Large-scale Antenna Systems Research Articles

Transmit Antenna Selection Strategies for SC- FDMA- IDMA Massive MIMO Systems

The 5 G wireless offers many advancements over the prevalent 4 G LTE communication networks, such as enhanced data transmission rates (in order of Gbps), Substantially reduced latency, many times increase in the Base Station Capacity and praiseworthy betterments in the QoS offered to the users. IDMA has already proven its potential in 5 G and it can be easily integrated in to massive MIMO systems. The ideology behind the concept of Massive MIMO is to match the requirement for efficient usage of spectrum, and is implemented using several numbers of antennas at the Base Station, catering to a number of subscribers concurrently using same band of the given frequency. However, the cost and complexity of implementation of such large-scale antenna systems is quite high. Thus for reduction in the number of radio frequency chains, the technique of Transmit Antenna Selection is used. In this paper, we propose two transmit antenna selection strategies considered for individual user or overall users based on selection criteria maximum sum rate or minimum bit error performance considering for (MIMO) multiuser multiple input and multiple output single carrier frequency division multiple access based interleave division multiple access wireless system (MIMO-SCFDMA-IDMA). We have taken this scheme for the uplink communication and selection of the subcarrier is through the bulk selection. The performance may get degraded in case of heavy load of multiple subcarriers attached to one antenna, which will in turn call for the role of power amplifier and thus efficiency of the system may go down. Therefore, here we prefer bulk subcarrier selection instead of per subcarrier selection. It is shown in this paper that enhanced sum rate can be achieved as more users are allowed to transmit concurrently, and thus multiuser gain is achieved. We also demonstrated the comparison with simulation result of sum rate performance and Bit error rate performance of varying users. Result shows that antenna selection based on overall system is better than the antenna selection considering each user.

Pilot-Efficient Scheduling for Large-Scale Antenna Aided Massive Machine-Type Communications: A Cross-Layer Approach

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS’ power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO.

Performance analysis and improvement of large-scale antenna array system

Performance analysis and improvement of large-scale antenna array system

Load-Aware Energy Efficient Adaptive Large Scale Antenna System

This paper proposes an adaptive large scale antenna system (ALSAS) for enhancing energy efficiency in low density wireless network scenarios. The proposed ALSAS comprises of two stages, a novel adaptive discontinuous transmission (ADTx) stage and an antenna array optimization (AAO) one. The basic idea is to utilize prior knowledge of the users' quality of service (QoS) requirements as well as precoding selection in the ADTx stage to maximize the transmitter hibernation periods subject to a certain complexity constraint. In the AAO stage, further power saving is achieved by reducing the number of active antenna elements subject to a certain QoS requirement. It is shown that, relative to conventional large scale antenna system (LSAS), the proposed ALSAS system achieves significant energy efficiency improvements under various scenarios. The results show that the proposed technique can provide energy efficiency improvement between 125% and 1124% in the suburban scenario, and between 196% and 952% in the rural scenario. It is also demonstrated that for rural environments with relatively small short inter-site-distance (ISD) values, ALSAS can provide up to 500% power saving for the fixed bit rate requirement case.

Partially-Connected Hybrid Beamforming for Multi-User Massive MIMO Systems

Due to the high power consumption and hardware cost of radio frequency (RF) chains, the conventional fully-digital beamforming will be impractical for large-scale antenna systems (LSAS). To address this issue, hybrid beamforming has been proposed to reduce the number of RF chains. However, the fully-connected structure assumed in most hybrid beamforming schemes is still cost-intensive. Recently, the partially-connected structure employing notably fewer phase shifters has received considerable attention in both academia and industry. But the design of partially-connected hybrid beamforming has not been fully understood, especially in multi-user systems. In this article, we directly address the challenging non-convex non-smooth partially-connected hybrid beamforming design problem with individual signal-to-interference-plus-noise ratio (SINR) constraints and unit-modulus constraints in a multi-user massive multiple-input multiple-output (MIMO) system. An iterative alternating algorithm based on a penalty method is proposed to obtain a stationary point, which inevitably has relatively high computational complexity. Thus, two low-complexity algorithms are then proposed by utilizing matrix approximation. Numerical results demonstrate significant performance gains of the proposed algorithms over existing hybrid beamforming algorithms. Moreover, the proposed low-complexity algorithms can achieve near-optimal performance with dramatically reduced computational complexity.

