Haleh Tabrizi

A Learning-Based Network Selection Method in Heterogeneous Wireless Systems

With the coexistence of various wireless technologies, next generation wireless communications will likely consist of an integrated system of networks, where the Access Points (APs) and Base Stations (BSs) work together to maximize the mobile-user Quality of Service (QoS). In such heterogeneous environment where handheld devices with different access technologies are not uncommon, it should be possible to select networks and seamlessly switch from one AP/BS to another in order to elevate user performance. In this paper, this type of network selection and handover mechanism with the goal of maximizing QoS is formulated as a Markov Decision Process (MDP). An algorithm based on Reinforcement Learning (RL) is then obtained that selects the best network based not only on the current network load but also the potential future network states. This algorithm aims at balancing the number of handovers and the achievable QoS. The results illustrate that while the QoS performance of the proposed algorithm is comparable to the performance of the optimum opportunistic selection algorithm, fewer number of network handovers (on average) are required. In addition, compared to the existing predefined network selection strategies with no handover, the MDP-based algorithm offers significantly better QoS.

QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces

Wireless networks have resource limitations; in a dense area, cellular spectrum resources are insufficient and affect the system performance. A Long Term Evolution (LTE) network aims to serve heterogeneous users with different QoS requirements. Traditional approaches need new infrastructure and degrade performance of delay sensitive applications, which may result in users with minimum rate requirements being in high blocking probability. To utilize wireless resources efficiently, users want to access the same medium to connect with the same multicast group and be overhauled at the same time. In this paper, a new technique is proposed for operator-controlled called the QoS-Aware Tethering in a Heterogeneous Wireless Network using LTE and TV White Spaces (QTHN) to improve QoS for Constant Bit Rate (CBR) and Best Effort (BE) users. The proposed QTHN converts the whole dense wireless network into hexagonal clusters via two layer network communication. In a cluster, one node is selected as a cluster head and all other nodes act as slaves. Within a cluster, a cluster head acts as an access point known as a Hotspot (H), which is further connected to the Base-station (BS). The proposed QTHN aims to improve QoS within heterogeneous wireless network using LTE and unused White Spaces in a wireless dense area. Simulation results show that the proposed QTHN reduced the numbers of blocked users and improved network utility.

Tethering over TV White-Space: Dynamic Hotspot Selection and Resource Allocation

In dense wireless areas where the cellular spectrum resources are insufficient, the conventional approach is to install more base-stations (BS) or offload some of the traffic onto unlicensed WiFi bands. Both approaches require adding new infrastructure that might be necessary for only a short time. Taking advantage of dual-hop communication, frequency reuse policies, and opportunistic use of white-spaces, this paper proposes an algorithm for operator-controlled tethering over TV white-space (TVWS). As such, in a dense wireless area, some nodes can act as hotspots and tether data to and from their corresponding slaves over TVWS. The proposed algorithm iteratively clusters the nodes into hotspots and slaves, and allocates resources with the objective of maximizing spectrum utilization. Evaluations show that the proposed algorithm can effectively exploit TVWS spectra, and given a fixed amount of network resources, it can significantly increase the number of supported users.

CaSRA: An algorithm for cognitive tethering in dense wireless areas

This paper investigates the performance gain obtained by creating a hotspot-slave configuration of nodes in densely populated areas. In doing so, a semi-distributed algorithm, referred to as CaSRA (Clustering and Spectrum assignment and Resource Allocation), that allows the hotspots to tether over locally available white-spaces is proposed. CaSRA, performs in three-steps: 1) clusters the nodes based on the K-means clustering algorithm, 2) assigns white-space spectrum to each cluster based on a distributed graph-coloring approach to maximize spectrum reuse, and 3) allocates physical layer resources to individual users based on local channel information. Unlike small cells (femtocells, relays, and WiFi networks), this approach does not require any additions to the existing infrastructure, but allows the nodes, themselves, to act as hotspots. Simulation results show that given fixed amount of network resources, the proposed algorithm can significantly improve the overall performance of network users.

Dynamic handoff decision in heterogeneous wireless systems: Q-learning approach

The satisfaction of a mobile user in a heterogeneous wireless environment relies heavily on the appropriate choice of network. With the presence of various wireless technologies and advances in smart mobile devices, the mobile terminal in next generation wireless communications will likely make intelligent handoff decisions to optimize the user Quality of Experience (QoE). This paper investigates network selection and handoff decision with the goal of maximizing user QoE. An algorithm based on Q-learning is obtained that chooses the best network based not only on the current network state but also the potential future network and device states. As opposed to other dynamic programming-based algorithms, this method does not require the knowledge of the statistics of the wireless environment, but learns an optimum policy by utilizing the mobile devices past experience. It is shown that the QoE results of the proposed Dynamic Handoff Decision (DHD) algorithm come very close to the performance of an optimum oracle algorithm, while on average fewer number of network handoffs are required.

