Multichannel wireless scheduling under limited terminal capability

Maintaining efficient and reliable communication services in vehicle networks is challenging due to the high mobility of vehicle users. One of the key issues is to deal with the large amount of channel feedback and make better use of limited wireless resources in real time. In this letter, we propose a trajectory data driven method to maximize the total user utilities, which is characterized by intelligent channel state information (CSI) feedback reduction and vehicle-to-vehicle (V2V)/vehicle-to-infrastructure (V2I) mode switching. A deep neural network (DNN) model is trained using optimization approaches and vehicles’ trajectory data. Based on the well trained model, V2V/V2I mode is selected adaptively and bandwidth is allocated optimally with partial channel feedback. Simulation results show that our proposed method outperforms the comparison baseline and can achieve 96.23% of the performance obtained by exhausting searching with full CSI feedback.

Tracking strategies for mobile wireless networks are studied. A cellular architecture in which base stations that are interconnected by a wired network communicate with mobile units via wireless links is assumed. The cost of utilizing the wireless links for the actual tracking of mobile users is considered. A tracking strategy in which a subset of all base stations is selected and designed as reporting centers is proposed. Mobile users transmit update messages only upon entering cells of reporting centers, while every search for a mobile user is restricted to the vicinity of the reporting center to which the user last reported. It is shown that, for an arbitrary topology of the cellular network (represented by the mobility graph), finding an optimal set of reporting centers is an NP-complete problem. Optimal and near-optimal solutions for important special cases of the mobility graph are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Energy-efficient opportunistic scheduling schemes in wireless networks

We address the problem of combining limited feedback information with long-term channel statistical information in the design of downlink SDMA schemes. A novel combining method is developed to improve the quality of channel knowledge at the base station. More specifically, a set of novel feedback parameters is proposed and a related method is developed to estimate a representation of the multiuser channel vectors at the base station. This method utilizes the hybrid information by combining instantaneous channel feedback and long-term channel statistics, and is based on a channel phase codebook designed using the generalized Lloyd algorithm. The estimated channel knowledge at the base station can be used for joint design of multiuser precoding and opportunistic scheduling. The advantage of the proposed scheme over existing CSI quantization based SDMA schemes is further confirmed by computer simulations.

It is well known that opportunistic scheduling by using feedback information significantly improves wireless network performance. Most opportunistic scheduling works have focused on the case where a single channel is shared by multiple users. However, emerging wireless technologies (e.g., MIMO, OFDMA, etc.) are characterized by multiple shared channels, which complicates the problem. Moreover, it is necessary for the network to be able to provide various levels of quality of service (QoS). To address these issues, we develop a QoS-aware selective feedback model and a method to do optimal resource allocation. In our feedback model, each user chooses those channel sets that meet its QoS requirements by exploiting user diversity, thus resulting in a significant reduction in the amount of feedback information. Given the feedback channel sets for each user, the base station then distributes channels to each user with the objective of maximizing the number of accommodated users or the sum of users' utility values. We use a graph theoretic approach to solve these maximization problems by mapping them to clique searching problems. We develop some interesting theoretical results and properties but show that the complexity of this problem can be exponential in the number of channels. Thus, we also develop two suboptimal algorithms to handle the case when the number of shared channels is large. Finally, we demonstrate the efficacy of our results through an extensive numerical study

The multi-user MIMO space time scheduling problem is the prime focus of the paper. Optimizing the link level performance of the MIMO system does not always imply achieving scheduling level optimization. Therefore,design optimization across the link layer and the scheduling layer is very important to exploit fully the temporal and spatial dimensions of the communication channel. We address the design of the optimal space time scheduler for a multi-user MIMO system based on an information theoretical approach. We assume a partial feedback channel, namely a scaler rate-feedback channel, is available to the transmitter, but the full channel matrix is unknown to the transmitter. With the partial feedback, we find that the optimal resource allocation strategy is water-filling in both the temporal domain and spatial domain. The optimal scheduler should allocate all the power to at most n/sub R/ users at any particular instant. The optimal scheduler performance depends on the total number of users (K), the number of receive antennas at the base station (n/sub R/), and the number of transmit antennas at the mobile (n/sub T/). The scheduling gain increases with n/sub R/ due to the distributed MIMO configuration formed between mobile users and base station. With a single power feedback channel, the scheduling gain reduces as n/sub T/ increases, illustrating that transmit diversity hurts the scheduling performance in the case of scalar feedback. Finally, scheduling performance improves as K increases due to the multi-user selection diversity.

