Mario H. Castañeda

On Downlink Intercell Interference in a Cellular System

The main difference between a non-cellular and a cellular communication system is the intercell interference. Therefore, modelling the intercell interference and analyzing its effects is of particular interest for cellular communication systems. On the one hand, the intercell interference can be modeled by system level simulations. On the other hand, it is also meaningful to assess the intercell interference without performing exhaustive simulations which nonetheless at the same time still capture the major effects that determine the interference. To this end, we consider the intercell interference as a random variable composed of many other random variables. In this paper, we present a semi-analytical method to analyze the downlink intercell interference in a cellular system. Through such an approach a quick and reliable assessment of the downlink intercell interference can be obtained. Our focus is on the methodology and we consider basic access schemes: WCDMA, TDMA, FDMA, and random OFDMA. However, our model can be extended to include other access schemes and other features.

Transceiver design in multiuser MISO systems with imperfect transmit CSI

Multiple transmit antennas can be employed in a downlink channel to serve multiple users under the assumption of perfect channel state information (CSI) of all users at the base station. In practice, however, the channel knowledge available at the base station is not perfect. For instance, in case of a time division duplex (TDD) system, the transmit CSI is the estimated uplink channel. On the other hand, in a frequency division duplex (FDD) system, feedback from the users is required to obtain transmit CSI. In this case, we assume the transmit CSI consists of a quantized version of the normalized estimated channel vector and the statistics of the channel norm of each user. In this work we consider the transceiver design of the downlink of a multiuser MISO system based on the minimum mean square error (MMSE) criterion with imperfect CSI as discussed above.

Optimal resource allocation in the downlink/uplink of single-user MISO/SIMO FDD systems with limited feedback

Consider a single-user FDD system with multiple antennas at the BS and a single antenna at the user. We assume that the BS performs coherent transmission and reception in the downlink and uplink, respectively. To this end, downlink resources are employed to estimate the downlink channel at the user, while uplink resources are used to estimate the uplink channel at the BS and to relay a quantized version of the downlink channel estimate back to the BS. The transmit CSI for the downlink is estimated, quantized, outdated and affected by feedback errors, while the receive CSI for the uplink is just estimated. Besides the well-known tradeoff between the training and data payload in a one-way system, in a two-way system like a downlink/uplink FDD system, there is an additional tradeoff between both links due to the feedback. We consider the resource allocation of the downlink and uplink jointly taking into account the feedback and imperfect CSI. As a figure of merit we employ the sum of the downlink and uplink capacities.

Transceiver Design in Multiuser MISO Systems with Limited Feedback

It is well known that the availability of M transmit antennas at the base station enables to serve K single-antenna users (K ≤ M), under the assumption of perfect channel state information (CSI) at the base station. However, in practical frequency division duplex (FDD) systems, the channel knowledge available at the base station is not perfect since it is obtained through a limited feedback in the uplink consisting of B bits per user. Part of the B feedback bits can be employed to quantize the channel direction information (CDI), while the rest can be used to quantize the channel magnitude information (CMI) of each user. In this work we address the transceiver design of a multiuser MISO system based on the minimum mean square error criterion and with transmit CSI obtained through a limited feedback composed of quantized CDI and CMI. We treat the case of K ≤ M users with different average channel gains and based on our findings we conclude that for uncorrelated channels it is optimum to employ all the B bits for quantizing the CDI under the assumption that the base station knows the statistics of the CMI of the users.

How much training is needed for interference coordination in cellular networks

Cooperative techniques for cellular networks promise very high data rates, but require additional and precise knowledge of the serving and interference channels. We show, how the pilot symbols required for achieving this information affect the possible data rates. The measurements of the channels are suffering from pilot contamination, due to the measurements in adjacent cells. On the one hand, with a too short pilot length, cooperation is not possible and the channels are learned too poorly, degrading the possible data rates. On the other hand, a too long pilot length reduces the efficiency of the system, leaving no resources for the data transmission. In addition, the channel measurements are outdated before they can be applied. With an upper bound to the sum rate of a system with interference coordination and a sub-optimal pilot allocation strategy, we discuss the pilot length trade-off.

