Iterative IA precoding and RIS beamforming based on rank-reduced MIMO interference channel

A Closed-form Solution for Robust Transceiver Design in MIMO Interference Channels under CSI Mismatch

Interference Minimization in Beyond-Diagonal RIS-Assisted MIMO Interference Channels

Distributed Robust Artificial-Noise-Aided Secure Precoding for Wiretap MIMO Interference Channels

An active reconfigurable intelligent surface (RIS) has been shown to be able to enhance the sum-of-degreesof-freedom (DoF) of a two-user multiple-input multiple-output (MIMO) interference channel (IC) with equal number of antennas at each transmitter and receiver. However, for any number of receive and transmit antennas, when and how an active RIS can help to improve the sum-DoF are still unclear. This paper studies the sum-DoF of an active RIS-assisted two-user MIMO IC with arbitrary antenna configurations. In particular, RIS beamforming, transmit zero-forcing, and interference decoding are integrated together to combat the interference problem. In order to maximize the achievable sum-DoF, an integer optimization problem is formulated to optimize the number of eliminating interference links by RIS beamforming. As a result, the derived achievable sum-DoF can be higher than the sum-DoF of twouser MIMO IC, leading to a RIS gain. Furthermore, a sufficient condition of the RIS gain is given as the relationship between the number of RIS elements and the antenna configuration.

Signal to interference plus noise ratio maximization (Max-SINR) algorithm are designed to increase the transmission rate of multiple-input multiple-output interference channels (MIMO IC). The algorithms are sensitive to channel estimation error, which makes the rate performance under imperfect channel state information (CSI) conditions become unsatisfactory. To deal with this, a relay assisted signal to interference plus noise ratio maximization (Relay-Max-SINR) algorithm is proposed in this work to improve the robustness of Max-SINR in MIMO ICs. Different from the traditional relay assisted using beamforming and forward transmission, we mathematically equivalent the signal processing process of relay to the receive filtering and transmit precoding, and design the relay processing matrix. In real communication process, the relay processing matrix is directly used for the signal processing at relay. Using the channel reciprocity, the receive filtering matrix at relay, the transmit precoding matrix at relay, the relay processing matrix, the receive filtering matrix at destination and the transmit precoding matrix at source are optimized iteratively according to the nature of generalized Rayleigh quotient. With the help of additional transmission links provided by the relay, interference is more effectively suppressed and the power loss of the desired signal is reduced. Simulations show that the proposed Relay-Max-SINR algorithm can significantly increase the capacity and the robustness of MIMO ICs.

An interference leakage minimisation transceiver design for multiple-input multiple-output Interference Channel networks is proposed by making use of the full rank constraint of the desired signal, the low rank constraint and low power constraint of the interference signal. The objective is to suppress interference leakage caused by not only the signal from other users, but also the other streams from the same user. To do so, the transmit precoding matrix and the receive filtering matrix are iteratively optimised through convex optimisation tools at stream level. Furthermore, a Min–Max interference leakage algorithm is also proposed to suppress the maximum interference from user, with the purpose of guaranteeing the fairness among users. Simulation results demonstrate that taking inter-stream interference into consideration can significantly improve the effectiveness of multiple-input multiple-output Interference Channel networks, while the Min–Max method can slightly increase the system capacity under certain conditions. It can be also confirmed that the trade-off among effectiveness, fairness and robustness exists in the transceiver optimisation of multiple-input multiple-output Interference Channel networks.

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.

We consider 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> -user Multiple Input Multiple Output (MIMO) Gaussian interference channel with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas at each transmitter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas at each receiver. It is assumed that channel coefficients are constant real numbers and are available at all transmitters and at all receivers. The main objective of this paper is to characterize the number of Degrees of Freedom (DoF) of this channel. Using the real interference alignment technique introduced in Motahari <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2014, we show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {MN}{M+N} K$ </tex-math></inline-formula> degrees of freedom can be achieved for almost all channel realizations. Also, a new upper-bound on the DoF of this channel is provided. This upper-bound coincides with our achievable DoF for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K\geq K_{u} \triangleq \frac {M+N}{\gcd (M,N)}$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gcd (M,N)$ </tex-math></inline-formula> denotes the greatest common divisor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> . This gives an exact characterization of DoF for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M\times N$ </tex-math></inline-formula> MIMO Gaussian interference channel in the case of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K\geq K_{u}$ </tex-math></inline-formula> . Since there is no cooperation between transmit (or receive) antennas of each user in our transmission scheme, this result shows that the DoF benefit of joint processing in collocated antennas vanishes when the number of users is greater than a certain threshold.

Recently cellular networks have been densely and heterogeneously deployed indoors and outdoors to expand the network capacity, and thus the in-building propagation loss and the transmit power diversity of access points will exacerbate link heterogeneity and result in partial unidirectional strong interference. To make full use of the strong interference feature, we propose the successive interference cancellation and alignment (SICA) scheme in the K-user interference channel with partial unidirectional strong interference. SICA is designed to transmit two kinds of data streams simultaneously, the alignment streams and superposition streams. The alignment streams will follow the interference alignment criterion to maintain the optimal degrees of freedom (DoF) performance; the superposition streams are handled via successive interference cancellation at all the strongly interfered receivers to improve the overall achievable rate. The joint transceiver designs for SICA is modeled as a weighted sum rate (WSR) maximization problem, and then can be alternately solved for a local optimum according to the optimality equivalence between WSR and its corresponding weighted mean square error (WMMSE) problem. Simulation results have confirmed the sum rate improvement and DoF optimality of the proposed SICA scheme.

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.

“Orthogonalized” sparsity enhanced mismatch models for wireless heterogeneous 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 transmit and receive beamforming for MIMO interference channels using the difference of convex programming

This letter shows that a multi-antenna relay using only partial and delayed channel state information (CSI) helps mitigate interference effects for multi-input multi-output interference channels when no CSI is available at transmitters. To this end, we present a novel relay-aided interference alignment, which comprises of two phases: 1) side-information learning and 2) overheard equation swapping via a relay. Leveraging the proposed scheme, we show that the achievable sum-degrees of freedom (sum-DoF) is equal to 2min(R, M)N/(min(R, M) + N) if min(R, M) > N where M, N, and R are the number of antennas at transmitter, receiver, and relay, respectively. One major implication of this result is that using the outdated CSI at the relay is sufficient to attain the optimal sum-DoF achieved with perfect CSI knowledge, provided that the number of antennas at the relay is larger than that of transmit and receive antennas.

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.

In this work, the authors propose a transceiver design strategy based on switched preprocessing (SP) for interference management in K ‐pair MIMO interference channels. Each transmitter performs SP by using a small number of permutation matrices to allocate the entries of its precoder output vector on different transmit antennas. Each arrangement of permutation matrices among the K transmitters gives rise to a set of K parallel point‐to‐point transceivers, referred to as MIMO latent transceiver set (MLTS). Based on the given channel state information (CSI), the optimum MLTS among the available ones is chosen by minimising the squared Euclidean distance between the pre‐estimated noiseless received vector and the true transmit symbol vector. In addition, they consider two CSI error models, i.e. the stochastic error model and the norm‐bounded error model, and for each type they propose robust algorithms for the design of the MLTS associated to the different choices of permutation matrices, which are based on minimising various types of mean square error criteria. A detailed study of computational complexity for the proposed SP‐based MIMO transceiver design algorithms is carried out. Simulation results verify the effectiveness of the new SP‐based designs for MIMO interference channels.

MIMO 간섭 채널을 위한 확장 선형 하이브리드 수신기

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference, i.e. , interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the $l_{2}$ and the Schatten- $p$ norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions.

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.