In this paper we discuss the pros and cons of non-precoded and beamformed Channel State Information (CSI) downlink (DL) reference signal schemes for Frequency Division Duplex (FDD) massive MIMO systems. We propose a hybrid mode of operation between the two, factorizing the antenna ports into a set of slow and fast ports with different update rates using a codebook with factorized precoders designed for a dual-polarized Uniform Planar Array (UPA). We show with simulation results that the proposed hybrid CSI feedback scheme can increase both average and 5th percentile spectral efficiency compared to the non-precoded CSI reference signal scheme. Compared to the beamformed CSI reference signal scheme, it can significantly increase the 5th percentile spectral efficiency with only a small loss in average spectral efficiency.

In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

The problem of designing simple and energy-efficient nonlinear distributed source-channel codes is considered. By demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. Based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. It is shown that the code has a substantial gain compared to a linear source-channel code.

The problem of designing simple and energy-efficient sensor nodes in a wireless sensor network is considered from a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers for orthogonal channels is proposed and evaluated. In particular the cases of the binary symmetric channel as well as the additive white Gaussian noise channel are studied. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non- ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.

The problem of designing simple and energy efficient nonlinear distributed source-channel codes is considered. By demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. Based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. One of the main advantages of the proposed scheme is that it is implementable for many sources, contrary to most existing nonlinear distributed source-channel coding systems.

In many communication applications one is interested in transmitting a time-discrete analog-valued (i.e. continuous alphabet) source over a time-discrete analog channel. We study this problem in the case of bandwidth expansion, in the sense that one source sample, X, is transmitted over N-orthogonal channels. An analog source-channel code based on orthogonal polynomials is proposed and analyzed. The code can be generated using a Gram-Schmidt procedure, to fit virtually any source distribution.

Sensor nodes in wireless sensor networks should preferable be both cheap and energy efficient. To cope with these requirements an algorithm for designing distributed scalar quantizers optimized for noisy channels is proposed and evaluated. Applying the algorithm results in locally optimal systems. It is demonstrated that the correlation between the sources can be used to reduce the quantization distortion when the channel is close to error-free. If, on the other hand, there are a lot of disturbances on the channel the correlation can be used to introduce protection against channel errors.

The problem of designing simple and energy efficient sensor nodes for a wireless sensor network is considered in a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers optimized for orthogonal additive white Gaussian noise channels is proposed and evaluated. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non-ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.

Summary form only given. This paper proposes a technique that would address the problem of designing multiple description source codes for J channels. Instead of implementing optimal combining we propose to simply average the decoded output of the individual channels and then adjust the length of the resulting vector based on a theoretical analysis valid for permutation codes. The choice of using permutation codes comes from the fact that their low complexity makes high dimensional vector quantization possible, i.e. large TV's, and our simulations have indicated that the random generation of rotation matrices works well when the dimension is high. For low dimensions, different outcomes of the generated rotation matrices seem to yield quite different performance, meaning that the random design may not be as appropriate for this case. Hence, any vector quantization scheme able to perform quantization in high dimensions could potentially replace the permutation coding in the proposed scheme. We also extend the method to use a fraction rho of the rate R to quantize the quantization error of the decoded data, when all descriptors are received, rather then using the whole rate to quantize the individual descriptors. This improves the performance when receiving all the descriptors at the cost of a decreased performance when some of the descriptors are lost. Varying rho therefore produces different operation points in the tradeoff between side and central distortion. The main advantages of the proposed method are its relatively low complexity and its ability to easily implement any number of descriptions

We introduce a new method for multiple description quantization (MDQ), based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. The sorting-based approach has a similar performance to multiple description scalar quantization and a flexible structure, providing straightforward implementation of multidimensional MDQ

Designing a communication system using joint source-channel coding in general makes it possible to achieve a better performance than when the source and channel codes are designed separately, especially under strict delay-constraints. The majority of work done in joint source-channel coding uses a discrete channel model, corresponding to an analog channel in conjunction with a hard decision modulation scheme. The performance of such a system can however be improved by using soft decision modulation. The main cost is a higher decoding complexity. An alternative is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this paper we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. We achieve a performance close to that of a system using unquantized soft information

The objective with multiple description coding (MDC) is to code one source of data into multiple bitstreams. The coding is done in such a way that multiple levels of quality is achieved. This means that even if one or a few of the bitstreams are lost, the received bits should make it possible to get an approximated version of the original data. One way to do this is to use pairwise correlating transforms which introduce correlation between the bitstreams. This correlation can be used in order to get an estimate of a lost stream. In this paper a new approach for MDC using pairwise correlating transforms is presented. In this approach, contrary to previous work, quantization of the source data is performed after the data has been transformed. This makes it possible to improve the shape of the quantization cells and to tailor these to the employed transform. We demonstrate that this offers a substantial performance gain compared with previous approaches to MDC using pairwise correlating transforms.