Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next generation communication systems. Due to a large number of antennas and radio frequency (RF) chains, complexity of the symbol detectors increased rapidly in a massive MIMO uplink receiver. Thus, the research to find the perfect massive MIMO detection algorithm with optimal performance and low complexity has gained a lot of attention during the past decade. A plethora of massive MIMO detection algorithms has been proposed in the literature. The aim of this paper is to provide insights on such algorithms to a generalist of wireless communications. We garner the massive MIMO detection algorithms and classify them so that a reader can find a distinction between different algorithms from a wider range of solutions. We present optimal and near-optimal detection principles specifically designed for the massive MIMO system such as detectors based on a local search, belief propagation and box detection. In addition, we cover detectors based on approximate inversion, which has gained popularity among the VLSI signal processing community due to their deterministic dataflow and low complexity. We also briefly explore several nonlinear small-scale MIMO (2-4 antenna receivers) detectors and their applicability in the massive MIMO context. In addition, we present recent advances of detection algorithms which are mostly based on machine learning or sparsity based algorithms. In each section, we also mention the related implementations of the detectors. A discussion of the pros and cons of each detector is provided.

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Massive MIMO Detection Techniques: A Survey

We analyze the effects of pilot-assisted channel estimation on achievable data rates (lower bound on information capacity) over a frequency flat time-varying channel. Under a block-fading channel model, the effects of the estimation error are evaluated in the case of the estimates being available at the receiver only (open loop) and in the case when the estimates are fed back to the transmitter allowing water pouring transmitter optimization (closed loop). Using a characterization of the effective noise due to estimation error, we analyze the achievable rates as a function of the power allocated to the pilot, the channel coherence time, the background noise level, as well as the number of transmit and receive antennas. The analysis presented here can be used to optimally allocate pilot power for various system and channel operating conditions, and to also determine the effectiveness of closed loop feedback.

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Pilot-assisted estimation of MIMO fading channel response and achievable data rates

A field-programmable gate array (FPGA) implementation of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. The algorithm overcomes the main drawback of the SD: its variable throughput, depending on the noise level and the channel conditions. Implementation results show that the algorithm is highly parallelizable and can be fully pipelined. This reduces the use of FPGA resources and results in a constant throughput, which is significantly higher than previous SD implementations at a cost of a very small bit error ratio (BER) degradation.

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Rapid Prototyping of a Fixed-Throughput Sphere Decoder for MIMO Systems

While the field of MIMO transmission has been explored over the past decade mainly theoretically, relatively few results exist on how these transmissions perform over realistic, imperfect channels The reason for this is that measurement equipment is expensive, difficult to obtain, and often inflexible when a multitude of transmission parameters are of interest This paper presents a flexible testbed developed to examine MIMO algorithms and channel models described in literature by transmitting data at 245 GHz through real, physical channels, supporting simultaneously four transmit and four receive antennas Operation is performed directly from Matlab allowing for a cornucopia of real-world experiments with minimum effort Examples measuring bit error rates on space-time block codes are provided in the paper

Vienna MIMO testbed

When designing complex communication systems, such as MIMO-OFDM transceivers, prototypes have become an important tool for understanding the implementation trade-offs and the system behavior. This paper presents a real-time FPGA prototype for a 4-stream MIMO-OFDM transceiver capable of transmitting 216 Mbit/s in 20 MHz bandwidth. The paper covers all parts of the system from RF to channel decoding and considers both algorithm and implementation aspects. In particular, we discuss the initial parameter estimation, channel estimation, MIMO detection, parameter tracking, and channel decoding. FPGA implementation results are reported along with measurements that demonstrate the throughput of spatial multiplexing with four spatial streams.

A Real-Time 4-Stream MIMO-OFDM Transceiver: System Design, FPGA Implementation, and Characterization

In this paper, a multiple-input-multiple-output (MIMO) extension for a third-generation (3G) wireless system is described. The integration of MIMO concepts within the existing UMTS standard and the associated space-time RAKE receiver are explained. An analysis is followed by a description of an actual experimental MIMO transmitter and receiver architecture, both realized on digital signal processors (DSPs) and FPGAs within a precommercial OneBTS base station. It uses four transmit and four receive antennas to achieve downlink data rates up to 1 Mb/s per user with a spreading factor of 32 and the UMTS chip rate of 3.84 MHz. Furthermore, different MIMO detectors are evaluated, comparing their performance and complexity. System performance is evaluated through simulations and indoor over-the-air measurements. Capacity and bit-error rate measurement results are presented.

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D. Haessig

Papers

Prototype experience for MIMO BLAST over third-generation wireless system