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1. A Cooperative Drone Assisted Mobile Access Network for Disaster Emergency Communications

   https://doi.org/10.1109/GLOBECOM38437.2019.9013813  

   Wu, Di; Sun, Xiang; Ansari, Nirwan (February 2020, 2019 IEEE Global Communications Conference (GLOBECOM))

   Multiple drone-mounted base stations (DBSs) are used to be deployed over a disaster struck area to help mobile users (MUs) communicate with working BSs, which are located beyond the disaster-struck area. DBSs are considered as relay nodes between MUs and working BSs. In order to relax the bottleneck in wireless backhaul links, we propose a cooperative drone assisted mobile access network architecture by enabling DBSs (whose backhaul links are congested) to offload their traffic to other DBSs (whose backhaul links are not congested) via DBS-to-DBS communications. We formulate the DBS placement and channel allocation problem in the context of the cooperative drone assisted mobile access network architecture, and design a COoperative DBS plAcement and CHannel allocation (COACH) algorithm to solve the problem. The performance of COACH is demonstrated via extensive simulations. 

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2. Uncoded Placement with Linear Sub-Messages for Private Information Retrieval from Storage Constrained Databases

   https://doi.org/10.1109/TCOMM.2020.3010988  

   Woolsey, Nicholas; Chen, Rong-Rong; Ji, Mingyue (July 2020, IEEE Transactions on Communications)

   We propose capacity-achieving schemes for private information retrieval (PIR) from uncoded databases (DBs) with both homogeneous and heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. In general, a PIR scheme is comprised of storage placement and delivery designs. Previous works have derived the capacity, or infimum download cost, of PIR with uncoded storage placement and sufficient conditions of storage placement to meet capacity. However, the currently proposed storage placement designs require splitting each message into an exponential number of sub-messages with respect to the number of DBs. In this work, when DBs have the same storage constraint, we propose two simple storage placement designs that satisfy the capacity conditions. Then, for more general heterogeneous storage constraints, we translate the storage placement design process into a “filling problem”. We design an iterative algorithm to solve the filling problem where, in each iteration, messages are partitioned into sub-messages and stored at subsets of DBs. All of our proposed storage placement designs require a number of sub-messages per message at most equal to the number of DBs. 

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3. SNR/CRB-Constrained Joint Beamforming and Reflection Designs for RIS-ISAC Systems

   https://doi.org/10.1109/TWC.2023.3341429  

   Liu, Rang; Li, Ming; Liu, Qian; Lee_Swindlehurst, A (July 2024, IEEE Transactions on Wireless Communications)

   In this paper, we investigate the integration of integrated sensing and communication (ISAC) and reconfigurable intelligent surfaces (RIS) for providing wide-coverage and ultrareliable communication and high-accuracy sensing functions. In particular, we consider an RIS-assisted ISAC system in which a multi-antenna base station (BS) simultaneously performs multiuser multi-input single-output (MU-MISO) communications and radar sensing with the assistance of an RIS. We focus on both target detection and parameter estimation performance in terms of the signal-to-noise ratio (SNR) and Cramér-Rao bound (CRB), respectively. Two optimization problems are formulated for maximizing the achievable sum-rate of the multi-user communications under an SNR constraint for target detection or a CRB constraint for parameter estimation, the transmit power budget, and the unit-modulus constraint of the RIS reflection coefficients. Efficient algorithms are developed to solve these two complicated non-convex problems. We then extend the proposed joint design algorithms to the scenario with imperfect self-interference cancellation. Extensive simulation results demonstrate the advantages of the proposed joint beamforming and reflection designs compared with other schemes. In addition, it is shown that more RIS reflection elements bring larger performance gains for directof- arrival (DoA) estimation than for target detection. 

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4. Rényi Differentially Private ADMM for Non-Smooth Regularized Optimization

   https://doi.org/10.1145/3374664.3375733  

   Chen, Chen; Lee, Jaewoo (March 2020, CODASPY '20: Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy)

   null (Ed.)

   In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as $$L_1$$ norm or nuclear norm, under Rényi differential privacy (RDP). To solve the problem, we propose two stochastic alternating direction method of multipliers (ADMM) algorithms: ssADMM based on gradient perturbation and mpADMM based on output perturbation. Both algorithms decompose the original problem into sub-problems that have closed-form solutions. The first algorithm, ssADMM, applies the recent privacy amplification result for RDP to reduce the amount of noise to add. The second algorithm, mpADMM, numerically computes the sensitivity of ADMM variable updates and releases the updated parameter vector at the end of each epoch. We compare the performance of our algorithms with several baseline algorithms on both real and simulated datasets. Experimental results show that, in high privacy regimes (small ε), ssADMM and mpADMM outperform baseline algorithms in terms of classification and feature selection performance, respectively. 

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5. Automatic Parallel Portfolio Selection

   https://doi.org/10.3233/FAIA230398  

   Kashgarani, Haniye; Kotthoff, Lars (September 2023, Frontiers in Artificial Intelligence and Applications, Volume 372: ECAI 2023)

   Algorithms to solve hard combinatorial problems often exhibit complementary performance, i.e. where one algorithm fails, another shines. Algorithm portfolios and algorithm selection take advantage of this by running all algorithms in parallel or choosing the best one to run on a problem instance. In this paper, we show that neither of these approaches gives the best possible performance and propose the happy medium of running a subset of all algorithms in parallel. We propose a method to choose this subset automatically for each problem instance, and demonstrate empirical improvements of up to 19% in terms of runtime, 81% in terms of misclassification penalty, and 26% in terms of penalized averaged runtime on scenarios from the ASlib benchmark library. Unlike all other algorithm selection and scheduling approaches in the literature, our performance measures are based on the actual performance for algorithms running in parallel rather than assuming overhead-free parallelization based on sequential performance. Our approach is easy to apply in practice and does not require to solve hard problems to obtain a schedule, unlike other techniques in the literature, while still delivering superior performance. 

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