Energy Efficient Hybrid Precoding for Cooperative Multicell Multiuser Massive MIMO Systems with Multiple Base Station Association

In this paper, we develop the weighted sum-rate maximization (WSRM) problem for the cooperative multicell multiuser massive MIMO (C4M) system with the limited cooperative scheme where each base station (BS) has user data of all the users in a system. It is shown that by the simple domain change technique, the WSRM problem can be exploited for hidden mono-tonicity and turn out to be a monotonic optimization problem. We propose a robust algorithm for discretized approximation of the Pareto boundary of achievable rate region. Using this algorithm, the normal boundary intersection (NBI) problem is solved. We solve the WSRM problem by using the outer polyblock approximation (OPA) algorithm. Through simulation results, we show that our proposed method has high accuracy as well as a fast convergence rate.

Massive MIMO technology is a promising candidate for the next generation wireless communication systems. The basic idea is to implement a large number of antennas at the base station (BS) of a cell to serve a relatively small number of users. Such an approach brings many benefits in terms of the throughput and energy efficiency. Two major challenges associated with massive MIMO systems are high power consumption and high system complexity. These problems can effectively be dealt with by performing antenna-subset selection at the BS. In this paper, we propose low-complexity antenna-subset selection schemes for both single-cell and multiple-cell cooperative massive MIMO systems for equal-rate zero-forcing beamforming transmission at the BS. These schemes sequentially select an antenna that contributes the most to the overall system sum rate. Simulation results show that our proposed schemes outperform other schemes with similar computational complexities and even surpass some of the higher computational complexity schemes in a massive MIMO setting.

In this paper, we delve into the maximized spectral efficiency (SE) of millimeter-wave (mmWave) multicell multiuser massive MIMO Systems for uplink transmission with low-resolution phase shifters (LRPSs). Millimeter-wave massive multiple-input multiple-output (mMIMO) is an important technology for upcoming cellular networks which will provide higher bandwidth and throughput than current wireless systems and networks. LRPSs are commonly used to minimize power consumption, maximize spectral efficiency and diminish the complexity of hybrid precoder and combiner. In this paper, we consider a hybrid analog-digital precoder and combiner design with LRPSs for mmWave multi-cell multiuser mMIMO systems for uplink transmission to spectral efficiency in terms of iterations. The proposed technique outperforms when compared to traditional optimization approaches concerning spectral efficiency and bit error rate (BER). We show through simulation results that our designs with LRPSs outperform standard iteration procedures.

Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.

This work focuses on the multi-cell multi-user distributed massive MIMO (DM-MIMO) systems, of which each user is equipped with single antenna and the base stations (BSs) consists of distributed antenna units. We first investigate the arbitrary BS antenna topology scenario. The derivation indicates that in this case the achievable uplink rate of an arbitrary user in central cell depends on both the number of BS's antennas and the users' access distance to each distributed antenna unit (DAU). As a result, the performance analysis based on the derivations is difficult. To overcome this issue and achieve clearer insight, we then consider a circularly distributed BS antenna array and obtain the asymptotic uplink rate of an arbitrary user by considering the asymptotic case where the number of antennas of BSs tends to infinity. It is achieved that the asymptotic uplink rate only depends on the distance from users' position to the center of reference cell. The presented numerical results show clearly that the distributed massive MIMO systems outperform the centralized ones. Moreover, it is also achieved that the interference from the adjacent cells imposes great impact on system performance. Besides this, in numerical analysis the average asymptotic uplink rate of a user is presented, which is free of the users' position and only depends on the radius of circular antenna arrays. It is achieved the maximum average uplink rate would be achieved when the radius of circularly distributed antenna array goes to its optimization location.

Hybrid precoding schemes have recently gained a great attention for millimeter wave (mmWave) massive multiple-input-multiple-output (massive MIMO) communication systems because of offered cost and low-power consumption advantages. The singular value decomposition (SVD) method which is exploited in the design of hybrid precoding causes several problems on system design, especially on bit allocation, precoding and equalizing processes. On the other hand, the previous works show that generalized triangular decomposition (GTD) method can present asymptotically optimal performance in conventional MIMO systems. In the light of this, we propose the use of GTD method for designing hybrid precoding operating in mmWave massive MIMO systems. The proposed design is analyzed via performed simulation studies including different system configurations. Moreover, the archived results are compared with the standard orthogonal matching pursuit (OMP) algorithm based hybrid precoding scheme. The simulation results show that the proposed hybrid precoding scheme can reach up to the performance of optimal precoding scheme.

This paper describes an interesting method to address the problem of pilot contamination, which adopts the staggered frame structure. The core idea is that different users in the same cell send pilot symbols in different time intervals, and users who send pilot sequences simultaneously in different cells use orthogonal pilot sequences. Furthermore, two channel estimation methods were utilized, which respectively are the Orthogonal Projection Least Square (OPLS) method and the Subtract Interference Least Square (SILS) method. Both two methods don’t directly estimate the whole channel parameters. Alternatively, they estimate the sectional channel response that is orthogonal to the other users’ channel response. The OPLS method is implemented by orthogonal projection operation, whose complexity is higher. So we proposed the SILS method, which combines signal detection to reduce the computational complexity. The simulation results show the staggered frame structure and the channel estimation methods can solve the pilot contamination effectively.

