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.

/pdf/massive-mimo-detection-techniques-a-survey-1hj40069of.pdf

Massive MIMO Detection Techniques: A Survey

The development of the fifth generation (5G) wireless networks is gaining momentum to connect almost all aspects of life through the network with much higher speed, very low latency and ubiquitous connectivity. Due to its crucial role in our lives, the network must secure its users, components, and services. The security threat landscape of 5G has grown enormously due to the unprecedented increase in types of services and in the number of devices. Therefore, security solutions if not developed yet must be envisioned already to cope with diverse threats on various services, novel technologies, and increased user information accessible by the network. This paper outlines the 5G network threat landscape, the security vulnerabilities in the new technological concepts that will be adopted by 5G, and provides either solutions to those threats or future directions to cope with those security challenges. We also provide a brief outline of the post-5G cellular technologies and their security vulnerabilities which is referred to as future generations (XG) in this paper. In brief, this paper highlights the present and future security challenges in wireless networks, mainly in 5G, and future directions to secure wireless networks beyond 5G.

/pdf/security-for-5g-and-beyond-4cj06e4muu.pdf

Security for 5G and Beyond

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

A Measurement-Based Stochastic Model for High-Speed Railway Channels

Modeling the variations in the local mean received power, the shadow fading is a relatively understudied effect in the literature. The inaccuracy of the universally accepted lognormal model is shown in many works. However, proposing other statistical distributions, such as gamma, which are not stemmed from the natural underlying physical process, cannot provide sufficient insights. Conceding the physical process of multiple reflections generating the lognormal distribution, in this paper, we propose a generalized mixture model that can address the modeling inaccuracies observed with a single lognormal distribution that may not correctly represent empirical data sets. To show that lognormal mixture model can be used under any shadow fading condition, we prove that an arbitrary probability density function can accurately be represented by a mixture of lognormal random variables (RVs). One of the main issues associated with mixture models is the determination of the mixture components. Here, we propose two solutions. Our first solution is a Dirichlet-process-mixture-based estimation technique that can provide the optimum number of components. Our second solution is an expectation–maximization (EM) algorithm-based technique for a more practical implementation. The proposed model and solution approaches are applied to our empirical data set, where the accuracy of the mixture model is verified by using both confidence-based and error-vector-norm-based techniques. Concluding this paper, we provide outage and cellular coverage probability expressions, where we show that more accurate shadow fading models yield more realistic performance estimates.

Lognormal Mixture Shadowing

This paper provides a performance evaluation of wireless Machine-to-Machine technologies for communication networks enabling Smart Distribution Grid operation. A challenging task raised by different architectural solutions for integrating households into the Smart Grid is given by the last mile connectivity. Nowadays sub-GHz wireless networks deployed for automated meter reading offer a reliable solution due to the lower frequency range and flexible infrastructures for outhouse installations and dedicated meter reading service on a low-bitrate long-term interval. By adding more functionality, like demand side management and distributed energy resources at the households into the Smart Grid, broadband radio service are required to cover the higher demands on data rate, message frequency and quality-of-service. Therefore several additional frequency options are discussed for existing broadband cellular systems, focusing on the integration of outdoor, indoor and basement installation of the Smart Grid devices. Within this work, these frequency options for cellular networks are analyzed for multiple installation scenarios based on ray tracing simulation, reference measurements and analytical channel models and a novel channel model for the prediction of the building penetration loss to basements is derived and presented. In a second step a traffic capacity analysis is performed in order to estimate the system performance parameters of different technologies and to show the influence of actual deployment scenarios for network planning of communication networks for Smart Distribution Grid scenarios.

Coverage and capacity analysis of wireless M2M technologies for smart distribution grid services

Requirements for higher data rates and lower power consumption set new challenges for implementation of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) receivers. Simple detectors have the advantage of low complexity and power consumption, but they cannot offer as good performance as more complex detectors. Therefore it would be beneficial to be able to adapt the detector algorithm to suit the channel conditions to minimize the receiver processing power consumption while satisfying the quality of service requirements. At low signal-to-noise ratio (SNR) and/or low rank channel, more power and computation resources could be used for detection in order to guarantee reliable communication, while in good conditions, a simple and less power consuming detector could be used. In this paper, we compare the performance of different detection algorithms. The performance results are based on simulations in long term evolution (LTE) system. The effect of precoding and hybrid automatic repeat request (HARQ) on the performance is shown. Implementation results based on the existing literature are included in the comparison. We discuss when it would be beneficial to use a complex detector and when a simple one would be sufficient. Also the switching criterion is discussed.

