Network-coded NOMA with antenna selection for the support of two heterogeneous groups of users

The combination of Non-Orthogonal Multiple Access (NOMA) and Transmit Antenna Selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity and high diversity gains. Meanwhile, Random Linear Coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low power devices with limited processing capabilities that have strict Quality of Service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework including NOMAbased TAS at the physical layer and RLC at the application layer. In particular, two low complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that, sliding window based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results

AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing

The enormous success of advanced wireless devices is pushing the demand for higher wireless data rates. Denser spectrum reuse through the deployment of more access points per square mile has the potential to successfully meet the increasing demand for more bandwidth. In theory, the best approach to density increase is via distributed multiuser MIMO, where several access points are connected to a central server and operate as a large distributed multi-antenna access point, ensuring that all transmitted signal power serves the purpose of data transmission, rather than creating "interference." In practice, while enterprise networks offer a natural setup in which distributed MIMO might be possible, there are serious implementation difficulties, the primary one being the need to eliminate phase and timing offsets between the jointly coordinated access points. In this paper we propose AirSync, a novel scheme which provides not only time but also phase synchronization, thus enabling distributed MIMO with full spatial multiplexing gains. AirSync locks the phase of all access points using a common reference broadcasted over the air in conjunction with a Kalman filter which closely tracks the phase drift. We have implemented AirSync as a digital circuit in the FPGA of the WARP radio platform. Our experimental testbed, comprised of two access points and two clients, shows that AirSync is able to achieve phase synchronization within a few degrees, and allows the system to nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC and higher layer aspects of a practical deployment. To the best of our knowledge, AirSync offers the first ever realization of the full multiuser MIMO gain, namely the ability to increase the number of wireless clients linearly with the number of jointly coordinated access points, without reducing the per client rate.Comment: Submitted to Transactions on Networkin

Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design

In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes

SYNCHRONIZATION AND RESOURCE ALLOCATION IN DOWNLINK OFDM SYSTEMS

The next generation (4G) wireless systems are expected to provide universal personal and multimedia communications with seamless connection and very high rate transmissions and without regard to the users’ mobility and location. OFDM technique is recognized as one of the leading candidates to provide the wireless signalling for 4G systems. The major challenges in downlink multiuser OFDM based 4G systems include the wireless channel, the synchronization and radio resource management. Thus algorithms are required to achieve accurate timing and frequency offset estimation and the efficient utilization of radio resources such as subcarrier, bit and power allocation. The objectives of the thesis are of two fields. Firstly, we presented the frequency offset estimation algorithms for OFDM systems. Building our work upon the classic single user OFDM architecture, we proposed two FFT-based frequency offset estimation algorithms with low computational complexity. The computer simulation results and comparisons show that the proposed algorithms provide smaller error variance than previous well-known algorithm. Secondly, we presented the resource allocation algorithms for OFDM systems. Building our work upon the downlink multiuser OFDM architecture, we aimed to minimize the total transmit power by exploiting the system diversity through the management of subcarrier allocation, adaptive modulation and power allocation. Particularly, we focused on the dynamic resource allocation algorithms for multiuser OFDM system and multiuser MIMO-OFDM system. For the multiuser OFDM system, we proposed a lowiv complexity channel gain difference based subcarrier allocation algorithm. For the multiuser MIMO-OFDM system, we proposed a unit-power based subcarrier allocation algorithm. These proposed algorithms are all combined with the optimal bit allocation algorithm to achieve the minimal total transmit power. The numerical results and comparisons with various conventional nonadaptive and adaptive algorithmic approaches are provided to show that the proposed resource allocation algorithms improve the system efficiencies and performance given that the Quality of Service (QoS) for each user is guaranteed. The simulation work of this project is based on hand written codes in the platform of the MATLAB R2007b

