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

Cyclic-shift diversity transmission and optional per-subcarrier transmit beamforming within a same time interval (e.g., OFDM symbol interval) takes place. The CSD transmission technique circularly shifts the IFFT output prior to any cyclic prefix insertion and has the effect of putting a subcarrier and antenna dependent phase shift in the effective channel response from each transmit antenna. To properly perform transmit adaptive array (TXAA) transmission within an OFDM symbol interval that is being circularly shifted by the CSD transmission technique, the TXAA weights will account for the frequency domain phase shift created by the CSD circular shift operation.

Method and apparatus for performing cyclic-shift diversity with beamforming

A 2D antenna array introduces a new level of control and additional degrees of freedom in multiple-input-multiple-output (MIMO) systems particularly for the so-called "massive MIMO" systems. To accurately assess the performance gains of these large arrays, existing azimuth-only channel models have been extended to handle 3D channels by modeling both the elevation and azimuth dimensions. In this paper, we study the channel correlation matrix of a generic ray-based 3D channel model, and our analysis and simulation results demonstrate that the 3D correlation matrix can be well approximated by a Kronecker production of azimuth and elevation correlations. This finding lays the theoretical support for the usage of a product codebook for reduced complexity feedback from the receiver to the transmitter. We also present the design of a product codebook based on Grassmannian line packing.

Kronecker Product Correlation Model and Limited Feedback Codebook Design in a 3D Channel Model

A wireless communication system is provided that spreads pilot signals, or channel state information reference signals (CSI-RSs), using a spreading code chosen from a set of mutually unbiased bases (MUBs) The advantages of such spreading with MUBs are that multiple base stations can send their pilot signals on a same time-frequency resources, making the pilot signal design very efficient and also improving channel estimation at a user equipment through orthogonal and quasi-orthogonal spreading which gives a gain above noise and interference A short spreading code chosen from MUBs may be used for spreading pilot signals transmitted from each antenna of a base station within a time-frequency resource comprising multiple closely-spaced subcarriers in frequency and/or multiple closely-spaced symbols in time

Method and apparatus for pilot signal processing in a wireless communication system

A receiver and methods of operation wherein Log-Likelihood-Ratio calculation are performed for arbitrary channel estimators with linear Minimum-Mean-Square-Error (MMSE) combining, successive cancellation combining, or joint detection. In some embodiments, the use of linear MMSE or successive cancellation combining may be employed to greatly lower the computational complexity over joint detection. In (401) a channel estimation MSE as a function of frequency, the transmitter modulation type, and a noise power are provided to the LLR component (313). A signal from a transmitter is received at one of the various antennas (301), (303) and respective receiver component (305), (307) in block (403). The channel estimation component (309) computes a channel estimate for the signal from the transmitter, or computes multiple channel estimates for multiple transmitter sources, in block (405). In block (407), decoder inputs are determined as a function of the channel estimates, the received signal, noise power, and channel estimation MSE.

Receiver Having Multi-Antenna Log Likelihood Ratio Generation With Channel Estimation Error

A method and apparatus for providing channel feedback is provided herein. During operation a covariance matrix at time t (R) is calculated as a function of a received downlink signal. In order to reduce overhead, R is normalized and quantized. The base unit then uses the covariance matrix estimate to determine appropriate channel beamforming weights, and instructs transmit beamforming circuitry to use the appropriate weights. In an embodiment, circuitry performs a method of calculating a first precoding matrix index I from a codebook using a received signal, calculating a second codebook index J* using the first precoding matrix index I to approximate a covariance matrix and calculating a quantized coefficient α* to approximate the covariance matrix wherein the quantized coefficient α* is determined using the first precoding matrix index I.

Timothy A. Thomas

Papers

Method and apparatus for performing cyclic-shift diversity with beamforming

Kronecker Product Correlation Model and Limited Feedback Codebook Design in a 3D Channel Model

Method and apparatus for pilot signal processing in a wireless communication system

Receiver Having Multi-Antenna Log Likelihood Ratio Generation With Channel Estimation Error

Closed-loop feedback in wireless communications system