Liangzhong Ruan

Interference Alignment for Partially Connected MIMO Cellular Networks

In this paper, we propose an iterative interference alignment (IA) algorithm for MIMO cellular networks with partial connectivity, which is induced by heterogeneous path losses and spatial correlation. Such systems impose several key technical challenges in the IA algorithm design, namely the overlapping between the direct and interfering links due to the MIMO cellular topology as well as how to exploit the partial connectivity. We shall address these challenges and propose a three stage IA algorithm. As illustration, we analyze the achievable degree of freedom (DoF) of the proposed algorithm for a symmetric partially connected MIMO cellular network. We show that there is significant DoF gain compared with conventional IA algorithms due to partial connectivity. The derived DoF bound is also backward compatible with that achieved on fully connected K-pair MIMO interference channels.

The Feasibility Conditions for Interference Alignment in MIMO Networks

Interference alignment (IA) has attracted great attention in the last few years for its breakthrough performance in interference networks. However, despite the numerous works dedicated to IA, the feasibility conditions of IA remains unclear for most network topologies. The IA feasibility analysis is challenging as the IA constraints are sets of high-degree polynomials, for which no systematic tool to analyze the solvability conditions exists. In this work, by developing a new mathematical framework that maps the solvability of sets of polynomial equations to the linear independence of their first-order terms, we propose a sufficient condition that applies to MIMO interference networks with general configurations. We have further proved that this sufficient condition coincides with the necessary conditions under a wide range of configurations. These results further consolidate the theoretical basis of IA.

CSI Feedback Reduction for MIMO Interference Alignment

Interference alignment (IA) is a linear precoding strategy that can achieve optimal capacity scaling at high SNR in interference networks. Most of the existing IA designs require full channel state information (CSI) at the transmitters, which induces a huge CSI signaling cost. Hence it is desirable to improve the feedback efficiency for IA and in this paper, we propose a novel IA scheme with a significantly reduced CSI feedback. To quantify the CSI feedback cost, we introduce a novel metric, namely the feedback dimension. This metric serves as a first-order measurement of CSI feedback overhead. Due to the partial CSI feedback constraint, conventional IA schemes can not be applied and hence, we develop a novel IA precoder/decorrelator design and establish new IA feasibility conditions. Via dynamic feedback profile design, the proposed IA scheme can also achieve a flexible tradeoff between the degree of freedom (DoF) requirements for data streams, the antenna resources and the CSI feedback cost. We show by analysis and simulations that the proposed scheme achieves substantial reductions of CSI feedback overhead under the same DoF requirement in MIMO interference networks.

Hierarchical Radio Resource Optimization for Heterogeneous Networks With Enhanced Inter-Cell Interference Coordination (eICIC)

Interference is a major performance bottleneck in Heterogeneous Network (HetNet) due to its multi-tier structure. We propose almost blank resource block (ABRB) for interference control in HetNet. When an ABRB is scheduled in a macro BS, a resource block (RB) with blank payload is transmitted and this eliminates the interference from this macro BS to the pico BSs. We study a two timescale hierarchical radio resource management (RRM) scheme for HetNet with dynamic ABRB control. The long term controls, such as dynamic ABRB, are adaptive to the large scale fading at a RRM server for co-Tier and cross-Tier interference control. The short term control (user scheduling) is adaptive to the local channel state information at each BS to exploit the multi-user diversity. The two timescale optimization problem is challenging due to the exponentially large solution space. We exploit the sparsity in the interference graph of the HetNet topology and derive structural properties for the optimal ABRB control. Based on that, we propose a two timescale alternative optimization solution for user scheduling and ABRB control. The solution has low complexity and is asymptotically optimal at high SNR. Simulations show that the proposed solution has significant gain over various baselines.

Dynamic Interference Mitigation for Generalized Partially Connected Quasi-Static MIMO Interference Channel

Recent works on MIMO interference channels have shown that interference alignment can significantly increase the achievable degrees of freedom (dof) of the network. However, most of these works have assumed a fully connected interference graph. In this paper, we investigate how the partial connectivity can be exploited to enhance system performance in MIMO interference networks. We propose a novel interference mitigation scheme which introduces constraints for the signal subspaces of the precoders and decorrelators to mitigate “many” interference nulling constraints at a cost of “little” freedoms in precoder and decorrelator design so as to extend the feasibility region of the interference alignment scheme. Our analysis shows that the proposed algorithm can significantly increase system dof in symmetric partially connected MIMO interference networks. We also compare the performance of the proposed scheme with various baselines and show via simulations that the proposed algorithms could achieve significant gain in the system performance of randomly connected interference networks.

