Iterative Near-Maximum-Likelihood Detection in Rank-Deficient Downlink SDMA Systems

Abstract—In this paper, a precoded and iteratively detected downlink multiuser system is proposed, which is capable of operating in rankdeficient scenarios, when the number of transmitters exceeds the number of receivers. The literature of uplink space division multiple access (SDMA) systems is rich, but at the time of writing there is a paucity of information on the employment of SDMA techniques in the downlink. Hence, we propose a novel precoded downlink SDMA (DL-SDMA) multiuser communication system, which invokes a low-complexity nearmaximum-likelihood sphere decoder and is particularly suitable for the aforementioned rank-deficient scenario. Powerful iterative decoding is carried out by exchanging extrinsic information between the precoder’s decoder and the outer channel decoder. Furthermore, we demonstrate with the aid of extrinsic information transfer charts that our proposed precoded DL-SDMA system has a better convergence behavior than its nonprecoded DL-SDMA counterpart. Quantitatively, the proposed system having a normalized system load of Ls = 1.333, i.e., 1.333 times higher effective throughput facilitated by having 1.333 times more DL-SDMA transmitters than receivers, exhibits a “turbo cliff” at an Eb/N0 of 5 dB and hence results in an infinitesimally low bit error rate (BER). By contrast, at Eb/N0 = 5 dB, the equivalent system dispensing with precoding exhibits a BER in excess of 10%. Index Terms—Iterative decoding, maximum likelihood detection, space division multiple access (SDMA) downlink, sphere decoding

Generalised MBER-based vector precoding design for multiuser transmission

We propose a generalized vector precoding (VP) design based on the minimum bit error rate (MBER) criterion for multiuser transmission in the downlink of a multiuser system, where the base station (BS) equipped with multiple transmitting antennas communicates with single-receiving-antenna mobile station (MS) receivers each having a modulo device. Given the knowledge of the channel state information and the current information symbol vector to be transmitted, our scheme directly generates the effective symbol vector based on the MBER criterion using the particle swarm optimization (PSO) algorithm. The proposed PSO-aided generalized MBER VP scheme is shown to outperform the powerful minimum mean-square-error (MMSE) VP and improved MMSE-VP benchmarks, particularly for rank-deficient systems, where the number of BS transmitting antennas is lower than the number of MSs supported

Scaling up MIMO: Opportunities and Challenges with Very Large Arrays

This paper surveys recent advances in the area of very large MIMO systems. With very large MIMO, we think of systems that use antenna arrays with an order of magnitude more elements than in systems being built today, say a hundred antennas or more. Very large MIMO entails an unprecedented number of antennas simultaneously serving a much smaller number of terminals. The disparity in number emerges as a desirable operating condition and a practical one as well. The number of terminals that can be simultaneously served is limited, not by the number of antennas, but rather by our inability to acquire channel-state information for an unlimited number of terminals. Larger numbers of terminals can always be accommodated by combining very large MIMO technology with conventional time- and frequency-division multiplexing via OFDM. Very large MIMO arrays is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation. The ultimate vision of very large MIMO systems is that the antenna array would consist of small active antenna units, plugged into an (optical) fieldbus.Comment: Accepted for publication in the IEEE Signal Processing Magazine, October 201

