Multiple-input Multiple-output Interfering Broadcast Channels Research Articles

User Scheduling for Sum-Rate Maximization Under Minimum Rate Constraints for the MIMO IBC

While the problem of sum-rate maximization of the multiple-input–multiple-output interference broadcast channel (MIMO IBC) has been extensively studied, most of the proposed solutions do not ensure a minimum rate for each scheduled user. In practice, many services require a minimum rate from the underlying communication links. Therefore, in this letter, we consider a sum-rate maximization problem with per-link minimum rate constraints for the MIMO IBC. The key idea is scheduling a suitable subset of the communication links for simultaneous transmissions, such that a minimum rate for each scheduled link can be ensured. To this end, we pose the sum-rate maximization problem as a combinatorial optimization problem, in which we introduce binary variables to the classical transceiver design problem. We propose a centralized solution based on branch-and-bound and a low-complexity semi-distributed scheme, in which a centralized unit is responsible for scheduling decisions, while the transceiver computations are distributed. Simulations show that the proposed solutions handle the user scheduling effectively, while the proposed semi-distributed scheme performs closely to the centralized scheme.

Multi-Agent Distributed Beamforming With Improper Gaussian Signaling for MIMO Interference Broadcast Channels

For rate optimization in interference limited networks, improper Gaussian signaling has shown its ability to outperform conventional proper Gaussian signaling. In this paper, we study a weighted sum-rate maximization problem with improper Gaussian signaling for the multiple-input multiple-output interference broadcast channel. To solve this nonconvex and NP-hard problem, we propose an effective separate covariance and pseudo-covariance matrices optimization algorithm. In the covariance optimization, a weighted minimum mean square error algorithm is adopted, and in the pseudo-covariance optimization, an alternating optimization (AO) algorithm is proposed, which guarantees convergence to a stationary solution and ensures a sum-rate improvement over proper Gaussian signaling. An alternating direction method of multipliers-based multi-agent distributed algorithm is proposed to solve an AO subproblem with the globally optimal solution in a parallel and scalable fashion. The proposed scheme exhibits favorable convergence, optimality, and complexity properties for future large-scale networks. The simulation results demonstrate the superior sum-rate performance of the proposed algorithm as compared with the existing schemes with proper as well as improper Gaussian signaling under various network configurations.

Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel

An interference alignment (IA) transceiver design scheme for multiple-input–multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI).

Energy Efficiency Optimization With Interference Alignment in Multi-Cell MIMO Interfering Broadcast Channels

Characterizing the fundamental energy efficiency (EE) performance of multiple-input–multiple-output interfering broadcast channels (MIMO-IFBC) is important for the design of green wireless system. In this paper, we propose a new network architecture proposition based on EE maximization for Multi-Cell MIMO-IFBC within the context of interference alignment (IA). Particularly, EE is maximized subject to maximum power and minimum throughput constraints. We propose two schemes to optimize EE for different signal-to-noise ratio (SNR) regions. For high-SNR operating regions, we employ a grouping-based IA scheme to jointly cancel intra- and inter-cell interferences and thus transform the MIMO-IFBC to a single-cell MIMO scenario. A gradient-based power adaptation scheme is proposed based on water-filling power adaptation and singular value decomposition to maximize EE for each cell. For moderate SNR cases, we propose an approach using dirty paper coding (DPC) with the principle of multiple access channel and broadcast channel duality to perform IA while maximizing EE in each cell. The algorithm in its dual form is solved using a subgradient method and a bisection searching scheme. Simulation results demonstrate the superior performance of the proposed schemes over several existing approaches. It also shows that interference-nulling-based IA approaches outperform hybrid DPC-IA approach in high-SNR region, and the opposite occurs in low-SNR region.

Performance Analysis of IA Techniques in the MIMO IBC With Imperfect CSI

In this work we consider the multiple-input multiple-output (MIMO) interference broadcast channel (IBC) and analyse the performance of interference alignment (IA) under imperfect channel state information (CSI), where the variance of the CSI error depends on the signal-to-noise ratio (SNR). First, we derive an upper bound on asymptotic mean loss in sum rate compared to the perfect CSI case and then we quantify the achievable degrees of freedom (DoF) with imperfect CSI. Both sum rate loss and achievable DoF are found to be dependent on the number of cells in the system and the amount of users per cell, in addition to the CSI error parameters themselves. Results show that when error variance is inversely proportional to SNR, full DoF are achievable and the asymptotic sum rate loss is bounded by a derived value. Additionally if the CSI imperfection does not disappear for asymptotically high SNR, then the full DoF gain promised by IA cannot be achieved; we quantify this loss in relation to the CSI mismatch itself. The analytically derived bounds are validated via system simulation, with the cellular counterparts of the maximum signal-to-interference-plus-noise ratio (Max-SINR) and the minimum weighted leakage interference (Min-WLI) algorithms being the IA techniques of choice. Secondly, inspired by the CSI mismatch model used to derive the bounds, we present a novel Max-SINR algorithm with stochastic CSI error knowledge (Max-SINR-SCEK) for the MIMO IBC. Simulations show that the proposed algorithm improves performance over the standard one under imperfect CSI conditions, without any additional computational costs.

