Multiuser Interference Channel Research Articles

Simplex Transformation Based Deep Unsupervised Learning for Optimization: Power Control With QoS Constraints in Multi-User Interference Channel

Simplex Transformation Based Deep Unsupervised Learning for Optimization: Power Control With QoS Constraints in Multi-User Interference Channel

Power Control With QoS Guarantees: A Differentiable Projection-Based Unsupervised Learning Framework

Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users’ quality-of-service (QoS) constraints, guaranteeing constraint satisfaction becomes a fundamental challenge. In this paper, we propose a novel unsupervised learning framework to solve the classical power control problem in a multi-user interference channel, where the objective is to maximize the network sum-rate under users’ minimum data rate or QoS requirements and power budget constraints. Utilizing a differentiable projection function, two novel deep learning (DL) solutions are pursued. The first is called Deep Implicit Projection Network (DIPNet), and the second is called Deep Explicit Projection Network (DEPNet). DIPNet utilizes a differentiable convex optimization layer to implicitly define a projection function. On the other hand, DEPNet uses an explicitly-defined projection function, which has an iterative nature and relies on a differentiable correction process. DIPNet requires convex constraints; whereas, the DEPNet does not require convexity and has a reduced computational complexity. To enhance the sum-rate performance of the proposed models even further, Frank-Wolfe algorithm (FW) has been applied to the output of the proposed models. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate but also achieve zero constraint violation probability, compared to the existing DNNs. The proposed solutions outperform the classic optimization methods in terms of computation time complexity.

Spatial Interference Alignment with Limited Precoding Matrix Feedback in a Wireless Multi-User Interference Channel for Smart Grids

Cellular communication provides an efficient, flexible, long-lived, and reliable communication technology for smart grids to improve the automated analysis, demand response, adoptive control, and coordination between the generator and consumers. With the expansion of wireless networks and the increase of access devices, interference has become a major problem that limits the performance of cellular wireless communication systems for smart grids. Spatial interference alignment (IA) is an effective method to eliminate interference and improve the capacity of wireless communication networks. This paper provides the sufficient conditions of spatial interference alignment operating with limited precoding matrix feedback for a K-user MIMO interference channel. Each receiver feeds the matrix index of the transmitting precoder back to the corresponding transmitter through an interference-free and error-free link. We calculated the number of feedback bits required to achieve the maximum theoretical multiplexing gain for the spatial interference alignment schemes considered and demonstrate the feasibility of spatial interference alignment under the limited feedback constraint investigated. It is shown that in order to maintain the same spatial multiplexing gain as that of the idealized scheme relying on perfect channel state information, the number of feedback bits per receiver scales as Nd≥di(M−di)log2SNR, where M and di denote the number of transmit (receive) antennas and the number of data steams for user i. Finally, the analytical results were verified by simulations for practical interference alignment schemes relying on limited precoding matrix feedback indices.

Joint Secure Transceiver Design and Power Allocation for AN-Assisted MIMO Networks

In this paper, we focus on antieavesdropping design in a multicell multiuser interference channel coexisting with a multiantenna eavesdropper, in which multiuser interference arises as a nonneglectable factor in securing communication. Supposing the eavesdropper is equipped with an arbitrary number of antennas, we jointly exploit the role of inherent multiuser interference and artificial noise (AN) to enhance security, and propose a noniterative secure transceiver design under a multiple input multiple output (MIMO) framework. The quantity relationship of system parameters is then analyzed to ensure feasibility. And the achievable secrecy rate is then derived without any knowledge of the eavesdropper. Finally, to balance the power allocated to AN and secrecy data, a power allocation strategy aiming at maximizing the achievable secrecy rate is designed, while guaranteeing legitimate users the required quality of service. With the adopted design, both the multiuser interference and AN are leveraged to facilitate communication security such that the proposed secure transceiver design can adapt to changes in eavesdropping antennas. Extensive numerical results have verified our analysis and demonstrated that the proposed power allocation strategy outperforms the baseline algorithms in terms of the achievable secrecy rate.

MIMO Interference Channels Assisted by Reconfigurable Intelligent Surfaces: Mutual Coupling Aware Sum-Rate Optimization Based on a Mutual Impedance Channel Model

We study a multi-user multiple-input multiple-output interference network in the presence of multiple reconfigurable intelligent surfaces (RISs). The entire system is described by using a circuit-based model for the transmitters, receivers, and RISs. This is obtained by leveraging the electromagnetic tool of mutual impedances, which accounts for the signal propagation and the mutual coupling among closely-spaced scattering elements. An iterative and provably convergent optimization algorithm that maximizes the sum-rate of RIS-assisted multi-user interference channels is introduced. Numerical results show that the sum-rate is enhanced if the mutual coupling among the RIS elements is accounted for at the optimization stage.

