K-user Multiple-input Multiple-output Interference Channel Research Articles

Optimal Transceiver Design for SWIPT in Interference Alignment Network

This paper studies simultaneous wireless information and power transfer in a K-user multiple-input multiple-output interference channel. A new scheme jointly designing interference ali ...

Low-Overhead Feedback Topology Design for the K-User MIMO Interference Alignment

Since designing a feedback topology is a practical way to implement interference alignment at reduced cost of channel state information (CSI) feedback, six feedback topologies have been presented in prior works for a K-user multiple-input multiple-output interference channel. To fully reveal the potential benefits of the feedback topology in terms of the saving of CSI overhead, we propose a new feedback topology in this paper. By efficiently performing dimensionality-decreasing at the transmitter side and aligning interference signals at a subset of receivers, we show that the proposed feedback topology obtains substantial reduction of feedback cost over the existing six feedback designs under the same antenna configuration.

Energy efficiency optimization for SWIPT in K-user MIMO interference channels

In this paper, we investigate the energy efficiency (EE) for K-user multiple-input multiple-output (MIMO) interference channels with simultaneous wireless information and power transfer (SWIPT) and interference alignment (IA). Specifically, power splitting architecture is leveraged at each receiver for information decoding (ID) and energy harvesting simultaneously. Taking the quality of service (Qos) requirement and harvested power constraints into consideration, we maximize the system EE by jointly designing transmit beamformers, transmit power, power splitters and receive filters. To solve the nonconvex EE optimization problem, we first eliminate the interference among users for ID via the maximal signal to interference plus noise ratio (Max-SINR) IA method, which yields a two-dimensional EE optimization problem. And then, we propose an EE-Max algorithm coupled with the Max-SINR, nonlinear fraction programming and one dimension searching to solve the reformulated problem. Simulation results show the effectiveness of the proposed EE optimization algorithm and reveal the influence of the maximum transmit power constraint and the QoS requirement on the system EE.

A Novel Joint Power and Feedback Bit Allocation Interference Alignment Scheme for Wireless Sensor Networks.

It is necessary to improve the energy efficiency of batteries in wireless sensor networks (WSNs). The multiple-input multiple-output (MIMO) technique has become an important means to ameliorate WSNs, and interference management is the core of improving energy efficiency. A promising approach is interference alignment (IA), which effectively reduces the interference and improves the throughput of a system in the MIMO interference channels. However, the IA scheme requires perfect channel state information (CSI) at all transceivers in practice, which results in considerable feedback overhead. Thus, limited IA feedback has attracted much attention. In this paper, we analyze the throughput loss of the K-user MIMO interference channels when each transmitter delivers multiple streams in one slot, and derives the upper-bound of the system interference leakage and throughput loss. Then, to reduce the interference leakage and throughput loss for the MIMO interference alignment with limited feedback, a joint power and feedback bit allocation optimization scheme is proposed. The simulation results show that, compared with the conventional schemes, the presented optimal scheme achieves less residual interference and better performance in the system throughput.

Achievable Sum DoF of the K-User MIMO Interference Channel with Delayed CSIT

This paper considers a $K$-user multiple-input-multiple-output (MIMO) interference channel (IC) where 1) the channel state information obtained by the transmitters (CSIT) is completely outdated, and 2) the number of transmit antennas at each transmitter, i.e., $M$, is greater than the number of receive antennas at each user, i.e., $N$. The usefulness of the delayed CSIT was firstly identified in a $K$-phase Retrospective Interference Alignment (RIA) scheme proposed by Maddah-Ali et al for the Multiple-Input-Single-Output Broadcast Channel, but the extension to the MIMO IC is a non-trivial step as each transmitter only has the message intended for the corresponding user. Recently, Abdoli et al focused on a Single-Input-Single-Output IC and solved such bottleneck by inventing a $K$-phase RIA with distributed overheard interference retransmission. In this paper, we propose two $K$-phase RIA schemes suitable for the MIMO IC by generalizing and integrating some key features of both Abdoli's and Maddah-Ali's works. The two schemes jointly yield the best known sum Degrees-of-Freedom (DoF) performance so far. For the case $\frac{M}{N}{\geq}K$, the achieved sum DoF is asymptotically given by $\frac{64}{15}N$ when $K{\to}\infty$.

