Multi-cell Multi-user Massive MIMO Systems Research Articles

Deep neural network design with SLNR and SINR criterions for downlink power allocation in multi-cell multi-user massive MIMO systems

In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.

Adaptive Pilot Allocation in Multi-Antenna Multi-Cell Multi-User Massive MIMO Systems for 5G Wireless Communication Network

Adaptive Pilot Allocation in Multi-Antenna Multi-Cell Multi-User Massive MIMO Systems for 5G Wireless Communication Network

Uplink millimeter-wave multi-cell multi-user massive multi-input multi-output systems

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.

Efficient Techniques for In-Band System Information Broadcast in Multi-Cell Massive MIMO

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 consider two different types of SI broadcast, i) synchronous broadcast of common (i.e., same) SI symbols from all cells, and ii) synchronous broadcast of cell-specific SI symbols from each cell. Through analysis we derive expressions for the achievable sum rate to STs in each cell and the rate of SI transmission to an IT for both these types of SI broadcast. We also derive expressions for the sum rate to STs and the rate to an IT for traditional orthogonal access (OA) where a fraction of physical resource is reserved for broadcast of SI. Simulations reveal that, just as in the single-cell scenario, for the multi-cell scenario also, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

On Hybrid Pilot for Channel Estimation in Massive MIMO Uplink

This paper introduces a hybrid pilot-aided channel estimation technique for mitigating the effect of pilot contamination for the uplink of multi-cell multiuser massive MIMO systems. The proposed hybrid pilot is designed such that it enjoys the complementary advantages between time-multiplexed (TM) pilot and time-superimposed (TS) pilot, and thereby, allows superior solution to the conventional pilot schemes. We mathematically characterize the impact of hybrid pilot on the massive MIMO uplink by deriving a closed-form approximation for the uplink achievable rate. In large-number-of-antennas regime, we obtain the asymptotically optimal solution for hybrid pilot by jointly designing the TM pilot and the TS pilot. It is shown that either TM pilot or TS pilot has the advantages for large frame-size and limited frame-size transmission, respectively, while the hybrid pilot scheme can offer a superior performance to that employing either TM pilot or TS pilot. Numerical results demonstrate the effectiveness of the proposed design.

Secure Transmission in Multi-Cell Multi-User Massive MIMO Systems With an Active Eavesdropper

In this letter, we investigate secure transmission for multi-cell multi-user massive multiple-input multiple-output systems with an active eavesdropper. During the uplink (UL) training, the eavesdropper can attack the training phase to cause pilot contamination. To resist such pilot contamination attack (PCA), we propose a secure transmission that does not require statistical information of the channels. In the proposed transmission strategy, we first detect PCA by a simple method. If the PCA is not detected, we then estimate the UL data of the users by a semi-blind method based on an asynchronous protocol. After that, we perform the data-aided channel estimation and design the corresponding precoder. Simulation results demonstrate that the proposed transmission can effectively resist the PCA even when the PCA detection method fails.

Optimal Low-Power Design of a Multicell Multiuser Massive MIMO System at 3.7 GHz for 5G Wireless Networks

Massive MIMO techniques are expected to deliver significant performance gains for the future wireless communication networks by improving the spectral and the energy efficiencies. In this paper, we propose a method to optimize the positions, the coverage, and the energy consumption of the massive MIMO base stations within a suburban area in Ghent, Belgium, while meeting the low power requirements. The results reveal that massive MIMO provides better performances for the crowded scenario where users’ mobility is limited. With 256 antennas, a massive MIMO base station can simultaneously multiplex 18 users at the same time-frequency resource while consuming 8 times less power and providing 200 times more capacity than a 4G reference network for the same coverage. Moreover, a pilot reuse pattern of 3 is recommended in a multiuser multicell environment to obtain a good tradeoff between the high spectral efficiency and the low power requirement.

On Detection and Mitigation of Reused Pilots in Massive MIMO Systems

In a time-division duplex multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multi-cell multi-user massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing semi-blind approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared with the in-cell users. This requires (perfect) power control. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. Finally, the estimated channels are used to detect information symbols, which, in turn, are used as pseudo-pilots to re-estimate the in-cell users’ channels. The proposed approach is illustrated via simulation examples and compared with some existing semi-blind methods.

Pilot Contamination Reduction Using Time-Domain Channel Sparsity in Massive MIMO-OFDM Systems

The pilot contamination problem in multi-cell multi-user massive MIMO systems is addressed in this paper. The overall performance of massive MIMO system is limited to the accuracy of the channel-state information (CSI) estimation at base station (BS). In time-division duplex systems, BS attains CSI using uplink-training sequence (TS) sent by users. The pilot contamination problem caused by using non-orthogonal TSs is the main challenge in this method. In this paper, a novel channel estimation method in the presence of pilot contamination is presented. The proposed method exploits sparse nature of the channel impulse response (CIR). Due to the pilot contamination and noise, the estimated CIR is not sparse anymore and consists of the desired and interference taps. To distinguish the desired taps from interferences, first, the initial estimation of the channel frequency response and equivalent CIR is achieved by taking EVD of received data covariance matrix. Finally, the initial estimated CIR will be modified using compressed-sensing methods. Simulation results indicate that the proposed approach achieves much better performance in terms of BER and sum-rate in comparison with the conventional methods.

Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Despite its promising performance gain, the realization of mmWave massive MIMO still faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds of RF chains matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of hybrid-beamforming where MIMO processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients, rendering the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the AoA of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed-form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length and the spatial separation between paths.

Achievable uplink rate analysis for distributed massive MIMO systems with interference from adjacent cells

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.

Uplink Sum-Rate Analysis of Massive MIMO System with Pilot Contamination and CSI Delay

We consider multi-cell multi-user massive MIMO system under correlated Rayleigh fading channels. Taking pilot contamination and CSI delay into consideration, we derive the equivalent channel model with MMSE channel estimation and one-tap prediction. Employing this equivalent channel model, the lower bound of the uplink sum-rate is derived, and its asymptotical performance is studied when the base station antenna number goes without bound. We find that if we schedule the $$k$$ k -th user of all cells who have the same prediction coefficient, the uplink sum-rate is the same as the one with no CSI delay when the number of BS antennas goes without bound at a much greater rate than the number of users. Simulation results show that the asymptotic approximation has good performance for large $$M$$ M , and suggest that large antenna array can compensate for the decay due to CSI delay. Simulation results also verify our guess that CSI delay does not necessarilly decrease the uplink sum-rate due to the impact of pilot contamination.