Partial Interference Alignment Research Articles

Efficient Subchannel Allocation Based on Partial Clustered Interference Alignment and Clusters Merging in Dense Small Cell Networks

This article investigates efficient subchannel allocation based on the proposed partial clustered interference alignment (PCIA) and clusters merging algorithms in dense small cell networks (SCNs), which well adapts to the randomly deployed dense SCNs and can further improve the achievable data streams. Specifically, we aim at maximizing the data streams achieved by all small cell user equipments (SUEs) while satisfying the requirement for data streams of each SUE. Due to the NP-hardness of the corresponding problem, we propose an efficient solution consisting of three phases: partial clustering, clusters merging, and subchannel allocation. Each phase requires much lower complexity and reduced feedback overhead. Simulations show that the proposed solution not only outperforms other related schemes but also has a smaller performance gap with the approximate optimal solution.

Smart Edge Caching-Aided Partial Opportunistic Interference Alignment in HetNets

The development of the next-generation wireless networks are regarded as the essentials to embrace of Internet of Things (IoT) and edge computing in heterogeneous networks (HetNets). Due to the the spectrum scarcity problem and the large number of connectivity demand of IoT users, intelligent interference management for IoT is worthy of thorough investigation and should be well discussed with consideration on edge computing in heterogeneous networks (HetNets). Two crucial challenges in the context are: 1) placing edge cache based on dynamic request of IoT users, and 2) cache-enabled interference management with time-varying wireless channels. In this paper, we proposed smart edge caching-aided partial opportunistic interference alignment(POIA) with deep reinforcement learning for IoT downlink system in HetNets. Towards this end, the proposed scheme can update the base station (BS) cache dynamically, and then select the optimal cache-enabled POIA user group considering the time-varying user’s requests and time-varying wireless channels. To solve this problem efficiently, the reinforcement learning is exploited that can take advantage of a deep Q-learing to replace the system action. Extensive evaluations demonstrate that the proposed method is effectiveness according to sum rate and energy efficiency of IoT downlink transmission for HetNets.

Partial Interference Alignment Scheme for Three-Tier Downlink Heterogeneous Networks

The introduction of the relay in heterogeneous networks (HetNets) can enhance cell-edge signals, but it will also bring coexistence of multiple interference which is composed of co-tier, cross-tier and cross time slot (caused by the half-duplex mode of the relay) interference, resulting in the ineffectiveness of the existing interference alignment (IA) schemes. To solve the above problem, the partial IA scheme, which achieves IA in the case of limited antennas by rationally designing the order and method of eliminating interference, is proposed in this paper. First, we align the interference from other base stations (BSs) into the interference space generated by pico BS to reduce the interference dimension of the relay, and then solve the interference between BSs and users by null space and concatenated precoding schemes. Furthermore, the feasibility condition of antenna configuration is analyzed. The simulation results show that the proposed scheme is superior to the traditional IA scheme in sum rate, and the performance of the proposed scheme is further improved as the relay transmission power increases.

A Cross Time Slot Partial Interference Alignment Scheme in Two-Cell Relay Heterogeneous Networks

The interference of a two-cell two-hop multi-input–multi-output (MIMO) heterogeneous network where the cell-center user (CCU) coexists with the cell-edge user (CEU) is complex to characterize and costly to suppress. In downlink, the CEU is assumed to be served by a half-duplex relay with amplify-and-forward mode. To suppress the intra-cell and inter-cell interference, a cross time slot partial interference alignment (TPIA) scheme, which aligns the interference terms generated in different sub-time slots to the same subspace, is proposed in this paper. Furthermore, the feasibility condition of antenna configuration is analyzed. Simulation results indicate that the TPIA scheme outperforms other existing conventional techniques in achievable sum degrees of freedom and sum-rate performance.

Partial Interference Alignment for Heterogeneous Cellular Networks

The deployment of small cell base stations (BSs) can efficiently boost the network capacity. Cross-tier interference management is critical for the performance enhancement in such heterogeneous networks. Interference alignment provides an efficient way to achieve optimal degree-of-freedom in interference channels. However, it is not necessarily optimal in terms of system sum capacity in the intermediate SNR region. In this paper, we propose a partial interference alignment scheme for heterogeneous network downlink transmission at intermediate SNR by considering the power imbalance among the macro cell and small cell BSs. The hard partial interference alignment adaptively selects the subspace for transmission and captures the trade-off of the transmission dimensions of small cell users and macro cell user. The soft partial interference alignment is used to further improve the hard interference alignment by iteratively balancing the interference and useful signals in each user side. Also, large dimensional analysis is performed to show that the number of transmit dimensions, which can be exploited by the small cell BS is determined by the SNR of the system and singular value distribution of the channel matrix. Simulation results suggest that the proposed partial interference alignment can achieve better achievable sum capacity in the intermediate SNR regime and the large dimension result is accurately verified.

