The total variation (TV) minimization can be utilized in a compressive sensing framework to recover a signal from a small number of measurements by searching for a signal with a sparse gradient. However, many natural signals of interest, such as natural images, generally have sparse representations in known transforms. Hence, the performance of the signal reconstruction procedure can be improved by also taking into account this transform-domain sparsity of the signal. Thus, the TV minimization problem can be modified by introducing an $l_1$-norm penalty term. The $l_1$-regularized TV minimization problem searches for a signal with a sparse gradient and a sparse representation in the given transform. The main contribution of this paper is the derivation of a customized interior-point method for solving the $l_1$-regularized TV minimization problem that computes the search direction of the Newton method efficiently by exploiting the specific structure of the Hessian.
The most widely used delay estimators in DS-CDMA receivers are based on the delay-locked loops (DLL). In a coherent RAKE receiver the complex channel coefficient estimate can be used to attain the phase coherency in DLL and the effect of data modulation can be removed via decision feedback. The resulting algorithms are called quasi-coherent delay-locked loops (QCDLL). The performance of four variations for QCDLLs based on lead-lag phase-locked loops has been analyzed by computer simulations. All the loops performed satisfactorily in a fading channel, but the quasi-coherent sample-correlate-choose-largest (QCSCCL) loop had 5-6 dB better performance than the other schemes.
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Based on the approximation of the sum of several Nakagami-m variables and an alternative expression of Q-function, an efficient bit error rate (BER) expression is described. The closed BER form is applicable to multicarrier code-division multiple-access (MC-CDMA) systems with equal gain combining (EGC) in frequency selective Nakagami fading channels.
We provide a distributed algorithm for the radio resource allocation problem in multicell downlink multi-input single-output systems, subject to satisfying quality of service requirements of each user. Specifically, the problem of minimizing total transmit power subject to signal-to-interference-plus-noise ratio constraints of each user is considered. We propose a consensus-based distributed algorithm based on alternating direction method of multipliers. Numerical results show that the proposed distributed algorithm can achieve the optimal centralized solution.
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Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The standard IEEE 802.11 has been shown to be quite inefficient in multihop networks. In addition to the hidden-terminal and exposed-terminal problems, there is also an unreachability problem, which may result in link/routing failures and unfairness among multiple traffic flows. In this paper, a medium access control (MAC) protocol, called <emphasis emphasistype="boldital">e</emphasis>MAC, is proposed. Under the proposed scheme, stations maintain double-hop neighborhood (DHN) graphs while exchanging designated <emphasis emphasistype="boldital">e </emphasis>MAC tables to share their knowledge about their neighborhood topology. Using a DHN graph and an adaptive unreachability reporting mechanism, stations are reliably informed about their neighbors' unreachability status. Hence, they avoid establishing link-layer connections with their unreachable neighbors, and consequently, network resources are not consumed for unsuccessful connection-establishment efforts. Furthermore, we propose an adaptive table broadcasting technique to facilitate topology information dissemination in mobile ad hoc networks (MANETs). The performance of the proposed schemes is evaluated and compared with that of earlier schemes through simulations. Our results show a performance enhancement due to better handling of unreachability, possible heterogeneous power distributions among contending stations, and mobility issues. </para>
Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment and bring the services closer to end-users. In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed that jointly controls the O-RAN functional splits, the allocated resources and hosting locations of the O-RAN/MEC services across geo-distributed platforms, and the routing for each O-RAN/MEC data flow. The goal is to minimize the long-term overall network operation cost and maximize the MEC performance criterion while adapting possibly time-varying O-RAN/MEC demands and resource availability. This orchestration problem is formulated as Markov decision process (MDP). However, the system consists of multiple BSs that share the same resources and serve heterogeneous demands, where their parameters have non-trivial relations. Consequently, finding the exact model of the underlying system is impractical, and the formulated MDP renders in a large state space with multi-dimensional discrete action. To address such modeling and dimensionality issues, a novel model-free RL agent is proposed for our solution framework. The agent is built from Double Deep Q-network (DDQN) that tackles the large state space and is then incorporated with action branching, an action decomposition method that effectively addresses the multi-dimensional discrete action with linear increase complexity. Further, an efficient exploration-exploitation strategy under a Bayesian framework using Thomson sampling is proposed to improve the learning performance and expedite its convergence. Trace-driven simulations are performed using an O-RAN-compliant model. The results show that our approach is data-efficient (i.e., converges faster) and increases the returned reward by 32\% than its non-Bayesian version.
