Massive Multiple-input Multiple-output Arrays Research Articles

Interference Alignment in Channel Estimation Using Hybrid Deep Neural Network with Multi-user Propagation Model for 5G in MM Wave Massive MIMO

Massive Multiple Input Multiple Output (MIMO) technologies, as well as higher frequencies utilization, such as MilliMeter Wave (MM Wave) frequencies, should be utilized in the 5G wireless network. This massive transmission will require a higher frequency band and in cellular massive MIMO, frequencies re-use leads to interference in channel estimation. Interference Alignment (IA) is a transmission strategy of the channel with higher capacities of signal-to-noise ratio. This interference needs to be aligned before the transmission of signal, so this research proposes a novel technique in IA by hybridization of deep learning techniques for Heterogeneous Networks (HetNets). The aim of this work is to develop a new interference alignment scheme based on multi-user with a neural network which can be efficient in mitigation of interference of the networks. The model that is proposed properly exploits the interference alignment concept by developing a multi-user group of small cells assisted by various BSs in the heterogeneous networks for MM Wave. The paper focuses on channel estimation by IA for a 5G network that transmits MMWave using neural networks. The neural network is utilized for learning the mapping functions between the received mmWave channels with negligible overhead. Here Hybrid Deep Neural Network is used with Multi-User Propagation (HDNN-MUP) model for IA and demonstrates a practical upper bound for MIMO array sizes that balance throughput and estimation overhead. The experimental analysis is done by utilizing the parameters namely Signal-to-noise ratio (SNR), Bit Error Rate (BER), computational time, energy efficiency, and spectral efficiency.

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

Compressive Sensing in Massive MIMO Array Testing: A Practical Guide

Compressive sensing (CS) has been deployed in a variety of fields including wideband spectrum sensing, active user detection, and antenna arrays. In massive multiple input multiple output (MIMO) arrays, CS has been applied to reduce the number of measurements required to verify the arrays excitation. To date, the literature has concentrated on the various methods of achieving CS and applying them to both linear and 2-D arrays, and aimed at detecting fully failed elements in an array, offering simple pass/fail testing. All follow the general approach of creating the sparsity needed for CS by subtracting the measured far field or near field of the test array from that of a “gold standard” array measured under exactly identical conditions. This article extended this work to the need for rapid but accurate reconstruction of element excitation in a production testing environment for massive MIMO arrays. The aim is to demonstrate that CS can offer accurate reconstruction of array excitation. Particularly, the work addresses the issues of optimal sampling, measurement noise, accuracy of faulty element detection, effects of beam scanning, and physical alignment of the gold standard array with the test array. We have restricted ourselves to considering production standard arrays with failure rates up to around 5% and conclude with a set of proposed modifications to the basic CS process as applied to array excitations that achieve a near 20 dB improvement in the accuracy of the reconstructed array excitation offering mean square errors (MSEs) near to −40 dB, with a sampling strategy of just 1.4% of the Nyquist rate. This is achieved with the number of measurements to array element size ratio of approximately 0.2.

Beam Focusing for Near-Field Multiuser MIMO Communications

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.

A Fully Packaged 135-GHz Multiuser MIMO Transmitter Array Tile for Wireless Communications

We report a packaging approach for building mm-wave massive multiple-input–multiple-output (MIMO) transmitter arrays in tiles. A single-channel 135-GHz transmitter, having a 22-nm fully-depleted (FD)-silicon on insulator (SOI) CMOS IC for baseband-RF conversion, an InP HBT power amplifier, and a linear microstrip patch antenna array, all packaged on a low-permittivity ceramic interposer (Kyocera GL771, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{r}=5.2\,\,\text {and}\,\,\delta =0.003$ </tex-math></inline-formula> ), had 27.5-dBm measured effective isotropic radiated power (EIRP) at saturation and showed 8.5% error vector magnitude (EVM) during 30-Gb/s, 64 quadratic-amplitude modulation (QAM) transmission. An eight-channel transmitter MIMO array tile module constructed on an low-temperature-cofired ceramic (LTCC) interposer, with the same antennas and ICs, after calibration and under the control of a field-programmable gate array (FPGA), had 38.5-dBm measured EIRP at saturation and showed −13-dB EVM in 1.92-Gb/s 16 QAM transmission. Steerable two-simultaneous beam transmission was demonstrated at 34-dBm peak EIRP per beam.