Rateless Space-Time Block Codes for 5G Wireless Communication Systems

This chapter presents a rateless space-time block code (RSTBC) for massive multiple-input multiple-output (MIMO) wireless communication systems. We discuss the principles of rateless coding compared to the fixed-rate channel codes. A literature review of rateless codes (RCs) is also addressed. Furthermore, the chapter illustrates the basis of RSTBC deployments in massive MIMO transmissions over lossy wireless channels. In such channels, data may be lost or are not decodable at the receiver end due to a variety of factors such as channel losses or pilot contamination. Massive MIMO is a breakthrough wireless transmission technique proposed for future wireless standards due to its spectrum and energy efficiencies. We show that RSTBC guarantees the reliability of the system in such highly lossy channels. Moreover, pilot contamination (PC) constitutes a particularly significant impairment in reciprocity-based multi-cell systems. PC results from the non-orthogonality of the pilot sequences in different cells. In this chapter, RSTBC is also employed in the downlink transmission of a multi-cell massive MIMO system to mitigate the effects of signal-to-interference-and-noise ratio (SINR) degradation resulting from PC. We conclude that RSTBC can effectively mitigate such interference. Hence, RSTBC is a strong candidate for the upcoming 5G wireless communication systems

Doctoral Thesis: Massive MIMO in Real Propagation Environments

Mobile communications are now evolving towards the fifth generation (5G). In the near future, we expect an explosive increase in the number of connected devices, such as phones, tablets, sensors, connected vehicles and so on. Much higher data rates than in today's 4G systems are required. In the 5G visions, better coverage in remote regions is also included, aiming for bringing the current "4 billion unconnected" population into the online world. There is also a great interest in "green communications", for less energy consumption in the ICT (information and communication technology) industry. Massive MIMO is a potential technology to fulfill the requirements and visions. By equipping a base station with a large number, say tens to hundreds, of antennas, many terminals can be served in the same time-frequency resource without severe inter-user interference. Through "aggressive" spatial multiplexing, higher data rates can be achieved without increasing the required spectrum. Processing efforts can be made at the base station side, allowing terminals to have simple and cheap hardware. By exploiting the many spatial degrees of freedom, linear precoding/detection schemes can be used to achieve near-optimal performance. The large number of antennas also brings the advantage of large array gain, resulting in an increase in received signal strength. Better coverage is thus achieved. On the other hand, transmit power from base stations and terminals can be scaled down to pursue energy efficiency. In the last five years, a lot of theoretical studies have been done, showing the extraordinary advantages of massive MIMO. However, the investigations are mainly based on theoretical channels with independent and identically distributed (i.i.d.) Gaussian coefficients, and sometimes assuming unlimited number of antennas. When bringing this new technology from theory to practice, it is important to understand massive MIMO behavior in real propagation channels using practical antenna arrays. Not much has been known about real massive MIMO channels, and whether the claims about massive MIMO still hold there, until the studies in this thesis were done. The thesis study connects the "ideal" world of theory to the "non-ideal" reality. Channel measurements for massive MIMO in the 2.6 GHz band were performed, in different propagation environments and using different types of antenna arrays. Based on obtained real-life channel data, the studies include • channel characterization to identify important massive MIMO properties, • evaluation of propagation conditions in real channels and corresponding massive MIMO performance, • channel modeling for massive MIMO to capture the identified channel properties, and • reduction of massive MIMO hardware complexity through antenna selection. The investigations in the thesis conclude that massive MIMO works efficiently in real propagation environments. The theoretical advantages, as observed in i.i.d. Rayleigh channels, can also be harvested in real channels. Important propagation effects are identified for massive MIMO scenarios, including channel variations over large arrays, multipath-component (MPC) lifetime, and 3D propagation. These propagation properties are modeled and included into the COST 2100 MIMO channel model as an extension for massive MIMO. The study on antenna selection shows that characteristics in real channels allow for significant reductions of massive MIMO complexity without significant performance loss. As one of the world's first research work on massive MIMO behavior in real propagation channels, the studies in this thesis promote massive MIMO as a practical technology for future communication systems