Asynchronous and Non-Stationary Interference Cancellation in Multiuser Interference Channels

A single antenna interference cancellation (SAIC) scheme is proposed. The scheme is motivated by the challenging asynchronous and non-stationary interference cancellation problem in wireless communications. Suppose the existence of the single-user region (SUR) of the desired signal, we propose to first recognize the SUR via a novel detection scheme. The SUR detection scheme proposed consists of the pseudo-observation matrix construction and the information theoretic criterion-based source number estimation. A basis set for the desired signal is then learnt over the SUR, via dictionary learning techniques. In the final signal recovery stage, a novel constrained sparse coding (CSC) process is proposed. The CSC reforms the conventional sparse coding (SC) via incorporating signal-specific constraints. Unlike existing SAIC algorithms, the proposed scheme is completely independent of a prior knowledge on the interferences. Numerical results are provided to demonstrate the effectiveness of the above proposed schemes under varied interference intensities and environmental noise levels. The proposed scheme outperformed competing SAIC schemes by about 5dB signal-to-interference-plus-noise ratio (SINR) improvement. The proposed CSC scheme outperformed the conventional SC by about 9dB SINR improvement, besides the superior recovery fidelity of signal property.

Interference alignment in two‐hop multiple‐input multiple‐output interference channel under various CSI and antenna correlation

In this paper, the problem of interference management is investigated in the downlink of multi-user multiple-input multiple-output two-hop interference channel under various scenarios of channel state information (CSI) and antenna correlation. Interference alignment (IA) in the space dimension is a powerful tool to solve this problem. Iterative IA algorithms are adopted in both the first and the second hops transmissions independently. After aligning interfering signals, the recovery of the transmitted signal vector is performed based on zero-forcing and semi-definite relaxation detectors. Furthermore, the impacts of the imperfect CSI and antenna correlation on sum-rate are investigated for decode-and-forward (DF) and amplify-and-forward relaying protocols. When the system is proper but IA is not completely feasible, the performance of minimum interference leakage (MIL) is compared with rank-constrained rank minimisation (RCRM) algorithm in DF protocol. RCRM algorithm is more sensitive to CSI such that the channel uncertainty easily leads to an increase in the interference matrix rank. According to the simulation results, for the case of perfect CSI, RCRM makes further interference-free dimensions which leads to better performance than MIL, but for the case of imperfect CSI, close performance is observed.

Distributed Interference Management: A Broadcast Approach

Effective interference management in the multiuser interference channel strongly hinges on the channel state information’s availability at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), acquiring the CSIT is prohibitive due to the extensive information exchange that it imposes. As a result, the interference management approaches that rely on the CSIT lose their effectiveness in such circumstances. This article focuses on the two-user interference channel and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are entirely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative gain between the direct and interfering channels, each receiver opportunistically decodes a subset of both transmitters’ received layers. An average achievable rate region is delineated, serving as an inner bound on the Gaussian interference channel’s average capacity region in the absence of CSIT. Finally, an upper bound on the gap between the achievable sum-rate and the sum-rate capacity is established.

Distributed Energy Efficiency Optimization for Multi-User Cognitive Radio Networks Over MIMO Interference Channels: A Non-Cooperative Game Approach

Energy efficiency (EE) optimization is investigated for a multi-user cognitive radio network (CRN) over multiple-input-multiple-output (MIMO) interference channels (ICs). To reduce the system overhead due to information exchange among the secondary CR uses (SUs), the EE optimization problem is formulated as a non-cooperative game, where each SU transmitter competes against the other SU pairs by optimizing its transmit covariance matrix. Specifically, each multi-antenna SU maximizes locally its energy efficiency in terms of the number of bits transmitted per unit energy consumption, subject to the per-SU transmit power constraint and the primary user (PU) perceived total interference constraint. It is proved that the formulated non-cooperative game admits at least one Nash equilibrium (NE), and the sufficient condition for a unique NE is derived subsequently. Primal decomposition is employed in the local EE optimization problem to relax the coupling PU perceived interference constraint such that fully distributed operation is allowed. A distributed iterative EE optimization algorithm (DIEEOA) is then proposed to obtain the unique NE, which is shown to converge to the global optimum. Linear precoding techniques are employed to mitigate the impacts of multi-user interference and imperfect channel state information (CSI). Through numerical simulations, effectiveness of the proposed scheme is validated and the system setting parameters’ impacts on the performance are studied.

