E. de Carvalho

Improving the rates in wireless relay systems through superposition coding

We introduce a new two-step relaying scheme based on superposition coding, SC-relaying. In Step 1, the source AS broadcasts a message, created by superposition coding, to the relay AR and the destination AD. After decoding the information from AS, AR relays a part of this message in Step 2, by using a codebook that is adapted to the link AR - AD. The information-theoretic design shows that the proposed scheme can improve the spectral efficiency, attaining almost optimal value. We demonstrate that the SC-relaying can be ported even to the case of uncoded systems, where it is shown how to select the transmission scheme in order to maximize the throughput between the source and the destination.

Efficient Cooperative Diversity Schemes and Radio Resource Allocation for IEEE 802.16j

This paper studies various cooperative diversity schemes for orthogonal frequency division multiple access (OFDMA)- time division duplex (TDD) based two-hop cellular networks in low mobility scenarios, where the instantaneous channel state information is available at the base station. A user scheduling and radio resource allocation technique is developed in order to efficiently integrate various cooperative diversity schemes for the emerging IEEE 802.16j based systems. The analysis of the system with this scheduler shows that a simple cooperative diversity scheme which dynamically selects the best scheme between conventional relaying and direct transmission is promising in terms of throughput and implementation complexity. The conventional relaying refers to the scheme where the destination relies solely on the signals received through the relay.

Blind and semi-blind FIR multichannel estimation: (global) identifiability conditions

Two channel estimation methods are often opposed: training sequence methods that use the information induced by known symbols and blind methods that use the information contained in the received signal and, possibly, hypotheses on the input symbol statistics but without integrating the information from known symbols, if present. Semi-blind methods combine both training sequence and blind information and are more powerful than the two methods separately. We investigate the identifiability conditions for blind and semi-blind finite impulse response (FIR) multichannel estimation in terms of channel characteristics, received data length, and input symbol excitation modes, as well as number of known symbols for semi-blind estimation. Two models corresponding to two different cases of a priori knowledge on the input symbols are studied: the deterministic model in which the unknown symbols are considered as unknown deterministic quantities and the Gaussian model in which they are considered as Gaussian random variables. This last model includes the methods using the second-order statistics of the received data. Semi-blind methods appear superior to blind and training sequence methods and allow the estimation of any channel with only few known symbols. Furthermore, the Gaussian model appears more robust than the deterministic one as it leads to less demanding identifiability conditions.

Sum-Rate Optimization in a Two-Way Relay Network with Buffering

A Relay Station (RS) uses a buffer to store and process the received data packets before forwarding them. Recently, the buffer has been exploited in one-way relaying to opportunistically schedule the two different links according to their channel quality. The intuition is that, if the channel to the destination is poor, then RS stores more data from the source, in order to use it when the channel to the destination is good. We apply this intuition to the case of half-duplex two-way relaying, where the interactions among the buffers and links become more complex. We investigate the sum-rate maximization problem in the Time Division Broadcast (TDBC): the users send signals to the RS in different time slots, the RS decodes and stores messages in the buffers. For downlink transmission, the RS re-encodes and sends using the optimal broadcast strategy. The operation in each time slot is not determined in advance, but depends on the channel state information (CSI). We derive the decision function for adaptive link selection with respect to CSI using the Karush-Kuhn-Tucker (KKT) conditions. The thresholds of the decision function are obtained under Rayleigh fading channel conditions. The numerical results show that the sum-rate of the adaptive link selection protocol with buffering is significantly larger compared to the reference protocol with fixed transmission schedule.

Cramer-Rao bounds for blind multichannel estimation

Certain blind channel estimation techniques allow the identification of the channel up to a scale or phase factor. This results in singularity of the Fisher information matrix (FIM). The Cramer-Rao bound, which is the inverse of the FIM, is then not defined. To regularize the estimation problem, one can impose constraints on the parameters. In general, many sets of constraints are possible but are not always relevant. We propose a constrained CRB, the pseudo-inverse of the FIM, which gives, for a minimum number of constraints, the lowest bound on the mean squared estimation error.

A Leakage-Based MMSE Beamforming Design for a MIMO Interference Channel

We propose a low complexity design of the linear transmit filters for a MIMO interference channel. This design is based on a minimum mean squared error (MMSE) approach incorporating the signal and the interference leakage for each transmitter. Unlike the previous methods, it allows a closed-form expression of the regularization factor for the MMSE transmit filter. Hence, it requires a lower computational complexity compared to the conventional MMSE approach that is optimally achieved by solving a polynomial equation to find the regularization factor. Furthermore, the mean squared error (MSE) performance of the proposed design is verified by simulations to have nearly no loss compared to the conventional MMSE approach.

A CSI Estimation Method for Wireless Relay Network

This letter proposes a method for channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). The proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link, which can reduce the required overhead to explicitly exchange CSI.

Channel estimation for multicarrier multiple input single output systems using the EM algorithm

This paper investigates the problem of blindly and semi-blindly acquiring the channel gains for an underdetermined synchronous multiuser multicarrier system. The special case of a multiple-input single-output (MISO) channel is considered where the different users transmit at the same time and in the same bandwidth. In order to separate the different users blindly, techniques exploiting the finite alphabet are used. For such techniques, and for a general underdetermined MIMO system, we study conditions under which the channel and the data for each user are blindly and semi-blindly identifiable. We consider the stochastic maximum likelihood (SML) criterion in which the unknown input symbols are modeled as discrete random variables. We apply the expectation-maximization (EM) algorithm in the frequency domain to get blind and semi-blind channel estimates for each user in the MISO case. We also present a recursive EM solution that updates the channel and noise estimates at each time instant. Simulations show that users can be separated, even at low SNR. Furthermore, semi-blind estimation allows for a more robust estimation solution since a possible singularity problem is avoided.

Performance Analysis of OFDM Systems with Adaptive Sub Carrier Bandwidth

Orthogonal frequency division multiplexing based wireless systems are spectrally efficient, but they are vulnerable to inter-carrier interference (ICI). In a wide area scenario, users will experience varying signal strength due to different individual path loss and also varying amount of Doppler spread because of their independent velocities. Therefore, the ICI among users will vary over a wide range. It is proposed in this work to use dynamically adaptive sub carrier bandwidth (ASB) along with adaptive bit loading to mitigate ICI in such conditions, which will keep receivers simple while maintaining maximum throughput in each situation. Results show that ASB can provide higher throughput than its fixed sub carrier bandwidth counterpart when both may use adaptive bit loading per sub carrier.

Receive diversity for mobile OFDM systems

We investigate receive diversity for OFDM-based broadband communication systems. We consider the use of multiple antennas to combat time-selective fading and OFDM transmission in combination with an appropriate guard interval to suppress intersymbol interference. The receive diversity scheme is located in the frequency-domain and we derive different linear estimators, hereafter referred to as diversity equalizers. We show that the carrier-based solution for the optimum linear MMSE estimator-which is the least complex solution that maximizes the overall SNR is the well known maximum ratio combiner (MRC). Furthermore, we present a performance analysis for the proposed diversity equalizers and compare theoretical results to a simulated system which uses channel state information for MRC.