Abstract:
A novel channel representation for a two-hop decentralized wireless relay network (DWRN) is proposed, where the relays operate in a completely distributive fashion. The modeling paradigm applies an analogous approach to the description method for a double-directional multipath propagation channel, and takes into account the finite system spatial resolution and the extended relay listening/transmitting time. Specifically, the double-directional information azimuth spectrum (IAS) is formulated to provide a compact representation of information flows in a DWRN. The proposed channel representation is then analyzed from a geometrically-based statistical modeling perspective. Finally, we look into the problem of relay network tomography (RNT), which solves an inverse problem to infer the internal structure of a DWRN by using the instantaneous doubledirectional IAS recorded at multiple measuring nodes exterior to the relay region.

Abstract:
In this paper, we consider the problem of exchanging channel state information in a wireless network such that a subset of the clients can obtain the complete channel state information of all the links in the network. We first derive the minimum number of required transmissions for such partial third-party information exchange problem. We then design an optimal transmission scheme by determining the number of packets that each client should send, and designing a deterministic encoding strategy such that the subset of clients can acquire complete channel state information of the network with minimal number of transmissions. Numerical results show that network coding can efficiently reduce the number of transmissions, even with only pairwise encoding.

Abstract:
Locally repairable codes (LRC) for distribute storage allow two approaches to locally repair multiple failed nodes: 1) parallel approach, by which each newcomer access a set of $r$ live nodes $(r$ is the repair locality$)$ to download data and recover the lost packet; and 2) sequential approach, by which the newcomers are properly ordered and each newcomer access a set of $r$ other nodes, which can be either a live node or a newcomer ordered before it. An $[n,k]$ linear code with locality $r$ and allows local repair for up to $t$ failed nodes by sequential approach is called an $(n,k,r,t)$-exact locally repairable code (ELRC). In this paper, we present a family of binary codes which is equivalent to the direct product of $m$ copies of the $[r+1,r]$ single-parity-check code. We prove that such codes are $(n,k,r,t)$-ELRC with $n=(r+1)^m,k=r^m$ and $t=2^m-1$, which implies that they permit local repair for up to $2^m-1$ erasures by sequential approach. Our result shows that the sequential approach has much bigger advantage than parallel approach.

Abstract:
In this paper, we give a unified performance analysis of interference alignment (IA) over MIMO interference channels. Rather than the asymptotic characterization, i.e. degree of freedom (DOF) at high signal-to-noise ratio (SNR), we focus on the other practical performance metrics, namely outage probability, ergodic rate and symbol error rate (SER). In particular, we consider imperfect IA due to the fact that the transmitters usually have only imperfect channel state information (CSI) in practical scenario. By characterizing the impact of imperfect CSI, we derive the exact closed-form expressions of outage probability, ergodic rate and SER in terms of CSI accuracy, transmit SNR, channel condition, number of antennas, and the number of data streams of each communication pair. Furthermore, we obtain some important guidelines for performance optimization of IA under imperfect CSI by minimizing the performance loss over IA with perfect CSI. Finally, our theoretical claims are validated by simulation results.

Abstract:
In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi-antenna technique, such as zero-forcing beamforming (ZFBF), is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the above two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi-antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.

Abstract:
We consider the problem of designing [n; k] linear codes for distributed storage systems (DSS) that satisfy the (r, t)-Local Repair Property, where any t'(<=t) simultaneously failed nodes can be locally repaired, each with locality r. The parameters n, k, r, t are positive integers such that r

Abstract:
In this paper, we address the problem of interference alignment (IA) over MIMO interference channels with limited channel state information (CSI) feedback based on quantization codebooks. Due to limited feedback and hence imperfect IA, there are residual interferences across different links and different data streams. As a result, the performance of IA is greatly related to the CSI accuracy (namely number of feedback bits) and the number of data streams (namely transmission mode). In order to improve the performance of IA, it makes sense to optimize the system parameters according to the channel conditions. Motivated by this, we first give a quantitative performance analysis for IA under limited feedback, and derive a closed-form expression for the average transmission rate in terms of feedback bits and transmission mode. By maximizing the average transmission rate, we obtain an adaptive feedback allocation scheme, as well as a dynamic mode selection scheme. Furthermore, through asymptotic analysis, we obtain several clear insights on the system performance, and provide some guidelines on the system design. Finally, simulation results validate our theoretical claims, and show that obvious performance gain can be obtained by adjusting feedback bits dynamically or selecting transmission mode adaptively.

Abstract:
We study amplified-and-forward (AF)-based two-way relaying (TWR) with multiple source pairs, which are exchanging information through the relay. Each source has single antenna and the relay has multi-antenna. The optimal beamforming matrix structure that achieves maximum signal-to-interference-plus-noise ratio (SINR) for TWR with multiple source pairs is derived. We then present two new non-zero-forcing based beamforming schemes for TWR, which take into consideration the tradeoff between preserving the desired signals and suppressing inter-pair interference between different source pairs. Joint grouping and beamforming scheme is proposed to achieve a better signal-to-interference-plus-noise ratio (SINR) when the total number of source pairs is large and the signal-to-noise ratio (SNR) at the relay is low.

Abstract:
We analyze the sensitivity of the capacity of a multi-antenna multi-user system to the number of users being served. We show analytically that, for a given desired sum-rate, the extra power needed to serve a subset of the users at low SNR (signal-to-noise ratio) can be very small, and is generally much smaller than the extra power needed to serve the same subset at high SNR. The advantages of serving only subsets of the users are many: multi-user algorithms have lower complexity, reduced channel-state information requirements, and, often, better performance. We provide guidelines on how many users to serve to get near-capacity performance with low complexity. For example, we show how in an eight-antenna eight-user system we can serve only four users and still be approximately 2 dB from capacity at very low SNR.

Abstract:
As a smart combination of cognitive radio networks and wireless sensor networks, recently introduced cognitive radio sensor network (CRSN) poses new challenges to the design of topology maintenance techniques for dynamic primary-user activities. This paper aims to provide a solution to the energy-efficient spectrum-aware CRSN clustering problem. Specifically, we design the clustered structure, establish a network-wide energy consumption model and determine the optimal number of clusters. We then employ the ideas from constrained clustering and propose both a centralized spectrum-aware clustering algorithm and a distributed spectrum-aware clustering (DSAC) protocol. Through extensive simulations, we demonstrate that DSAC can effectively form clusters under a dynamic spectrum-aware constraint. Moreover, DSAC exhibits preferable scalability and stability with its low complexity and quick convergence under dynamic spectrum variation.