Multicell Multiuser Multiple-input Multiple-output Research Articles

Fair Licensed Spectrum Sharing Between Two MNOs Using Resource Optimization in Multi-Cell Multi-User MIMO Networks

Licensed spectrum sharing has been a promised approach to provide mobile network operators (MNOs) with required spectrum at times of increased traffic, or to improve the mobile user data rate with limited spectral resources. In this paper, we investigate the use of multi-user multiple-input, multiple-output (MIMO) techniques to enable licensed spectrum sharing. Specifically, we present a fair spectrum sharing system between two MNOs in multi-cell multi-user MIMO networks. We impose fairness by ensuring that each MNO receives spectrum in proportion to the amount it contributes. We formulate a constrained optimization problem to determine resource allocation and user scheduling across two MNOs. Since the problem is non-convex, we develop an algorithm to provide an effective solution through fractional programming and block coordinate descent. Our numerical results illustrate that the proposed spectrum sharing scheme can achieve up to 60 % improvement in terms of the average user rate among the two operators while ensuring that neither MNO is exploited for participating in the sharing mechanism. This improvement is in relation to the baseline of each MNO using multi-user MIMO communications on its own. In addition, most users, especially cell-center users close to the BSs, take advantage of our proposed spectrum sharing framework.

A Network-Assisted Game-Theoretic Design to Power Control in Autoregressive Fading Channels

Several previous works have proposed game-theoretic approaches to controlling the pilot and data power levels in the uplink of both single and multi-cell multi-user multiple input multiple output (MU-MIMO) systems. Unfortunately, the vast majority of existing works design these power control schemes under the assumption that the wireless channels between the mobile terminals and the serving base station are block fading. Meanwhile, several letters have shown that modeling fast fading channels as autoregressive (AR) processes with known or estimated state transition matrices give much more accurate results than those suggested by block fading models. Thus, this letter proposes a game-theoretic approach to controlling the uplink pilot and data power levels in a MU-MIMO system, in which the wireless channels are AR processes with mobile terminal-specific state transition matrices. We find that the proposed approach outperforms a classical cellular path-loss compensating fractional power control scheme and a game-theoretic power control scheme designed for block fading channels.

Spatial Multiplexing Superimposed Pilots for Multicell Multiuser MIMO Uplink Systems

For a multiple-input multiple-output (MIMO) system, or a multiuser MIMO system, it is important to acquire the accurate channel state information. In this article, a superimposed-pilot-based channel estimation scheme is proposed for multicell multiuser MIMO uplink systems. In this scheme, the pilot sequences and data sequences are superimposed, but the space generated by the pilot sequences and the space spanned by data sequences are separated far enough in order to reduce interference. Based on this scheme, the minimum mean-square error channel estimation is deduced, and the optimal power allocation between the pilots and the data signals is derived to maximize throughput. These results show that our proposed scheme can overcome the pilot contamination and achieve better performance than the traditional time-multiplexing scheme with mutually orthogonal pilot sequences for different users when the coherent time slot is limited.

SLINR-Based Downlink Optimization in MU-MIMO Networks

Optimizing the downlink of multi-cell multiuser multiple input multiple output (MU-MIMO) networks has received substantial attention; however, the schemes in the literature consider centralized solutions requiring significant overhead in information exchange (e.g., global channel state information or CSI) and computation load (the need to solve a single large problem). This paper presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized</i> weighted sum-rate (WSR) maximization algorithm for the multiuser downlink, accounting for beamforming, scheduling, and power allocation. We show that the signal-to-leakage-plus-noise ratio (SLNR) used in previous work suffers from significant drawbacks that limit its potential use in WSR maximization. We address this by proposing a new performance measure, the signal-to-leakage-plus-interference-plus-noise ratio (SLINR), which incorporates <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra-cell interference</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inter-cell leakage</i> . The SLINR exploits the benefits of the SLNR approach, but by explicitly including interference, avoids many of its flaws. We derive an iterative and decentralized resource allocation approach under imperfect CSI, and our simulation results show that, despite BSs using only <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local information</i> , the proposed algorithm comes within 3.8% of the throughput achieved by centralized schemes.