Coordinated Tethering Over White Spaces

Coordinated tethering over a white-space spectrum is investigated herein to increase mobile broadband spectrum efficiency in densely populated areas. This paper proposes an algorithm for operator-controlled tethering over white spaces. The proposed approach does not add to the existing infrastructure but instead allows the individual nodes to act as “hotspots” and to tether data to and from other nodes. The proposed algorithm iteratively clusters the nodes into hotspots and slaves and allocates resources to maximize spectrum utility. The proposed methods dynamic characteristics allow cellular systems to hierarchically evolve in dense areas as necessary. A signaling framework for node-to-node and base-station-to-node communication that enables such operator-controlled tethering is also presented. Simulation results show that given a fixed amount of network resources, the proposed algorithm can significantly increase the number of supported users.

An intelligent power save mode mechanism for IEEE 802.11 WLAN

Maximizing the run-time of battery-limited mobile devices is contingent on efficient energy management at the mobile device wireless interface. IEEE 802.11 wireless local area network (WLAN) power save mode (PSM) minimizes the mobile device energy consumption by allowing the device to de-activate its wireless interface periodically while the access point (AP) stores its incoming packets. Further increase in energy conservation occurs through the proposed decision-making algorithm, Intelligent-PSM (IPSM), that determines dynamic, as opposed to fixed, activation times based on the state of the mobile user. IPSM can be implemented with minimal change to the standard PSM transmission scheme. Simulation results show that with a maximum allowable packet delay of 1 second and limited AP buffering capacity, energy conservation of up to about 37% compared to standard PSM can be achieved.

BER-based wear leveling and bad block management for NAND flash

One of the main challenges keeping flash memories from achieving widespread distribution is their limited endurance. The programming and erasing from re-writes damages the cells, progressively increasing the number of errors until information can no longer be stored reliably. Most manufacturers employ powerful ECC techniques, but there is a limit to the number of errors that these can correct. When the number of errors goes beyond the capability of the ECC, it is necessary to invoke RAID, which introduces significant latency and jeopardizes the speed of the memory if used too often. This paper introduces a method for estimating the BER that a flash page will exhibit after retention and uses this estimate for wear leveling. Instead of leveling out the number of PE cycles in all the blocks, the proposed scheme attempts to wear all the blocks evenly so that they all suffer the same BER. Additionally, the estimate will be used to detect bad blocks, those likely to exhibit a number of errors beyond the ECC correction capability, and retire them from further use.

Spatial Reuse in Dense Wireless Areas: A Cross-Layer Optimization Approach via ADMM

This paper introduces an efficient method for communication resource use in dense wireless areas where all nodes must communicate with a common destination node. The proposed method groups nodes based on their distance from the destination and creates a structured multi-hop configuration in which each group can relay its neighbors data. The large number of active radio nodes and the common direction of communication toward a single destination are exploited to reuse the limited spectrum resources in spatially separated groups. Spectrum allocation constraints among groups are then embedded in a joint routing and resource allocation framework to optimize the route and amount of resources allocated to each node. The solution to this problem uses coordination among the lower-layers of the wireless-network protocol stack to outperform conventional approaches where these layers are decoupled. Furthermore, the structure of this problem is exploited to obtain a semi-distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) where each node can optimize its resources independently based on local channel information.

Improving NAND flash read performance through learning

Two important performance metrics for a storage system are the latency associated with retrieving data from its storage medium and the effective lifetime of its storage medium. Both metrics are directly affected by the number of raw read errors (i.e. errors prior to exploiting error-correction mechanisms). This paper focuses on NAND flash memories, where a read is performed by comparing stored voltages with a threshold voltage. The unwanted variation of stored voltages causes read errors. This paper identifies number of flash program-erase (PE) cycles, time elapsed between writing and reading, and the page number (physical location) as the main sources of voltage variations. It then proposes a method for learning how read thresholds should vary with these parameters such that the storage controller can dynamically vary thresholds and minimize read errors. Lab experiments show that at the flash end-of-life, the proposed method lowers the raw bit-error-rate up to a factor of 6, as compared to manufacturers default read settings.