In this paper we consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We propose two different multi-cell approaches to the broadcast of SI, i) synchronous broadcast of same SI symbols from all cells (SAME-SI), and ii) synchronous broadcast of statistically independent SI symbols from each cell (DIFF-SI). We also consider the traditional orthogonal access (OA) approach where a fraction of physical resource is reserved for broadcast of SI. Through analysis and simulations, it is observed that for both SAME-SI and DIFF-SI, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

This paper expands our previously proposed semi-blind uplink interference suppression scheme on multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and decision feedback channel estimation (DFCE). It employed the constant modulus algorithm (CMA) as the BAA scheme which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter- cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for the constant envelope signal such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper newly proposes to apply multi modulus algorithm (MMA) and data-aided sample matrix inversion based minimum mean square error (MMSE-SMI) weight derivation. It can successfully realize interference suppression under the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance.

The massive MIMO systems are the more popular field in the present era for the 5G wireless communication system. The MIMO system is a demanding research topic for the last four decades. This topic is under implementation and observation from the last few years. These systems have many advantages and many research sub-areas but this paper investigates the modified model of the massive MIMO receiver system. The traditional receiver system model of massive MIMO system reduces the channel noise using a linear filter in the receive combiner bank (RCB) but the proposed model removes the channel noise before the RCB using an adaptive filter bank (AFB). The AFB is the combination of LMS adaptive filters. The analysis parameters are channel noise, signal-to-noise ratio (SNR) and bit-error-rate (BER) using the hybrid precoder and combiner computation algorithms – Quantized Sparse Hybrid Beamforming (QSHB) and Hybrid Beamforming Peak Search (HBPS). Therefore, the proposed massive MIMO system model gives the less channel noise in the received signal, higher SNR and lower BER as compared to the traditional massive MIMO receiver systems.

Channel estimation is a very challenging task for an on-grid massive MIMO over the mmWave system with hybrid precoding. The problem arises due to more number of radio frequency chains that are comparatively smaller than the antennas used in the systems. The conventional massive MIMO channel estimation system over mm waveband is based on the off-grid learning models parameters and virtual channel estimators. The resolution loss is arises based on the high computational complexity of channel estimation by its Bayesian learning framework. The accuracy of the on-grid channel estimation, the work proposes a singular value decomposition-based iterative reweighted channel estimation scheme for massive MIMO over mmWave band. The objective function-based gradient descent method is used for the optimization. This proposed objective functions can be used to optimize the channel estimation by iteratively moving the estimate of an angle of arrival and departs. The iteratively reweighted parameters are used for the optimization of trade-off errors.KeywordsMassive MIMOAngle of arrivalmmWave bandChannel estimationAngle of departure

SummaryMillimeter wave communication and massive MIMO technology are important ingredients for the future generation communication system. In this context, hybrid precoding (HP) is one of the most promising solutions, and it becomes utterly important in the case of the multi‐user scenario. In this paper, authors have analyzed the performance of the lattice reduction aided fully connected structured precoder for millimeter wave massive MIMO under multi‐user scenario. This paper deals with Korkine–Zolotareff (KZ) and Lenstra–Lenstra–Lovász (LLL) algorithms for the lattice reduction (LR). And the presented results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE)‐based linear precoder with LLL algorithm‐based reduction‐based ZF (LLR‐ZF), LLR‐MMSE, KZ‐ZF, KZ‐MMSE, and Kalman filter‐based precoder. Here, the performance of the precoders has been evaluated based on spectral efficiency, energy efficiency diversity order, and bit error rate. This work conveys some important findings: (i) effectiveness of the lattice reduction aided precoder in multi‐user scenario; (ii) numerical results pointed out that at low SNR condition, KZ‐aided MMSE hybrid precoder provides 4‐dB performance than a Kalman filter‐based precoder; (iii) KZ‐MMSE hybrid precoder outperforms the other precoder.

<abstract> <p>Due to an increase in the number of users and a high demand for high data rates, researchers have resorted to boosting the capacity and spectral efficiency of the next-generation wireless communication. With a limited RF chain, hybrid analog digital precoding is an appealing alternative. The hybrid precoding approach divides the beamforming process into an analog beamforming network and a digital beamforming network of a reduced size. As a result, numerous hybrid beamforming networks have been proposed. The practical effects of signal processing in the RF domain, such as the additional power loss incurred by an analog beamforming network, were not taken into account. The effectiveness of hybrid precoding structures for massive MIMO systems was examined in this study. In particular, a viable hardware network realization with insertion loss was developed. Investigating the spectral and energy efficiency of two popular hybrid precoding structures, the fully connected structure, and the subconnected structure, it was found that in a massive MIMO, the subconnected structure always performed better than the fully connected structure. Characterizing the effect of quantized analog precoding, it was shown that the subconnected structure was able to achieve better performance with fewer feedback bits than the fully connected structure.</p> </abstract>

This paper investigates the hybrid precoding design for millimeter wave multiple-input multiple-output systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. This paper presents three main contributions. First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> satisfies N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> = min{rank(H), N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rf</sub> }, where H represents the channel matrix and N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rf</sub> is the number of radio frequency chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.

This chapter, the techniques of massive multiple-input-multiple-output (MIMO) and network MIMO are studied. First, massive MIMO is compared with the conventional point-to-point MIMO used in current cellular systems in terms of capacity and energy efficiency. The comparison shows that massive MIMO has huge gains even under the modest conditions using simple linear processing. Then, the implementation of massive MIMO is discussed by focusing on the channel estimation method, the signal detection method and the precoding method. Following this, several main issues in massive MIMO are examined and relevant measures are discussed. Finally, network MIMO is studied by introducing cooperation between base stations to completely remove the inter-cell interference, at the cost of even higher complexity that places limits on the backhaul and fronthaul links.