System Level Aspects for Multiple Cell Scenarios

With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.

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.

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.

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.

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 this paper, we address the problem of beamforming (BF) design in a cognitive relay network (CRN), where the cognitive network not only shares the spectrum with the primary one but also acts as a relay to assist the primary signal transmission. Considering that all of the wireless channels are subject to the correlated Rayleigh fading distribution, we first formulate a constrained optimization problem to minimize the total transmit power of the cognitive base station (CBS) with multiple antennas, while guaranteeing the quality-of-service (QoS) of the primary and secondary users and keeping the mutual interference below an acceptable level. Then, by adopting the available partial channel state information (CSI), and proposing an approximation method to relax the constraints, a support vector machine (SVM) based algorithm is presented to solve the optimization problem for the BF weight vectors. The benefit of the new approach is that the slow change of the partial CSI can be taken into account. Finally, computer simulation results are provided to confirm the superiority of the proposed BF design strategy.

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.

In order to effectively utilise the spectrum of wireless network, secondary users (SUs) are often authorised to share spectrum with primary users (PUs) in cognitive networks. To guarantee effective transmission of PUs and SUs, the interference from the SUs to the PUs is limited and only the SUs that have the information rates (rates) than the corresponding thresholds are allowed to transmit their data. In order to select proper SUs to transmit and to maximise their achievable weighted sum rate (WSR) under rate requirement and interference constraint, the authors propose a joint admission control and beamforming design algorithm for partial channel state information (CSI) case. The WSR maximisation problem is first constructed by using approximation and convex relaxation, according to the statistical property of the CSI. Then a penalty factor is introduced to check the feasibility of such problem and instruct the admission of the SUs. Beyond the feasible SUs set, the WSR is maximised by updating the achievable rate of each user with rate profile technique and polyblock outer approximation method. Simulation results show that the proposed beamforming can achieve higher WSR than the beamforming of the existing baseline method.

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.

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.

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.