Thang Van Nguyen

Sensing and Probing Cardinalities for Active Cognitive Radios

In a cognitive radio network, opportunistic spectrum access (OSA) to the underutilized spectrum involves not only sensing the spectrum occupancy but also probing the channel quality in order to identify an idle and good channel for data transmission-particularly if a large number of channels is open for secondary spectrum reuse. Although such a joint mechanism, referred to as active sensing, may improve the OSA performance due to diversity, it inevitably incurs additional energy consumption. In this paper, we consider a wideband cognitive radio network with limited available frame energy and treat a fundamental energy allocation problem: how available energy should be optimally allocated for sensing, probing, and data transmission to maximize the achievable average OSA throughput. By casting this problem into the multiarmed bandit framework under probably approximately correct (PAC) learning, we put forth a proactive strategy for determining the optimal sensing cardinality (the number of channels chosen to sense) and probing cardinality (the number of channels chosen to probe) that maximize the average throughput of the secondary user with limited available frame energy. This framework determines the optimal amount of pure exploration for the active sensing OSA bandit problem in which we refine the action (median) elimination algorithm for channel probing to minimize the sample complexity in PAC learning. Numerical results show that the optimal active sensing achieves a significant throughput gain over the (even optimal) sensing alone. Therefore, this work provides an energy allocation policy to optimally balance the available energy between exploration (sensing and probing) and exploitation (data transmission), giving the optimal diversity-energy tradeoff for the average OSA throughput.

Power Allocation and Achievable Secrecy Rates in MISOME Wiretap Channels

We consider transmission of confidential data over multiple-input single-output multiple-eavesdropper (MISOME) Rayleigh-fading wiretap channels. The transmitter has access to full channel state information (CSI) of a legitimate link but only partial CSI of an eavesdropper link in forms of the average received signal-to-noise power ratio (SNR). In this location-aware scenario, we develop the optimal power allocation strategy that maximizes the secrecy rate achievable by beamforming. The optimal power control is regulated in an on-off fashion with the threshold depending only on the numbers of antennas and the average SNRs. By rescuing the vanishing high-SNR degree of freedom for secure communication from this optimal on-off beamforming with the artificial noise scheme, we further derive the achievable ergodic secrecy rate of MISOME wiretap channels in closed form.

Least Square Cooperative Localization

Location awareness is becoming essential for emerging wireless applications where most network activities require the location information of network nodes, e.g., routing between nodes in ad-hoc sensor networks, positioning vehicles on the road, or tracking targets in underwater acoustic sensor networks. In particular, cooperation among nodes is highly beneficial for the localization accuracy and coverage in harsh environments. In this paper, we study least square (LS) cooperative localization in the presence of arbitrary non-line-of-sight (NLOS) ranging bias. To develop the network position error bound (PEB), we first derive the Fisher information matrix (FIM) for a general NLOS bias model and show that a Gaussian bias due to NLOS effects is the worst case that produces the extremal FIM, whereas a constant bias or equivalently full line-of-sight is the best situation leading to the largest FIM in a sense of Löwner partial ordering. We then analyze the asymptotic performance, such as uniform convergence, consistency, and efficiency, of LS cooperative localization to quantify the deviations of localization accuracy for LS, squared-range LS, and squared-range weighted LS solutions from the fundamental limit (i.e., Cramér-Rao lower bound) due to their practical tractability. We also propose a simple distributed algorithm for LS cooperative localization by integrating squared-range relaxation into Gaussian variational message passing on the localization network. To account for stochastic natures of node locations and populations, we further characterize the network PEB for Gilberts disk localization network, where anchors and/or agents are randomly distributed in the network according to point processes.

Machine Learning for Wideband Localization

Wireless localization has a great importance in a variety of areas including commercial, service, and military positioning and tracking systems. In harsh indoor environments, it is hard to localize an agent with high accuracy due to non-line-of-sight (NLOS) radio blockage or insufficient information from anchors. Therefore, NLOS identification and mitigation are highlighted as an effective way to improve the localization accuracy. In this paper, we develop a robust and efficient algorithm to enhance the accuracy for (ultrawide bandwidth) time-of-arrival localization through identifying and mitigating NLOS signals with relevance vector machine (RVM) techniques. We also propose a new localization algorithm, called the two-step iterative (TSI) algorithm, which converges fast with a finite number of iterations. To enhance the localization accuracy as well as expand the coverage of a localizable area, we continue to exploit the benefits of RVM in both classification and regression for cooperative localization by extending the TSI algorithm to a centralized cooperation case. For self-localization setting, we then develop a distributed cooperative algorithm based on variational Bayesian inference to simplify message representations on factor graphs and reduce communication overheads between agents. In particular, we build a refined version of Gaussian variational message passing to reduce the computational complexity while maintaining the localization accuracy. Finally, we introduce the notion of a stochastic localization network to verify proposed cooperative localization algorithms.

