LS Interference Alignment Algorithm Based on Symbol Detection Assistance

Interference alignment (IA) with symbol extensions in the quasi-static flat-fading K-user multipleinput multiple-output (MIMO) interference channel (IC) is considered in this paper. In general, long symbol extensions are required to achieve the optimal fractional degrees of freedom (DOF). However, long symbol extensions over orthogonal dimensions produce structured (diagonal or block diagonal) channel matrices from transmitters to receivers. Most of existing approaches are limited in cases where the channels have some special structures, because they align the interference without preserving the dimensionality of the desired signal explicitly. To overcome this common drawback of most existing IA algorithms, two novel iterative algorithms for IA with symbol extensions are proposed. The first algorithm designs transceivers for IA based on the mean square error (MSE) criterion which minimizes the total MSE of the system while preserving the dimensionality of the desired signal. The novel IA algorithm is a constrained optimization problem which can be solved by Lagrangian method. Its convergence is proven as well. Utilizing the reciprocity of alignment, the second algorithm is proposed based on the maximization of the multidimensional case of the generalized Rayleigh Quotient. It maximizes each receiver’s signal to interference plus noise ratio (SINR) while preserving the dimensionality of the desired signal. In simulation results, we show the superiority of the proposed algorithms in terms of four aspects, i.e., average sum rate, the fraction of the interfering signal power in the desired signal subspace, bit error rate (BER) and the relative power of the weakest desired data stream.

Multiple-input multiple-output (MIMO) wireless communication systems can achieve higher throughput through interference alignment. For a small number of users, determining the maximum possible degrees of freedom as well as the feasibility of interference alignment in MIMO systems is well studied. However, the issues of scheduling in systems employing interference alignment and serving a large number of users have received little attention so far. In this paper, we study the problem of joint scheduling, interference alignment, and packet admission control in MIMO wireless systems with the goal of maximizing system throughput subject to stability constraints. We formulate a stochastic network optimization problem and propose a scheduling and interference alignment (SIA) algorithm. In each time slot, SIA schedules some users among many competing ones to transmit data, and determines encoding and decoding matrices for the selected users. Packet admission control is performed in each time slot. In addition, we propose a heuristic semi-distributed algorithm (SDSIA), which has a lower computational complexity than the SIA algorithm. Via simulation, we evaluate the performance of SIA and SDSIA for different algorithm parameters and different numbers of users. We also compare the performance of SDSIA with other approaches which do not simultaneously exploit interference alignment and scheduling and find that the combination of these two techniques increases the achievable data rate dramatically.

Assuming perfect channel state information, the existing interference alignment (IA) algorithm proposed in [2] suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback, resulting in residual ICI. In this letter, we propose the optimization of the reference vector for regulating the residual ICI. Specifically, it is shown that the reference vector that minimizes an upper bound on the residual ICI power is the eigenvector corresponding to the largest eigenvalue of the sum of the interference-channel matrices multiplied by their corresponding Hermitian matrices. Moreover, the performance gain of the proposed IA algorithm compared with the existing one in [2] is analyzed and demonstrated by simulation to be significant.

Energy-efficient interference alignment (IA) algorithms that simultaneously satisfy continuous coverage and green communications requirements are an open problem in 5G cellular networks. IA is one of the most promising techniques to eliminate interference. However, a recent assumption in green communications is to utilize interference signals as an energy supply for electronic devices. In this scenario, simultaneous wireless information and power transfer (SWIPT) schemes are a common technique to harvest energy from wireless signals. This paper addresses a performance comparison of different IA algorithms to guarantee the best trade-off between sum-rate and the amount of harvested energy, with an in-depth analysis.

In wireless interference networks, orthogonal medium access techniques like time division multiple access (TDMA) and frequency division multiple access (FDMA) achieve merely one degree of freedom (DoF). However, by using interference alignment (IA) the achievable DoF can be linearly scaled up with the number of users. Nevertheless, this outperformance of IA heavily depends on the availability of perfect channel state information (CSI), which is not a realistic assumption in practice. Since under imperfect CSI, the performance of IA may become severely degraded, design of enhanced IA algorithms by relying on the availability of merely imperfect CSI is of particular interest. In this paper, we propose a least squares (LS)-based IA algorithm which adaptively designs the beamformers based on the knowledge of the channel estimation error variance, which is possible to be known in advance. Then, we compare the performance of the proposed scheme with interference leakage minimization algorithms. It is shown that under both perfect and imperfect CSI, the proposed LS-based IA outperforms interference leakage minimization algorithms.