Multi-user MIMO wireless communications

Mehrantennensysteme sind auf Grund der erhöhten Bandbreiteneffizienz und Leistung eine Schlüsselkomponente von Mobilfunksystemen der Zukunft. Diese ermöglichen das gleichzeitige Senden von mehreren, räumlich getrennten Datenströmen zu verschiedenen Nutzern. Die zentrale Fragestellung in der Praxis ist, ob der ursprünglich vorausgesagte Kapazitätsgewinn in realistischen Szenarios erreicht wird und welche spezifischen Gewinne durch zusätzliche Antennen und das Ausnutzen von Kanalkenntnis am Sender und Empfänger erzielt werden, was andererseits einen Zuwachs an Overhead oder nötiger Rechenleistung bedeutet. In dieser Arbeit werden neue lineare und nicht-lineare MU-MIMO Precoding- Verfahren vorgestellt. Der verfolgte Ansatz zur Bestimmung der Precoding- Matrizen ist allgemein anwendbar und die entstandenen Algorithmen können zur Optimierung von verschiedenen Kriterien mit beliebig vielen Antennen an der Mobilstation eingesetzt werden. Das wurde durch die Berechnung der Precoding- Matrix in zwei Schritten erreicht. Im ersten Schritt wird die Überschneidung der Zeilenräume minimiert, die durch die effektiven Kanalmatrizen verschiedener Nutzer aufgespannt werden. Basierend auf mehreren parallelen Einzelnutzer-MIMO- Kanälen wird im zweiten Schritt die Systemperformanz bezüglich bestimmter Kriterien optimiert. Aus der gängigen Literatur ist bereits bekannt, dass für Nutzer mit nur einer Antenne das MMSE Kriterium beim precoding optimal aber nicht bei Nutzern mit mehreren Antennen. Deshalb werden in dieser Arbeit zwei neue Mehrnutzer MIMO Strategien vorgestellt, die vom MSE Kriterium abgeleitet sind, nämlich sukzessives MMSE und RBD. Bei der sukzessiven Verarbeitung mit einer entsprechenden Anpassung der Sendeleistungsverteilung kann die volle Diversität des Systems ausgeschöpft werden. Die Kapazität nähert sich dabei der maximalen Summenrate des Systems an. Bei gemeinsamer Verarbeitung der MIMO Kanäle wird unabhängig vom Grad der Mehrnutzerinterferenz die maximale Diversität erreicht. Die genannten Techniken setzen entweder eine aktuelle oder eine über einen längeren Zeitraum gemittelte Kanalkenntnis voraus. Aus diesem Grund müssen die Auswirkungen von Kanal-Schätzfehlern und Einflüsse des Transceiver Front-Ends auf die Verfahren näher untersucht werden. Für eine weitergehende Abschätzung der Mehrantennensysteme muss die Performanz des Gesamtsystems untersucht werden, da viele Einflüsse auf die räumliche Signalverarbeitung bei Betrachtung eines einzelnen Links nicht erkennbar sind. Es wurde gezeigt, dass mit MIMO Precoding Strategien ein Vielfaches der Datenrate eines Systems mit nur einer Antenne erzielt werden kann, während der Overhead durch Pilotsymbole und Steuersignale nur geringfügig zunimmt.Multiple-input, multiple-output (MIMO) systems are a key component of future wireless communication systems, because of their promising improvement in terms of performance and bandwidth efficiency. An important research topic is the study of multi-user (MU) MIMO systems. Such systems have the potential to combine the high throughput achievable with MIMO processing with the benefits of space division multiple access (SDMA). The main question from a practical standpoint is whether the initially predicted capacity gains can be obtained in more realistic scenarios and what specific gains result from adding more antennas and overhead or computational power to obtain channel state information (CSI) at the transceivers. In this thesis we introduce new linear and non-linear MU MIMO processing techniques. The approach used for the design of the precoding matrix is general and the resulting algorithms can address several optimization criteria with an arbitrary number of antennas at the user terminals (UTs). This is achieved by designing the precoding matrices in two steps. In the first step we minimize the overlap of the row spaces spanned by the effective channel matrices of different users. In the next step, we optimize the system performance with respect to the specific optimization criterion assuming a set of parallel single-user MIMO channels. As it was previously reported in the literature, minimum mean-squared-error (MMSE) processing is optimum for single-antenna UTs. However, MMSE suffers from a performance loss when users are equipped with more than one antenna. The two MU MIMO processing techniques that result from the two different MSE criteria that are proposed in this thesis are successive MMSE and regularized block diagonalization. By iterating the closed form solution with appropriate power loading we are able to extract the full diversity in the system and empirically approach the maximum sum-rate capacity in case of high multi-user interference. Joint processing of MIMO channels yields maximum diversity regardless of the level of multi-user interference. As these techniques rely on the fact that there is either instantaneous or long- term CSI available at the base station to perform precoding and decoding, it was very important to investigate the influence of the transceiver front-end imperfections and channel estimation errors on their performance. For a comprehensive assessment of multi-antenna techniques, it is mandatory to consider the performance at system level, since many effects of spatial processing are not tractable at the link level. System level investigations have shown that MU MIMO precoding techniques provide several times higher data rates than single-input single-output systems with only slightly increased pilot and control overhead

Performance Optimization Over Wireless Links With Operating Constraints

Wireless communication is one of the most active areas of technological innovations and groundbreaking research ranging from simple cellular phones to highly complex military monitoring devices. The emergence of radios with cognitive capabilities like software defined radios has revolutionized modern communication systems by providing transceivers which can vary their output waveforms as well as their demodulation methods. This adaptability plays a pivotal role in efficient utilization of radio spectrum in an intelligent way while simultaneously not interfering with other radio devices operating on the same frequency band. Thus, it is safe to say that current and future wireless systems and networks depend on their adaptation capability which in turn presents many new technical challenges in hardware and protocol design, power management, interference metrics, distributed algorithms, Quality of Service (QoS) requirements arid security issues. Transmitter adaptation methods have gained importance, and numerous transmitter optimization algorithms have been proposed in recent years. The main idea behind these algorithms is to optimize the transmitted signals according to the patterns of interference in the operating environment such that some specific criterion is optimized. In this context, the objective of this dissertation is to propose transmitter adaptation algorithms in conjunction with power control for wireless systems focusing on performance optimization based on operating constraints. Specifically, this dissertation achieves joint transmitter adaptation and power control in the uplink and downlink of wireless systems with applications to Multiple-Input-Multiple-Output (MIMO) wireless systems and cognitive radio networks. In addition, performance of the proposed algorithms are evaluated in the context of fading channels, taking into consideration the time-varying nature of wireless channels