Study of adaptive detection for MIMO-OFDM systems

In this paper, the results of outdoor-to-indoor multiple-input multiple-output (MIMO) channel measurements at 780 MHz are presented. The motivation for these measurements was to provide actual measurement data needed to extend the International Mobile Telecommunications -Advanced (IMT-A) channel models to the frequencies around 400 – 900 MHz. There are publications reporting measurement results for these frequencies, but the results for the outdoor-to-indoor propagation scenario are basically non-existing. The measurement results presented in this paper include path loss, shadow fading, delay spread, Ricean K-factor and angular spread. The cross-correlations between large scale parameters are also given. The path loss results were compared to two modified versions of COST 231 path loss model [1] for building penetration loss. The comparison showed that the COST 231 path loss model with modified indoor and outdoor loss sections can be applied also to frequencies around 800 MHz with suitable parameter values.

Characterization of propagation in an outdoor-to-indoor scenario at 780 MHz

Cognitive radio (CR) systems require flexible and adaptive implementations of signal processing algorithms. An adaptive symbol detector is needed in the baseband receiver chain to achieve the desired flexibility of a CR system. This paper presents a novel design of an adaptive detector as an application-specific instruction-set processor (ASIP). The ASIP template is based on transport triggered architecture (TTA). The processor architecture is designed in such a manner that it can be programmed to support different suboptimal multiple-input multiple-output (MIMO) detection algorithms in a single TTA processor. The linear minimum mean-square error (LMMSE) and three variants of the selective spanning for fast enumeration (SSFE) detection algorithms are considered. The detection algorithm can be switched between the LMMSE and SSFE according to the bit error rate (BER) performance requirement in the TTA processor. The design can be scaled for different antenna configurations and different modulations. Some of the algorithm architecture co-optimization techniques used here are also presented. Unlike most other detector ASIPs, high level language is used to program the processor to meet the time-to-market requirements. The adaptive detector delivers 4.88-49.48 Mbps throughput at a clock frequency of 200 MHz on 90 nm technology.

/pdf/an-adaptive-detector-implementation-for-mimo-ofdm-downlink-aneovl0rex.pdf

An Adaptive Detector Implementation for MIMO-OFDM Downlink

In this paper, the results of wideband multiple-input multiple-output (MIMO) measurements around 800 MHz in urban macro (UMa) and outdoor to indoor (O2I) scenarios are presented. The motivation for conducting the measurements was to extend the frequency range of the International Mobile Telecommunications-Advanced (IMT-A) channel model down to 700–800 MHz. The measurement campaigns on the lower frequency bands reported in literature are mostly narrowband measurements and therefore new measurements were needed. The results reported in this paper include path loss, shadow fading, delay spread, angular spread and Ricean K-factor. Angular spreads for both the mobile and base station are not previously presented in the literature for radio channel propagation at 800 MHz range. Also extensive measurement results for outdoor to indoor scenario around 800 MHz are non-existing in literature. [1]

Wideband radio channel measurements around 800 MHz in outdoor to indoor and urban macro scenarios

Higher data rate and lower power consumption requirements set new challenges for implementation of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) re-ceivers. Simple detectors have low complexity and power consumption, but the performance is worse than with more complex detectors. Therefore the detector can be adapted to suit the channel conditions to minimize the power consumption while satisfying the quality of service requirements. The performances of the linear minimum mean-square error (LMMSE) detector and the K-best list sphere detector (LSD) in a long term evolution (LTE) system are compared. We also investigate least squares (LS) channel estimation performance with different mobile speeds and correlation scenarios. Theoretical complexities of the detectors are also given. In the 4 × 4 system, high order modulation and bad channel estimate impacts the performance of the K-best detector, resulting it to be outperformed by the LMMSE detector. In the 8 × 8 case, the K-best LSD outperforms the LMMSE detector, since the channel conditions are more challenging for the LMMSE detector.

/pdf/detection-and-channel-estimation-in-8-8-mimo-ofdm-3y62xfvqxy.pdf

Essi Suikkanen

Papers

Study of adaptive detection for MIMO-OFDM systems

Characterization of propagation in an outdoor-to-indoor scenario at 780 MHz

An Adaptive Detector Implementation for MIMO-OFDM Downlink

Wideband radio channel measurements around 800 MHz in outdoor to indoor and urban macro scenarios

Detection and channel estimation in 8 × 8 MIMO-OFDM