Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes

Both physical and MAC-layer need to be jointly optimized to maximize the autonomy of IoT devices. Therefore, a cross-layer design is imperative to effectively realize Low Power Wide Area networks (LPWANs). In the present paper, a cross-layer assessment framework including power modeling is proposed. Through this simulation framework, the energy consumption of IoT devices, currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a cross-layer approach significantly improves energy efficiency and overall throughput. Two major contributions are made. First, an open-source LPWAN assessment framework has been conceived. It allows testing and evaluating hypotheses and schemes. Secondly, as a representative case, the LoRaWAN protocol is assessed. The findings indicate how a cross-layer approach can optimize LPWANs in terms of energy efficiency and throughput. For instance, it is shown that the use of larger payloads can reduce up to three times the energy consumption on quasi-static channels yet may bring an energy penalty under adverse dynamic conditions

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

Out-of-Band Radiation from Antenna Arrays Clarified

Non-linearities in radio-frequency (RF) transceiver hardware, particularly in power amplifiers, cause distortion in-band and out-of-band. Contrary to claims made in recent literature, in a multiple-antenna system this distortion is correlated across the antennas in the array. A significant implication of this fact is that out-of-band emissions caused by non-linearities are beamformed, in some cases into the same direction as the useful signal.Comment: IEEE Wireless Communications Letters, 2018, to appea

Toward Energy-Efficient Massive MIMO: Graph Neural Network Precoding for Mitigating Non-Linear PA Distortion

Massive MIMO systems are typically designed assuming linear power amplifiers (PAs). However, PAs are most energy efficient close to saturation, where non-linear distortion arises. For conventional precoders, this distortion can coherently combine at user locations, limiting performance. We propose a graph neural network (GNN) to learn a mapping between channel and precoding matrices, which maximizes the sum rate affected by non-linear distortion, using a high-order polynomial PA model. In the distortion-limited regime, this GNN-based precoder outperforms zero forcing (ZF), ZF plus digital pre-distortion (DPD) and the distortion-aware beamforming (DAB) precoder from the state-of-the-art. At an input back-off of -3 dB the proposed precoder compared to ZF increases the sum rate by 8.60 and 8.84 bits/channel use for two and four users respectively. Radiation patterns show that these gains are achieved by transmitting the non-linear distortion in non-user directions. In the four user-case, for a fixed sum rate, the total consumed power (PA and processing) of the GNN precoder is 3.24 and 1.44 times lower compared to ZF and ZF plus DPD respectively. A complexity analysis shows six orders of magnitude reduction compared to DAB precoding. This opens perspectives to operate PAs closer to saturation, which drastically increases their energy efficiency

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology

Cell-Free networking is one of the prime candidates for 6G networks. Despite being capable of providing the 6G needs, practical limitations and considerations are often neglected in current research. In this work, we introduce the concept of federations to dynamically scale and select the best set of resources, e.g., antennas, computing and data resources, to serve a given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing, further increasing the complexity of such systems. Therefore, each federation is self-managing and is distributed over the area in a cell-free manner. Next to the dynamic federations, new accompanying terminology is proposed to design cell-free systems taking into account practical limitations such as time synchronization and distributed processing. We conclude with an illustration with four federations, serving distinct applications, and introduce two new testbeds to study these architectures and concepts

A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is a promising technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes in groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency

Channel Hardening in Massive MIMO: Model Parameters and Experimental Assessment

Reliability is becoming increasingly important for many applications envisioned for future wireless systems. A technology that could improve reliability in these systems is massive MIMO (Multiple-Input Multiple-Output). One reason for this is a phenomenon called channel hardening, which means that as the number of antennas in the system increases, the variations of channel gain decrease in both the time- and frequency domain. Our analysis of channel hardening is based on a joint comparison of theory, measurements and simulations. Data from measurement campaigns including both indoor and outdoor scenarios, as well as cylindrical and planar base station arrays, are analyzed. The simulation analysis includes a comparison with the COST 2100 channel model with its massive MIMO extension. The conclusion is that the COST 2100 model is well suited to represent real scenarios, and provides a reasonable match to actual measurements up to the uncertainty of antenna patterns and user interaction. Also, the channel hardening effect in practical massive MIMO channels is less pronounced than in complex independent and identically distributed (i.i.d.) Gaussian channels, which are often considered in theoretical work.Comment: Accepted to IEEE Open Journal of the Communications Societ

Dynamic Federations for 6G Cell-Free Networking: Concepts and Terminology

Cell-Free networking is one of the prime candidatesfor 6G networks. Despite being capable of providing the 6Gneeds, practical limitations and considerations are often neglectedin current research. In this work, we introduce the conceptof federations to dynamically scale and select the best set ofresources, e.g., antennas, computing and data resources, to servea given application. Next to communication, 6G systems are expected to provide also wireless powering, positioning and sensing,further increasing the complexity of such systems. Therefore,each federation is self-managing and is distributed over thearea in a cell-free manner. Next to the dynamic federations,new accompanying terminology is proposed to design cell-freesystems taking into account practical limitations such as timesynchronization and distributed processing. We conclude withan illustration with four federations, serving distinct applications,and introduce two new testbeds to study these architectures andconcepts

Channel Hardening in Massive MIMO - A Measurement Based Analysis

Wireless-controlled robots, cars and other critical applications are in need of technologies that offer high reliability and low latency. Massive MIMO, Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems and is one part of the solution to increase the reliability of wireless systems. More specifically, when increasing the number of base station antennas in a massive MIMO systems the channel variations decrease and the so-called channel hardening effect appears. This means that the variations of the channel gain in time and frequency decrease. In this paper, channel hardening in massive MIMO systems is assessed based on analysis of measurement data. For an indoor scenario, the channels are measured with a 128-port cylindrical array for nine single-antenna users. The analysis shows that in a real scenario a channel hardening of 3.2-4.6 dB, measured as a reduction of the standard deviation of the channel gain, can be expected depending on the amount of user interaction. Also, some practical implications and insights are presented.Comment: Accepted to SPAWC 201