Degrees of freedom for MIMO interference broadcast channels

Multiple-input multiple-output (MIMO) interference broadcast channel (IBC) plays an important role in the modern wireless communications. The upper bound of degrees of freedom (DoF) and its Corresponding achievable schemes was investigated. However, all the achievable schemes require perfect channel state information at transmitters (CSIT). In absence of CSIT, the DoF value is still unknown. This article mainly focuses on the G-cell K-user MIMO IBC, where there are M antennas at each transmitter and N antennas at each receiver. The transmitters only know channel coherence time internals rather than the values of channel coefficients. The users in the same cell are assumed to be able to share the channel information. Based on a heterogeneous semi-staggered block fading model, a blind interference alignment (IA) scheme was proposed for this scenario. It is shown that when R≥1/(G-1) and R = min(M,KN)/max(M,KN), a total of GKMN/(M + KN) DoF can be achieved. The inner bound is same with the decomposition DoF upper bound. Since the complexity is an important performance index to evaluate the achievable scheme, the quantitative analysis for the complexity is presented.

The Degrees of Freedom for MIMO Interference Broadcast Channels with no CSIT

Multiple-input multiple-output (MIMO) interference broadcast channel (IBC) plays an important role in the modern wireless communications. The upper bound of degree of freedom (DoF) and corresponding achievable schemes have been investigated. However, all the achievable schemes require perfect channel state information at transmitters (CSIT). In the absence of CSIT, the DoF value is still unknown. This paper mainly focuses on theG-cellK-user MIMO IBC, where there areMantennas at each transmitter andNantennas at each receiver. The transmitters only know channel coherent time internals rather than the values of channel coefficients. The users in the same cell are assumed to be able to share the channel information. Based on a heterogeneous block fading model, a blind interference alignment (IA) scheme is proposed for this scenario. We show that when and , then a total of degrees of freedom (DoF) can be achieved. The inner bound is same with the decomposition DoF upper bound.

On the Degrees of Freedom of Interference Alignment for Multicell MIMO Interfering Broadcast Channels

The interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance capacity of a wireless network. This paper proposes an interference alignment scheme for a cellular network with L cells and K users under a multiple-input multiple-output (MIMO) Gaussian interfering broadcast channel (IFBC) scenario. The proposed IA scheme aligns intercell interferences (ICI) into a small dimensional subspace through a cooperative receive beamforming and cancels both the ICI and interuser interferences (IUI) simultaneously through a transmit beamforming. We characterize the feasibility condition for the proposed IA to achieve a total number of degrees of freedom (DoF) of LK in terms of the numbers of transmit antennas and receive antennas. Then we derive the maximum number of DoF achieved by the proposed IA by finding an optimal dimension of ICI alignment subspace for a given antenna configuration. The numerical results show that the proposed IA scheme has a better DoF performance than the conventional schemes.

Regularized Zero-Forcing Interference Alignment for the Two-Cell MIMO Interfering Broadcast Channel

We present linear transmit-receive filter design strategies for the two-cell multiple-input multiple-output (MIMO) interfering broadcast channel (IBC) where inter-cell interference (ICI) exists in addition to inter-user interference (IUI) within the cell. We first formulate a generalized zero-forcing interference alignment (ZF-IA) method capable of handling multiple data streams for each user. We proceed to further adjust the transmit-receive filters based on the minimum weighted-mean-square-error (WMSE) principle using the weights obtained from the initial generalized ZF-IA design, in an effort to to enhance the sum-rate performance. In contrast to the existing weighted-sum-rate-maximizing scheme, our method does not require an iterative calculation of the weights. Because of this, the proposed scheme, while not designed specifically to maximize the sum-rate, is computationally efficient and delivers favorable tradeoffs between performance and information exchange overhead.

An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel

Consider the multiple-input multiple-output (MIMO) interfering broadcast channel whereby multiple base stations in a cellular network simultaneously transmit signals to a group of users in their own cells while causing interference to each other. The basic problem is to design linear beamformers that can maximize the system throughput. In this paper, we propose a linear transceiver design algorithm for weighted sum-rate maximization that is based on iterative minimization of weighted mean-square error (MSE). The proposed algorithm only needs local channel knowledge and converges to a stationary point of the weighted sum-rate maximization problem. Furthermore, the algorithm and its convergence can be extended to a general class of sum-utility maximization problem. The effectiveness of the proposed algorithm is validated by numerical experiments.

On the Design of Interference Alignment Scheme for Two-Cell MIMO Interfering Broadcast Channels

The interference alignment (IA) is a promising technique to effectively mitigate interferences in wireless communication systems. To show the potential benefits of such an IA scheme, this letter focuses on a two-cell multiple-input multiple-output (MIMO) Gaussian interfering broadcast channels (MIMO-IFBC) with M transmit antennas and N receive antennas. It corresponds to a downlink scenario for cellular networks with two base stations (BSs) with M transmit antennas per BS, and two users with N receive antennas per user, on the cell-boundary of each BS. In this scenario, we propose a novel IA technique jointly designing transmit and receive beamforming vectors in a closed-form expression without iterative computation. It is also analytically shown that the proposed IA algorithm achieves the optimal degrees of freedom (DoF) of 2N in the case of [¾N] ≤ M <; 2N. The simulations demonstrate that not only the analytical results are valid, but the sum-rate of our proposed scheme also outperforms those of conventional techniques, especially in the high signal-to-noise ratio (SNR) regime.