With the ever increasing data and computational demands from mobile users, heterogenous wireless networks (HetNets) and mobile cloud computing (MCC) have been advocated as a promising solution to meet these demands. Insufficient bandwidth is one of the most important challenges being faced by a successful implementation of the MCC technology due to heavy data traffic. The MCC implementation on HetNet increases the bandwidth available to each base station (BS) by frequency reuse. In this paper, a novel data storage method for big data files is proposed, along with a data correction technique to deal with the issue of failure of a data chunk retrieval. The proposed algorithm exploits the multiple paths that are available between a user and the cloud storage system in a MCC-HetNet environment. Since the bottleneck in the MCC is the wireless link between the user equipment (UE) and the BS, we have implemented the algorithm on the wireless links between the UE and the BS. The simulated results show that the proposed method outperforms the conventional data storage method between the mobile device and the cloud system.

The effective reuse of the radio resources in D2D communication underlaying cellular system is pivotal for enhancing the total system capacity. Developing practical and effective scheduling schemes is critical to the success of D2D-based cellular systems. An optimal proportionally fair (PF) scheduling scheme that maximizes the logarithmic sum of the user data rates is proposed in this paper. Considering the fact that the computational complexity of the optimal PF scheduling is significantly high, low-complexity heuristic algorithm has also been proposed. It has been shown that the heuristic algorithm achieves high logarithmic sum of the average user data rates while maintaining low-computational complexity. Simulation results are used to verify the performance of the proposed algorithm, and to compare it with the optimal PF scheduling algorithms.

The Proportional Fair (PF) scheduler has been extensively studied in wireless communications research. Most of the research done, however, focuses on theoretical or simplistic simulations. In this paper, both theory and practical measurements for a PF scheduler are studied. Two data collections are conducted to verify the performance of the scheduler in an actual LTE-A network (small cells) environment. Allocated Physical Resource Blocks (PRBs) and throughput of each phone used in the data collection are estimated. Three different types of PF schedulers are implemented to predict user throughput. The results show that the scheduler maintains good fairness for both user throughputs and PRB allocation. Further it is shown that our results, derived from actual recorded data are different from those derived from simulation models presented in the literature [1] [2]. Similarly, the cell throughput and fairness values are dynamic and randomly distributed with the time in an actual LTE-A network in contrast to simulation models. From our study, we show that the generalize PF Scheduler performs more accurately to predict the user throughput values. It is concluded that this real-world LTE-A network study is more meaningful and valuable in enhancing the understanding of actual 4G and future 5G networks.

This paper investigates the system performances of the IEEE 802.16j system with mobile relays such as cell throughput and service coverage. It also focuses on resource management problems such as overlapped subchannel allocation (OVSA) and orthogonal subchannel allocation (ORSA) and relay power control such as simplified on-off mobile relay control (SOMRC). The performance of the proposed schemes is evaluated with both proportional fair (PF) and mobility assisted opportunistic scheduling (MAOS) by SH. Ali and V.C.M. Leung (2005) schedulers. Simulation results show that the cell throughputs of the proposed schemes are greater than one without relay, and the cell throughput of OVSA scheme is improved with increasing the number of mobile relay stations (MRSs). OVSA with SOMRC also provides larger cell throughput than OVSA without MRS power control. The service coverage of ORSA scheme is greater than one of OVSA scheme, and OVSA with SOMRC offers expanded service coverage than OVSA without MRS power control.

When the traditional heterogeneous multi-core scheduling algorithm performs tasks with high resource density, a large amount of idle time often occurs on the processor core. Therefore, based on the environment of heterogeneous multi-core processors, this paper studies the static heuristic table scheduling algorithm, and proposes an optimization approach for the problem of single priority assignment and too simple task assignment. We design optimization in the static heuristic scheduling algorithm list generation phase and task allocation phase, and propose a hybrid task allocation method with three strategies to improve the standby time utilization of processor core. Then, DVFS technology is used to optimize the scheduling results, so that the task can run with lower energy consumption without increasing makespan. Finally, the new algorithm is compared with three traditional scheduling algorithms through design experiments, and it is proved that the new algorithm has better performance when executing more tasks.

