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As the excellent tools for aiding communication,intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) can extend the coverage area, remove blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance at directional modulation (DM) networks using IRS and UAV in combination. To fully explore the benefits of IRS and UAV, two efficient methods are proposed to enhance SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix, which is called Max-SR-SLNR. Here, Eve is maximally interfered by transmitting artificial noise (AN) along the direct path and null-space projection (NSP) on the remaining two channels. To reduce the computational complexity, the CM, AN beamforming and IRS phase shift design are independently designed in the following methods. The CM beamforming vector is constructed based on maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, and phase shift matrix of IRS is directly given by phase alignment (PA) method. This method is called MRT-NSP-PA. Simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as IRS tends to lager-scale.

We consider a quantum and classical version multi-party function computation problem with $n$ players, where players $2, \dots, n$ need to communicate appropriate information to player 1, so that a ``generalized'' inner product function with an appropriate promise can be calculated. The communication complexity of a protocol is the total number of bits that need to be communicated. When $n$ is prime and for our chosen function, we exhibit a quantum protocol (with complexity $(n-1) \log n$ bits) and a classical protocol (with complexity $(n-1)^2 (\log n^2$) bits). In the quantum protocol, the players have access to entangled qudits but the communication is still classical. Furthermore, we present an integer linear programming formulation for determining a lower bound on the classical communication complexity. This demonstrates that our quantum protocol is strictly better than classical protocols.

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side; this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface.

In this paper, we investigate the employment of reconfigurable intelligent surfaces (RISs) into vehicle platoons, functioning in tandem with a base station (BS) in support of the high-precision location tracking. In particular, the use of a RIS imposes additional structured sparsity that, when paired with the initial sparse line-of-sight (LoS) channels of the BS, facilitates beneficial group sparsity. The resultant group sparsity significantly enriches the energies of the original direct-only channel, enabling a greater concentration of the LoS channel energies emanated from the same vehicle location index. Furthermore, the burst sparsity is exposed by representing the non-line-of-sight (NLoS) channels as their sparse copies. This thus constitutes the philosophy of the diverse sparsities of interest. Then, a diverse dynamic layered structured sparsity (DiLuS) framework is customized for capturing different priors for this pair of sparsities, based upon which the location tracking problem is formulated as a maximum a posterior (MAP) estimate of the location. Nevertheless, the tracking issue is highly intractable due to the ill-conditioned sensing matrix, intricately coupled latent variables associated with the BS and RIS, and the spatialtemporal correlations among the vehicle platoon. To circumvent these hurdles, we propose an efficient algorithm, namely DiLuS enabled spatial-temporal platoon localization (DiLuS-STPL), which incorporates both variational Bayesian inference (VBI) and message passing techniques for recursively achieving parameter updates in a turbo-like way. Finally, we demonstrate through extensive simulation results that the localization relying exclusively upon a BS and a RIS may achieve the comparable precision performance obtained by the two individual BSs, along with the robustness and superiority of our proposed algorithm as compared to various benchmark schemes.

In this paper we study multi-task oriented communication system via studying analog encoding method for multiple estimation tasks. The basic idea is to utilize the correlation among interested information required by different tasks and the feature of broadcast channel. For linear estimation tasks, we provide a low complexity algorithm for multi-user multi-task system based on orthogonal decomposition of subspaces. It is proved to be the optimal solution in some special cases, and for general cases, numerical results also show significant improvements over baseline methods. Further, we make a trial to migrate above method to neural networks based non-linear estimation tasks, and it also shows improvement in energy efficiency.

This paper investigates the multi-antenna covert communications assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, to shelter the existence of communications between transmitter and receiver from a warden, a friendly full-duplex receiver with two antennas is leveraged to make contributions to confuse the warden. Considering the worst case, the closed-form expression of the minimum detection error probability (DEP) at the warden is derived and utilized as a covert constraint. Then, we formulate an optimization problem maximizing the covert rate of the system under the covertness constraint and quality of service (QoS) constraint with communication outage analysis. To jointly design the active and passive beamforming of the transmitter and STAR-RIS, an iterative algorithm based on globally convergent version of method of moving asymptotes (GCMMA) is proposed to effectively solve the non-convex optimization problem. Simulation results show that the proposed STAR-RIS-assisted scheme highly outperforms the case with conventional RIS.

