Emmanuel C. Manasseh

Preamble and pilot symbol design for channel estimation in OFDM systems with null subcarriers

In this article, design of preamble for channel estimation and pilot symbols for pilot-assisted channel estimation in orthogonal frequency division multiplexing system with null subcarriers is studied. Both the preambles and pilot symbols are designed to minimize the l2 or the l∞ norm of the channel estimate mean-squared errors (MSE) in frequency-selective environments. We use convex optimization technique to find optimal power distribution to the preamble by casting the MSE minimization problem into a semidefinite programming problem. Then, using the designed optimal preamble as an initial value, we iteratively select the placement and optimally distribute power to the selected pilot symbols. Design examples consistent with IEEE 802.11a as well as IEEE 802.16e are provided to illustrate the superior performance of our proposed method over the equi-spaced equi-powered pilot symbols and the partially equi-spaced pilot symbols.

Pilot symbol assisted channel estimation for OFDM-based cognitive radio systems

In this article, challenges regarding the provision of channel state information (CSI) in non-contiguous orthogonal frequency division multiplexing (NC-OFDM) cognitive radio (CR) systems are addressed. We propose a novel scheme that utilizes cross entropy (CE) optimization together with an analytical pilot power distribution technique to design pilot symbols that minimizes the channel estimate mean squared error (MSE) of frequency-selective channels. The optimal selection of pilot subcarriers is a combinatorial problem that requires heavy computations. To reduce the computational complexity, the CE optimization is utilized to determine the position of pilot subcarriers. Then, for a given pilot placement obtained by the CE algorithm, a closed form expression to obtain optimal pilot power distribution is employed. Simulation results indicate that, the proposed pilot symbol design provides better channel estimate MSE as well as the bit error rate (BER) performance when compared with the conventional equal powered pilot design.

Combined channel estimation and PAPR reduction technique for MIMO-OFDM systems with null subcarriers

In this article, we address the challenges regarding the provision of channel state information as well as reducing peak-to-average power ratio (PAPR) of a multiple input multiple output orthogonal frequency multiplexing (MIMO–OFDM) system. The mean squared error (MSE) of the channel estimate is adopted as the optimization criterion to design pilot symbols for channel estimation in MIMO–OFDM systems with null subcarriers. We design the placement and power distribution to the pilot symbols for multiple transmit antennas to minimize the MSE of the least square (LS) channel estimates. To reduce interference of the pilot symbols transmitted from different antennas, an algorithm to guarantee that pilot symbols are disjoint from any other transmitter pilot set is proposed. To efficiently reduce the PAPR of the MIMO–OFDM signals, a method that mixes dummy symbols and phase information of the pilot symbols is presented. Simulation results based on IEEE 802.16e are presented to illustrate the superior performance of our proposed channel estimation method over the existing standard and the partially equi-spaced pilot symbols. We also demonstrate that, by mixing the dummy symbols and phase information of the pilot symbols, the PAPR of the MIMO–OFDM signals can significantly be reduced.

Distributed Demand-side management with load uncertainty

In this paper, we consider load control in a multiple residence setup where energy consumption scheduling (ECS) devices in smart meters are employed for Demand-side management (DSM). Several residential end-users share the same energy source and each residential user has non adjustable loads, adjustable loads and a storage device. The ECS devices interact automatically by running an iterative distributed algorithm to find the optimal energy consumption schedule for each end-user, in order to reduce the total energy cost as well as the peak-to-average-ratio (PAR) in load demand in the system. Simulation results are provided to evaluate the performance of the proposed game theoretic based distributed DSM technique.

Efficient training design for estimation of channel, CFO, and receiver I/Q imbalance in MIMO-OFDM systems

In this paper, training design for estimation of frequency selective channels, carrier frequency offset (CFO) and in-phase/quadrature-phase (I/Q) imbalance in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. We utilize convex optimization technique to optimize power of all active subcarriers, then we employ adaptive Markov chain Monte Carlo (AMCMC) techniques to select training sequence that minimizes the channel estimate mean squared error (MSE) while suppressing the effect of the I/Q imbalance. To estimate CFO, maximum likelihood (ML) based scheme that utilizes two successive OFDM training symbols is deployed. Numerical simulations are provided to corroborate the efficacy of the proposed design.

Training Symbol Design for Channel Estimation and IQ Imbalance Compensation in OFDM Systems

In this paper, training symbol designs for estimation of frequency selective channels and compensation of in-phase (I) and quadrature (Q) imbalances on OFDM transmitters and receivers are studied. We utilize cross entropy (CE) optimization techniques together with convex optimization to design training sequence that minimizes the channel estimate mean squared error (MSE) as well as estimating the effect of I/Q mismatch while lowering the peak power of the training signals. The proposed design provide better channel estimate MSE and bit error rate (BER) performances. The efficacies of the proposed designs are corroborated by analysis and simulation results.

OPTIMIZATION OF TRANSMIT SIGNALS TO INTERFERE EAVESDROPPING IN A WIRELESS LAN

We consider physical-layer security of a wireless LAN where multiple receivers collude to eavesdrop the information from the base station to the intended receiver. To enhance the physical-layer security, we design the interference signals to combat the eavesdropping. Our design problems are resolved using semidefinite relaxation problems, which can be numerically solved efficiently by the existing convex optimization solvers. Simulation results are provided to demonstrate the efficiency of the proposed designs.

How many known symbols are required for linear channel estimation in OFDM

Training sequences for estimation of channel parameters have been well designed under the condition that the number of the unknown channel parameters is not greater than the number of the known symbols. Without the condition, we develop a method to obtain the optimal number of known pilot symbols for pilot-aided linear channel estimation in orthogonal frequency division multiplexing (OFDM). We derive an expression for channel estimation error covariance from which performance measures can be numerically evaluated to obtain the optimal number of pilot symbols. Using capacity lower bound as a performance measure, we demonstrate that for channels with exponential power profiles, it is better to estimate a truncated version of the channel response, which reveals the disadvantage of the existing design based on the condition.

Autonomous demand-side optimization with load uncertainty

Demand-side management (DSM) will play an important role in balancing the energy distribution and demand in future smart grids. Indeed DSM is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and help the energy providers reduce the peak load demand and reshape the load profile [1]-[4]. Achieving effective DSM is crucial to the success of the smart grid. The success of DSM programs mainly depends on how big a portion of the total energy load is controllable. Efficient DSM, promote immediate change of consumption by shifting or reducing load through incentives or pricing mechanisms [3]. In this article, we consider load control in a multiple residence setup, where energy consumption scheduler (ECS) devices in smart meters are employed for DSM. Several residential endusers share the same energy source and each residential user has non-adjustable loads, adjustable loads and a storage device. Residential users utilize ECS deployed inside their smart meters for the adjustable loads as well as charging and discharging of their storage devices. The smart meters with ECS functions interact automatically by running a centralized algorithm to find the optimal energy consumption schedule for each user in order to reduce the total energy cost as well as the peak-to-average-ratio (PAR) in load demands. The objective is to minimize the energy cost in the system as well as PAR. Simulation results demonstrate that our proposed scheme significantly reduces the PAR and the total cost of electricity.

Low complexity equalization for single carrier transmissions over doubly-selective channels using linear approximation

In this paper, a low complexity equalizer for single carrier transmissions over doubly selective channels is developed. The main attribute of the proposed equalizer is its low complexity compared to the existing equalizers, which is attained at the expense of a slight performance loss. Simulation results show that the performance of our proposed low complexity equalizer is as good as that of the conventional equalizers with extensive computation complexity.