Ricardo Blasco-Serrano

Polar Codes for Cooperative Relaying

We consider the symmetric discrete memoryless relay channel with orthogonal receiver components and show that polar codes are suitable for decode-and-forward and compress-and-forward relaying. In the first case we prove that polar codes are capacity achieving for the physically degraded relay channel; for stochastically degraded relay channels our construction provides an achievable rate. In the second case we construct sequences of polar codes that achieve the compress-and-forward rate by nesting polar codes for source compression into polar codes for channel coding. In both cases our constructions inherit most of the properties of polar codes. In particular, the encoding and decoding algorithms and the bound on the block error probability O(2-Nβ) which holds for any 0<;β<;1/2.

Multi-antenna transmission for underlay and overlay cognitive radio with explicit message-learning phase

We consider the coexistence of a multiple-input multiple-output secondary system with a multiple-input single-output primary link with different degrees of coordination between the systems. First, for the uncoordinated underlay cognitive radio scenario, we fully characterize the optimal parameters that maximize the secondary rate subject to a primary rate constraint for a transmission strategy that combines rate splitting and interference cancellation. Second, we establish a model for the coordinated overlay cognitive radio scenario that consists of a message-learning phase followed by a communication phase. We then propose a transmission strategy that combines techniques for cooperative communication and for the classical cognitive radio channel. We optimize our system to maximize the rate of communication for the secondary users under a primary-user rate constraint and find efficient algorithms to compute the optimal system parameters. Finally, we compare both cognitive radio strategies to assess their relative merits and to evaluate the effect of the message-learning phase. We observe that for closely located transmitters, the overlay strategy outperforms the underlay strategy. In this situation, learning the primary message is very beneficial for the secondary systems, especially if they are interference-limited rather than power-limited. The situation is reversed when the distance between the transmitters is large. In either case, we observe that there is room for significant improvement if the transmitter implements both strategies and decides adaptively which one to use according to the channel conditions. We conclude our work with a discussion on the extension to the coexistence with multiple-input multiple-output primaries.

Optimal beamforming in MISO cognitive channels with degraded message sets

In this paper we consider the coexistence of a single-input single-output (SISO) primary link with a multiple-input single-output (MISO) secondary user pair that has non-causal knowledge of the primary message. We study an achievable rate region that exploits this knowledge by combining selfless relaying to maintain the rate supported by the primary link with dirty paper coding to pre-cancel the interference at the secondary receiver. We find the optimal choice of power allocation between these operating modes at the secondary transmitter as well as the optimal beamforming vectors. Moreover, we address the robustness of the solution to uncertainties in the channel knowledge. Finally, we show by numerical evaluation the gains obtained due to the additional knowledge of the primary message.

Polar codes for compress-and-forward in binary relay channels

We construct polar codes for binary relay channels with orthogonal receiver components. We show that polar codes achieve the cut-set bound when the channels are symmetric and the relay-destination link supports compress-and-forward relaying based on Slepian-Wolf coding. More generally, we show that a particular version of the compress-and-forward rate is achievable using polar codes for Wyner-Ziv coding. In both cases the block error probability can be bounded as O(2−Nβ) for 0 &#60; β &#60; 1over2 and sufficiently large block length N.

Compress-and-Forward Relaying Based on Symbol-Wise Joint Source-Channel Coding

We propose a new compress-and-forward implementation for the relay channel based on joint source-channel coding techniques. The relay performs scalar quantization of its observation in combination with a redundant index mapping. Our system utilizes the correlation between the quantized signal and the direct-link observation of the transmitted symbols as redundancy for error protection on the relay-to-destination link. In order to fully exploit this correlation the destination requires iterative decoding to recover the quantized observation sent by the relay. Once regenerated, this quantized signal is optimally combined with the direct-link observation to decode the message conveyed by the source. By quantizing the observed signal itself rather than a measure on the reliability of the information bits (e.g. a posteriori probabilities from a decoder), and by using digital communication methods on the relay- to-destination link our system yields superior performance to that of amplify-and-forward, decode- and-forward and previous implementations of compress-and-forward based on soft decoding.

Bandwidth efficient compress-and-forward relaying based on joint source-channel coding

We propose a new code design for compress-and-forward relaying over bandlimited relay-to-destination channels. The main contribution of this paper is a code design based on joint (source-channel) coding and modulation that uses the correlation between the observations at the relay and the destination as protection against channel errors. This allows for relay nodes with reduced complexity, shifting most of the processing requirements to the destination node. Moreover, by using scalar quantizers with an entropy constraint our system provides remarkable performance in channel conditions where neither amplify-and-forward nor compress-and-forward efficiently exploit the presence of a relay node. Simulation results confirm the benefits of our proposed system.

Linear precoding in MISO cognitive channels with degraded message sets

In this work, the coexistence of a single-input single-output (SISO) primary link and a multiple-input single-output (MISO) secondary link is considered, where the secondary transmitter has non-causal knowledge of primary message and transmits both primary and secondary messages. The optimal beamforming vectors and power allocation at the secondary transmitter are derived to maximize the achievable secondary rate while satisfying the primary rate requirement. Moreover, the optimal linear precoding is obtained by semidefinite relaxation and rank-one decomposition, when the number of antennas at the secondary transmitter is larger than two. Finally, the performance of the proposed scheme is evaluated through numerical simulations.

Linear precoding in MISO cognitive channels with causal primary message

The coexistence of a single-input single-output (SISO) primary link and a multiple-input single-output (MISO) secondary link is considered in an extended cognitive radio channel setup, where the secondary transmitter has to obtain (“learn”) the primary message in a first phase rather than having non-causal knowledge of it. An achievable rate region is derived that combines decode-and-forward relaying with linear precoding in the second phase. The optimal transmission strategy is found that maximizes the secondary rate with the primary rate requirement. The performance of the proposed strategy is compared, where dirty-paper coding (DPC) is deployed in the second phase, in terms of average secondary rate. The performance degradation is negligible at certain SNR and primary link load, and the implementation is of lower complexity. The comparison with the underlay strategy is also performed, where the secondary transmitter has no knowledge of the primary message.

An achievable measurement rate-MSE tradeoff in compressive sensing through partial support recovery

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example.

A Measurement Rate-MSE Tradeoff for Compressive Sensing Through Partial Support Recovery

We consider the problem of estimating sparse vectors from noisy linear measurements in the high dimensionality regime. For a fixed number k of nonzero entries, we study the fundamental relationship between two relevant quantities: the measurement rate, which characterizes the asymptotic behavior of the dimensions of the measurement matrix in terms of the ratio m/log n (with m being the number of measurements and n the dimension of the sparse vector), and the estimation mean square error. First, we use an information-theoretic approach to derive sufficient conditions on the measurement rate to reliably recover a part of the support set that represents a certain fraction of the total vector power. Second, we characterize the mean square error of an estimator that uses partial support set information. Using these two parts, we derive a tradeoff between the measurement rate and the mean-square error. This tradeoff is achievable using a two-step approach: first support set recovery, and then estimation of the active components. Finally, for both deterministic and random vectors, we perform a numerical evaluation to verify the advantages of the methods based on partial support set recovery.