Interference alignment (IA) is a multi-user wireless communication strategy that enables several transmitters simultaneous communicate with their paired receivers without interfering each other. IA is a multiplexing gain optimal strategy at the expense of requiring the global channel state information (CSI) at the transmitters. Most previous works assume the availability of perfect CSI which is not possible in practice. The performance of IA algorithms based on perfect CSI drop dramatically in circumstance where CSI errors exist. In this paper we propose a robust minimum mean square error (MMSE) IA algorithm with imperfect CSI. Based on a Gaussian statistic channel error model, we give the mean square error objective function with per transmitter power constraint. The objective function is not joint convex in precoders and decoders, but convex in precoders with fixed decoders and vice versa. We convert the problem to two convex sub problems and iteratively find the solutions. Numerical results are presented to validate the effectiveness of proposed algorithm when CSI errors exist.

In the traditional interference channel (IC), each transmitter works in the mode of one-to-one. For serving more user terminals (UTs), a multiple-multicast network is proposed, in which there are multiple transmitters, and each transmitter works in the mode of one-to-many. Thus, each UT would suffer from both the intra-transmitter and inter-transmitter interference. Given the transmitting and receiving degrees of freedom, the desired signal would be more difficult to be separated from the numerous interference signals. For managing the interference, an interference subspace alignment (ISA) algorithm is proposed to decrease the dimension of the interference subspace. Different from the traditional iterative interference alignment (IA) algorithms, which iteratively calculate the precoding and receiving combining matrices to minimize the strength of interference, the ISA algorithm directly aligns the interference subspace spanned by the $T-N$ transmitters within that spanned by the residual $N$ transmitters, in which $T$ denotes the number of all the transmitters, and $N$ is the cardinality of the transmitter set used for IA. Now, the dimension of each UT's interference subspace is determined via $N$ transmitters ( $N ). During the subspace alignment, we, respectively, construct the composite IC state matrices for the $T-N$ and $N$ transmitters, and the composite precoding vector, which lies in the null space of the composite IC matrices. Thus, all the precoding matrices can be obtained once. After the subspace alignment, each UT's receiving combining matrix can be designed to lie in the null space of the aligned interference subspace. According to the simulation results, the proposed ISA algorithm can effectively suppress the interference and improve the system sum rate compared with the traditional interference management algorithms.

Based on closed-form interference alignment (IA) solutions, a low overhead distributed interference alignment (LOIA) scheme is proposed in this paper for the $K$-user SISO interference channel, and extension to multiple antenna scenario is also considered. Compared with the iterative interference alignment (IIA) algorithm proposed by Gomadam et al., the overhead is greatly reduced. Simulation results show that the IIA algorithm is strictly suboptimal compared with our LOIA algorithm in the overhead-limited scenario.

In recent years, wireless LAN systems that utilize a wideband channel have been used in many cases, so that the problem of wireless LAN interference often occurs. In order to solve the wireless LAN interference problem, various interference management methods have been proposed. Among them, interference alignment algorithm is the most popular. In order to overcome the limitations of existing interference alignment algorithms in wideband WLAN interfering environment, in this paper, we propose a new SA-OIA (Sub-band Allocated Opportunistic Interference Alignment) method and analyze the performance.

The requirement for global channel state information (CSI) throughout the network is a significant hurdle to overcome in interference alignment (IA) technologies for mobile applications. In this letter, a distributed IA algorithm is proposed that uses local CSI at the receivers to determine desirable precoding matrices for all interfering transmitters. These matrices are fed back to the transmitters where they are combined, using scalar weighting, to determine the next set of transmit precoding matrices. Results herein show that, with equivalent feedback limitations, the proposed algorithm provides better SINR performance than an IA algorithm requiring full CSI-based feedback, due to the smaller dimensions of the precoding matrices.