Broadcasting Scalable Video With Generalized Spatial Modulation in Cellular Networks

This paper considers the transmission of scalable video via broadcast and multicast to increase spectral and energy efficiency in cellular networks. To address this problem, we study the use of generalized spatial modulation (GSM) combined with non-orthogonal hierarchical M-QAM modulations due to the capability to exploit the potential gains of large scale antenna systems and achieve high spectral and energy efficiencies. We introduce the basic idea of broadcasting/multicasting scalable video associated to GSM, and discuss the key limitations. Non-uniform hierarchical QAM constellations are used for broadcasting/multicasting scalable video while user specific messages are carried implicitly on the indexes of the active transmit antennas combinations. To deal with multiple video and dedicated user streams multiplexed on the same transmission, an iterative receiver with reduced complexity is described. 5G New Radio (NR) based link and system level results are presented. Two different ways of quadruplicating the number of broadcasting programs are evaluated and compared. Performance results show that the proposed GSM scheme is capable of achieving flexibility and energy efficiency gain over conventional multiple input multiple output (MIMO) schemes.

Low-Complexity Analog Beamforming for mmWave Large-Scale MISOME Wiretap Channel

With the popularization of large-scale antenna arrays in wireless communication, the conventional fully digital beamforming, which requires dedicated radio frequency (RF) chain per antenna element, is not viable for large-scale antenna systems due to its prohibitively high hardware cost. On the contrary, the analog beamforming design only requires one RF chain for all antenna elements, and has been widely investigated in recent years. In this letter, we consider the secure analog beamforming design that maximizes the secrecy rate in the large-scale multi-input single-output multi-eavesdropper (MISOME) wiretap channel. However, the associated secrecy rate maximization (SRM) problem is naturally non-convex due to the constant envelope constraint induced by the analog beamformer. To handle the problem, a low-complexity algorithm that combines the Dinkelbach approach and iterative coordinate ascent (ICA) algorithm is proposed to obtain a high-quality suboptimal solution. Numerical results illustrate that the proposed algorithm achieves both higher secrecy rate performance and lower complexity as compared to the existing schemes.

Multi-Group Multicast Beamforming: Optimal Structure and Efficient Algorithms

This paper considers the multi-group multicast beamforming optimization problem, for which the optimal solution has been unknown due to the non-convex and NP-hard nature of the problem. By utilizing the successive convex approximation numerical method and Lagrangian duality, we obtain the optimal multicast beamforming solution structure for both the quality-of-service (QoS) problem and the max-min fair (MMF) problem. The optimal structure brings valuable insights into multicast beamforming: We show that the notion of uplink-downlink duality can be generalized to the multicast beamforming problem. The optimal multicast beamformer is a weighted MMSE filter based on a group-channel direction: a generalized version of the optimal downlink multi-user unicast beamformer. We also show that there is an inherent low-dimensional structure in the optimal multicast beamforming solution independent of the number of transmit antennas, leading to efficient numerical algorithm design, especially for systems with large antenna arrays. We propose efficient algorithms to compute the multicast beamformer based on the optimal beamforming structure. Through asymptotic analysis, we characterize the asymptotic behavior of the multicast beamformers as the number of antennas grows, and in turn, provide simple closed-form approximate multicast beamformers for both the QoS and MMF problems. This approximation offers practical multicast beamforming solutions with a near-optimal performance at very low computational complexity for large-scale antenna systems.

