Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels

Η παρούσα διδακτορική διατριβή επικεντρώνεται στη δυνατότητα που έχουν τα συστήματα ΜΙΜΟ να επιτυγχάνουν υψηλότερη χωρητικότητα από ένα συμβατικά συστήματα SISO. Όμως η χωρητικότητα που επιτυγχάνουν τα συστήματα MIMO σχετίζεται με τη γνώση/πληροφορία την οποία έχουν ο πομπός και ο δέκτης για το κανάλι. Θεωρώντας εργοδικό κανάλι με μιγαδική κανονική κατανομή, στο οποίο ο δέκτης έχει πλήρη γνώση του καναλιού και ο πομπός γνωρίζει μόνο την κατανομή αυτού, επιδιώκεται η μεγιστοποίηση της μέσης αμοιβαία πληροφορίας. Στην περίπτωση εκπομπής beamforming, τη μέγιστη μέση αμοιβαία πληροφορία μεταξύ πομπού-δέκτη επιτυγχάνει ο «βέλτιστος beamformer» και η επιτυγχανόμενη μέγιστη τιμή αναφέρεται ως «εργοδική beamforming χωρητικότητα». Στα πλαίσια της παρούσας διατριβής μελετάται ο τρόπος υπολογισμού του «βέλτιστου beamformer» για την περίπτωση χωρικώς συσχετισμένων καναλιών ΜΙΜΟ με κατανομή Rice και αποδεικνύεται ότι, ο υπόψη υπολογισμός προκύπτει από την επίλυση ενός απλού, μονοδιάστατου (1-Δ) προβλήματος βελτιστοποίησης. Η ανωτέρω απόδειξη βασίζεται σε γεωμετρικές ιδιότητες, κατάλληλους μετασχηματισμούς βάσης και στις συνθήκες Karush-Kuhn-Tucker. Στη συνέχεια υλοποιήθηκε πληθώρα προσομοιώσεων η οποία ανέδειξε την χαμηλή πολυπλοκότητα της προτεινόμενης μονοδιάστατης μεθόδου, καθώς και την υψηλή απόδοση του «βέλτιστου beamformer» ως πολιτική εκπομπής. Επιπρόσθετα, εφαρμόστηκε το μοντέλο προσομοίωσης καναλιών ΜΙΜΟ της 3GPP, με σκοπό την περαιτέρω μελέτη της απόδοσης του «βέλτιστου beamformer» σε πρακτικά λειτουργικά σενάρια. Τα αποτελέσματα επιβεβαίωσαν εκ νέου την υψηλή απόδοση του «βέλτιστου beamformer» και τη σημασία της προτεινόμενης μεθόδου υπολογισμού του.In this doctoral thesis, the focus is on the capability of MIMO systems to achieve much higher capacity than SISO systems. However, the capacity achieved by MIMO systems is closely related to the “channel knowledge” model which is assumed at both ends of the MIMO link. Considering the case of MIMO complex Gaussian ergodic channels, where the receiver has perfect Channel State Information (CSI) whereas the transmitter has Channel Distribution Information (CDIT), we aim at the maximization of the average mutual information between them. For the case of beamforming transmission, the maximum average mutual information is achieved by the “optimum beamformer” and is referred to as “ergodic beamforming capacity”. In this work, the calculation of the optimum beamformer is studied for spatially correlated MIMO Rician fading channels and it is proven that this calculation is achieved by solving a simple 1-D optimization problem. The proof was based on geometrical properties, basis transformations and the Karush-Kuhn-Tucker (KKT) conditions. Extended simulations were performed which demonstrated the low computational complexity of the proposed method as well as the high performance of the optimum beamformer. Additionally the 3GPP MIMO channel model was employed in order to study further the performance of the optimum beamformer in practical operational scenarios. The results confirmed the high performance of the optimum beamformer and the significance of the proposed solutions

Asymptotic Mutual Information Statistics of Separately-Correlated Rician Fading MIMO Channels