For the hierarchical structure and frequency reuse of cognitive heterogeneous networks, it is inevitable to effectively deal with interference while gaining capacity. In this article, we consider the downlink cognitive heterogeneous network, in which a macro base station can serve a certain number of macro users, and each macro cell is composed of a certain number of micro cells. An opportunistic downlink interference alignment (ODIA) algorithm for cognitive heterogeneous networks based on puncture scheduling is proposed. First, we designed a pair of precoding and decoding matrices to eliminate co-layer interference. Second, in order to reduce the overhead required to transmit system state information(CSI), we proposed a user scheduling algorithm that not only can obtain a larger interference-free size, but also improvements in fairness, system capacity and overflow probability. The simulation results show that the proposed ODIA scheme is superior to the existing interference management algorithms in terms of interference, system capacity and fairness of system resource allocation.

5.1 Link Adaptation link adaptation 5.2 System Concept for a MIMO-OFDM-Based Self-Organizing Data Transmission Network 5.3 Pricing Algorithms for Power Control, Beamformer Design, and Interference Alignment in Interference Limited Networks 5.4 Interference Reduction: Cooperative Communication with Partial CSI in Mobile Radio Cellular Networks

Physical layer techniques have come a long way and can achieve very close to Shannon capacity for point-to-pint links. It is apparent that, to further improve network capacity significantly, we have to resort to concurrent transmissions. Many concurrent transmission techniques (e.g., zero forcing, interference alignment and distributed MIMO) are proposed in which multiple senders jointly encode signals to multiple receivers so that interference is aligned and each receiver is able to decode its desired information. In this paper, we investigate the constraints and challenges of using interference alignment. Our main contribution is conducting the first systematic investigation on the key issue of identifying opportunities for interference alignment. We identify diverse, novel scenarios for using interference alignment. We show that identifying opportunities for interference alignment in the general case is computational challenging. However, we also present a promising, distributed algorithm for identifying a wide range of opportunities for interference alignment using a unifying framework based on the degree of freedom. Our second contribution is evaluating key practical implementation issues.

In this paper, we propose a linear transceiver design based on interference alignment (IA) for multiple-input multiple-output (MIMO) heterogeneous networks, which consist of overlaying macrocell and picocells. By exploiting the concept of IA, we show that the proposed transceiver design can mitigate the effect of interference efficiently. We also provide a transceiver design based on IA for additional deployments of femtocells by little modification of the proposed design. From simulation results, we show that the proposed transceiver design can provide additional degrees of freedom (DOF) compared to the conventional interference coordination schemes. Furthermore, we also demonstrate that the proposed transceiver design can be easily generalized to the network with arbitrary number of cells and users per macrocell.

본 논문에서는 다중 셀 환경에서 전체 생 용량을 향상시키기 위하여 부분공간 간섭 정렬 (Sub-space Interference Alignment) 기법을 위한 새로운 레퍼런스 벡터 설정 알고리즘을 제안한다. 송신단에서 임의의 동일한 레퍼런스 벡터를 사용하여 전송 벡터를 생성하고, 수신단에서 이와 직교하는 벡터를 사용하여 정렬된 간섭 신호를 제거하는 기존의 부분공간 간섭 정렬 기법의 경우, 사용되는 레퍼런스 벡터 및 채널 상황에 따라 전체 시스템 용량이 달라지는 문제점을 가지고 있다. 이에 본 논문에서는 레퍼런스 벡터와 채널의 변화에 의해 전체 시스템의 합용량이 변화하는 문제점 및 레퍼런스 벡터 원소들의 크기 분산이 작아질수록 합용량이 향상되는 경향을 보임을 분석한다. 이러한 분석을 바탕으로 새로운 레퍼런스 벡터 설정 방법으로 레퍼런스 벡터 원소들의 크기 분산을 고려하여 설정하는 알고리즘을 제안하며, 모의실험을 통해 기존 알고리즘과 비교하여 제안된 알고리즘이 평균적으로 약 50% 정도 향상된 합용량을 나타냄을 확인한다. In this paper, novel sub-space interference alignment algorithms are proposed to boost the capacity in multi-cell environment. In the case of conventional sub-space alignment, arbitrary reference vectors have been adopted as transmitting vectors at the transmitter side, and the inter-cell interference among users are eliminated by using orthogonal vectors of the chosen reference vectors at the receiver side. However, in this case, sum-rate varies using different reference vectors even though the channel values keep constant, and vice versa. Therefore, the relationship between reference vectors and channel values are analyzed in this paper, and novel interference alignment algorithms are proposed to increase multi-cell capacity. Reference vectors with similar magnitude are adopted in the proposed algorithm. Simulation results show that the proposed algorithms provide about 50 % higher sum-rate than conventional algorithm.