Cognitive radios have the ability to sense the radio spectrum environment and to switch dynamically to available frequency ranges. Mobile WiMAX is an emerging wireless networking standard that could potentially benefit from cognitive radio technology. We develop a framework for applying cognitive radio technology to mobile WiMAX networks to increase capacity and simplify network operations. In the proposed cognitive WiMAX architecture, base stations are equipped with sensitive detectors and assign channels to subscriber stations dynamically based on spectrum availability. Power control is employed to increase frequency reuse in conjunction with spectrum sensing. Using computer simulation, we evaluate the performance of “cognitive channel assignment” relative to conventional dynamic channel assignment. Our numerical results show that cognitive radios can substantially increase the capacity of emerging WiMAX networks by exploiting inherent spectrum hole opportunities. The key performance parameters determining the achievable capacity of cognitive WiMAX networks are the detection and interference range, which depend in turn on characteristics of the radio propagation environment.

We propose a local averaging technique for processing the received pilot signal strength, which can significantly improve handoff performance in cellular networks. In handoff algorithms, the received pilot signal strength is typically averaged to diminish the undesirable effect of the fast fading component. Unfortunately, the averaging process can substantially alter the characteristics of path loss and shadowing components, causing increased handoff delay. The proposed local averaging method provides significant improvement for handoff delay performance, especially in the non-line-of-sight case, when the mobile station turns around a corner. An important feature of local averaging is that the handoff performance is insensitive to the speed of the mobile station, such that velocity estimation is not needed. We develop efficient numerical procedures to compute the handoff performance metrics under local averaging

In this correspondence, we develop a formal method for solving a class of level-crossing problems in discrete-time with application to the analysis of soft handoff performance in cellular networks such as code-division multiple-access (CDMA) systems. In such networks, proper dimensioning of soft handoff parameters is critical to overcoming propagation impairments and providing a transparent radio access service for multiple user profiles. We obtain exact expressions for the cell assignment and active set update probabilities of a mobile station traveling along an arbitrary straight-line trajectory. We develop recursive algorithms to compute performance measures such as outage probability, macrodiversity gain, and signaling load. The discrete-time level-crossing analysis yields an accurate and efficient computational tool for designing and dimensioning high performance soft handoff algorithms while avoiding the need for approximations or time-consuming computer simulations.

We introduce an important system performance measure related to handoff called handoff interference, which characterizes the additional interference noise induced by the handoff process. The handoff interference experienced by a mobile unit is determined by the parameters of the handoff algorithm and is critical for accurate evaluation of the wireless system capacity in the context of multiple cell and user scenarios. We develop an efficient numerical procedure to compute the handoff interference along arbitrary trajectories in a cellular network. We present numerical results showing the tradeoff between the handoff interference and the number of handoffs along a given trajectory, which should be taken into account in dimensioning the handoff parameters to optimize system performance.

The handoff algorithm employed in a cellular network has a significant impact on overall network performance, but evaluation of handoff performance has for the most part been done using only crude approximations or brute-force computer simulation. We introduce a new discrete-time approach to analyze the performance of handoff algorithms based on pilot signal strength measurements. We derive exact analytical expressions and develop a recursive numerical procedure to evaluate handoff performance metrics for a mobile station moving along a straight-line trajectory in a cellular network employing hard handoff. The numerical procedure provides a computational solution to a level-crossing problem in discrete time. Our discrete-time approach provides valuable analytical insight into the performance impact of handoff algorithms. Moreover, our numerical procedure for discrete-time handoff analysis provides an accurate and efficient tool for the design and dimensioning of high-performance handoff algorithms. The accuracy of the numerical procedure is validated by simulation.

We introduce a new hard handoff algorithm that employs local averaging, a drop timer, and hysteresis. The proposed algorithm achieves better performance than conventional hysteresis-based handoff algorithms that employ exponential averaging, particularly in microcellular environments. We develop an analytical model and an efficient numerical procedure to evaluate accurately the performance and evaluation technique greatly simplify the practical task of dimensioning the cellular network for optimum performance.

Proper design of soft handoff mechanisms for CDMA cellular networks is critical to overcoming propagation impairments and providing a transparent radio access service for multiple data user profiles. The connectivity of the mobile user to the core network via an active set of access points is determined dynamically by the handoff algorithm via measurements of received pilot signal strengths. Using a discrete-time framework, we derive analytical expressions and develop associated numerical procedures for evaluating the soft handoff performance of a mobile unit travelling along an arbitrary straight-line trajectory in a CDMA cellular network. In particular, we obtain expressions for the cell assignment probabilities, which can be used to compute performance measures such as outage probability, macrodiversity gain, and signaling load. Our methodology provides an accurate and efficient means of designing and dimensioning high performance soft handoff algorithms for CDMA networks, while avoiding the need for approximations or time-consuming computer simulation.