Dual-Band Dual-Polarized Massive MIMO Array for Maritime Applications

A dual-band dual-polarized massive multiple-input multiple-output (MIMO) antenna array is presented for maritime communications and sensing applications in the 5.1- and 5.9-GHz bands. Passive elements are introduced in the array to achieve a low-gain pattern variation in both bands. The optimal element spacing is studied, relying on the impact of mutual coupling and the radiation performance of the embedded elements. The topology of the MIMO array is also studied based on the system-level performance. The array was prototyped, validating the proposed concept.

A Comprehensive Study of Massive MIMO Antenna Arrays in the Next Generation of Communication Networks

The need of a capacity expansion becomes an inevitable factor concerning the increasing of mobile data applications and the unlimited expectation of the fifth generation of communication. Currently, massive Multiple Input Multiple Output (MIMO) technique is the main part of the continuous researches and it has become the major key for the technologies of modern cellular communications not only because it offers very high system throughput but also it improves the energy efficiency in multi-user scenarios. One of the first Base Stations (BSs) was implemented in 2018 for the chain of sixty four digital transceivers. Other BS deployed in many countries, designed by many companies, theoretically verified by many researches and published in many papers, and designed smartphones for working on 3G, 4G, and 5G technologies. Within a few years, designing a massive MIMO antenna array with digital transmitters and receivers will have a master streaming characteristic at both centimeter wave (sub of 6GHz) and millimeter wave (mm wave) frequencies. Recently, diverse design challenges of these arrays have appeared and characterized by using huge number of antennas in the vicinity, integrating them with Radio Frequency (RF) frontends, surpassing the hardware restrictions, and resolving the beamforming techniques. To highlight on these developments, the present paper concentrates on the evolution and growth of the existing massive MIMO array antenna which are designed, implanted, and measured indoors or outdoors for wireless communication systems in both BSs and smartphones. The paper is also expanded to explain the antenna configuration and its integration with beamforming techniques which has a great impact on reducing the cost and the energy consumption matched with the energy-saving design vision of 5G. Some of the challenges that have faced the design and the proposed solutions are also discussed in this paper. In addition, massive MIMO antenna array implemented in some countries around the world are discussed and future ideas to support the improvements in the antenna array communication networks are highlighted.

Low Complexity Optimization of the Asymptotic Spectral Efficiency in Massive MIMO NOMA

Massive multiple-input multiple-output (MIMO) technology facilitates huge increases in the capacity of wireless channels, while non-orthogonal multiple access (NOMA) addresses the problem of limited resources in traditional orthogonal multiple access (OMA) techniques, promising enhanced spectral efficiency. This work uses asymptotic capacity computation results to reduce the complexity of a power allocation algorithm for small-scale MIMO-NOMA, so that it may be applied for systems with massive MIMO arrays. The proposed method maximizes the sum-capacity of the considered system, subject to power and performance constraints, and demonstrates greater accuracy than alternative approaches despite remaining low-complexity for arbitrarily large antenna arrays.

Guest Editorial Special Issue on Multiple Antenna Technologies for Beyond 5G-Part II

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multiple antenna technologies,</i> which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

The Design of a Trimodal Broadside Antenna Element for Compact Massive MIMO Arrays: Utilizing the Theory of Characteristic Modes

Massive multiple-input, multiple-output (MIMO) arrays are becoming critical elements in cellular base station infrastructure. We utilize the theory of characteristic modes (TCM) to design a novel trimodal broadside antenna element that is suitable for constructing compact massive MIMO arrays. The proposed antenna element consists of three ports, formed by reviewing an existing compact two-port Y-shaped patch antenna from a TCM perspective. Using this perspective, the Y-shaped antenna is modified into a snowflake-shaped patch antenna and excited by three ports via capacitive coupling. The advantage of this design approach is that the size of the three-port antenna is approximately the same as a conventional dual-polarized patch antenna, hence allowing 50% more antenna elements for the same array aperture. By cutting the ground plane into a hexagonal shape, a multiple of the proposed three-port canonical antennas are concatenated together to form 21- and 102-port massive MIMO arrays with all radiation patterns pointing in the broadside direction. Key performance characteristics of the massive MIMO arrays, i.e., the Hermitian product of the simulated massive MIMO channel and mutual coupling, validate the effectiveness of the compact design.

Guest Editorial Special Issue on Multiple Antenna Technologies for Beyond 5G-Part—I

Recently, the first version of the fifth-generation (5G) new radio (NR) standard with massive multiple-input multiple-output (MIMO) has been finished by the 3rd Generation Partnership Project (3GPP), with initial deployments occurring in 2018. Despite the major advances in 5G, there are still many challenges remaining. 6G and beyond will require even higher data rates, lower latencies, better energy efficiency, and improved robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multiple antenna technologies,</i> which have played important roles in nearly all recent wireless standards, will be key to addressing these challenges. MIMO research continues to evolve, and new MIMO research topics such as enhanced massive MIMO techniques and array architectures hold much potential for 6G and beyond. Cell-free massive MIMO utilize a large number of distributed access points (APs) that jointly serve users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free massive MIMO can be analyzed using a similar methodology as in cellular massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in millimeter wave (mmWave) massive MIMO systems, beamspace MIMO has been proposed to significantly reduce the number of required radio-frequency (RF) chains by using lens antenna arrays or phase shifters. Alternatively, the intelligent reflecting surface (IRS) concept involves electromagnetically controllable surfaces that can be integrated into large-scale infrastructure such as building walls, airports, and stadiums. There are active and partially passive forms of large intelligent surface (LIS), and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models. This special issue aims to highlight recent research on multiple antenna technologies.

A N260 Band 64 Channel Millimeter Wave Full-Digital Multi-Beam Array for 5G Massive MIMO Applications

In this paper, a 37-42.5GHz (N260 Band) 64 channel full-digital multi-beam array for 5G massive multiple input multiple output (MIMO) applications is developed. The multi-beam array is built on a time division duplexing (TDD) architecture in which the transmitter and receivers are independent and working separately to enable more flexible channel configuration and hardware distribution. The 64 antenna elements of the array are aligned in a way that 16 elements lie in the horizontal and 4 in the vertical direction to deliver higher beam resolution in the horizontal plane. By using cables of unequal lengths, the $16\times 4$ antenna elements are connected to a cluster of $8\times 8$ transmitters/receivers. The hardware design of the transmitters, receivers, antennas and other circuits is demonstrated, with excellent measured RF performance achieved. Furthermore, the calibration of the massive MIMO array is presented, and the influence of inequality of array feedlines on calibration accuracy in a wide bandwidth is analyzed and experimentally verified. Finally, experimental studies on single beam scanning, multiple concurrent beam generation and over-the-air (OTA) performance of the transmitting and receiving array have been done. Simulation and measurement results suggest that the array has an excellent beam-forming capability that supports beam steering and generation of multiple concurrent beams. The OTA test reveals that the array can provide high modulation and demodulation accuracy for communication.

Engineering the Eigenspace Structure of Massive MIMO Links Through Frequency-Selective Surfaces

We propose an efficient finite-difference time-domain (FDTD) computational paradigm to estimate the channel matrix of generic massive multiple-input–multiple-output (MIMO) systems. Working with a practical scenario with 196 base station (BS) elements and 6 equispaced user-equipment devices, we compute the dominant eigenspace for the uplink channel. The full-wave results are compared to conventional ray-tracing methods, with emphasis laid upon observing and understanding the electromagnetic (EM) impact of interelement mutual coupling and spherical wavefront illumination on the wireless MIMO channel properties. In particular, we investigate EM manipulation of massive MIMO channel structures by utilizing, especially, engineered wire-grid frequency-selective surfaces placed in the vicinity of the BS massive MIMO array. Using the in-house FDTD code, we demonstrate the capability of such passive structures to redistribute the illumination Poynting vector magnitude over the BS array aperture, as well as modifying the dominant eigenspace at certain design frequencies. This letter demonstrates the advantage of the synergy between rigorous EM analysis and stochastic communication theories and techniques, with expected applications in emerging wireless technologies (5G and beyond).

Massive MIMO is a reality—What is next?: Five promising research directions for antenna arrays

Massive MIMO (multiple-input multiple-output) is no longer a “wild” or “promising” concept for future cellular networks—in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies—once viewed prohibitively complicated and costly—is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6GHz and mmWave frequencies.In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.

Eigenspace Structure Estimation for Dual-Polarized Massive MIMO Systems Using an IDM-CGF Technique

In this letter, we propose an infinitesimal dipole model, cross-correlation Green's function algorithm suitable for the efficient determination of spatial correlation matrix data for massive multiple-input–multiple-output (MIMO) antennas with arbitrary element polarization and interelement spacing. Through observations of the dominant eigenspace of the massive MIMO correlation matrix, we show that one can easily assess the available degrees of freedom in channel-hardened scenario of user-equipment-to-base-station communication. We provide several examples featuring $8 \times 8$ and $16 \times 16$ massive MIMO arrays composed of infinitesimal dipoles, lending crucial insights into the correlation matrix eigenspace structural modifications incurred by changing element polarization, spatial positioning, and interelement spacing.

Precoding and Detection for Broadband Single Carrier Terahertz Massive MIMO Systems Using LSQR Algorithm

The terahertz (THz) communication utilizes the frequency spectrum above 300 GHz and is widely considered as a promising solution to the future high-speed short-range wireless communication beyond millimeter wave communication. While providing tens of gigahertz bandwidth, it is subjected to high-propagation path loss, inter-symbol, and inter-user interferences. The massive multiple-input-multiple-output (MIMO) can be applied to address these problems by cooperation between many access point antennas. However, the THz channel characteristics, including high-propagation path loss, frequency selectivity, and a big number of samples per channel impulse response, require carefully tailored algorithm for massive MIMO signal processing. In this paper, we propose a single carrier minimum mean square error precoding and detection algorithm for frequency selective THz channels. The MIMO signal transmission is described with the block matrices. A gain control heuristic is introduced to reduce the complexity. The sparsity property of the channel is utilized to construct sparse channel matrices and the least square QR algorithm is applied to efficiently solve the problems. Besides the uniform antenna array, the hybrid array consisting of several subarrays is considered as well. The simulation results show that the massive MIMO array can provide a satisfactory performance in terms of bit error rate.

Cooperative Dynamic Angle of Arrival Estimation Considering Space–Time Correlations for Terahertz Communications

Angle of arrival (AoA) estimation is required by adaptive directive antennas in order to realize a high antenna gain, which is necessary for future indoor terahertz communications due to its extremely high path loss. This paper proposes an AoA estimation algorithm using belief propagation in a dynamic scenario, where the user equipment (UE) is moving during the data transmission, based on the space-time correlations of AoA change. The temporal correlation of AoA change is due to the limited moving speed and statistical movement pattern. Furthermore, if we have distributed antennas or hybrid massive multiple-input-multiple-output (MIMO) array, the AoA changes of different antennas reveal spatial correlation because all the AoA changes are caused by the same spatial displacement of UE. This spatial correlation is utilized in this paper to further improve the estimation accuracy by passing messages between antennas and combining the intrinsic estimate by each antenna and extrinsic information from other antennas. In order to demonstrate the algorithm performance, a distributed antenna system and a hybrid massive MIMO array (an array of multiple directive antenna arrays) are considered as application scenarios. The simulation results show that the cooperative estimation brings significant advantage in both scenarios in respect of mean effective antenna gain and level crossing rate.

A Message Passing Approach to Acquire mm-Wave Channel State Information Based on Out-of-Band Data

Millimeter-wave (mm-wave) communication is a practicable scheme for big data communication, such as next-generation cellular communication. However, because mm-wave frequencies have an extremely large path loss, in order to mitigate the mm-wave path loss, a large number of antennas are packed for beamforming. Conventional multiple-input multiple-output (MIMO) beamforming uses digital processing, which leads to needing extremely giant energy. Hybrid analog/digital beamforming could serve as an awesome technique to reduce the cost. It is essential for achieving high beamfroming gain that accurate mm-wave channel information should be attained at the transmitter. This paper concentrates on the channel state information (CSI) acquirement problem in mm-wave communication systems with massive MIMO arrays. Because of the CSI acquirement, a method of significant overhead, we consider an accurate channel estimation scheme with low overhead. Not only do we in this paper propose using support information extracted at Sub-6GHz to aid the mm-wave CSI acquirement but also introduce a strategy used in the mm-wave channel estimation. We, in particular, formulate mm-wave CSI acquirement as a compressive sensing problem and obtain the CSI by using Bernoulli–Gaussian generalized approximate message passing (BG-GAMP) algorithm. We also extend the BG-GAMP algorithm with support distribution information from sub-6-GHz channel. Furthermore, based on the $K$ nearest neighbor idea, we redesign the BG-GAMP algorithm depending on sub-6-GHz support distribution information. Details of BG-GAMP with the nearest neighbor learning algorithm that is built on support distribution information from sub-6-GHz channel would also be revealed. Simulation results show the out-of-band information aided mm-wave channel estimation is capable of reducing the pilot overhead greatly and channel estimation accuracy improved as well.

Two-Dimensional DOA Estimation Using Arbitrary Arrays for Massive MIMO Systems

With the quick advancement of wireless communication networks, the need for massive multiple-input-multiple-output (MIMO) to offer adequate network capacity has turned out to be apparent. As a portion of array signal processing, direction-of-arrival (DOA) estimation is of indispensable significance to acquire directional data of sources and to empower the 3D beamforming. In this paper, the performance of DOA estimation for massive MIMO systems is analyzed and compared using a low-complexity algorithm. To be exact, the 2D Fourier domain line search (FDLS) MUSIC algorithm is studied to mutually estimate elevation and azimuth angle, and arbitrary array geometry is utilized to represent massive MIMO systems. To avoid the computational burden in estimating the data covariance matrix and its eigenvalue decomposition (EVD) due to the large-scale sensors involved in massive MIMO systems, the reduced-dimension data matrix is applied on the signals received by the array. The performance is examined and contrasted with the 2D MUSIC algorithm for different types of antenna configuration. Finally, the array resolution is selected to investigate the performance of elevation and azimuth estimation. The effectiveness and advantage of the proposed technique have been proven by detailed simulations for different types of MIMO array configuration.

Design of Energy- and Cost-Efficient Massive MIMO Arrays

Large arrays of radios have been exploited for beamforming and null steering in both radar and communication applications, but cost and form factor limitations have precluded their use in commercial systems. This paper discusses how to build arrays that enable multiuser massive multiple-input–multiple-output (MIMO) and aggressive spatial multiplexing with many users sharing the same spectrum. The focus of the paper is the energy- and cost-efficient realization of these arrays in order to enable new applications. Distributed algorithms for beamforming are proposed, and the optimum array size is considered as a function of the performance of the receiver, transmitter, frequency synthesizer, and signal distribution within the array. The effects of errors such as phase noise and synchronization skew across the array are analyzed. The paper discusses both RF frequencies below 10 GHz, where fully digital techniques are preferred, and operation at millimeter (mm)-wave bands where a combination of digital and analog techniques are needed to keep cost and power low.