Atom search optimization algorithm based hybrid antenna array receive beamforming to control sidelobe level and steering the null

Modern and upcoming 5G cellular communication networks require large-scale antenna systems to improve gain in order to face the weaknesses in the suggested millimeter-wave (mmWave) signals and to meet the higher capacity demand. These systems implement digital beamforming which requires radio frequency (RF) chain connected to each antenna. Hybrid analog-digital (HAD) beamforming has been proposed as a solution to many of the challenges facing the implementation of fully-digital beamforming, namely, energy consumption, hardware cost, and complexity. The main goals of this paper are to reduce the peak sidelobe level (SLL) of the beam pattern and steering the nulls in the desired directions along with reducing the power consumption, hardware cost, and complexity. To achieve these goals, a novel partial-connected hybrid analog-digital receive beamformer based on an atom search optimization (ASO) algorithm is proposed. ASO is a recently invented metaheuristic optimization algorithm imitating the physical movements of atoms as described in molecular dynamics simulation with the advantage of having few parameters to tune. The desired optimization weights are accomplished by controlling the amplitudes-only of the digital beamformer’s vector along with the phase shifts of the analog beamformer’s vectors. Finally, to demonstrate the effectiveness of the proposed beamformer, a comparison with other state-of-the-art metaheuristic algorithms through several scenarios is conducted, in terms of peak SLL reduction, null depth, and convergence rates. Simulation results show that the proposed algorithm has always been outperforming other types of beamformers.

Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI

Large intelligent surface (LIS)-assisted wireless communications have drawn attention worldwide. With the use of low-cost LIS on building walls, signals can be reflected by the LIS and sent out along desired directions by controlling its phases, thereby providing supplementary links for wireless communication systems. In this paper, we evaluate the performance of an LIS-assisted large-scale antenna system by formulating a tight upper bound of the ergodic spectral efficiency and investigate the effect of the phase shifts on the ergodic spectral efficiency in different propagation scenarios. In particular, we propose an optimal phase shift design based on the upper bound of the ergodic spectral efficiency and statistical channel state information. Furthermore, we derive the requirement on the quantization bits of the LIS to promise an acceptable spectral efficiency degradation. Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.

Improved ADMM-based algorithms for multi-group multicasting in large-scale antenna systems with extension to hybrid beamforming

In this paper, multi-group multicast beamforming is considered for the full digital and hybrid beamforming. The wireless system comprises of a multiple-antenna base station and single-antenna users. Quality-of-service (QoS)-aware design is investigated where the optimization objective is to minimize the total transmitted power subject to the signal-to-interference-plus-noise ratio (SINR) constraint at each user. In addition to the SINR constraints, per-antenna power constraint is included for each antenna of the base station. The original optimization problem for full digital beamforming is transformed into an equivalent form such that the alternating direction method of multipliers (ADMM) can be applied in an effective and computationally inexpensive manner for the large-scale antenna systems. In this new formulation, the beamformer weight vectors are decomposed into two subspaces in order to decrease the number of dual variables and multiplications. The optimum update equations are obtained for the proposed ADMM algorithm. This new reformulation is used for two different hybrid beamforming structures employing phase shifters and vector modulators. Optimum updates are derived for each system. The proposed algorithms decrease computational complexity of the existing ADMM algorithms due to the effective reformulation as well as the direct solution of the nonconvex problem. In the simulation results, it is shown that the proposed methods have better convergence behavior and less computational time than the benchmark algorithms. Furthermore, the proposed method for hybrid beamforming with vector modulators performs better than its counterpart in the literature in terms of transmitted power.

Effective Channel Hardening in an Indoor Multiband Scenario

We evaluate channel hardening for a large scale antenna system by means of indoor channel measurements over four frequency bands, 1.472 ,hbox {GHz}, 2.6 ,hbox {GHz}, 3.82 ,hbox {GHz} and 4.16 ,hbox {GHz}. NTNU’s Reconfigurable Radio Network Platform has been used to record the channel estimates for 40 radio links to a 64 element array with wideband antennas in a rich scattering environment. We examine metrics for channel hardening, namely, the coherence bandwidth, the rms delay spread and the normalized effective subcarrier power, for the effective channel perceived by a single user after precoding and superposition in the downlink. We describe these metrics analytically and demonstrate them with measured data in order to characterize the rate of hardening of the effective channel as the number of antenna elements at the base station increases. The metrics allow for direct insight into the benefits of channel hardening with respect to radio system requirements.

