Hyosung Kim

Doubly Selective Channel Estimation Using Exponential Basis Models and Subblock Tracking

Three versions of a novel adaptive channel estimation approach, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented for doubly selective channels, where we track the BEM coefficients rather than the channel tap gains. Since the time-varying nature of the channel is well captured in the CE-BEM by the known exponential basis functions, the time variations of the (unknown) BEM coefficients are likely much slower than those of the channel, and thus more convenient to track. We propose a ?subblockwise? tracking scheme for the BEM coefficients using time-multiplexed (TM) periodically transmitted training symbols. Three adaptive algorithms, including a Kalman filtering scheme based on an assumed autoregressive (AR) model of the BEM coefficients, and two recursive least-squares (RLS) schemes not requiring any model for the BEM coefficients, are investigated for BEM coefficient tracking. Simulation examples illustrate the superior performance of our approach over several existing doubly selective channel estimators.

A channel-based hypothesis testing approach to enhance user authentication in wireless networks

We consider a physical layer approach to enhance wireless security by using the unique wireless channel state information (CSI) of a legitimate user to authenticate subsequent transmissions from this user, thereby denying access to any spoofer whose CSI would significantly differ from that of the legitimate user by virtue of a different spatial location. In some existing approaches, multicarrier systems have been considered where the channel frequency response at distinct frequencies is used to devise a hypothesis testing approach: is the CSI of the current transmission (packet) the same as that of the previous transmission? In this paper we investigate a single-carrier timedomain approach via either residual testing or time-domain CSI comparison. A hypothesis testing approach is formulated to test whiteness of residuals from current transmission where the residuals are generated using the estimated channel from the previous transmission. We also consider a hypothesis testing approach where the time-domain CSI of the current transmission is compared with that of the previous transmission. Two binary hypothesis testing approaches are formulated and illustrated via simulations.

Turbo equalization for doubly-selective fading channels using nonlinear kalman filtering and basis expansion models

We present a turbo (iterative) equalization receiver with fixed-lag nonlinear Kalman filtering for coded data transmission over doubly-selective channels. The proposed receiver exploits the complex exponential basis expansion model (CEBEM) for the overall channel variations, and an autoregressive (AR) model for the BEM coefficients. We extend an existing turbo equalization approach based on symbol-wise AR modeling of channels to channels based on BEMs. In the receiver an adaptive equalizer using nonlinear Kalman filters with delay is coupled with a soft-input soft-output (SISO) decoder to iteratively perform equalization and decoding. The adaptive equalizer jointly optimizes the estimates of the BEM coefficients and data symbols, thereby automatically accounting for correlation between data symbols and channel tap gains. An extrinsic information transfer (EXIT) chart analysis of the proposed approach is also presented. Simulation examples demonstrate that our CE-BEM-based approach significantly outperforms the existing symbol-wise AR model-based turbo equalizer.

Doubly-selective mimo channel estimation using exponential basis models and subblock tracking

We present a subblock-wise tracking approach to doubly-selective MIMO channel estimation, exploiting the oversampled complex exponential basis expansion model (CE-BEM) for the overall channel variations, and an autoregressive (AR) model to update the BEM coefficients. The time-varying nature of the channel is well captured by the CE-BEM while the time-variations of the (unknown) BEM coefficients are likely much slower than that of the channel. We track the BEM coefficients via Kalman filtering, based on time-multiplexed periodically transmitted training symbols. Simulation examples demonstrate its superior performance over some existing doubly-selective channel tracking schemes.

Recursive least-squares decision-directed tracking of doubly-selective channels using exponential basis models

We present a decision-directed tracking approach to doubly-selective channel estimation exploiting the complex exponential basis expansion model (CE-BEM). The time-varying nature of the channel is well captured by the CE-BEM while the time-variations of the (unknown) BEM coefficients are likely much slower than those of the channel. We track the BEM coefficients via the exponentially-weighted recursive least-squares (RLS) algorithm, aided by symbol decisions from a decision-feedback equalizer (DFE). Simulation examples demonstrate its superior performance over an existing subblock-wise channel tracking scheme.

On channel-based user authentication for mobile terminals

We consider a physical layer approach to enhance wireless security by using the unique wireless channel state information (CSI) of a legitimate user to authenticate subsequent transmissions from this user, thereby denying access to any spoofer whose CSI would significantly differ from that of the legitimate user by virtue of a different spatial location. A hypothesis testing approach is formulated to test whiteness of residuals from current transmission where the residuals are generated using the estimated channel from the previous transmission. In some existing approaches, multicarrier systems have been considered where the channel frequency response at distinct frequencies is used to devise a hypothesis testing approach: is the CSI of the current transmission (packet) the same as that of the previous transmission? In this paper we investigate a single-carrier time-domain approach via residual testing. A binary hypothesis testing approach is formulated and illustrated via simulations.

Turbo equalization for doubly-selective MIMO fading channels using exponential basis models

We present an adaptive soft-input soft-output (SISO) turbo equalization receiver for doubly-selective multiple-input-multiple-output (MIMO) channels. The proposed receiver exploits the complex exponential basis expansion model (CE-BEM) for the overall channel variations, and an autoregressive (AR) model for the BEM coefficients. We extend an existing single-user turbo equalization approach based on symbol-wise AR modeling of channels to multiuser/MIMO channels based on BEMs. Simulation examples demonstrate that our CE-BEM-based approach has superior performance over a symbol-wise AR model-based turbo equalizer.

Iterative (Turbo) equalization for doubly-selective channels using exponential basis expansion models

We present an adaptive soft-input soft-output (SISO) turbo equalization receiver for doubly-selective channels. The proposed receiver exploits the complex exponential basis expansion model (CE-BEM) for the overall channel variations, and an autoregressive (AR) model for the BEM coefficients. We extend an existing turbo equalization approach based on symbol-wise AR modeling of channels to channels based on BEMs. Simulation examples and an EXIT chart analysis demonstrate that our CE-BEM-based approach outperforms the existing symbol-wise AR model-based turbo equalizer.

Recursive least-squares doubly-selective MIMO channel estimation using exponential basis models

An adaptive MIMO channel estimation scheme, exploiting the oversampled complex exponential basis expansion model (CE-BEM), is presented for doubly-selective fading channels where we track the BEM coefficients. The time-varying nature of the channel is well captured by the CE-BEM while the time-variations of the (unknown) BEM coefficients are likely much slower than that of the channel. We apply the exponentially-weighted and sliding-window recursive least-squares (RLS) algorithms to track the BEM coefficients subblock-by-subblock, using time-multiplexed periodically transmitted training symbols. Simulation examples demonstrate its superior performance over the conventional block-wise channel estimator.

Forgetting factor selection in RLS decision-directed tracking of doubly-selective channels

We consider a decision-directed tracking approach to doubly-selective channel estimation exploiting the complex exponential basis expansion model (CE-BEM). The time-varying nature of the channel is well captured by the CE-BEM while the time-variations of the (unknown) BEM coefficients are likely much slower than those of the channel. We track the BEM coefficients via the exponentially-weighted recursive least-squares (RLS) algorithm, aided by symbol decisions from a decision-feedback equalizer (DFE). Such a scheme was recently presented in a conference paper by the authors [1]. In this paper we investigate selection of the forgetting factor in the RLS algorithm. We show that its selection depends upon how often the BEM coefficients are updated and we provide simple guidelines for its choice. Simulation examples demonstrate superior performance of the proposed decision-directed scheme over an existing subblock-wise channel tracking scheme.