Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to user). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated tradeoffs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphics processing unit (GPU) cluster.

Decentralized Baseband Processing for Massive MU-MIMO Systems

Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in fifth-generation (5G) wireless systems as it offers significant improvements in spectral efficiency compared to existing multi-antenna technologies. The presence of hundreds of antenna elements at the base station (BS), however, results in excessively high hardware costs and power consumption, and requires high interconnect throughput between the baseband-processing unit and the radio unit. Massive MU-MIMO that uses low-resolution analog-to-digital and digital-to-analog converters (DACs) has the potential to address all these issues. In this paper, we focus on downlink precoding for massive MU-MIMO systems with 1-bit DACs at the BS. The objective is to design precoders that simultaneously mitigate MU interference and quantization artifacts. We propose two nonlinear 1-bit precoding algorithms and corresponding very large-scale integration (VLSI) designs. Our algorithms rely on biconvex relaxation, which enables the design of efficient 1-bit precoding algorithms that achieve superior error-rate performance compared with that of linear precoding algorithms followed by quantization. To showcase the efficacy of our algorithms, we design VLSI architectures that enable efficient 1-bit precoding for massive MU-MIMO systems, in which hundreds of antennas serve tens of user equipments. We present corresponding field-programmable gate array (FPGA) reference implementations to demonstrate that 1-bit precoding enables reliable and high-rate downlink data transmission in practical systems.

https://publications.lib.chalmers.se/records/fulltext/254483/local_254483.pdf

1-bit Massive MU-MIMO Precoding in VLSI

As 5G New Radio (NR) is being rolled out, research effort is being focused on the evolution of what is to come in the post-5G era. In order to meet the diverse requirements of future wireless communication in terms of increased capacity and reduced latency, technologies such as distributed massive Multiple-Input Multiple-Output (MIMO), sub-millimeter wave and Tera-hertz spectrum become technology components of interest. Furthermore, to meet the demands on connectivity anywhere at anytime, non-terrestrial satellite networks will be needed, which brings about challenges both in terms of implementation as well as deployment. Finally, scaling up massive Internet-of-Things (IoT), energy harvesting and Simultaneous Wireless Information and Power Transfer (SWIPT) is foreseen to become important enablers when deploying a large amount of small, low-power radios. In this paper, we will discuss some of the important opportunities these technologies bring, and the challenges faced by the microwave and wireless communication communities.

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Implementation Challenges and Opportunities in Beyond-5G and 6G Communication

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 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, and 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.

/pdf/efficient-dsp-and-circuit-architectures-for-massive-mimo-53o5edq0ui.pdf

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

Linear data-detection algorithms that build on zero forcing (ZF) or linear minimum mean-square error (L-MMSE) equalization achieve near-optimal spectral efficiency in massive multi-user multiple-input multiple-output (MU-MIMO) systems. Such algorithms, however, typically rely on centralized processing at the base station (BS) which results in 1) excessive interconnect and chip input/output (I/O) data rates and 2) high computational complexity. Decentralized baseband processing (DBP) partitions the BS antenna array into independent clusters that are associated with separate radio-frequency circuits and computing fabrics in order to overcome the limitations of centralized processing. In this paper, we investigate decentralized equalization with feedforward architectures that minimize the latency bottlenecks of existing DBP solutions. We propose two distinct architectures with different interconnect and I/O bandwidth requirements that fuse the local equalization results of each cluster in a feedforward network. For both architectures, we consider maximum ratio combining, ZF, L-MMSE, and a nonlinear equalization algorithm that relies on approximate message passing. For these algorithms and architectures, we analyze the associated post-equalization signal-to-noise-and-interference-ratio. We provide reference implementation results on a multigraphics processing unit system which demonstrate that decentralized equalization with feedforward architectures enables throughputs in the Gb/s regime and incurs no or only a small performance loss compared to centralized solutions.

Decentralized Equalization With Feedforward Architectures for Massive MU-MIMO

Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally-complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to users). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated trade-offs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphic processing unit (GPU) cluster.

Conventional centralized data detection algorithms for massive multi-user multiple-input multiple-output (MU-MIMO) systems, such as minimum mean square error (MMSE) equalization, result in excessively high raw baseband data rates and computing complexity at the centralized processing unit. Hence, practical base-station (BS) designs for massive MU-MIMO that rely on state-of-the-art hardware processors and I/O interconnect standards must find new means to avoid these bottlenecks. In this paper, we propose a novel decentralized data detection method, which partitions the BS antenna array into separate clusters. Each cluster is associated with independent computing hardware to perform decentralized data detection, which requires only local channel state information and receive data, and a minimum amount of information exchange between clusters. To demonstrate the benefits of our approach, we map our algorithm to a Xeon Phi cluster, which shows that BS designs with hundreds or thousands of BS antennas can be supported.

Decentralized data detection for massive MU-MIMO on a Xeon Phi cluster

In the massive multi-user multiple-input multiple-output (MU-MIMO) downlink, traditional centralized beamforming (or precoding), such as zero-forcing (ZF), entails excessive complexity for the computing hardware, and generates raw baseband data rates that cannot be supported with current interconnect technology and chip I/O interfaces. In this paper, we present a novel decentralized beamforming approach that partitions the base-station (BS) antenna array into separate clusters, each associated with independent computing hardware. We develop a decentralized beamforming algorithm that requires only local channel state information and minimum exchange of consensus information among the clusters. We demonstrate the efficacy and scalability of decentralized ZF beamforming for systems with hundreds of BS antennas using a reference implementation on a GPU cluster.

/pdf/decentralized-beamforming-for-massive-mu-mimo-on-a-gpu-vuhvv4tq6k.pdf

Decentralized beamforming for massive MU-MIMO on a GPU cluster

A wideband circularlypolarized (CP) wearable antenna for 5 GHz (5.15–5.825 GHz) wireless body area network is presented and analyzed. The emphasis of this work is on the additional resonance generated by the mutual coupling of a 2×2 compact patch array. As the proposed antenna is fed by a sequential-phase feeding network, the resonance can be used to achieve unidirectional radiation with improved CP bandwidth. To alleviate the loading effects of the human body, a modified strip feedline, comprising two pairs of arc-shaped slots cut on the bottom ground, is introduced. Moreover, the adoption of textiles as substrate and conductor provides flexibility and conformability, allowing the antenna to be integrated with garments. The prototype with the dimensions of 40 mm × 40 mm × 4 mm (0.73 λ0 × 0.73 λ0 × 0.07 λ0) was fabricated. The measured results indicate that a wide impedance bandwidth of 43% (4.25–6.63 GHz), a half-power beamwidth of 60°, and a 3 dB AR bandwidth of 34% (4.71–6.67 GHz) are achieved. Furthermore, the antenna performance is proved to be stable under structural deformation and human body loading. Finally, transmission losses and link budgets are evaluated, enabling the antenna to be a robust solution for off-body communications.

Yujun Chen

Papers

Decentralized Baseband Processing for Massive MU-MIMO Systems

Decentralized Baseband Processing for Massive MU-MIMO Systems

Decentralized data detection for massive MU-MIMO on a Xeon Phi cluster

Decentralized beamforming for massive MU-MIMO on a GPU cluster

Wearable Wideband Circularly Polarized Array Antenna for Off-Body Applications