Wireless OFDM Systems and Cross-Layer Optimization

The increasing popularity of wireless broadband services nowadays indicates that, future wireless systems will witness a rapid growth of high-data-rate applications with very diverse quality of service requirements. To support such applications under limited radio resources and harsh wireless channel conditions, dynamic resource allocation, which achieves both higher system spectral efficiency and better QoS, has been identified as one of the most promising techniques. In particular, jointly optimizing resource allocation across adjacent and even nonadjacent layers of the protocol stack leads to dramatic improvement in overall system performance. In this article an overview of recent research on dynamic resource allocation, especially for OFDM systems is provided. Recent work and open issues on cross-layer resource allocation and adaptation are also discusse

Spatial Domain Resource Sharing for Overlapping Cells in Indoor Environment

As microcell wireless systems become more widespread, intercell interference among the access points will increase due to the limited frequency resource. In the overlapping cell scenario, radio resources should be shared by multiple cells. Although time and frequency resource sharing has been described in many papers, there is no detailed report on dynamic spatial resource sharing among multiple cells for microcell wireless systems. Thus, we present the effectiveness of spatial resource sharing among two access points. We introduce two scenarios based on the zero forcing method; one is the primary-secondary AP scenario and the other is the cooperative AP scenario. To evaluate the transmission performance of spatial resource sharing, channel matrices are measured in an indoor environment. The simulation results using the measured channel matrices show the potential of spatial resource sharing

Transmit without regrets: Online optimization in MIMO-OFDM cognitive radio systems

In this paper, we examine cognitive radio systems that evolve dynamically over time due to changing user and environmental conditions. To combine the advantages of orthogonal frequency division multiplexing (OFDM) and multiple-input, multiple-output (MIMO) technologies, we consider a MIMO-OFDM cognitive radio network where wireless users with multiple antennas communicate over several non-interfering frequency bands. As the network's primary users (PUs) come and go in the system, the communication environment changes constantly (and, in many cases, randomly). Accordingly, the network's unlicensed, secondary users (SUs) must adapt their transmit profiles "on the fly" in order to maximize their data rate in a rapidly evolving environment over which they have no control. In this dynamic setting, static solution concepts (such as Nash equilibrium) are no longer relevant, so we focus on dynamic transmit policies that lead to no regret: specifically, we consider policies that perform at least as well as (and typically outperform) even the best fixed transmit profile in hindsight. Drawing on the method of matrix exponential learning and online mirror descent techniques, we derive a no-regret transmit policy for the system's SUs which relies only on local channel state information (CSI). Using this method, the system's SUs are able to track their individually evolving optimum transmit profiles remarkably well, even under rapidly (and randomly) changing conditions. Importantly, the proposed augmented exponential learning (AXL) policy leads to no regret even if the SUs' channel measurements are subject to arbitrarily large observation errors (the imperfect CSI case), thus ensuring the method's robustness in the presence of uncertainties.Comment: 25 pages, 3 figures, to appear in the IEEE Journal on Selected Areas in Communication

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.Comment: To be published in IEEE Communications Surveys and Tutorial

An improved resource allocation scheme for WiMAX using channel information

In recent years, tremendous progress has been made in wireless communication systems to provide wireless coverage to end users at different data rates. WiMAX technology provides wireless broadband access over an extended coverage area in both fixed and mobility environments. Most of the existing resource allocation schemes allocate resources based on respective service class of the incoming users’ requests. However, due to variation in channel conditions, user mobility and diverse resource requirements QoS based resource allocation either results in over or under utilization of allocated resources. Therefore, resource allocation is a challenging task in WiMAX. This research proposes an improved resource management mechanism that performs resource allocation by taking into consideration not only the user service class but also the respective channel status. Based on these two parameters, this research aims to achieve improved resource allocation in terms of resource utilization, fairness and network throughput. First, a Channel Based Resource Allocation scheme is introduced where priority in resource allocation is given to users’ requests with relatively higher service classes and better channel status. To maintain fairness in resource allocation process, a Fair Resource Allocation Based Service mechanism is developed where priority is given to users’ requests having less additional resources demand. Finally, to improve throughput of the network, a Channel Based Throughput Improvement approach is proposed which dynamically selects a threshold level of channel gain based on individual channel gain of users. During resource allocation process, users above the threshold level are selected for resource allocation such that priority is given to users with high channel gain. Different simulation scenario results reveal an overall improved resource utilization from 87% to 91% and the throughput improves up to 15% when compared to existing schemes. In conclusion the performance of resource utilization is improved if channel status is considered as an input parameter