Precise characterization of the mutual information of MIMO systems is required to assess the throughput of wireless communication channels in the presence of Rician fading and spatial correlation. Here, we present an asymptotic approach allowing to approximate the distribution of the mutual information as a Gaussian distribution in order to provide both the average achievable rate and the outage probability. More precisely, the mean and variance of the mutual information of the separatelycorrelated Rician fading MIMO channel are derived when the number of transmit and receive antennas grows asymptotically large and their ratio approaches a finite constant. The derivation is based on the replica method, an asymptotic technique widely used in theoretical physics and, more recently, in the performance analysis of communication (CDMA and MIMO) systems. The replica method allows to analyze very difficult system cases in a comparatively simple way though some authors pointed out that its assumptions are not always rigorous. Being aware of this, we underline the key assumptions made in this setting, quite similar to the assumptions made in the technical literature using the replica method in their asymptotic analyses. As far as concerns the convergence of the mutual information to the Gaussian distribution, it is shown that it holds under some mild technical conditions, which are tantamount to assuming that the spatial correlation structure has no asymptotically dominant eigenmodes. The accuracy of the asymptotic approach is assessed by providing a sizeable number of numerical results. It is shown that the approximation is very accurate in a wide variety of system settings even when the number of transmit and receive antennas is as small as a few units.Comment: - submitted to the IEEE Transactions on Information Theory on Nov. 19, 2006 - revised and submitted to the IEEE Transactions on Information Theory on Dec. 19, 200

Statistical Eigenmode Transmission over Jointly-Correlated MIMO Channels

We investigate MIMO eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly-correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed low-complexity transmitter optimization approach.Comment: 32 pages, 6 figures, to appear in IEEE Transactions on Information Theor

Intelligent Reflecting Surface Enhanced Wireless Network: Two-timescale Beamforming Optimization

Intelligent reflecting surface (IRS) has drawn a lot of attention recently as a promising new solution to achieve high spectral and energy efficiency for future wireless networks. By utilizing massive low-cost passive reflecting elements, the wireless propagation environment becomes controllable and thus can be made favorable for improving the communication performance. Prior works on IRS mainly rely on the instantaneous channel state information (I-CSI), which, however, is practically difficult to obtain for IRS-associated links due to its passive operation and large number of elements. To overcome this difficulty, we propose in this paper a new two-timescale (TTS) transmission protocol to maximize the achievable average sum-rate for an IRS-aided multiuser system under the general correlated Rician channel model. Specifically, the passive IRS phase-shifts are first optimized based on the statistical CSI (S-CSI) of all links, which varies much slowly as compared to their I-CSI, while the transmit beamforming/precoding vectors at the access point (AP) are then designed to cater to the I-CSI of the users' effective channels with the optimized IRS phase-shifts, thus significantly reducing the channel training overhead and passive beamforming complexity over the existing schemes based on the I-CSI of all channels. For the single-user case, a novel penalty dual decomposition (PDD)-based algorithm is proposed, where the IRS phase-shifts are updated in parallel to reduce the computational time. For the multiuser case, we propose a general TTS optimization algorithm by constructing a quadratic surrogate of the objective function, which cannot be explicitly expressed in closed-form. Simulation results are presented to validate the effectiveness of our proposed algorithms and evaluate the impact of S-CSI and channel correlation on the system performance.Comment: 15 pages, 12 figures, accepted for publication in IEEE Transactions on Wireless Communication

Hardware-Impaired Rician-Faded Cell-Free Massive MIMO Systems With Channel Aging

We study the impact of channel aging on the uplink of a cell-free (CF) massive multiple-input multiple-output (mMIMO) system by considering i) spatially-correlated Rician-faded channels; ii) hardware impairments at the access points and user equipments (UEs); and iii) two-layer large-scale fading decoding (LSFD). We first derive a closed-form spectral efficiency (SE) expression for this system, and later propose two novel optimization techniques to optimize the non-convex SE metric by exploiting the minorization-maximization (MM) method. The first one requires a numerical optimization solver, and has a high computation complexity. The second one with closed-form transmit power updates, has a trivial computation complexity. We numerically show that i) the two-layer LSFD scheme effectively mitigates the interference due to channel aging for both low- and high-velocity UEs; and ii) increasing the number of AP antennas does not mitigate the SE deterioration due to channel aging. We numerically characterize the optimal pilot length required to maximize the SE for various UE speeds. We also numerically show that the proposed closed-form MM optimization yields the same SE as that of the first technique, which requires numerical solver, and that too with a much reduced time-complexity.Comment: This work has been submitted to the IEEE Transactions on Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible, 32 pages, 14 figure

Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas $N$ and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than $N$, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the $N$ data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/one-or-multiple-stream