A Novel Two-Stage Beam Selection Algorithm in mmWave Hybrid Beamforming System

A hybrid beamforming structure, which consists of digital precoding and analog beamforming, is a low-cost solution to millimeter wave (mmWave) large-scale antenna systems. In this letter, we study the sum rate maximization problem where an analog beamformer is selected from a discrete codebook. A two-stage scheme is proposed in order to avoid an exhaustive search. First, the lower bound of the sum rate is derived, and the trace inverse of the equivalent channel is determined as a novel metric for beam selection. Based on this metric, we formulate a convex problem to derive an initial beam set. Finally, an iterative algorithm is designed to update beams for further performance improvement. Simulation results demonstrate that the proposed scheme achieves a near-optimal sum rate performance and outperforms existing schemes.

Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. First, we estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high-estimation accuracy. The simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting the promising application of the proposed scheme in large-scale antenna array systems.

Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization

This paper presents an efficient optimization technique for gridless 2-D line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm minimization (ANM) is considered, which has been successfully applied in 1-D problems to allow super-resolution frequency estimation for correlated sources even when the number of snapshots is highly limited. The state-of-the-art 2-D ANM approach vectorizes the 2-D measurements to their 1-D equivalence, which incurs huge computational cost and may become too costly for practical applications. We develop a novel decoupled approach of 2-D ANM via semi-definite programming (SDP), which introduces a new matrix-form atom set to naturally decouple the joint observations in both dimensions without loss of optimality. Accordingly, the original large-scale 2-D problem is equivalently reformulated via two decoupled one-level Toeplitz matrices, which can be solved by simple 1-D frequency estimation with pairing. Compared with the conventional vectorized approach, the proposed D-ANM technique reduces the computational complexity by several orders of magnitude with respect to the problem size, at no loss of optimality. It also retains the benefits of ANM in terms of precise signal recovery, small number of required measurements, and robustness to source correlation. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO, radar signal processing and radio astronomy.

Joint User Scheduling and Beam Selection for Maritime Large-scale Antenna Systems

Since the overall distribution of users in the sea area is sparse, it will appear densely in a local angle domain. Specifically, the 3D massive MIMO antenna system are established, which are with the ability to distinguish users in the same horizontal direction as well as vertical direction. According to this, a joint user scheduling and beam selection scheme based on the automatic identification system (AIS) is proposed to solve the problems that of the interference cancellation for the users in the angle domain. In particular, the proposed scheme divides the beam according to the users’ location information. Furthermore, we select the user with the highest priority in the beam to form a set of the users in serving, in the meanwhile, the greedy algorithm is used to schedule the user combination to achieve the greatest sum rate. By means of the numerical results, it is shown that the proposed scheme can be used to schedule and serve users in the case of crowded angle domain, and the scheme can obtain near-optimal sum rate.

An Efficient Hybrid Beamforming Design for Massive MIMO Receive Systems via SINR Maximization Based on an Improved Bat Algorithm

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for practical implementation on the large-scale antenna systems. Compared to fully-digital beamforming, partially-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. This paper aims to mitigate interference and increase robustness against direction-of-arrival (DOA) mismatch along with lowering hardware complexity, cost, and power dissipation. To achieve these goals, a novel robust HAD beamforming receiver with partially-connected structure is proposed. It is based on methods of an improved bat algorithm (I-BA) and robust adaptive beamformers (RABs) in the digital domain. Since most of the RAB methods are sensitive to the DOA mismatch and depending on the complex weights which lead to an expensive receiver, the I-BA is proposed. In the analog part, analog phase alignment by linear searching (APALS) with sufficiently fine grid points is employed to optimize the analog beamforming matrix. The performance of the proposed I-BA is assessed using MATLAB simulation and compared with BA, and particle swarm optimization (PSO) algorithms. It shows better performance in terms of convergence speed, stability, and convergence rate. Besides, the proposed hybrid I-BA-APALS approach showed better performance compared to other proposed robust hybrid techniques, i.e., diagonal loading (DL) APALS (DL-APALS) and DL spatial matched filter (SMF) APALS (DL-SMF-APALS).

