themselves, namely to expand localization coverage by iterative cooperation. Simulation results demonstrate that ICDV-Hop algorithm is more effective to improve the localization accuracy and coverage. Moreover, it is more reliable and robust compared to the traditional DV-Hop, especially when the ratio of beacon nodes is low and the network topology is sparse.

IEEE 802.16j mobile multi-hop relay network is proposed to enhance the system coverage, user throughput and the capacity of fixed/mobile broadband wireless access system in IEEE 802.16d/802.16e. Relay station (RS) enables data transmission between base station and end user when the channel is not good for transmission. Besides, one relay station also communicates with a pair of other relay stations to accomplish the communication via multi-hop relay path. An efficient scheme for path selection and per user throughput in the multi-hop relay network is an important issue to fulfill the purpose of a relay station. In this paper, we proposed a cross-layer tree based path construction mechanism to adjust the system deployment when new mobile station (MS) access to the network. Furthermore, an interference based path selection scheme is suggested to facilitate the improvement of per-user throughput as well as system throughput in multi-hop relay network. Simulation on OPNET shows our mechanism achieves significant progress in system throughput and efficiency of transmission.

This paper presents a novel joint network and channel decoding-interference canceling (JNCD-IC) algorithm for HARQ in wireless broadcasting system. In this system, the base station uses network coding for retransmission. In the retransmission phase, JNCD-IC scheme is proposed for the receivers to recover multiple error packets from the composite packet which is formed by XORing error packets from different receivers. Traditionally, receivers can only recovery one error packet from the composite packet using XOR method when it knows all the other packets in this composite packet as a prior, while the JNCD-IC scheme is trying to recovery multiple error packets from the composite packet by treating the network code and channel code as a single parity check product code. Simulation results for Rayleigh block fading channel show a significant increase in throughput compared to the current schemes.

In this paper, a two-hop amplify-and-forward cooperative diversity system using OFDM modulation was considered. We proposed an associated waterfllling power allocation scheme under separate power constraints at source and relay, which allocated source and relay power in an associated fashion. The scheme first allocated power to subcarriers uniformly, then gave an approximate equivalent channel gain model, and last employed the classic waterfilling algorithm to optimize the power allocation at relay and source, respectively. Moreover, we investigated the subcarrier pairing problem and presented a modified sorted subcarrier pairing algorithm, which considered the effect of source-destination link as well as source-relay link. The simulation results show that the associated waterfilling power allocation scheme and the modified sorted subcarrier pairing algorithm both could achieve higher average rate alone, and that the combination of them could improve the performance further.

A novel coding scheme called the distributed generalized low-density (GLD) code is proposed for cooperative relay networks. In this scheme, each relay node decodes and forwards some of the constituent codes of the GLD code to cooperatively form a distributed GLD code using the partial error-detecting and error-correcting capabilities of the GLD code. Distributed GLD codes can easily be adjusted to the variation of the relay number while the overall code rate of the system is constant. At the destination, the soft information from different paths can be combined for the GLD decoder thus diversity gain and coding gain are achieved simultaneously. Verified by simulations, the scheme can obtain significant performance gains over the strategy without relay in quasi-static fading channels and the performance is further improved when more relays are employed.

We propose a new connection admission control algorithm called DAC for real-time services in packet-switched OFDM wireless cellular networks. The main idea of DAC is to use maximum acceptance ratio for real-time services according to the desired packet-level and call-level QoS requirements. The control mechanism is periodical. At the beginning of the control period, the admission controller gathers the loading information from its own and neighboring cells. Then, according to the information, the maximum acceptance ratio is calculated by using a Markovian queueing model and time-dependent distribution of the number of connections. Finally the admission controller uses the computed ratio to admit connection requests in this control period. Extensive simulation results show that our algorithm satisfies packet-level and call-level QoS requirements for real-time services while maintaining high bandwidth utilization.

This paper deals with the problem of downlink scheduling in SDMA packet access networks. The performance of the SDMA system is predominantly affected by the mutual interference between users. In order to ensure the quality of service (QoS) and enhance the resource utilization, the scheduler needs to jointly consider the constraints from the physical layer (spatial separability for instance) and the data link layer (such as QoS requirements). In this paper, we propose a simple cross-layer downlink scheduler for SDMA based systems. As an example, it is implemented on an OFDMA/SDMA system. Analysis and simulations are given to show the effectiveness of our proposal.