This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Spatially Sparse Precoding in Millimeter Wave MIMO Systems

Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered, and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR

MIMO Broadcast Channels With Finite-Rate Feedback

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

5G : A tutorial overview of standards, trials, challenges, deployment, and practice

A high peak-to-average-power ratio (PAPR) is a primary drawback of orthogonal frequency-division multiplexing (OFDM). Many methods have been proposed to reduce the OFDM PAPR for single-antenna and for MIMO systems. Some systems, however, use multiple antennas for frequency-selective transmit beamforming either to a single or multiple simultaneous receivers. This paper presents a new method for PAPR reduction that minimizes the peak power across all antennas while preserving the maximal ratio transmission (MRT) gain and zero-crosstalk criteria at the receivers. Because the gain and zero crosstalk is preserved at the receivers, no performance loss is seen by the receivers when using the PAPR-reduced transmit vector. With eight transmit antennas, the algorithm demonstrates an average of 5.5 dB PAPR reduction for single-user beamforming and about 4.5 dB PAPR reduction for four-user transmit spatial division multiple access (Tx-SDMA)

Papr Reduction for Beamforming Ofdm Transmitters

Statistical eigen-beamforming (EBF) is a widely used multiple antenna transmission technique in which one antenna weight is used over potentially a large bandwidth (e.g., all subcarriers in OFDM). With N transmit antennas, the maximum gain for EBF at a receiver is 20 log N decibels over a single antenna transmission (assuming a power unfair comparison where each transmit antenna has a fixed power so the addition of new antennas brings a beamforming gain as well as a total transmit power increase). Based on statistical analysis of field data and observations, this gain is nearly achievable with line-of-sight transmissions. While useful for characterizing the benefits of EBF, the statistical analysis does not indicate the realizable gain for a particular location. This paper presents a relationship between the EBF gain, spatial correlation of the channel, and the number of transmit antennas. The conjectured gain is compared to the expected gain based on an analysis of field data. The results of this comparison show that the spatial correlation as defined in this paper can be used to predict the EBF gain accurately for a variety of antenna spacings and polarizations.

Estimating Statistical Eigen-Beamforming Gains Using Spatial Channel Correlation

Semi-blind channel estimators promise to dramatically reduce the number of pilots needed for accurate channel estimation over pilot-only based techniques while providing much better performance than blind algorithms. This paper presents a novel semi-blind multi-user channel estimator for OFDM based on the maximum likelihood estimator. The estimator works by iterating between estimating the channels between multiple transmit antennas and each receive antenna and the respective symbols for each transmitter. Simulation results show that the semi-blind channel estimator is effective even when the frequency-domain pilot spacing is greater than the Nyquist spacing.

Semi-blind channel identification in OFDM

A method and apparatus for determining stream weights is provided herein. During operation, an uplink direction of arrival (DOA) and a downlink direction of departure (DOD) calibration procedure is implemented using uplink signals and GPS information from a subset of mobiles without the need of calibration circuitry at the base. Because the presented calibration procedure needs no new hardware it can be deployed in existing deployments with only a soft-ware upgrade.

Manifold calibration for a communication system

This paper presents two new multi-user techniques that aid in the decision-directed tracking of a rapidly time-varying OFDM channel. The first is based on applying gradient optimization techniques to a least squares multi-user frequency-domain channel estimation problem. The second technique is based on a method for modeling a time-varying frequency response as a linear combination of a small number of time-invariant "Doppler channels." This technique provides a formula for updating these Doppler channels, which greatly improves the performance of decision-directed tracking. These techniques are tested for different decision-directed methods in a simulated 6.0 MHz OFDM system with a 330 Hz maximum Doppler frequency and an 8.0 /spl mu/sec maximum delay spread.

Timothy A. Thomas

Papers

Papr Reduction for Beamforming Ofdm Transmitters

Estimating Statistical Eigen-Beamforming Gains Using Spatial Channel Correlation

Semi-blind channel identification in OFDM

Manifold calibration for a communication system

Decision-directed tracking for multi-user broadband mobile OFDM