We develop the joint optimization schemes for traffic-offloading and resource-allocation over radio-frequency (RF) powered backscatter-based mobile wireless networks, where a macro-cell base station (BS), several small-cell access points (SAPs), and multiple energy harvesting (EH) mobile users (MUs) coexist. By optimizing MUs' network access (through traffic-offloading) and system resource-allocation, we aim to minimize the energy consumption of MUs by using the low-energy consumption of the backscatter communications. First, we develop a distributed traffic-offloading and resource-allocation scheme, when the short-range ambient backscatter (AB) communication (generally with communication range being several meters) is employed to assist MUs' data transmission. Based on our developed scheme, MUs choosing to access nearby SAPs can backscatter ambient RF signals for data transmission to reduce energy consumption. Then, we employ the long-range bistatic backscatter (BB) communication (with communication up to 270 meters) to support data transmission between MUs and the BS, and thus MUs can convey data by backscattering RF signals emitted from dedicated carrier emitters. Accordingly, we develop a joint traffic-offloading and resource-allocation scheme for the scenarios when the AB and BB communications are adopted by MUs accessing the SAPs and the BS, respectively. Finally, we validate and evaluate the performance of our developed schemes through numerical analyses.

Decentralized distributed systems, such as grids, clouds or networks of sensors, have been widely investigated recently. An important nature of such systems is the heterogeneity of their resources; in order to archive the availability, scalability and flexibility. As a consequence, managing the systems to meet requirements is obviously a nontrivial work. The issue is even more challenging in term of job scheduling when the task dependency within each job exists. In this paper, we address such problem of job scheduling, so called workflow-based job scheduling, in the decentralized distributed systems with heterogeneous resources. As such problem is proven to be an NP-complete problem, an efficient heuristic algorithm to address this problem is proposed. The algorithm is based on an observation that the heterogeneity of the resources can affect the execution time of the scheduling. We compare the effectiveness and efficiency of the proposed algorithm with a baseline algorithm. The result shows that our algorithm is highly effective and efficient for the scheduling problem in the decentralized distributed system with heterogeneous resources environment both in terms of the solution quality and the execution time respectively.KeywordsDecentralized algorithmHeterogeneous resourcesWorkflow-based job scheduling

In this paper, we consider the problem of Angle of Departure (AoD) based channel feedback in Frequency Division Duplex (FDD) massive Multiple-Input Multiple-Output (MIMO) systems with multiple antennas at the users. We consider the use of Zero-Forcing Block Diagonalization (BD) as the downlink precoding scheme. We consider two different cases; one in which the number of streams intended for a user equals the number of antennas at that user and the other case in which the number of streams is less than the number of user antennas. BD requires the feedback of the subspace spanned by the channel matrix at the user or a subspace of it in the case of having less number of streams than the number of antennas at a specific user. Based on our channel model, we propose a channel feedback scheme that requires less feedback overhead compared to feeding back the whole channel matrix. Then, we quantify the rate gap between the rate of the system with perfect Channel State Information (CSI) at the massive MIMO Basestation (BS) and our proposed channel feedback scheme for a given number of feedback bits. Finally, we design feedback codebooks based on optimal subspace packing in the Grassmannian manifold. We show that our proposed codes achieve performance that is very close to the performance of the system with perfect CSI at the BS.

In this paper, we evaluate the tradeoff between the amount of uplink channel feedback information and the orthogonal frequency-division multiple access (OFDMA) downlink performance with opportunistic frequency-domain packet scheduling. Three candidate channel feedback schemes are investigated, including practical aspects, such as the effects of terminal measurement errors, bandwidth measurement granularity, quantization, and uplink signaling delays. The performance is evaluated by means of system-level simulations with detailed modeling of various radio resource-management algorithms, etc. Our results show that the optimal tradeoff between the channel feedback and the downlink OFDMA system performance depends on the radio channel frequency coherence bandwidth. We conclude that the so-called average best-M scheme is the most attractive channel feedback solution, where only the average channel quality for the best M subbands is reported, together with a bit mask identifying those subbands. The latter scheme only has a channel feedback word size in the range of 10-20 bits for the considered OFDMA system with 10-MHz bandwidth.