Asymptotic Performance Analysis of MMSE Receivers in Multicell MU-MIMO Systems

For large-scale antenna array multiple-input multiple-output (MIMO) systems, a typical solution often put forward for uplink detection consists in applying low-complexity linear receivers such as zero-forcing (ZF) and minimum mean-square error (MMSE) combiners. In this paper, the performance of uplink MMSE detection has been analyzed in the context of a multi-cell multiuser MIMO system. An approximate expression for the signal-to-interference-plus-noise ratio (SINR) is proposed, which is in simple form and becomes exact in the low SNR regime. This approximate SINR applies to systems where the base station (BS) has the statistical information of the out-of-cell interfering signals, and systems where the BS ignores the presence of such interfering signals. This approximate SINR is tight for arbitrary user powers, arbitrary number of antennas at the base station, and arbitrary channel correlation matrix associated with any particular user. Approximate expressions for capacity and bit-error rate/symbol-error rate have also been derived. Particularly, it can be proven that, when the MMSE receiver ignores the presence of out-of-cell interfering signals, the performance gap between ZF and MMSE vanishes in the high SNR regime.

Joint Resource Allocation and Transceiver Design for Sum-Rate Maximization Under Latency Constraints in Multicell MU-MIMO Systems

Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model. Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model.

The interference alignment scheme based on subspace differentiation for multicell multiuser multiple‐input‐multiple‐output uplink channels

AbstractThe traditional subspace interference alignment (IA) schemes in the multicell multiuser multiple‐input‐multiple‐output (MIMO) network align interference into the multidimensional subspace to reduce the dimension of the interference space, but the choice of subspace is random, and the difference between users is not considered. Such schemes may leave large intercell interference when the difference between users is large. To solve this problem, this article proposes the IA scheme based on subspace differentiation for multicell multiuser MIMO uplink channels. First, based on the transmit power and the distance to base stations in other cells, each user analyzes the interference value it will cause. The interference value of each user is represented by actual interference power (AIP). Within each cell, the users are reordered according to the AIP. When performing interference reduction, the signals of the users with large interference are merged into the subspace composed of the signals of the users with small interference, which can further reduce the final intercell interference. The multidimensional merge weight is assigned according to the AIP. The smaller the interference value of the dimension, the more interference it merges. The experimental simulation data show that the scheme implemented in this article can improve the proportion of the desired signals in the received signals of the receivers, thereby improving the overall system rate. When the user difference in transmit power and distance is large, the proposed scheme has a particularly obvious advantage.

Multi-Objective Power Minimization Design for Energy Efficiency in Multicell Multiuser MIMO Beamforming System

In this study, we propose a multi-objective power optimization design in a multicell multiuser multiple-input multiple-output (MIMO) beamforming system in the future era of green communications for energy efficiency. First, we consider a multicell multiuser MIMO system with multiple base stations (BSs) and each BS could serve multiple downlink mobile stations (MSs) simultaneously. Second, we formulate a multi-objective optimization problem (MOP) to simultaneously minimize the downlink powers of different groups of cells subject to the signal to interference plus noise ratio (SINR) constraints. An indirect method is proposed to transform the MOP of multi-objective power minimization design of multicell multiuser MIMO beamforming system to a semidefinite programming (SDP)-constrained MOP. In order to efficiently solve the multi-objective power minimization problem with a low computational complexity, a novel multi-objective evolutionary algorithm (MOEA) with modification is proposed to guarantee the global convergence of Pareto optimal solutions. Finally, simulation results are provided to validate the performance and show the superiority of the proposed multi-objective beamforming green design to minimize the downlink power consumption of MIMO beamforming system in different groups of cells simultaneously.

Joint User Selection, Power Allocation, and Precoding Design With Imperfect CSIT for Multi-Cell MU-MIMO Downlink Systems

In this paper, a new optimization framework is presented for the joint design of user selection, power allocation, and precoding in multi-cell multi-user multiple-input multiple-output (MU-MIMO) systems when imperfect channel state information at transmitter (CSIT) is available. By representing the joint optimization variables in a higher-dimensional space, the weighted sum-spectral efficiency maximization is formulated as the maximization of the product of Rayleigh quotients. Although this is still a non-convex problem, a computationally efficient algorithm, referred to as generalized power iteration precoding (GPIP), is proposed. The algorithm converges to a stationary point (local maximum) of the objective function and therefore it guarantees the first-order optimality of the solution. By adjusting the weights in the weighted sum-spectral efficiency, the GPIP yields a joint solution for user selection, power allocation, and downlink precoding. The GPIP can be extended to the multi-cell scenario where cooperative base stations perform joint user-cell selection and design their precodes by taking into account the inter-cell interference by sharing global imperfect CSIT. System-level simulations show the gains of the proposed approach with respect to conventional user selection and linear downlink precoding.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

A Game Theoretic Approach to Uplink Pilot and Data Power Control in Multi-Cell Multi-User MIMO Systems

In multi-user multiple-input–multiple-output (MU-MIMO) systems that employ pilot-symbol aided channel estimation, the pilot-to-data power ratio (PDPR) has a large impact on the system performance. In this paper, we consider the problem of setting the PDPR in multi-cell MU-MIMO systems in the presence of channel estimation errors, intercell interference and pilot contamination. To analyze and address this problem, we first develop a model of the multi-cell MU-MIMO system and derive a closed-form expression for the mean squared error of the uplink received data symbols. Building on this result, we then propose two decentralized PDPR-setting algorithms based on game theoretic approaches that are applicable in multi-cell systems. We find that both algorithms converge to a Nash equilibrium and provide performance improvements over systems that do not properly set the PDPR, while they maintain different levels of fairness.