Estimation of rank deficient covariance matrices with Kronecker structure

Given a set of observations, the estimation of covariance matrices is required in the analysis of many applications. To this end, any know structure of the covariance matrix can be taken into account. For instance, in case of separable processes, the covariance matrix is given by the Kronecker product of two factor matrices. Assuming the covariance matrix is full rank, the maximum likelihood (ML) estimate in this case leads to an iterative algorithm known as the flip-flop algorithm in the literature. In this work, we first generalize the flip-flop algorithm to the case when the covariance matrix is rank deficient, which happens to be the case in several situations. In addition, we propose a non-iterative estimation approach which incurs in a performance loss compared to the ML estimate, but at the expense of less complexity.

Information-Preserving Transformations for Signal Parameter Estimation

The problem of parameter estimation from large noisy data is considered. If the observation size N is large, the calculation of efficient estimators is computationally expensive. Further, memory can be a limiting factor in technical systems where data is stored for later processing. Here we follow the idea of reducing the size of the observation by projecting the data onto a subspace of smaller dimension M ≪ N, but with the highest possible informative value regarding the estimation problem. Under the assumption that a prior distribution of the parameter is available and the output size is fixed to M, we derive a characterization of the Pareto-optimal set of linear transformations by using a weighted form of the Bayesian Cramér-Rao lower bound (BCRLB) which stands in relation to the expected value of the Fisher information measure. Satellite-based positioning is discussed as a possible application. Here N must be chosen large in order to compensate for low signal-to-noise ratios (SNR). For different values of M, we visualize the information-loss and show by simulation of the MAP estimator the potential accuracy when operating on the reduced data.

Multi-satellite time-delay estimation for reliable high-resolution GNSS receivers

Reliable estimation of position in time and space has become a key necessity in several technical applications like mobile navigation, precision farming or network synchronization. While the increasing amount of operating Global Navigation Satellite Systems (GNSS) offers diverse possibilities to receive GNSS signals worldwide and to determine position accurately, inter- and intrasystem interference has been identified as a problem of growing importance. The performance of receivers which track all in-view satellites individually degrades if mutual interference is not taken into account. Therefore, we consider the problem of joint signal parameter estimation. For scenarios where the signals of different satellites superimpose at the receiver a joint maximum likelihood estimator for all relevant signal parameters is derived. In order to keep the computation of the related likelihood function, and the determination of its maximum, feasible for a low-complexity receiver, an iterative Expectation-Maximization (EM) algorithm is applied. Simulations for different scenarios show that this approach is efficient in the estimation theoretic sense, and robust against interference that is caused by signals with known structure.

Multiuser diversity with limited feedback

Multiple antennas at the base station can be employed to serve multiple single-antenna users in the downlink of a system. When the number of active users K is larger than the number of transmit antennas M, the base station can exploit multiuser diversity by cleverly scheduling a set of M users. For this purpose, the base station needs to know the downlink channel state information of all the users in the system. In a frequency division duplex (FDD) system, this is attained with limited feedback from the users. Albeit most of the literature assumes a per-user limited feedback, a system limited feedback is actually more appropriate. Given a constraint on the total amount of airtime available for feedback, in this work we are interested in determining the optimum number of users that should feed back. We observe a tradeoff between the attainable degree of multiuser diversity and the feedback quality. The users are scheduled based on minimizing the sum of the mean square error (MSE) for the selected set of users.

FDD overhead optimization for a multiuser two-way system with imperfect CSI

In this paper, we find the optimum lengths of TUL, TDL and B i.e. the uplink (UL) and downlink (DL) pilots and feedback bits respectively maximizing the sum of the lower bounds of the UL and the DL rates. With the aid of TUL symbols, the Base Station (BS) estimates the UL channel of each user. TDL symbols are means of estimating the downlink channel, which after being normalized and quantized with B bits, is made available at the BS. Estimated Channel State Information (CSI) and quantized Channel Direction Information (CDI), which may be received erroneously, are thus available at the BS for computing the receive and the transmit Zero-forcing (ZF) filters, respectively. The use of ZF enables the derivation of analytical DL and UL mean squared error (MSE) expressions that give a lower bound on the DL and UL rates, respectively, needed for our optimization.