Interference alignment in K ‐user multiple‐input–multiple‐output interference channels with partially coordinated receivers

In a K-user multiple-input multiple-output (MIMO) interference channel where each N-antenna transmitter communicates with its corresponding M-antenna receiver by using d degrees of freedom (DoF), the feasibility condition for conventional interference alignment (IA) implies that M + N > d(K + 1). In this paper, we consider a K-user MIMO interference channel where averagely half of the total decoded data can be shared by receive nodes via partial coordination. We propose IA algorithms such that first, their feasibility condition can now be expressed as M + N > d(K + 4)/2, which further implies less number of antennas is needed in comparison with conventional IA techniques to achieve the same number of DoF; and second, even with this reduced number of antennas, their throughput is still comparable to that of conventional IA methods. This is particularly pronounced for asymptotically large K where the required number of antennas for the proposed IA schemes can be half of that of conventional IA techniques to secure the same number of DoF. Moreover, we also examine the performance of the proposed IA algorithms in the presence of both error propagations and channel estimation errors. Simulation results show that in these cases, the throughput of the proposed algorithms can be comparable to that of conventional IA techniques.

A reconfigurable iterative algorithm for the K-user MIMO interference channel

Interference alignment (IA) is a recently proposed transmission technique for the K-user interference channel (IFC), which is proven to achieve a sum-rate multiplexing gain of K/2 at the high interference regime. Motivated by our recent work [1] that showed how the sum-rate scaling can range between K/2 and K for moderate-to-low interference conditions, in this paper we present a novel iterative algorithm for the K-user multiple-input multiple-output (MIMO) IFC with arbitrary number of transceiver antennas. The proposed algorithm automatically adjusts itself to the interference regime at hand, in the above sense, as well as to the wireless channel in order to achieve the appropriate sum-rate scaling. Our reconfigurable algorithm combines the system-wide mean squared error minimization criterion with the single-user waterfilling solution to maximize each user's transmission rate according to the interference levels and channel conditions. Extensive computer simulation results for the sum-rate performance of the proposed reconfigurable algorithm over various Ricean fading channels are presented. It is shown that, in the interference-limited regime, the proposed algorithm reconfigures itself so as to achieve the IA scaling whereas, in the moderate-to-low interference regime, it chooses interference-myopic MIMO transmissions for all K communication pairs.

Interference alignment for MIMO cognitive networks: a complex FDPM-based subspace tracking approach

Interference alignment (IA) for multiple-input multiple-output (MIMO) cognitive networks is considered by modelling the unlicensed secondary transmitter-receiver pairs which coexist with the licensed multi-antenna primary user as a K-user MIMO interference channel. Starting from investigating the constraint conditions of IA scheme in MIMO cognitive networks, a practical IA algorithm is developed by using a subspace tracking approach based on the complex fast data projection method (FDPM). In the proposed algorithm, first, each secondary transmitter aligns its transmitted signal into the null space of the channel matrix from itself to the primary user without causing any interference to the primary user. Then secondary transmitters and receivers, requiring no channel knowledge of secondary networks, alternately design the precoding and postprocessing matrices through a training period which exploits the complex FDPM-based minor subspace tracking, thus eliminating interference among secondary users. Moreover, the case where secondary transmitters have partial knowledge of channels from themselves to the primary user is also discussed. Simulation results reveal that the proposed algorithm can achieve a high sum rate performance while requiring low computational complexity.

Taylor approximation pricing for K-user multiple-input multiple-output (MIMO) interference channels

This study considers the immense problem of sum rate maximisation in K-user multiple-input multiple-output interference channels (IFC). In order to address this problem, the authors propose an iterative Taylor approximation algorithm to find the optimal transmit covariance matrices, where each user maximises the sum rate function through Taylor expansion. Owing to the distributed nature of the ITA algorithm, Taylor terms can be treated as prices, where each user collects current prices from the others to perform update. This process further leads us modelling it as a more general concave n-person game to analyse the existence and uniqueness of the Nash equilibrium (NE). Then an iterative covariance-price updating (ICP) algorithm is proposed for flexible updating and establishing a convergence process to obtain the optimal solution under NE uniqueness conditions. Simulation results show that both algorithms come with similar performance and both significantly outperform the conventional iterative waterfilling algorithm.