The-Hanh Pham

Joint channel and frequency offset estimation in distributed MIMO flat-fading channels

In this paper, we consider the problem of joint channel and frequency offset estimation in a flat-fading multi- input multi-output (MIMO) system. We assume that each pair of transmit and receive antennas has a different frequency offset. We present two computationally efficient iterative algorithms based on expectation conditional maximization (ECM) and space-alternating generalized expectation-maximization (SAGE) algorithms. The mean-square-error (MSE) performance of the interested parameters for the proposed algorithms is compared with the Cramer-Rao Bound (CRB). Simulation results show that the proposed iterative algorithms achieve the CRB and overcome the drawbacks of existing algorithms.

Optimal training sequences for channel estimation in bi-directional relay networks with multiple antennas

In this letter, we consider a bi-directional relay network in which two users, U1 and U2, exchange their information via a relay station, RS. Multiple antennas are deployed at both users and at RS. Single carrier cyclic prefix (SCCP) is used to combat intersymbol interference (ISI) in frequency-selective fading channels. The transmission process is divided into two time slots. At the first time slot, both users send their information to RS concurrently. RS then amplifies and broadcasts its received signals at the second time slot. We propose an algorithm to estimate the channel information at end users based on the least square (LS) principle. To further minimize the mean square error (MSE) of the estimate, a method to design the optimal training sequences is also proposed. Simulation results show that the performance achieved by our optimal design is close to that with perfect channel information.

Spectrum Sensing for OFDM Signals Using Pilot Induced Auto-Correlations

Orthogonal frequency division multiplex (OFDM) has been widely used in various wireless communications systems. Thus the detection of OFDM signals is of significant importance in cognitive radio and other spectrum sharing systems. A common feature of OFDM in many popular standards is that some pilot subcarriers repeat periodically after certain OFDM blocks. In this paper, sensing methods for OFDM signals are proposed by using such repetition structure of the pilots. Firstly, special properties for the auto-correlation (AC) of the received signals are identified, from which the optimal likelihood ratio test (LRT) is derived. However, this method requires the knowledge of channel information, carrier frequency offset (CFO) and noise power. To make the LRT method practical, we then propose an approximated LRT (ALRT) method that does not rely on the channel information and noise power, thus the CFO is the only remaining obstacle to the ALRT. To handle the problem, we propose a method to estimate the composite CFO and compensate its effect in the AC using multiple taps of ACs of the received signals. Computer simulations have shown that the proposed sensing methods are robust to frequency offset, noise power uncertainty, time delay uncertainty, and frequency selectiveness of the channel.

How many RF chains are optimal for large-scale MIMO systems when circuit power is considered?

Multiple antennas at the transmitter and the receiver can increase the channel capacity significantly. However, this is at the expense of linearly increasing circuit power consumption due to the use of multiple radio frequency (RF) chains to support the antennas, which is quite significant for large-scale MIMO systems but is largely ignored in the literature. Hence, in this paper, we assess the performance of a point-to-point large-scale MIMO channel considering the overall power consumption on both the transmitter and the receiver, and study the optimal RF chain configurations to maximize the transmission rate under such a total power constraint. Both configurations with and without the channel state information (CSI) of the channel matrix are considered. While the former requires the design of optimal selection of RF chains, the latter only needs to determine the optimal number of RF chains. We find through numerical results that the gain of configuration with CSI over that without CSI diminishes as the dimension of the channel gets large. We also provide guidelines for selecting the optimal number RF chains for configuration without CSI. In particular, for MISO case, we find that it is near-optimal to choose half of the maximum number of transmit RF chains, the circuits of which are affordable to be powered on by the total power budget.

On the Design of Optimal Training Sequence for Bi-Directional Relay Networks

In this letter, the problem of training sequence design in a bi-directional relay network is investigated. The two nodes, Tx1 and Tx2, communicate with each other via a relay (RL). The RL receives the signals that are transmitted simultaneously from both nodes and then broadcasts the superposition back to the two nodes. Single carrier cyclic prefix (SCCP) is deployed in the transmission from the two nodes and the RL as well for combating intersymbol interference (ISI). We are interested in the channel estimation problem in this scenario. We propose a design of training sequences from two nodes to minimize the mean-square error (MSE) of the channel estimation according to zero forcing (ZF) criterion. We do not separate the links between each node to RL but incorporate them into the composite channels. Simulation results show that our design performance suffers only 1-dB loss in terms of bit-error rate (BER) as compared to the case with perfect channel information.