Feasibility-Aware Partial Interference Alignment for Hybrid D2D and Cellular Communication Networks

Device-to-Device (D2D) communication has been a promising technology for the fifth generation (5G) wireless network owing to the higher throughput, less energy consumption, and lower latency. As the 5G network becomes denser, the interference in a hybrid D2D and cellular communication system becomes much severe and more difficult to deal with. In this paper, a novel feasibility aware partial interference alignment (FA-PIA) scheme is investigated. Partial IA has already been studied to manage severe interference for symmetric interference network or simple asymmetric network with limited users. In this paper, we consider an asymmetric interference network with an arbitrary number of D2D users transmitting concurrently. We aim to boost the degrees of freedom and achievable sum rate. To achieve this goal, a lower complexity three-step-based scheme is proposed. Specifically, the pressure transfer tree-based interference selection algorithm is first developed, which is to select the appropriate interference links to be aligned under the IA feasibility constraints. Then, the minimum interference leakage-based iterative partial IA scheme is given to mitigate the selected interference. At last, a constrained concave convex procedure-based iterative power optimization algorithm is provided to further manage the residual interference. Numerical results demonstrate that the proposed three-step-based FA-PIA scheme can significantly improve the achievable sum rate.

Role of Interference Alignment in Wireless Cellular Network Optimization

The emergence of interference alignment (IA) as a degrees-of-freedom optimal strategy motivates the need to investigate whether IA can be leveraged to aid conventional network utility maximization algorithms, which are typically only capable of finding locally optimal solutions. To test the usefulness of IA in this context, this paper proposes a two-stage optimization framework for the downlink of a $G$ -cell multi-antenna network with $K$ users/cell. The first stage of the proposed framework focuses on nulling interference from a set of dominant interferers using IA, while the second stage optimizes transmit and receive beamformers to maximize a network-wide utility using the IA solution as the initial condition. Further, this paper establishes a set of new feasibility results for partial IA that can be used to guide the number of dominant interferers to be nulled in the first stage. Through simulations on specific topologies of a cluster of base stations, it is observed that the impact of IA depends on the choice of the utility function and the presence of out-of-cluster interference. In the absence of out-of-cluster interference, the proposed framework outperforms straightforward optimization when maximizing the minimum rate, while providing marginal gains when maximizing sum rate. However, the benefit of IA is greatly diminished in the presence of significant out-of-cluster interference.

SER for Optimal Combining in the Presence of Multiple Correlated Co-Channel Interferers

Symbol error rate (SER) performance has been analyzed for a receive diversity system in the presence of multiple interferers and noise over fading channels. Both unequal power interferers and correlated interferers have been considered. The derived approximate SER expressions are easy to evaluate and are applicable to any QAM constellation and arbitrary number of receive antennas and interferers. Further, the impact of correlation between interferer channels is studied and it is shown that correlation behaves like partial interference alignment.

Partial interference alignment for downlink multi‐cell multi‐input–multi‐output networks

Interference alignment (IA) is a novel technique to achieve the optimal degree of freedom of wireless communication systems through efficient interference management. In a large size multi-cell network, IA over a full connected model requires the impractical number of transmit/receive antennas. Moreover, extensive channel state information is delivered over the backhaul between different base-stations. For realistic scenarios with limited quantity of transceiver antennas, such a full IA scheme may even become infeasible. In this study, by exploiting the heterogeneous path losses, the authors propose a novel partial IA scheme to enhance the throughput of multi-cell networks, which requires relatively small amounts of antennas and hence can be practically implemented. They first formulate the partial IA problem in terms of a mixed integer bi-level non-linear optimal program. Then, they decompose the problem into two sub-problems to reduce the computation complexity and, furthermore, introduce two algorithms for interference links selection. It is shown that, in a 19 hexagonal wrap-around-cell layout, their proposed algorithm outperforms a standard multi-user multi-input–multi-output technique with far less transmit antennas. The present scheme is therefore of great promise to practical applications.

Partial Interference Alignment Schemes for the K-User MIMO Interference Channel

Partial interference alignment (PIA) strategies for the K-user interference channel are considered. Two schemes are proposed to align the partial interference for the downlink interference channel in the case ofK>3. In the first scheme, strong interferences are limited as much as possible by aligning the two strongest interferers into a same subspace at the receiver. While the second scheme is to design its precoding matrix (or vector) by selecting the precoder pair which maximizes the chordal distance between designed signal subspace and interference signal subspace in the set of pecoding matrices (or vectors) at each transmitter. Simulation results show that compared with the existing schemes which don’t optimize the precoder, such as Fixed-PIA, the proposed schemes can improve the sum rates significantly by selecting IA forms in multi-input multi-output (MIMO) systems.

Partial Interference Alignment for $K$-User MIMO Interference Channels

In this paper, we consider a Partial Interference Alignment and Interference Detection (PIAID) design for $K$-user quasi-static MIMO interference channels with discrete constellation inputs. Each transmitter has M antennas and transmits L independent data streams to the desired receiver with N receive antennas. We focus on the case where not all K-1 interfering transmitters can be aligned at every receiver. As a result, there will be residual interference at each receiver that cannot be aligned. Each receiver detects and cancels the residual interference based on the constellation map. However, there is a window of unfavorable interference profile at the receiver for Interference Detection (ID). In this paper, we propose a low complexity Partial Interference Alignment scheme in which we dynamically select the user set for IA so as to create a favorable interference profile for ID at each receiver. We first derive the average symbol error rate (SER) by taking into account of the non-Guassian residual interference due to discrete constellation. Using graph theory, we then devise a low complexity user set selection algorithm for the PIAID scheme,which minimizes the asymptotically tight bound for the average end-to-end SER performance. Moreover, we substantially simplify interference detection at the receiver using Semi-Definite Relaxation (SDR) techniques. It is shown that the SER performance of the proposed PIAID scheme has significant gain compared with various conventional baseline solutions.