Towards Optimal Power Control via Ensembling Deep Neural Networks

A deep neural network (DNN) based power control method that aims at solving the non-convex optimization problem of maximizing the sum rate of a fading multi-user interference channel is proposed. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. We then present PCNet+, which enhances the generalization capacity of PCNet by incorporating noise power as an input to the network. Observing that a single PCNet(+) does not universally outperform the existing solutions, we further propose ePCNet(+), a network ensemble with multiple PCNets(+) trained independently. Simulation results show that for the standard symmetric K -user Gaussian interference channel, the proposed methods can outperform state-of-the-art power control solutions under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.

Stringently Delay-Constrained Group Decoding and Adaptive Transmission for Multi-User Interference Channel

Low latency and high reliability are key features of the fifth-generation (5G) machine-type communications. In this paper, we provide a stringently delay-constrained model for multi-user interference channel, and formulate the rate and power allocation problem based on Markov decision process (MDP), which is solved by value iteration algorithm. In particular, to make timely transmission of each packet after being encoded, we characterize the delay constraint on sum of buffering delay and transmission delay. Then, the rate splitting and group decoding are employed at transmitters and receivers, respectively, which can offer significant improvement of achievable rate region. In addition, we also consider the receiving latency and propose novel delay-constrained partial group decoding (DC-CPGD), where all desired messages can be decoded within allowable interference cancellation steps to effectively control this latency. Two rate allocation algorithms are proposed with limited information exchange. Numerical results demonstrate that the proposed DC-CPGD can perform close to exhaustive search, and the proposed MDP-based adaptive transmission combined with DC-CPGD can significantly reduce the transmission cost under stringent delay requirement.

On Necessary and Sufficient Conditions of Interference Alignment Feasibility in a MIMO Interference Channel

One of the major problems of applying interference alignment (IA) for handling interference in a multi-user MIMO interference channel, with constant coefficients over time and without symbol extension is the feasibility problem. The feasibility of IA is to determine whether an IA is capable to omit all interferer signals or not, when the parameters of the system such as the number of users and the number of antennas are fixed. Comparing the Ruan sufficient condition of feasibility with Gonzalez feasibility test, their corresponding matrices are shown to have the same structure in this paper. We claim the row rank fullness of Ruan and Gonzalez matrices with generic channel matrices satisfy properness condition, which are necessary condition of IA feasibility. Other necessary conditions are extracted by investigating row rank fullness of these matrices. An optimization problem based on the extracted conditions is introduced to obtain the maximum sum degrees of freedom (DoF). The simulation results reveal an upper bound on the sum DoF with the optimization subject to the point to point constraint, the two-user information theory constraint and the extracted constraints.

다중 사용자 MISO 간섭 채널에서 물리 계층 보안을 고려한 간단한 프리코딩 기법

본 논문은 다중 사용자 다중 입력 단일 출력 (MISO) 간섭 채널에서 복수의 도청자가 존재하는 경우에, 보안 채널 수율을 향상시키기 위한 간단한 프리코딩 벡터 설계 기법을 제안한다. 제안하는 기법에서는 보안 채널 수율을 최적화하는 문제에 대해 다루었으며, 수학식으로 주어지는 분석해를 구하는 것을 목표로 하였다. 이를 위해서 신호대 누수 잡음비 (SLNR) 기반으로 프리코딩 벡터를 설계하는 방법을 제시한다. 구체적으로는, 도청자가 정보 신호를 전혀 검출할 수 없도록 도청 채널 수율을 완전히 제거하면서, 송신단-수신단 링크 간의 채널 수율을 최대화시키도록 프리코딩 벡터를 설계한다. 성능 검증을 위하여 모의실험을 수행하였으며, 송신 안테나 개수, 송신단-수신단 링크 개수, 도청자 수 간에 특별한 조건식을 만족하지 않는 환경에서는, 제안하는 기법이 모든 신호대 잡음비 범위에서 기존 기법보다 더 우수한 보안 채널 수율을 나타낸다는 것을 제시한다.

Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.

Finite-Horizon Throughput Region for Wireless Multi-User Interference Channels

This paper studies a wireless network consisting of multiple transmitter-receiver pairs where interference is treated as noise. Previously, the throughput region of such networks was characterized for either one time slot or an infinite time horizon. We aim to fill the gap by investigating the throughput region for transmissions over a finite time horizon. Unlike the infinite-horizon throughput region, which is simply the convex hull of the throughput region of one time slot, the finite-horizon throughput region is generally non-convex. Instead of directly characterizing all achievable rate-tuples in the finite-horizon throughput region, we propose a metric termed the rate margin, which not only determines whether any given rate-tuple is within the throughput region (i.e., achievable or unachievable), but also tells the amount of scaling that can be done to the given achievable (unachievable) rate-tuple such that the resulting rate-tuple is still within (brought back into) the throughput region. Furthermore, we derive an efficient algorithm to find the rate-achieving policy for any given rate-tuple in the finite-horizon throughput region.

