The Feasibility of Interference Alignment for Full-Duplex MIMO Cellular Networks

Abstract

The feasibility conditions of interference alignment (IA) are analyzed for reverse TDD systems, i.e., one cell operates as downlink (DL) but the other cell operates as uplink (UL). Under general multiple-input and multiple-output (MIMO) antenna configurations, a necessary condition and a sufficient condition for one-shot linear IA are established, i.e., linear IA without symbol or time extension. In several example networks, optimal sum degrees of freedom (DoF) is characterized by the derived necessary condition and sufficient condition. For symmetric DoF within each cell, a sufficient condition is established in a more compact expression, which yields the necessary and sufficient condition for a class of symmetric DoF. An iterative construction of transmit and received beamforming vectors is further proposed, which provides a specific beamforming design satisfying one-shot IA. Simulation results demonstrate that the proposed IA not only achieve lager DoF but also significantly improve the sum rate in the practical signal-to-noise ratio (SNR) regime.Index Terms-Degrees of freedom, dynamic TDD, feasibility conditions, heterogeneous networks, interference alignment, multiple-input and multiple-output (MIMO), reverse TDD.

Transmit Power Optimization and Feasibility Analysis of Self-Backhauling Full-Duplex Radio Access Systems

“…B. Different Communication Schemes, SINRs, and Achievable Rates 1) Full-duplex: The scheme where everything is done in full-duplex mode is illustrated in Even though there are also advanced methods for attenuating the IUI [34], [35], in this paper we assume that the only factors affecting its power level are the transmit power of the UL UEs and the path loss. In addition to serving the UEs, the AN must also maintain the bidirectional backhaul link using the same frequency band.…”

Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

“…Condition (3) preserves the required interference-free degree of freedom (DoF) for the desired signal of each IoT node. In the Rayleigh fading channel where the entries are independently distributed with no special structure, Condition (3) would be satisfied naturally when Condition (2) is satisfied [25]. If the feasibility condition is held for the tiered network, the received signal of the IoT node k can be given as:${\mathrm{bold-italicy}}_k={boldnormalU}_k^H{boldnormalH}_{k,k}{boldnormalV}_k{\mathrm{bold-italicx}}_k+{boldnormalU}_k^H{\mathrm{bold-italicn}}_k.$…”

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

“…$|vec{false(\Delta H_{k,i}false)}^H{e}_{k,i}^{d,j}{|}^2$ is beta-distributed with an expectation that equals $1/false(N^2-1false)$. The expectation of ${\sin }^2{\theta }_{k,i}$ is given by $\overline{\Gamma }false(N^{2}false)2^{-B_{k,i}/false(N^2-1false)}$ from [10] where $\overline{\Gamma }false(N^{2}false)=\Gamma false(1/false(N^2-1false)false)/false(N^2-1false)$. Assuming $B_{k,i}$ is the number of feedback bits from transmitter i to receiver j , we can rewrite Equation (14) as $\mathrm{normalE}\left[{\stackrel{false¯}{I}}_{k,2}^d\right]\le true\sum _{i=1,i\ne k}^{K}true\sum _{j=1}^D{\eta }_{k,i}{p}_{k,j}\frac{\overline{\Gamma }false(N^{2}false)}{N^2-1}{2}^{-\frac{B_{k,i}}{N^2-1}}$…”

“…In the K -user interference channels, each user can achieve 1/2 DoF, and guarantee the total K/2 DoF, although the number of users increases [6]. Encouraged by this surprising result, much work has been done to apply IA to different interference networks, such as MIMO networks [7], cellular networks [8,9,10], cognitive radio [11], ad hoc networks [12], etc.…”