Power control and performance analysis of cognitive radio systems under dynamic spectrum activity and imperfect knowledge of system state

Power control plays a critical role in cognitive radio (CR) system to achieve a balance between efficient sharing of licensed spectrum and interferences to primary system (PU). Most of the existing works considered power control under perfect knowledge of system state (such as channel state information (CSIT) and sensing information (SIT)). In this paper, we shall focus on the design and analysis of power control scheme under imperfect knowledge of system state for SISO and OFDM systems. We obtained asymptotically optimal power control policy, taking into account of imperfect system state knowledge and packet errors due to channel outage. In addition, we found that system parameters affect asymptotic average goodput by O(-ln(rho_pR_p)e^-sigma ^e ² ^/1-sigma ^e ²) and O(e^-rho ^p ^R ^pe^-sigma ^e ² ^/1-sigma ^e ²) under low and high PU activities respectively where rho_pR_p, sigma_e ² represents PU activity level and CSIT quality respectively.

Multi-tier network secrecy in the ether

Communications in the ether are highly susceptible to eavesdropping due to the broadcast nature of the wireless medium. To improve communication confidentiality in wireless environments, research efforts have been made to complement cryptography with physical layer security. A recent view of the role of interference, especially in multi-tier wireless networks, suggested that interference engineering can increase the level of communication confidentiality. The design of interference engineering strategies (IESs) requires a thorough characterization of concurrent effects of wireless emissions on legitimate and eavesdropping receivers. This article advocates IESs for achieving a new level of communication confidentiality in multi-tier wireless networks (namely multi-tier network secrecy) with different degrees of coordination among the tiers. Insights on how IES benefits wireless network secrecy are provided, guiding the design of such strategies for a new level of communication confidentiality.

The feasibility conditions of interference alignment for MIMO interference networks

Attributed by its breakthrough performance in interference networks, interference alignment (IA) has attracted great attention in the last few years. However, despite the tremendous works dedicated to IA, the feasibility conditions of IA processing remains unclear for most network typologies. The IA feasibility analysis is challenging as the IA constraints are sets of high-degree polynomials, for which no systematic tool to analyze the solvability conditions exists. In this work, by developing a new mathematical framework that maps the solvability of sets of polynomial equations to the linear independence of their first order terms, we propose a sufficient condition that applies to K-pairs MIMO interference networks with general typologies. We have further proved that the sufficient condition aligns with the necessary conditions under some special configurations.

Generalized Interference Alignment—Part II: Application to Wireless Secrecy

In contrast to its wired counterpart, wireless communication is highly susceptible to eavesdropping due to the broadcast nature of the wireless propagation medium. Recent works have proposed the use of interference to reduce eavesdropping capabilities in wireless wiretap networks. However, the concurrent effect of interference on both eavesdropping receivers (ERs) and legitimate receivers has not been thoroughly investigated, and careful engineering of the network interference is required to harness the full potential of interference for wireless secrecy. This two-part article addresses this issue by proposing a generalized interference alignment (GIA) technique, which jointly designs the transceivers at the legitimate partners to impede the ERs without interfering with LRs. In Part I, we have established a theoretical framework for the GIA technique. In Part II, we will first propose an efficient GIA algorithm that is applicable to large-scale networks and then evaluate the performance of this algorithm in stochastic wireless wiretap network via both analysis and simulation. These results reveal insights into when and how GIA contributes to wireless secrecy.

Generalized Interference Alignment—Part I: Theoretical Framework

Interference alignment (IA) has attracted enormous research interest as it achieves optimal capacity scaling with respect to signal to noise ratio on interference networks. IA has also recently emerged as an effective tool in engineering interference for secrecy protection on wireless wiretap networks. However, despite the numerous works dedicated to IA, two of its fundamental issues, i.e., feasibility conditions and transceiver design, are not completely addressed in the literature. In this two part paper, a generalized interference alignment (GIA) technique is proposed to enhance the IAs capability in secrecy protection. A theoretical framework is established to analyze the two fundamental issues of GIA in Part I and then the performance of GIA in large-scale stochastic networks is characterized to illustrate how GIA benefits secrecy protection in Part II. The theoretical framework for GIA adopts methodologies from algebraic geometry, determines the necessary and sufficient feasibility conditions of GIA, and generates a set of algorithms for solving the GIA problem. This framework sets up a foundation for the development and implementation of GIA.