VLSI Implementation of Low Power Reconfigurable MIMO Detector

Multiple Input Multiple Output (MIMO) systems are a key technology for next generation high speed wireless communication standards like 802.11n, WiMax etc. MIMO enables spatial multiplexing to increase channel bandwidth which requires the use of multiple antennas in the receiver and transmitter side. The increase in bandwidth comes at the cost of high silicon complexity of MIMO detectors which result, due to the intricate algorithms required for the separation of these spatially multiplexed streams. Previous implementations of MIMO detector have mainly dealt with the issue of complexity reduction, latency minimization and throughput enhancement. Although, these detectors have successfully mapped algorithms to relatively simpler circuits but still, latency and throughput of these systems need further improvements to meet standard requirements. Additionally, most of these implementations don’t deal with the requirements of reconfigurability of the detector to multiple modulation schemes and different antennae configurations. This necessary requirement provides another dimension to the implementation of MIMO detector and adds to the implementation complexity. This thesis focuses on the efficient VLSI implementation of the MIMO detector with an emphasis on performance and re-configurability to different modulation schemes. MIMO decoding in our detector is based on the fixed sphere decoding algorithm which has been simplified for an effective VLSI implementation without considerably degrading the near optimal bit error rate performance. The regularity of the architecture makes it suitable for a highly parallel and pipelined implementation. The decoder has intrinsic traits for dynamic re-configurability to different modulation and encoding schemes. This detector architecture can be easily tuned for high/low performance requirements with slight degradation/improvement in Bit Error Rate (BER) depending on needs of the overlying application. Additionally, various architectural optimizations like pipelining, parallel processing, hardware scheduling, dynamic voltage and frequency scaling have been explored to improve the performance, energy requirements and re-configurability of the design

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems, based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose - to the best of our knowledge - the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin

Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions

Massive MIMO is a compelling wireless access concept that relies on the use of an excess number of base-station antennas, relative to the number of active terminals. This technology is a main component of 5G New Radio (NR) and addresses all important requirements of future wireless standards: a great capacity increase, the support of many simultaneous users, and improvement in energy efficiency. Massive MIMO requires the simultaneous processing of signals from many antenna chains, and computational operations on large matrices. The complexity of the digital processing has been viewed as a fundamental obstacle to the feasibility of Massive MIMO in the past. Recent advances on system-algorithm-hardware co-design have led to extremely energy-efficient implementations. These exploit opportunities in deeply-scaled silicon technologies and perform partly distributed processing to cope with the bottlenecks encountered in the interconnection of many signals. For example, prototype ASIC implementations have demonstrated zero-forcing precoding in real time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing of 8 terminals). Coarse and even error-prone digital processing in the antenna paths permits a reduction of consumption with a factor of 2 to 5. This article summarizes the fundamental technical contributions to efficient digital signal processing for Massive MIMO. The opportunities and constraints on operating on low-complexity RF and analog hardware chains are clarified. It illustrates how terminals can benefit from improved energy efficiency. The status of technology and real-life prototypes discussed. Open challenges and directions for future research are suggested.Comment: submitted to IEEE transactions on signal processin