No Arabic abstract
The emergence of OpenFlow and Software Defined Networks brings new perspectives into how we design the next generation networks, where the number of base stations/access points as well as the devices per subscriber will be dramatically higher. In such dense environments, devices can communicate with each other directly and can attach to multiple base stations (or access points) for simultaneous data communication over multiple paths. This paper explores how networks can maximally enable this multi-path diversity through network programmability. In particular, we propose programmable flow clustering and set policies for inter-group as well as intra-group wireless scheduling. Further, we propose programmable demultiplexing of a single network flow onto multiple paths before the congestion areas and multiplexing them together post congestion areas. We show the benefits of such programmability first for legacy applications that cannot take advantage of multi-homing without such programmability. We then evaluate the benefits for smart applications that take advantage of multi-homing by either opening multiple TCP connections over multiple paths or utilizing a transport protocol such as MP-TCP designed for supporting such environments. More specifically, we built an emulation environment over Mininet for our experiments. Our evaluations using synthetic and trace driven channel models indicate that the proposed programmability in wireless scheduling and flow splitting can increase the throughput substantially for both the legacy applications and the current state of the art.
Mobile quality of experience and user satisfaction are growing research topics. However, the relationship between a users satisfaction with network quality and the networks real performance in the field remains unexplored. This paper is the first to study both network and non-network predictors of user satisfaction in the wild. We report findings from a large sample (2224 users over 12 months) combining both questionnaires and network measurements. We found that minimum download goodput and device type predict satisfaction with network availability. Whereas for network speed, only download factors predicted satisfaction. We observe that users integrate over many measurements and exhibit a known peak-end effect in their ratings. These results can inform modeling efforts in quality of experience and user satisfaction.
Small cell enchantment is emerging as the key technique for wireless network evolution. One challenging problem for small cell enhancement is how to achieve high data rate with as-low-as-possible control and computation overheads. As a solution, we propose a low-complexity distributed optimization framework in this paper. Our solution includes two parts. One is a novel implicit information exchange mechanism that enables channel-aware opportunistic scheduling and resource allocation among links. The other is the sub-gradient based algorithm with a polynomial-time complexity. What is more, for large scale systems, we design an improved distributed algorithm based on insights obtained from the problem structure. This algorithm achieves a close-to-optimal performance with a much lower complexity. Our numerical evaluations validate the analytical results and show the advantage of our algorithms.
Due to the merit without requiring charging cable, wireless power transfer technologies have drawn rising attention as a new method to replenish energy to Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we study mobile charger scheduling problem for multi-node recharging with deadline-series. Our target is to maximize the overall effective charging utility, and minimize the traveling time as well. Instead of charging only once over a scheduling cycle, we incorporate the multiple charging strategy for multi-node charging with deadline constraint, where charging spots and tour are jointly optimized. Specifically, we formulate the effective charging utility maximization problem as to maximize a monotone submodular function subject to a partition matroid constraint, and propose a simple but effective 1/2 -approximation greedy algorithm. After that, we present the grid-based skip-substitute operation further to save the traveling time, which can increase the charging utility. Finally, we conduct the evaluation for the performance of our scheduling scheme. Comparing to the Early Deadline First scheme, the simulation and field experiment results show that our algorithm outperform EDF by 37.5% and 37.9%, respectively.
In this paper, we consider resource allocation in the 3GPP Long Term Evolution (LTE) cellular uplink, which will be the most widely deployed next generation cellular uplink. The key features of the 3GPP LTE uplink (UL) are that it is based on a modified form of the orthogonal frequency division multiplexing based multiple access (OFDMA) which enables channel dependent frequency selective scheduling, and that it allows for multi-user (MU) scheduling wherein multiple users can be assigned the same time-frequency resource. In addition to the considerable spectral efficiency improvements that are possible by exploiting these two features, the LTE UL allows for transmit antenna selection together with the possibility to employ advanced receivers at the base-station, which promise further gains. However, several practical constraints that seek to maintain a low signaling overhead, are also imposed. In this paper, we show that the resulting resource allocation problem is APX-hard and then propose a local ratio test (LRT) based constant-factor polynomial-time approximation algorithm. We then propose two enhancements to this algorithm as well as a sequential LRT based MU scheduling algorithm that offers a constant-factor approximation and is another useful choice in the complexity versus performance tradeoff. Further, user pre-selection, wherein a smaller pool of good users is pre-selected and a sophisticated scheduling algorithm is then employed on the selected pool, is also examined. We suggest several such user pre-selection algorithms, some of which are shown to offer constant-factor approximations to the pre-selection problem. Detailed evaluations reveal that the proposed algorithms and their enhancements offer significant gains.
With the advantages of Millimeter wave in wireless communication network, the coverage radius and inter-site distance can be further reduced, the ultra dense network (UDN) becomes the mainstream of future networks. The main challenge faced by UDN is the serious inter-site interference, which needs to be carefully addressed by joint user association and resource allocation methods. In this paper, we propose a multi-agent Q-learning based method to jointly optimize the user association and resource allocation in UDN. The deep Q-network is applied to guarantee the convergence of the proposed method. Simulation results reveal the effectiveness of the proposed method and different performances under different simulation parameters are evaluated.