Revisiting the MIMO Capacity With Per-Antenna Power Constraint: Fixed-Point Iteration and Alternating Optimization

In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are 1) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems and/or 2) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared with current methods. We then extend the proposed solutions to multiuser MIMO systems with dirty paper coding-based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches.

Separable linearly constrained minimum variance beamformers

Abstract Large-scale antenna systems offer many attractive features, including large array gain and improved spatial resolution, for example. However, classical beamforming methods, such as the linearly constrained minimum variance (LCMV) filter, do not perform well on this scenario due to large computational costs involved. To deal with this issue, we propose Kronecker-separable extensions of the LCMV filter and its stochastic gradient implementation, known as Frost’s algorithm, for uniform rectangular arrays. We study the convergence of the proposed methods, investigate their computational complexity, and assess their source recovery performance with computer simulations. Our results show that our methods exhibit important computational savings while the source recovery performance losses are small.

Analysis of energy efficiency in cloud based heterogeneous RAN with large-scale antenna systems

To address the capacity requirements resulting from huge growth in mobile data traffic, the mobile network operators (MNOs) are densifying their networks with more base stations, and with more spectrum layers. The addition of base stations and spectrum layers increases the energy consumption of the access network. Hence the network needs to be optimized to lower the power consumption (e.g.) through cloud, or deployment of spectral and power efficient radio units like Large-Scale Antenna Systems (LSAS) or massive Multiple Input Multiple Output (MIMO) radio units.In this paper, we analyse the network evolution towards Cloud based Radio Access Network (C-RAN) for a mix of base stations with Macro and LSAS Remote Radio Units (RRU). We derive the computational complexity of the base station components using a flexible power model. We evaluate the need for base station architecture split, when C-RAN is used with LSAS radio units and large antenna configurations. We use a combination of simulators viz.NS3 (for LTE radio network simulation) and CloudSIM Plus (for Cloud based base station simulation). For each base station type, we compare the computational complexity of different sub-components. Further, based on the selected power model, we study the effect of base station's scheduling algorithm (viz.) Proportionate-Fair or Round-Robin, on the computational complexity of the various base station sub-components. We consider Time and Wavelength Division Multiplexed Passive Optical Network (TWDM-PON) for the high capacity low latency fronthaul network. Using the overall radio cluster throughput and power consumption information from Cloud base station, Radio site, and fronthaul network, we assess the energy efficiency metrics for the Cloud RAN and LSAS (with different antenna configurations) with Macro RRUs as reference.Through the simulations, we observe that, the computation complexity was relatively higher for RR scheduling (compared to PF scheduling). But the variations of energy efficiency metric were similar for both the scheduling schemes for different base station configurations.We observe that compared to Macro2TR, LSAS variants are highly energy efficient. This gain is mainly associated with higher throughput due to MU-MIMO (where multiple UEs are accommodated in the same set of radio resources) and lower power consuming components in LSAS. Between LSAS32TR, LSAS64TR and LSAS128TR, we find that LSAS32TR had lower energy efficiency. Further, we observe that the energy efficiency of LSAS64TR is almost same as LSAS128TR, though LSAS64TR supports just half the number of MU-MIMO layers. This implies that the additional number of layers that LSAS128TR system supports, is compensated by power consumed to support the higher antenna chains and spatially multiplexed layers.Thus, MNOs interested in deploying high spectral efficiency solution along with high energy efficiency, may prefer to deploy LSAS64 systems for tower-top deployments of Radio unit deployment, since it offers higher energy efficiency with lower wind-load effect due to smaller antenna size.