Joint Precoder and Equalizer Design for Multi-User Multi-Cell MIMO VLC Systems

In multi-user multi-cell multiple-input multiple-output (MIMO) visible light communication (VLC) systems, inter-cell interference (ICI) and inter-user interference (IUI) are the two key factors that could severely degrade the system performance. In this paper, for the first time, a novel joint precoder and equalizer design based on interference alignment is proposed to mitigate both IUI and ICI in multi-user multi-cell MIMO VLC systems under imperfect channel state information (CSI). The proposed approach aims to choose proper precoder and receiving equalizer to minimize the mean square error (MSE) under the unique optical power constraints in VLC. Furthermore, we consider optical channel estimation error in our formulated joint optimization problem when designing the joint precoder and receiving equalizer in multi-user multi-cell MIMO VLC systems. Numerical results show that the proposed design achieves a better performance under different users locations, channel estimation errors, and transmitter/receiver spacing, compared to the existing minimum mean square error (MMSE) and max-rate designs. Especially, when the transmit optical power is 12 W per light emitting diode (LED) lamp, our proposed design achieves a sum rate improvement of up to 18.2% and 28.7% as compared with the MMSE and max-rate designs, respectively. In addition, the proposed design can save about 1.4 and 4.3 W optical power per LED lamp as compared with the MMSE and max-rate designs, respectively, at a target bit error rate (BER) of 10 $^{-3}$ .

Distributed probabilistic data association detector with turbo base-station cooperations in multi-user multi-cell MIMO systems

We address the severe interference condition with unity frequency re-use factor in each cell and propose a novel distributed receiver for reliable detection of the uncoded data transmitted in multi-user multi-cell multiple input, multiple output (MIMO) systems. The new algorithm employs distributed probabilistic data association (PDA) with turbo base-station (BS) cooperations (PDA-TB). From a Bayesian point of view, we modify the original complex PDA detector to incorporate the a priori probability, which makes the complex PDA detector applicable to turbo processing: the soft information is extracted using PDA algorithm at local BS and then used as the a priori probability for further detection at neighboring BSs. Final soft decision is made after the algorithm converges. In this way, the benefit of macro-diversity is achieved and the inter-cell interferences are mitigated. In heavy interference condition, the proposed algorithm outperforms the distributed PDA aided soft combining (PDA-SC) and achieves the near optimal performance. Meanwhile, low complexity is maintained due to the rapid convergence.

Non‐cooperative interference alignment for multicell multiuser MIMO uplink channels

Linear interference alignment (IA) is considered a promising interference management technique for future cellular networks. However, its application to practical cellular networks still faces many challenges, two of which are the overhead of sharing global channel state information (CSI) and the cost of signalling dimensionality. These two challenges are resolved in this study via a new linear IA scheme that not only prevents global CSI sharing but also keeps the number of signalling dimensions manageable. While making linear IA more applicable, the proposed scheme provides comparable degrees of freedom to the existing IA schemes in symmetric multicell multiuser multiple-input multiple-output (MIMO) uplink channels.

Diversity-Multiplexing-Nulling Trade-Off Analysis of Multiuser MIMO System for Intercell Interference Coordination

A fundamental performance trade-off of multicell multiuser multiple-input multiple-output (MU-MIMO) systems is explored for achieving intercell and intracell interference-free conditions. In particular, we analyze the three-dimensional diversity-multiplexing-nulling trade-off (DMNT) among the diversity order (i.e., the slope of the error performance curve), multiplexing order (i.e., the number of users that are simultaneously served by MU-MIMO), and nulling order (i.e., the number of users with zero interference in a victim cell). This trade-off quantifies the performance of MU-MIMO in terms of its diversity and multiplexing order, while nulling the intercell interference toward the victim cell in the neighbor. First, we design a precoding matrix to mitigate both intercell and intracell interference for a linear precoding-based MU-MIMO system. Then, the trade-off relationship is obtained by analyzing the distribution of the signal-to-noise ratio (SNR) at the user terminals. Furthermore, we demonstrate how DMNT can be applied to estimate the long-term throughput for each mobile station, which allows for determining the optimal number of multiplexing order and throughput loss due to the interference nulling.

