Joint Interference Alignment Research Articles

Joint Interference Alignment and Subchannel Allocation in Ultra-Dense Networks

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -colorable subgraph (MKCS) problem in graph theory for a given <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme.

Joint Interference Alignment and Probabilistic Caching in MIMO Small-Cell Networks

Cache-enabled small base stations (SBS) are capable of relieving the heavy burden of the backhaul link and reducing the transmission latency. The hit probability depends on the coverage probability and caching placement probabilities. However, the interference in the small-cell networks may significantly degrade the coverage probability. In this paper, for MIMO small-cell networks consisting of SBS and users, where both of them are equipped with multiple antennas, a joint interference alignment (IA) and probabilistic caching (JIA-ProbC) scheme is proposed. Using tools from stochastic geometry, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -th order Voronoi cells are constructed to form clusters, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> SBSs cooperatively serve users within each of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -th order Voronoi cells. Then, the IA scheme for MIMO interference channel (IC) is employed to cancel the intra-cluster interference within each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -th order Voronoi cell. By exploiting the advantage of multiples antennas at users, the IA scheme can simultaneously support more users interference-free than both the zero forcing (ZF) based interference cancellation scheme for MISO systems and SISO systems without interference management, as more interference can be canceled. Furthermore, the coverage probability is analytically approximated by the a closed-form expression. Moreover, the optimal caching placement probability is analytically derived. Numerical Simulation results show that the proposed JIA-ProbC can significantly outperform the existing joint ZF and probabilistic caching (JZF-ProbC) scheme for MISO systems and SISO probabilistic caching (SISO-ProbC) scheme as well as the joint IA and most popular caching (JIA-MPC) caching scheme.

Joint Interference Alignment and Power Control for Dense Networks via Deep Reinforcement Learning

This letter proposes a joint interference suppression scheme in heterogeneous networks (HetNets) with dense small cells (SCs) and users. Different from the majority of existing studies, we adopt the co-tier intra-cell interference alignment (IA), while the co-tier inter-cell and cross-tier interference is suppressed by centralized power control in the macro base station (MBS). Specifically, the power control problem is modeled as a Markov Decision Process (MDP) with the aim of maximizing the sum spectrum efficiency. Considering the exponential growth of the output layer neurons faced by general deep reinforcement learning (DRL) algorithms, we propose a deep deterministic policy gradient (DDPG)-based algorithm to solve the problem. Simulation results demonstrate that the proposed algorithm is able to achieve better performance and wider application scope comparing with existing algorithms.

Joint Interference Alignment and Power Allocation Based on Stackelberg Game in Device-to-Device Communications Underlying Cellular Networks

As a promising wireless communication technique, Device-to-Device (D2D) can improve the utilization of spectrum resources, overall system throughput and reduce end-to-end delay, energy consumption by exploiting the direct radio link between local devices. Meanwhile, introducing D2D in cellular networks is very challenging due to the complex interference problem. To deal with the complex interference of D2D Underlying Cellular Networks, Interference alignment (IA) as an efficient interference management technique is extensively studied for D2D communications underlaying the cellular networks. However, most of the previous researches on IA only consider the equal power allocation for users, which leads to suboptimal overall system throughput. In order to further improve the system performance, in this paper, we propose a joint power allocation based on stackelberg game and interference alignment algorithm (STPA-IA) to eliminate the complex interference in D2D communications underlaying cellular networks. In this joint approach, the base station (BS) is modeled as the leader while the D2D pairs are followers. The performance of network system and that of D2D users are regarded as the revenues of leader and followers in stackelberg game. The strategy of the leader (BS) is to get the maximum benefits by reducing the interference, while the followers tend to (D2D pairs) maximize their own utility according to leader's strategy. The non-cooperative game is proved to get the nash equilibrium by the above strategies. Moreover, the close-form expression of the allocated power by maximum the unity function of leader and followers is derived. Extensive simulation results demonstrate that the proposed algorithm outperforms previous works in terms of sum-rate with lower time complexity.

Joint interference alignment and power allocation for NOMA-based multi-user MIMO systems

Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multi-user MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.

Joint Interference Alignment and Power Allocation for K-User Multicell MIMO Channel through Staggered Antenna Switching.