Joint Channel Estimation and Data Detection for MIMO-OFDM Two-Way Relay Networks

In this paper, we consider multi-input multi-output (MIMO) two-way relay networks with orthogonal frequency-division multiplexing (OFDM) modulation scheme. The networks consist of two end users exchanging their information via a relay. One information exchange between two end users is divided into two Phases. In Phase 1, two users send their information to the relay. The relay then amplifies and broadcasts its received signals to the two users in Phase 2, i.e., the relay works in the amplify-and-forward (AF) mode. By doing so, two-way relay networks require half as many time slots to accomplish one information exchange as the traditional one-way relay networks. We propose at each end user an iterative algorithm to jointly estimate the channel information and detect the data transmitted from the other user. The channel information consists of the \textit{composite} channels which are the combinations of individual channels in the networks and noise covariance matrices resulting from the AF working mode of the networks. We apply the Expectation Conditional Maximization (ECM) algorithm to estimate the necessary channel information for detection. Simulation results show that the performance of our proposed iterative algorithm is close to that given by the perfect channel information.

A joint channel estimation and data detection receiver for multiuser MIMO IFDMA systems

In this paper, we are interested in the problem of joint channel estimation and data detection for multi-input multi-output (MIMO) interleaved frequency division multiple access (IFDMA) systems. Although IFDMA is free from the multiple access interference (MAI), it suffers from intersymbol interference (ISI). MIMO-IFDMA system suffers from both ISI and multi-stream interference (MSI). The block iterative generalized decision feedback equalizer (BI-GDFE) is an iterative and effective interference cancellation scheme which could provide near maximum likelihood (ML) performance with very low complexity. However, BI-GDFE needs the channel state information (CSI). In this paper, we utilize the soft estimates of the transmitted symbols provided by the BI-GDFE to estimate the channel via an expectation maximization (EM)-based algorithm. By doing so, a joint channel estimation and data detection receiver is developed. To evaluate the performance of the proposed channel estimation algorithm, we derive the Cramer-Rao lower bound (CRLB). Computer simulations show that the bit error rate (BER) performance of the proposed joint channel estimation and signal detection receiver can reach the performance of the BI-GDFE with perfect CSI.

Iterative Receiver for Multi-Input Multi-Output (MIMO) Two-Way Wireless Relay Systems

In this paper, we propose an iterative receiver for a multi-input multi-output (MIMO) two-way wireless relay system. The two-way relay system requires two time slots to accomplish one information exchange. Compared to the traditional one-way relay system, which demands four time slots to complete one information exchange, our system only uses half as many time slots. Our proposed iterative receiver consists of two loops. One is the minimum mean square error (MMSE)-based iterative soft interference cancelation (SIC) algorithm which is extended from the algorithm in [1] to cover correlated noise environment incurred from the nature of signalling. The other is the Expectation Conditional Maximization (ECM)-based estimation algorithm. The MMSE-SIC algorithm provides the soft information of the transmitted signals and this information is transferred to the ECM-based estimation algorithm to update the channel information. Simulation results show that the performance of the iterative receiver proposed here is close to that given by perfect channel information with small number of iterations.

Joint Channel Information Estimation and Data Detection for OFDM-Based Systems under Unknown Interference

In this paper we consider an orthogonal frequency-division multiplexing (OFDM)-based system under unknown narrow-band interference (NBI). We propose an iterative receiver to jointly estimate the channel information, which consists of channel coefficients and noise-plus-interference variances of each sub-carrier, and detect the transmitted signals. The simulation results show that our proposed receiver provides an extremely close bit-error-rate (BER) to that of the case where perfect channel information is available at the receiver. Besides, Cramer-Rao lower bound (CRLB) of interested parameters are also derived. The mean-square-error (MSE) of the estimated parameters given by our proposed algorithm reaches the CRLB.

Channel estimation and training design for MIMO-OFDM two-way relay systems

In this paper we consider two-way relaying systems consisting of two end users, N1 and N2, which exchange their information with the help of a relay, R. The three terminals are equipped with multiple antennas. The information exchange process between N1 and N2 are divided into two phases. Both N1 and N2 send their information to R at the first phase. The relay will amplify its received signals and transmit the resultant signals back to N1 and N2 at the second phase. The end users estimate the necessary channel coefficients to decode the signals; therefore, the processing burden on the relay is reduced. In this paper, a least-squared based estimation method is proposed to estimate those coefficients. Optimal pilot vectors are also proposed to minimize the mean-square-error (MSE) of channel estimation. Furthermore, the proposed training signals has the peak-to-average-power ratio (PAPR) of 1.