A Receiver-centric Approach to Interference Management: Fairness and Outage Optimization

Effective interference management in the multiuser interference channel necessitates that the users form their transmission and interference management decisions in coordination, and adapt them to the state of the channel. Establishing such coordination, often facilitated through information exchange, is prohibitive in fast-varying channels, especially when the network size grows. This paper focuses on the multiuser Gaussian interference channel and offers a receiver-centric approach to interference management. In this approach, the transmitters deploy rate-splitting and superposition coding to generate their messages according to independent Gaussian codebooks. The receivers can freely decode any arbitrary set of interfering messages along with their designated messages in any desired joint or ordered fashion, and treat the rest of the interferers as Gaussian noise. The proposed receiver-centric interference management approach is applied to two class of problems (outage optimization and fairness-constrained rate allocation), and constructive proofs are provided to establish the following properties for the proposed approach: 1) the optimal set of codebooks to be decoded by each receiver is a local decision made by each receiver based on its local channel state information (CSI); 2) the globally optimal transmission rates are related to locally optimal rates computed by the receivers based on their local information, which implies that the transmitters do not require explicit knowledge of the CSI and can determine their rates via limited feedback from the receivers; and 3) obtaining the optimal interference management strategy at each receiver has controlled complexity.

Probabilistic Radio Resource Allocation Over CDMA-Based Cognitive Radio Networks

A novel probabilistic radio resource allocation algorithm is proposed for code-division multiple-access cognitive radio networks. Our method incorporates a sensing-based hybrid access- and interference-limited spectrum sharing strategy to maximize the achievable spectral efficiency of cognitive users (CUs) while guaranteeing the quality-of-service (QoS) requirements of licensed services. Specifically, the enhanced performance for CUs is achieved through optimal joint power and rate adaptation to the time-varying multiuser interference and frequency-selective fading channel conditions. We show that this scheme can improve the average spectral efficiency of CUs, while satisfying the peak and average received-interference constraints at the primary receiver.

Macro-pico amplitude-space sharing with layered interference alignment

Inter-cell interference leads to severe performance degradation in cellular networks, and the study of multi-user interference channel is the corner stone for solving this problem. Amplitude-space layered interference alignment (IA), as an effective complementation to the vector-space IA, is a promising method to increase the data rate in static interference channels. However, recent studies of layered IA has been focused on analyzing the degrees of freedom (DoF) or the achievable rate under specific channel constraints. In this paper, we propose a layered IA scheme that can work with arbitrary channel coefficients. We develop a layer partitioning method and optimize the active layer assignment through linear programming. An implementation scheme is then introduced with multi-level nested lattice codes where the signal and interference are nested in amplitude space, and the interference from different users is nestedly aligned. The performance of the proposed scheme is finally evaluated in homogeneous and heterogeneous cellular networks with practical settings.

MSE-Based Hybrid RF/Baseband Processing for Millimeter-Wave Communication Systems in MIMO Interference Channels

We consider the design of a hybrid multiple-input multiple-output (MIMO) processor consisting of a radio frequency (RF) beamformer and a baseband MIMO processor for millimeter-wave communications over multiuser interference channels. Sparse approximation problems are formulated to design hybrid MIMO processors approximating the minimum-mean-square-error transmit/receive processors in MIMO interference channels. They are solved by orthogonal-matching-pursuit-based algorithms that successively select RF beamforming vectors from a set of candidate vectors and optimize the corresponding baseband processor in the least squares sense. It is shown that various beamformers can be designed by considering different types of candidate vector sets. Simulation results demonstrate the advantage of the proposed design over the conventional method that designs the baseband processor after steering the RF beams.

A Review on Interference Alignment in Multiuser Interference Channels

As the density of base stations in wireless networks becomes higher, system performance is limited not by noise but by interference from other transmitters and so research on interference channels has been actively undertaken. In this article, we study interference alignment (IA) techniques, which are known to maximize the achievable degrees of freedom (DoF) under multiuser interference channels. First, the concepts and history of IA are introduced, and the results on achievable DoF are explained. From a practical aspect, beam design for multiple input multiple output interference channels is revisited. Additionally, channel information feedback, performance with feedback delay, blind techniques, cellular interference alignment and other recent results are explained. Based on these results, the feasibility and role of interference alignment are examined under various conditions.