Interference Alignment for Multicell Multiuser MIMO Uplink Channels

This paper proposes a linear interference alignment (IA) scheme, which can be used for uplink channels in a general multicell multiuser multiple-input-multiple-output (MIMO) cellular network. The proposed scheme aims to align interference caused by signals from a set of transmitters into a subspace that is established by the signals from only a subset of those transmitters, thereby effectively reducing the number of interfering transmitters. The total degrees of freedom (DoF) achievable by the proposed scheme is given in closed-form expression, and a numerical analysis shows that the proposed scheme can achieve the optimal DoF in certain scenarios and provides a higher total DoF than other related schemes in most cases.

Uplink symbol error rate analysis of multicell multiuser‐multiple‐input–multiple‐output systems with minimum mean square error receiver under pilot contamination

This paper considers the uplink of a multicell multiuser multiple-input-multiple-output (MIMO) time-division duplexing system, where K mobile users in the target cell send their uncoded M-ary phase shift keying (M-PSK) signals to the target base station (BS) equipped with a linear minimum mean square error (MMSE) receiver in the presence of receiver antenna correlations. By employing the equivalent channel model, the uplink is analysed in terms of symbol error rate (SER), giving approximated closed-form expressions. The complicated functions of the SER can be simplified for the case where all the eigenvalues of the correlation matrix of the target BS are identical or distinct. It is proved that in the high signal to noise ratio (SNR) region, the SER is independent of the correlations between the BS antennas. And due to the existence of pilot contamination and intercell interference, the SER tends to a constant whose value is depended on the number of mobile users in each cell and the interference strength between the cells as well. Finally computer simulations are conducted to show that our approximated expressions have good performance in low SNR even for not large K.

LS Channel Estimation in Multicell Multiuser MIMO OFDM with Zadoff Chu Sequence as Pilots

High spectral efficiency and the ability to combat multipath fading are the key requirements of future wireless communication systems. Multiple-input multiple-output (MIMO) combined with orthogonal frequency- division multiplexing (OFDM) has been widely considered to be a promising candidate. Moreover Multi user MIMO OFDM can provide good spatial multiplexing with single antenna for each user. In the case of multicell multiuser MIMO OFDM system main challenge is to acquire the channel state information. In order to get the channel state information, channel estimation is to be done. Here pilot aided LS channel estimation is used to obtain the estimates. For multi cell multi user MIMO OFDM, channel estimation using pilot subcarriers causes pilot contamination. This is due to the reuse of pilot subcarriers in different cells. In order to reduce the impact of pilot contamination, pilot sequences must select properly. Here Zadoff Chu sequences are used for the design of pilots in different cells.

On Superimposed Pilot for Channel Estimation in Multicell Multiuser MIMO Uplink: Large System Analysis

This paper concerns the uplink of multicell multiuser multiple-input multiple-output (MIMO) systems. To tackle the effect of pilot contamination that is generally viewed as a bottleneck in previous work, this paper presents a superimposed pilot (SP)-aided uplink channel estimation scheme and mathematically characterizes the impact that an SP has on the performance of such a very large MIMO system. It is shown that there are two types of interference components that do not vanish, even when the number of antennas $M$ grows to infinity. The first type, which is referred to as cross-contamination, is due to the correlation between the SP and data among different cells. The second type, which is referred to as self-contamination, is due to the dependence between channel estimation and estimation error. Cross-contamination is, in principle, similar to pilot contamination in a conventional pilot-based multicell MIMO system, whereas self-contamination is unique for the SP-aided scheme. Both theoretical and simulation results demonstrate that the SP-aided scheme can effectively reduce the estimation contamination by increasing the data frame size and, in turn, achieve a significant improvement over the spectral efficiency, in comparison with conventional pilot-based methods.

Distributed Block Diagonalization with Selective Zero Forcing for Multicell MU-MIMO Systems

This letter proposes a distributed beamforming scheme based on block diagonalization (BD) for multicell multiuser multiple-input multiple-output (MU-MIMO) downlink systems. Although conventional BD can be directly extended to these systems in a distributed manner, it suffers from sum-rate degradation due to strict zero-forcing (ZF) constraints. To overcome this problem, we introduce a sum-rate maximization problem for BD, to find the optimal selection of ZF constraints. Then we propose a search algorithm to solve the problem in a distributed and heuristic manner. Simulation results show that the proposed scheme effectively finds a near-optimal solution and significantly improves simple extended BD such that it can provide higher sum rate than the conventional schemes.