Compressive Sensing Based Channel Feedback Protocols for Spatially-Correlated Massive Antenna Arrays

Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.Engineering and Applied Science

Transmission of Still Images Using Low-Complexity Analog Joint Source-Channel Coding

[Abstract] An analog joint source-channel coding (JSCC) system designed for the transmission of still images is proposed and its performance is compared to that of two digital alternatives which differ in the source encoding operation: Joint Photographic Experts Group (JPEG) and JPEG without entropy coding (JPEGw/oEC), respectively, both relying on an optimized channel encoder–modulator tandem. Apart from a visual comparison, the figures of merit considered in the assessment are the structural similarity (SSIM) index and the time required to transmit an image through additive white Gaussian noise (AWGN) and Rayleigh channels. This work shows that the proposed analog system exhibits a performance similar to that of the digital scheme based on JPEG compression with a noticeable better visual degradation to the human eye, a lower computational complexity, and a negligible delay. These results confirm the suitability of analog JSCC for the transmission of still images in scenarios with severe constraints on power consumption, computational capabilities, and for real-time applications. For these reasons the proposed system is a good candidate for surveillance systems, low-constrained devices, Internet of things (IoT) applications, etc.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Wireless interference networks with limited feedback

Wir betrachten das Problem der Akquirierung von Kanalzustandsinformationen an den Sendern von drahtlosen Netzwerken und entwickeln Feedbackverfahren und Sendestrategien für verschiedene Netzwerk Architekturen. Die entwickelten Verfahren werden analysiert und die Skalierung der Performance des Gesamtsystems anhand bestimmter Systemparameter bestimmt. Zuerst betrachten wir eine einzelne Zelle eines zellularen Systems und nehmen an, dass die Beamformingvektoren durch ein festes Codebuch vorgegeben sind. Wir entwickeln und analysieren ein neues Feedbackverfahren, dass Flexibilität und Robustheit vereint und dadurch effiziente und zuverlässige Kommunikation mit den Empfängern ermöglicht. Eine Analyse des Verfahrens zeigt, dass die Skalierung des Ratenverlustes durch quantisierte Kanalzustandsinformation besser ist als bei vergleichbaren Verfahren. Für das Feedbackverfahren wird ein spezieller Algorithmus entwickelt der es ermöglicht Codebücher für verschiedene Kanalmodelle zu generieren und zu optimieren. Die analytischen Ergebnisse werden durch Simulationen validiert und bestätigen einen Gewinn gegenüber vergleichbaren Verfahren. Anschließend betrachten wir zellulare Systeme mit mehreren Zellen. Wir charakterisieren die Freiheitsgrade (degrees of freedom) unter verschiedenen Annahmen über das Kanalmodell. Des weiteren entwickeln wir verschiedene Algorithmen, die die optimalen Freiheitsgrade erreichen können. Anschließend wird ein Feedbackverfahren entwickelt, dass den Feedbackaufwand für die entwickelten Algorithmen signifikant reduziert. Wir analysieren eine breite Klasse von zellularen Systemen die beliebige koordinierte Sendestrategien verwenden. Für diese Klasse von Systemen leiten wir die Skalierung des Ratenverlustes relativ zum Feedbackaufwand her. Abschließend zeigen wir, wie die analytischen Ergebnisse auf das entwickelte Feedbackverfahren angewendet werden können. Im letzten Kapitel entwickeln wir ein Framework, dass das Potenzial von Compressed Sensing nutzt um den Messaufwand und Feedbackaufwand in zellularen Systemen mit vielen Teilnehmern signifikant zu reduzieren. Das Framework ermöglicht es die Datenraten der Nutzer innerhalb gegebener Fehlerschranken zu schätzen. Grundlage ist neben Compressed Sensing ein neues Messverfahren, dass die Überlagerung von Signalen im Kanal nutzt, um zufällige nicht adaptive Messungen der Kanalkoeffizienten am Empfänger zu ermöglichen. Diese Messungen werden zu einer zentralen Steuereinheit übertragen und dort dekodiert. Wir analysieren die Genauigkeit der Rekonstruktion für einen linearen und einen nicht-linearen Dekodierer und leiten die Skalierung mit der Anzahl der Messungen her. Abschließend zeigen wir, wie der entwickelte Ansatz in zellularen Systemen angewendet werden kann.We consider the problem of acquiring accurate channel state information at the transmitters of a wireless network. We develop different feedback and transmit strategies for different network architectures and analyze their performance. First, we consider a single cell of cellular system and assume that the beamforming vectors are given by a fixed transmit codebook. We develop and analyze a new feedback and transmit strategy which combines flexibility and robustness needed for efficient and reliable communication. We prove that it has better scaling properties compared to classical results on the limited feedback problem in the broadcast channel and that this benefit improves with an increasing number of transmit antennas. We show how feedback codebooks can be designed for different propagation environments. Link level and system level simulations sustain the analytic results showing performance gains of up to 50 % or 70 % compared to zeroforcing when using multiple antennas at the base station and multiple antennas or a single antenna at the terminals, respectively. We characterize the degrees of freedom (i.e. the multiplexing gain) of multi-cellular systems under different assumptions on the channel model and for different system setups. We propose different algorithms that possibly achieve the optimal degrees of freedom. The first algorithm aims on aligning the interference at each receiver in a subspace of the available receive space. Our second algorithm aims on directly maximizing the signal-to-interference-plus-noise ratio (SINR) of all receivers. By allowing symbol extensions over time or frequency and including a user selection we are able to achieve the alignment of interference for many system setups and exploit multi-user diversity. For coordinated transmit strategies we find the scaling of the performance loss with the feedback load. A distributed interference alignment algorithm is introduced. The algorithm makes efficient use of quantized channel state information and significantly reduces the feedback overhead. We develop a framework that we call compressive rate estimation. To this end, we assume that the composite channel gain matrix (i.e. the matrix of all channel gains between all network nodes) is compressible which means it can be approximated by a sparse or low rank representation. We develop a sensing protocol that exploits the superposition principle of the wireless channel and enables the receiving nodes to obtain non-adaptive random measurements of columns of the composite channel matrix. The random measurements are fed back to a central controller who decodes the composite channel gain matrix (or parts of it) and estimates individual user rates. We analyze the rate loss for a linear and a non-linear decoder and find the scaling laws according to the number of non-adaptive measurements

Feedback Subsampling in Temporally-Correlated Slowly-Fading Channels using Quantized CSI

This paper studies the problem of feedback subsampling in temporally-correlated wireless networks utilizing quantized channel state information (CSI). Under both peak and average power constraints, the system data transmission efficiency is studied in two scenarios. First, we focus on the case where the codewords span one fading block. In the second scenario, the throughput is determined for piecewise slowly-fading channels where the codewords are so long that a finite number of correlated gain realizations are experienced during each codeword transmission. Considering different temporal correlation conditions in both scenarios, substantial throughput increment is observed with feedback rates well below 1 bit per slot

Downlink Achievable Rate Analysis for FDD Massive MIMO Systems

Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods