Large-scale Antenna Systems Research Articles

Printed millimeter-wave MIMO-based slot antenna arrays for 5G networks

In this work, a broadband millimeter-wave (mm-wave) multiple-input–multiple-output (MIMO) antenna system for upcoming fifth generation (5G) networks is presented. The MIMO antenna system is two ports and realized using two antenna arrays, aligned in opposite directions. Each array consist of three elements in each, as each element is a simple recognized printed wide-slot antenna proximity excited by microstrip line with a widened tuning stub; manipulated for operating in the Ka-band, which includes the 28 and 38 GHz bands, as potential candidates for 5G communications. An electromagnetic band-gap (EBG) reflector is placed behind the antenna structure toward the feeding network to decrease the backward radiation and improve the front-to-back (F/B) ratio. Results show that the proposed MIMO antenna system with EBG reflector provides wideband impedance bandwidth >27 GHz (from 22.5 to >50 GHz) and good radiation characteristics with a total realized gain up to 11.5 and 10.9 dBi at the two frequencies of interest, respectively. The envelope correlation coefficient (ECC) and diversity gain (DG) were evaluated and showed good MIMO performance. These remarkable features with the benefits of design simplicity and easily expansion to large-scale antenna system make the proposed design suitable for mm-wave communications.

Energy-Efficient Resource Allocation in Multicell Large-Scale Distributed Antenna System with Imperfect CSI

ABSTRACT In this paper, we propose an energy-efficient resource allocation algorithm in the downlink of a multicell large-scale distributed antenna system. A non-convex optimization problem is formulated to maximize the energy efficiency (EE) of the entire network. The optimization problem considers the circuit power consumption, the minimum data rate requirements, and the maximum allowed transmit power per remote radio head. The optimization problem is transformed into an equivalent optimization problem in the subtractive form using the Dinkelbach method. An iterative algorithm for the optimization solution is proposed in which antenna selection with user scheduling and power allocation are implemented separately. To tackle this significant computational complexity, a norm-based joint antenna and the user selection algorithm that uses Tabu Search are proposed to select optimum antennas and users for transmission. Next, a Lagrangian dual decomposition method is adopted to derive a power allocation strategy that can run independently in each cell. Simulation results show that the proposed algorithm can achieve its maximum EE in several iterations, and that increasing the number of antennas or users does not necessarily result in a higher EE. In addition, the proposed iterative scheme is always able to achieve a better performance than the traditional norm-based algorithm and the simplified cloud radio access network based energy efficient power allocation scheme, no matter how many antennas or users are selected.

Structured Random Compressed Channel Sensing for Millimeter-Wave Large-Scale Antenna Systems

In millimeter-wave (mmWave) communications, large-scale antenna system (LSAS) is considered an essential technology to realize beamforming gain to compensate for huge propagation loss. However, channel estimation for LSASs poses a formidable challenge, especially when hybrid analog–digital structures are adopted for ensuring reasonable complexity and cost. To address this challenge, compressed channel sensing (CCS) is leveraged to measure mmWave channels via a random sensing codebook. By exploiting the sparse nature in mmWave channels, only a small number of measurements is required for channel recovery. However, signaling or storing the configuration of a full random sensing codebook leads to a huge burden. Moreover, because the sensing beam of a full random sensing codebook always spreads its power over the channel, the CCS has a stringent requirement on the signal-to-noise ratio (SNR) for robust channel recovery. To overcome these issues, we propose a structured random sensing codebook inspired by the random convolutional measurement process. Owing to its structured nature, signaling or storing overhead from the codebook configuration is significantly reduced. Additionally, the structured random sensing codebook can concentrate its power in a local angle coverage for a sectorized cell, thus improving the robustness in low-SNR regimes. Simulation results demonstrate that the recovery performance of the proposed structured random sensing codebook design is comparable to that of the full random design. Regarding the robustness in low-SNR regimes, the recovery performance is substantially improved by the structured random sensing codebook with power concentration for a local angle coverage.

A Simple and Effective Approach for Transmit Antenna Selection in Multiuser Massive MIMO Leveraging Submodularity

Massive MIMO systems are expected to enable great improvements in spectral and energy efficiency. Realizing these benefits in practice, however, is hindered by the cost and complexity of implementing large-scale antenna systems. A potential solution is to use transmit antenna selection for reducing the number of radio-frequency (RF) chains at the base station. In this paper, we consider the NP-hard discrete optimization problem of performing transmit antenna selection in the downlink of a single cell, multiuser massive MIMO system by maximizing the downlink sum-rate capacity with fixed user power allocation subject to various RF switching constraints. Whereas prior work has focused on using convex relaxation based schemes, which lack theoretical performance guarantees and can be computationally demanding, we adopt a very different approach. We establish that the objective function of this antenna selection problem is monotone and satisfies an important property known as submodularity , while the RF switching constraints are expressible as the independent sets of a matroid. This implies that a simple greedy algorithm can be used to guarantee a constant-factor approximation for all problem instances. Simulations indicate that greedy selection yields a near-optimal solution in practice and captures a significant fraction of the total downlink channel capacity at substantially lower complexity relative to convex relaxation based approaches, even with very few RF chains. This paves the way for substantial reduction in hardware complexity of massive MIMO systems while using very simple algorithms.

Channel Characterization for Wideband Large-Scale Antenna Systems Based on a Low-Complexity Maximum Likelihood Estimator

Wideband large-scale array systems operating at millimeter-wave bands are expected to play a key role in future communication systems. It is recommended by standardization groups to use spherical-wave models (SWMs) to characterize the channel in near-field cases because of the large array apertures and the small cell size. However, this feature is not widely reflected in channel models yet, mainly due to the high computational complexity of SWMs compared to that of the conventional plane-wave model (PWM), especially when ultrawideband signals are considered. In this paper, a maximum likelihood estimator of low computational complexity is implemented with an SWM for ultrawideband signals. The measurement data obtained from an ultrawideband large-scale antenna array system at 28–30 GHz are processed with the proposed algorithm. The power azimuth-delay profiles estimated from the SWM and the PWM are compared to those obtained from rotational horn antenna measurement, respectively. It shows that the multipath components (MPCs) are well-estimated with the proposed algorithm, and significant improvement in estimation performance is achieved with the SWM compared to the PWM. Moreover, the physical interpretation of the estimated MPCs is also given along with the estimated scatterers.

Efficient Compressive Channel Estimation for Millimeter-Wave Large-Scale Antenna Systems

Large-scale antenna systems are considered as a viable technology to compensate for huge path loss in millimeter-wave (mmWave) communications. However, due to the massive antennas, the channel state information (CSI) acquisition is costly and challenging. In this paper, we develop a novel compressive channel estimation framework based on multiple measurement vectors (MMV). Compared with conventional single measurement vector (SMV)-based approach, the proposed framework exploits structural sparsity exhibited in the relatively rich local scattering mmWave channels to greatly reduce the training and computational overheads. Moreover, we propose a channel subspace matching pursuit (CSMP) algorithm based on the MUltiple SIgnal Classification (MUSIC) as an MMV solver. By leveraging the benefits of MUSIC, the proposed CSMP can properly exploit the diversity gain from structural sparsity, and further improve the estimation quality via the superresolution capability. Meanwhile, an efficient implementation method of the proposed CSMP is also presented. Compared to the conventional MMV solver, the proposed CSMP exhibits much lower complexity. Finally, several simulation results show that the MMV-based CSMP achieves significant performance gains over other estimation algorithms, especially when the angular resolutions are high. Regarding the computational cost, the simulation result shows that the MMV-based estimation algorithms are approximately two orders of magnitude smaller than the SMV-based estimation algorithms.

Achieving massive connectivity with non-orthogonal pilots and dirty-RFs

Emerging 5G wireless networks need to support sufficient connectivity for a large number of machine-type communication devices, which may have poor radio-frequency (RF) circuit quality due to cost- and energy-efficient design. In this paper, the connectivity is analyzed for a training-based large-scale antenna system employing both non-orthogonal pilots and dirty-RFs on both the transmitter and receiver sides. By considering the effects of imperfect hardware and interference caused by non-orthogonal pilots, the performance of the linear minimum mean-square error (MMSE) channel estimator is derived, and the corresponding average rate of the maximum ratio combiner (MRC) is obtained. From these results, the connectivity maximization problem is formulated and the closed-form solutions for the optimal training length and the optimal number of simultaneously served users are provided. Asymptotic analysis further reveals that allowing only orthogonal pilots limits both the connectivity even when a large number of antenna is employed. However, by allowing non-orthogonal pilots, both the connectivity and the energy efficiency can be improved significantly, even when dirty-RFs are taken into account.

Over-the-Air Radiated Testing of Millimeter-Wave Beam-Steerable Devices in a Cost-Effective Measurement Setup

With the severe spectrum congestion of sub-6 GHz cellular systems, large-scale antenna systems in the millimeter-wave (mmWave) bands can potentially meet the high data rate envisioned for fifth generation communications. Performance evaluation of antenna systems is an essential step in the product design and development stage. However, conventional cable conducted test methods are not applicable for mmWave devices. There is a strong need for over-the-air (OTA) radiated methods, where mmWave device performance can be evaluated in a reliable, repeatable, and feasible way in laboratory conditions. In this article, radiated testing methods are reviewed, with a focus on their principle and applicability for beam steerable mmWave devices. To explore the spatial sparsity of mmWave channel profiles, a cost-effective simplified 3D sectored multi-probe anechoic chamber system with an OTA antenna selection scheme is proposed. This setup is suitable for evaluation of beam-steerable devices, including both base station and user equipment devices. The requirements for the test system design are analyzed, including the measurement range, number of OTA antennas, number of active OTA antennas, and amount of channel emulator resource. Finally, several metrics to validate system performance are described for evaluation of mmWave devices.

Assessment of Pilot Pollution Problem for Multi-Cell Multi-User MIMO

Focused research and standardization work in wireless throughput, subscribers will increase day by day. One can prospect that, millions of users in a mega city will want to transmit and receive data, for instance, 100 megabits per second per user. Massive MIMO (Large Scale Antenna Systems) is a new technology which will be used for resolving the mentioned issue. Spectral efficiency improvements over fourth generation (4G) technology are frequently mentioned. Adding more antennas is always beneficial for increased throughput, reduced radiated power, increase the capacity everywhere in the cell and greater simplicity in signal processing. In these days the main problem is RF interference and noise which can be generated by almost any device that produces an electro-magnetic signal, such as cordless phones to Bluetooth headsets, microwave ovens, repeaters and even smart phones, which is caused call drop and bad quality in the network. In this article, the Signal-to-interference-plus-noise ratio (SINR) and the value of mean capacity in the Non-cooperative cellular wireless have been increased by using infinite number of base station antennas. While employing advanced features, this is illustrated by network densification, Multi-cell Multi-User MIMO and inter cell interference mitigation techniques. The propagation model is not clear for both terminals and base stations which is calculated taking in to consideration path loss, specular reflection, environment models, earth’s elevation, fast fading, log-normal shadowing fading and geometric attenuation. The conjugate transpose of the channel estimation is used for forward and reverse precoding. Numerical results show that, by using unlimited number of antennas in the base station, the inter-cell interference, the effect of uncorrelated noise and fast fading have been vanished, although the inter-cell interference that caused by reuse of the pilot sequence in other cells does not disappear. And also average capacity improves with increment of base station antennas. In this study, MATLAB based simulation tool has been developed to calculate the SIR and also the mean capacity.

Online compressive diagnosis of massive MIMO calibration state

With hundreds of antennas at one single base station (BS), it is critical to take full advantage of time-division duplexing (TDD) channel reciprocity to learn the downlink (DL) channel state information (CSI) from the uplink (UL) channel measurements at the BS. However, due to different radio frequency branches for transmitting and receiving, calibrating the antenna array at the BS is a necessary and prerequisite task to effect this reciprocity between the end-to-end DL and UL channels. This calibration procedure becomes extremely challenging in massive multiple-input multiple-output (MIMO) since all the existing calibration approaches do not scale well with the size of the antenna array. To release all the potentials promised by massive MIMO, we rely on compressive sensing theory to develop an efficient and robust way to diagnose the massive antenna array and further restore the full calibration at the BS without service interruption. The diagnosis only requires CSI feedback in the amount of O(log N) instead of O(N) as required by those conventional calibration schemes. Extensive simulations demonstrate our approach can maintain the whole large scale antenna system in a calibrated state with only limited amount of feedback overhead. The proposed novel scheme thus allows us to make full use of the channel reciprocity in massive MIMO under TDD operation.

Hybrid Analog–Digital Precoding Design for Secrecy mmWave MISO-OFDM Systems

Millimeter-wave large-scale antenna systems typically apply hybrid analog-digital precoders to reduce hardware complexity and power consumption. In this paper, we design hybrid precoders for physical-layer security under two types of channel knowledge. With full channel knowledge at transmitter, we provide sufficient conditions on the minimum number of RF chains needed to realize the performance of the fully digital precoding. Then, we design the hybrid precoder to maximize the secrecy rate. By maximizing the average projection between the fully digital precoder and the hybrid precoder, we propose a low-complexity closed-form hybrid precoder. We extend the conventional projected maximum ratio transmission scheme to realize the hybrid precoder. Moreover, we propose an iterative hybrid precoder design to maximize the secrecy rate. With partial channel knowledge at transmitter, we derive a secrecy outage probability upper-bound. The secrecy throughput maximization is converted into a sequence of secrecy outage probability minimization problems. Then, the hybrid precoder is designed to minimize the secrecy outage probability by an iterative hybrid precoder design. Performance results show the proposed hybrid precoders achieve performance close to that of the fully digital precoding at low and moderate signal-to-noise ratios (SNRs), and sometimes at high SNRs depending on the system parameters.

Energy Efficiency Gain of Cellular Base Stations with Large-Scale Antenna Systems for Green Information and Communication Technology

Due to the ever-increasing data demand of end users, the number of information and communication technology (ICT)-related devices and equipment continues to increase. This induces large amounts of heat emissions, which can cause serious environmental pollution. In recent times, signal transmission systems such as cellular base stations (BSs) have been constructed everywhere and these emit a large carbon footprint. Large-scale antenna systems (LSASs) that use a large amount of transmission antennas to serve a limited number of users can increase energy efficiency (EE) of BSs based on the beamforming effect, and thus can be a promising candidate to reduce the carbon footprint of the ICT field. In this paper, we discuss the necessary schemes to realize LSASs and show the expected EE gain of the LSAS with enough practicality. There are many obstacles to realize the high EE LSAS, and even though several studies have shown separate schemes to increase the EE and/or throughput (TP) of LSASs, few have shown combinations of schemes, and presented how much EE gain can be achieved by the schemes in the overall system. Based on the analysis in this paper, we believe more detailed work for the realization of high energy efficient BSs with LSASs is possible because this paper shows the necessary schemes and the maximum achievable energy efficiency gain as a reference. Extensive analysis and simulation results show that with proper implementation of the power amplifier/RF module and a robust channel estimation scheme, LSASs with 600 transmitter (TX) antennas can achieve 99.4 times more EE gain compared to the current systems, thereby resulting in significant reduction of carbon footprints.

RaPro: A Novel 5G Rapid Prototyping System Architecture

We propose a novel fifth-generation (5G) rapid prototyping (RaPro) system architecture by combining FPGA-privileged modules from a software defined radio (or FPGA-coprocessor) and high-level programming language for advanced algorithms from multi-core general purpose processors. The proposed system architecture exhibits excellent flexibility and scalability in the development of a 5G prototyping system. As a proof of concept, a multi-user full-dimension multiple-input and multiple-output system is established based on the proposed architecture. Experimental results demonstrate the superiority of the proposed architecture in large-scale antenna and wideband communication systems.

Pairing and Scheduling for Large Array MIMO Using Regularized Channel Inversion Receivers Over Nakagami-m Fading

Multiple-antenna (MIMO) technology is becoming mature for wireless communications and has been incorporated into wireless broadband standards like LTE and WiFi. Basically, the more the number of antennas the transmitter/receiver is equipped with, the more the possible independent signal paths and the better the performance in terms of data rate and link reliability. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO and full-dimension MIMO) makes a clean break with current practice through the use of a very large number of service antennas (e.g., hundreds or thousands) that are operated fully coherently and adaptively. Extra antennas help by focusing the transmission and reception of signal energy into ever-smaller regions of space. This brings huge improvements in throughput and energy efficiency, in particularly when combined with simultaneous scheduling for a large number of user terminals (e.g., tens or hundreds). This Paper focuses on the efficiency of the throughput and fairness of large MIMO. In multi-user MIMO (MU-MIMO), fewer antennas were used, so the only concern was to mitigate the interference of the users. The interference was mitigated by using zero forcing (ZF) receivers while the fairness of the system was maintained by using Rayleigh Fading channel. MU-MIMO was sufficient for only smaller number of users as the number of antennas were fixed and all the users had to be entertained within the same bandwidth. But, in Massive MIMO, along with the interference of the users, the interference of the antennas themselves is also a big concern, as, the number of antennas were increased contrary to the fixed fewer number of antennas in MU-MIMO. Rayleigh Fading channel and ZF receivers were not sufficient for increased number of users and antennas respectively. The problem of interference amongst the antennas was addressed by using regularized channel inversion receivers that increase the throughput of the system while, the fairness of the system is increased by using Nakagami-m channel.

Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems

In this paper, we study the physical layer multicasting to multiple co-channel groups in large-scale antenna systems. The users within each group are interested in a common message and different groups have distinct messages. In particular, we aim at designing the precoding vectors solving the so-called quality of service (QoS) and weighted max-min fairness (MMF) problems, assuming that the channel state information is available at the base station (BS). To solve both problems, the baseline approach exploits the semidefinite relaxation (SDR) technique. Considering a BS with $N$ antennas, the SDR complexity is more than $\mathcal {O}(N^{6})$ , which prevents its application in large-scale antenna systems. To overcome this issue, we present two new classes of algorithms that, not only have significantly lower computational complexity than existing solutions, but also largely outperform the SDR-based methods. Moreover, we present a novel duality between transformed versions of the QoS and the weighted MMF problems. The duality explicitly determines the solution to the weighted MMF problem given the solution to the QoS problem, and vice versa. Numerical results are used to validate the effectiveness of the proposed solutions and to make comparisons with existing alternatives under different operating conditions.

Novel Detection Scheme for LSAS Using Power Allocation in Multi User Scenario with LTE-A and MMB Channels

Massive MIMO (also known as the "Large-Scale Antenna System") enables a significant reduction of latency on the air interface with the use of a large excess of service-antennas over active terminals and time division duplex operation. For large-scale MIMO, several technical issues need to be addressed (e.g., pilot pattern design and low-antenna power transmission design) and theoretically addressed (e.g., channel estimation and power allocation schemes). In this paper, we analyze the ergodic spectral efficiency upper bound of a large-scale MIMO, and the key technologies including channel uplink detection. We also present new approaches for detection and power allocation. Assuming arbitrary antenna correlation and user distributions, we derive approximations of achievable rates with linear detection techniques, namely zero forcing, maximum ratio combining, minimum mean squared error (MMSE) and eigen-value decomposition power allocation (EVD-PA). While the approximations are tight in the large system limit with an infinitely large number of antennas and user terminals, they also match our simulations for realistic system dimensions. We further show that a simple EVD-PA detection scheme can achieve the same performance as MMSE with one order of magnitude fewer antennas in both uncorrelated and correlated fading channels. Our simulation results show that our proposal is a better detection scheme than the conventional scheme for LSAS. Also, we used two channel environment channels for further analysis of our algorithm: the Long Term Evolution Advanced channel and the Millimeter wave Mobile Broadband channel.

Modular Precoding Based on Zero Forcing in Large Scale Antenna Systems with Imperfect Channel State Information

Modular Precoding Based on Zero Forcing in Large Scale Antenna Systems with Imperfect Channel State Information

Optimal transmission power determination to increase energy efficiency of large-scale MIMO antenna systems

Large-scale (LS) MIMO antenna systems use excessive amount of TX antennas and serve limited number of users to make very high channel gain, and it is obvious that the channel gain increases spectral efficiency of the system. The transmission power of LS-MIMO systems also can be reduced from the increment of channel gain to increase energy efficiency (EE). In this situation, a determination of transmission power which is reduced from total available transmission power can be an important scheme to achieve high EE. This paper proposes a determination scheme of the EE optimum transmission power for LS-MIMO systems. The approximated optimum transmission power is derived analytically as a closed-form function based on various parameters, such as number of TX antennas, number of users, system bandwidth, interference, and so on. Using the closed-form function with the measurable parameters, we can easily get the optimum EE transmission power in real-time. The effectiveness of proposed scheme is validated with numerical simulations. According to the results, the proposed scheme can significantly increase the EE with fairly small loss of channel capacity, compared with ordinary LS-MIMO systems.

Optimal Semi-Persistent Uplink Scheduling Policy for Large-Scale Antenna Systems

In this paper, the uplink semi-persistent scheduling policy problem of minimizing network latency is considered for a training-based large-scale antenna system employing two simple linear receivers, a maximum ratio combiner and a zero-forcing receiver, while satisfying each user’s reliability and latency constraints under an energy constraint. The network latency is defined as the air-time requested either to serve all users with a minimum quality-of-service, including reliability constraints and minimum throughput levels, or to maximize the spectral efficiency. Optimal non-orthogonal pilots are used to decrease the network latency. An optimization algorithm for determining the latency-optimal uplink scheduling policy using binary-integer programming (BIP) with an exponential-time complexity is proposed. In addition, it is proven that a linear programming relaxation of the BIP can provide an optimal solution with a polynomial-time complexity. Numerical simulations demonstrate that the proposed scheduling policy can provide several times lower network latency in realistic environments than conventional policies. The proposed optimal semi-persistent scheduling policy provides critical guidelines for designing 5G and future cellular systems, particularly for their ultra-reliable low-latency communication services.

Design of an Energy Efficient Future Base Station with Large-Scale Antenna System

Due to the continuous increase in data demanded by end-users, an energy-efficient base station (BS) is a vital topic of interest that would not only result in a substantial economic impact on service providers, but would also reduce the carbon footprint of operating a network. In this regard, we propose the structure and systematic operation of a BS with a large-scale (LS) antenna system that can increase the energy efficiency (EE) of cellular systems. The proposed BS structure includes various power-related units, such as a central management apparatus, power controller, EE calculator, radio site-dependent parameter space (RSD-PS) and determiner. With the information provided from each unit, the decision unit determines how to adjust each component of the BS in order to maximize the EE. Extensive simulations show that the proposed BS improves the EE performance by about 83.05% relative to the reference BS.