In this paper, we characterise the joint interference alignment (IA) and power allocation strategies for a K-user multicell multiple-input multiple-output (MIMO) Gaussian interference channel. We consider a MIMO interference channel with blind-IA through staggered antenna switching on the receiver. We explore the power allocation and feasibility condition for cooperative cell-edge (CE) mobile users (MUs) by assuming that the channel state information is unknown. The new insight behind the transmission strategy of the proposed scheme is premeditated (randomly generated transmission strategy) and partial cooperative CE MUs, where the transmitter is equipped with a conventional antenna, the receiver is equipped with a reconfigurable multimode antenna (staggered antenna switching pattern), and the receiver switches between preset T modes. Our proposed scheme assists and aligns the desired signals and interference signals to cancel the common interference signals because the received signal must have a corresponding independent signal subspace. The capacity for a K-user multicell MIMO Gaussian interference channel with reconfigurable multimode antennas is completely characterised. Furthermore, we show that the proposed K-user multicell MIMO scheduling and K-user L-cell CEUs partial cooperation algorithms elaborate the generalisation of K-user IA and power allocation strategies. The numerical results demonstrate that the proposed intercell interference scheme with partial-cooperative CE MUs achieves better capacity and signal-to-interference plus noise ratio (SINR) performance compared to noncooperative CE MUs and without intercell interference schemes.

Joint Interference Alignment and Power Allocation for K-User Multicell MIMO Channel Through Staggered Antenna Switching

Joint Interference Alignment and Power Allocation for K-User Multicell MIMO Channel Through Staggered Antenna Switching

Joint interference alignment precoding based on the optimization algorithm on the Grassmannian manifold

To solve the interference problem between users of the multiuser MIMO system, we first transform the system sum rate maximization problem into joint optimization of interference signal power and useful signal power. On this basis, we propose a weighted interference alignment objective function, causing the system to obtain a higher sum rate by adjusting the weight with different signal-to-noise ratios. Then, we model the transmit subspace and the interference subspace on the Grassmannian manifold and propose joint interference alignment precoding based on the Grassmannian conjugacy gradient algorithm (GCGA-JIAP algorithm). In contrast to conventional interference alignment algorithms, our proposed algorithm can reduce the computational cost by transforming the constrained optimization of the complex Euclidean space into unconstrained optimization with the degenerate dimension on the Grassmannian manifold. Computer simulation shows that the proposed algorithm improves the convergence of the iterative optimization of the transmitter precoding matrix and the receiver postprocessing matrix and also improves the sum rate performance of the multiuser MIMO interference system.

Joint Interference Alignment and Power Allocation for Multi-User MIMO Interference Channels Under Perfect and Imperfect CSI

We present centralized-based iterative algorithms that jointly determine the optimal transceiver filters as well as the optimum power allocation for a $K$ -user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is developed on the basis of the average per user multiplexing gain and the achievable sum-rate in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transceiver filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so-called uplink-training phase. At the CU, using the alternating optimization strategy, various iterative algorithms are proposed to design the filters. In other related studies, the equal transmit power policy for all user-BS pairs is chosen ignoring the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system’s total power. Thus, another key aspect of this paper is to make optimal power allocation decisions for all the user-BS pairs based on the sum-rate maximization strategy and under a sum power constraint.

Joint IA and SFBC Macrocells and Small-Cells Coexistence under Minor Information Exchange

The deployment of small-cells within the boundaries of a macrocell is considered to be an effective solution to cope with the current trend of higher data rates and improved system capacity. In the current heterogeneous configuration with the mass deployment of small-cells, it is preferred that these two cell types will coexist over the same spectrum, because acquiring additional spectrum licenses for small-cells is difficult and expensive. However, the coexistence leads to cross-tier/intersystem interference. In this context, this contribution investigates interference alignment (IA) methods in order to mitigate the interference of macrocell base station towards the small-cell user terminals. More specifically, we design a diversity-oriented interference alignment scheme with space-frequency block codes (SFBCs). The main motivation for joint interference alignment with SFBC is to allow the coexistence of two systems under minor intersystem information exchange. The small-cells just need to know what space-frequency block code is used by the macrocell system and no intersystem channels need to be exchanged, contrarily to other schemes recently proposed. Numerical results show that the proposed method achieves a performance close to the case where full cooperation between the tiers is allowed.

Joint Interference Alignment and Avoidance for Downlink Heterogeneous Networks

Joint Interference Alignment and Avoidance for Downlink Heterogeneous Networks