Location-aware visual radios

Location awareness creates a new paradigm for distributing scalable multimedia data over wireless networks, enabling a variety of context-aware applications that require precise location information of network nodes. An emerging concept for robust and accurate network localization is to exploit cooperation and heterogeneous design for harnessing multimodal fusion of sensing measurements to extract location information. This article gives a brief introduction to vision- and radio-based positioning technologies, and then presents illustrative machine-learning methodology to successfully integrate vision information and radio time-of-arrival measurements for cooperative localization of ultra-wideband visual radios in harsh indoor environments.

Secrecy diversity in MISOME wiretap channels

The use of multiple-antenna arrays can leverage the physical-layer security of wireless systems. It is therefore important to characterize such systems in a realistic situation where we can apply the so-called artificial-noise solution in multiple-antenna systems to enhance the communication confidentiality by only exploiting a fading nature of wireless environments. In this paper, we consider secure communication over a multiple-input single-output Rayleigh-fading channel in the presence of a multiple-antenna eavesdropper—referred to as a multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, secure beamforming with artificial noise is treated when the transmitter has access to full channel state information (CSI) of a legitimate channel but only channel distribution information of an eavesdropper channel. We first put forth a new notion of the symbol error probability (SEP) of confidential information—called the δ-secrecy SEP—to connect the reliability and confidentiality of the legitimate communication. We then quantify the diversity impact of secure beamforming with artificial noise on the δ-secrecy SEP in the MISOME wiretap channel and show that the artificial-noise strategy preserves the secrecy diversity of order nt - ne for nt transmit and ne eavesdropper antennas.

Optimal Sensing Cardinality for Cognitive Radios

This letter puts forth a proactive approach for determining the sensing cardinality (the number of subchannels to sense) in a multiband cognitive radio network with limited available block energy. Specifically, we present the sensing cardinality that maximizes the achievable block rate of the cognitive user by optimally allocating available energy to the wideband spectrum sensing and data transmission. We further derive the closed-form expression for this optimal sensing cardinality when each subchannel has the identical primary spectrum occupancy statistics.

Power Optimization With BLER Constraint for Wireless Fronthauls in C-RAN

Cloud radio access network (C-RAN) is a novel architecture for future mobile networks to sustain the exponential traffic growth thanks to the exploitation of centralized processing. In C-RAN, one data processing center or baseband unit (BBU) communicates with users via distributed remote radio heads (RRHs), which are connected to the BBU via high capacity, low latency fronthaul links. In this letter, we study C-RAN with wireless fronthauls due to their flexibility in deployment and management. First, a tight upper bound of the system block error rate (BLER) is derived in closed-form expression via union bound analysis. Based on the derived bound, adaptive transmission schemes are proposed. Particularly, two practical power optimizations based on the BLER and pair-wise error probability (PEP) are proposed to minimize the consumed energy at the RRHs while satisfying the predefined quality of service (QoS) constraint. The premise of the proposed schemes originates from practical scenarios where most applications tolerate a certain QoS, e.g., a nonzero BLER. The effectiveness of the proposed schemes is demonstrated via intensive simulations.

Learning dictionary and compressive sensing for WLAN localization

Localization using the received signal strength (RSS) is a popular technique in the indoor location aware service because of the wide deployment of wireless local area networks (WLANs) and the spreading of mobile device with the measuring RSS function. In this paper, we investigate the RSS-based WLAN indoor positioning system using ℓ0-norm recovery support of sparse representation. Based on the fingerprinting method, the radio map (RM) constructed in offline phase is decomposed into a dictionary and a corresponding sparse representation matrix, using the K-SVD learning overcomplete dictionary algorithm. The learned dictionary guarantees the condition of stable recovery sparse representation. The position of each reference point (RP) in the RM is characterized by an unique support in each vector of sparse representation. We use the orthogonal matching pursuit algorithm to find the support of sparse representation of the real-time measured RSS vector over the learned dictionary and thereby determine which RP is closest to the user. This is an ℓ0-norm minimization problem. We also study the effect of the other RPs to the recovery solution of real-time measurement vector. We first derive the weighted vector that reflects the contribution of each RP in the localization formulation, then the user position is estimated by this vector and the positions of RPs.

Switched power allocation for MISOME wiretap channels

In this paper, we consider secure communication over a multiple-input single-output Rayleigh-fading channel in the presence of a multiple-antenna eavesdropper - referred to as a multiple-input single-output multiple-eavesdropper (MISOME) wiretap channel. Specifically, secure beamforming with artificial noise is treated when the transmitter has access to full channel state information (CSI) of a legitimate channel but only partial statistical CSI of an eavesdropper channel. We first derive the optimal power allocation between the information-bearing signal and artificial noise (or simulated interference) to maximize the achievable secrecy rate in the presence of a weak or strong eavesdropper. We then develop a near-optimal power allocation strategy in a switched fashion for a general case and derive a closed-form expression for the ergodic secrecy rate achieved by secure beamforming with this switched power allocation in the MISOME wiretap channel.