Multipath QUIC is a transport protocol that allows for the use of multiple network interfaces for a single connection. It thereby offers, on the one hand, the possibility to gather a higher throughput, while, on the other hand, multiple paths can also be used to transmit data redundantly. Selective redundancy combines these two applications and thereby offers the potential to transmit time-critical data. This paper considers scenarios where data with real-time requirements are transmitted redundantly while at the same time, non-critical data should make use of the aggregated throughput. A new model called congestion window reservation is proposed, which enables an immediate transmission of time-critical data. The performance of this method and its combination with selective redundancy is evaluated using emulab with real data. The results show that this technique leads to a smaller end-to-end latency and reliability for periodically generated priority data.

We consider the multiwinner election problem where the goal is to choose a committee of $k$ candidates given the voters' utility functions. We allow arbitrary additional constraints on the chosen committee, and the utilities of voters to belong to a very general class of set functions called $\beta$-self bounding. When $\beta=1$, this class includes XOS (and hence, submodular and additive) utilities. We define a novel generalization of core stability called restrained core to handle constraints and consider multiplicative approximations on the utility under this notion. Our main result is the following: If a smooth version of Nash Welfare is globally optimized over committees within the constraints, the resulting committee lies in the $e^{\beta}$-approximate restrained core for $\beta$-self bounding utilities and arbitrary constraints. As a result, we obtain the first constant approximation for stability with arbitrary additional constraints even for additive utilities (factor of $e$), and the first analysis of the stability of Nash Welfare with XOS functions even with no constraints. We complement this positive result by showing that the $c$-approximate restrained core can be empty for $c<16/15$ even for approval utilities and one additional constraint. Furthermore, the exponential dependence on $\beta$ in the approximation is unavoidable for $\beta$-self bounding functions even with no constraints. We next present improved and tight approximation results for simpler classes of utility functions and simpler types of constraints. We also present an extension of restrained core to extended justified representation with constraints and show an existence result for matroid constraints. We finally generalize our results to the setting with arbitrary-size candidates and no additional constraints. Our techniques are different from previous analyses and are of independent interest.

In strong line-of-sight millimeter-wave (mmWave) wireless systems, the rank-deficient channel severely hampers spatial multiplexing. To address this inherent deficiency, multiple reconfigurable-intelligent-surfaces (RISs) are introduced in this study to customize the wireless channel. Utilizing the RIS to reshape electromagnetic waves, we theoretically show that a favorable channel with an arbitrary tunable rank and a minimized truncated condition number can be established by elaborately designing the placement and reflection matrix of RISs. Different from existing works on multi-RISs, the number of elements needed for each RIS to combat the path loss and the limited phase control is also considered. On the basis of the proposed channel customization, a joint transmitter-RISs-receiver (Tx-RISs-Rx) design under a hybrid mmWave system is investigated to maximize the spectral efficiency. Using the proposed scheme, the optimal singular value decomposition-based hybrid beamforming at the Tx and Rx can be obtained without matrix decomposition for the digital and analog beamforming. The bottoms of the sub-channel mode in the water-filling algorithm, which are conventionally uncontrollable, are proven to be independently adjustable by RISs. Moreover, the transmit power required for realizing multi-stream transmission is derived. Numerical results are presented to verify our theoretical analysis and exhibit substantial gains over systems without RISs.

We present a novel and first-of-its-kind information-theoretic framework for the key design consideration and implementation of a ground-to-UAV (G2U) communication network to minimize end-to-end transmission delay in the presence of interference. The proposed framework is useful as it describes the minimum transmission latency for an uplink ground-to-UAV communication must satisfy while achieving a given level of reliability. To characterize the transmission delay, we utilize Fano's inequality and derive the tight upper bound for the capacity for the G2U uplink channel in the presence of interference, noise, and potential jamming. Subsequently, given the reliability constraint, the error exponent is obtained for the given channel. Furthermore, a relay UAV in the dual-hop relay mode, with amplify-and-forward (AF) protocol, is considered, for which we jointly obtain the optimal positions of the relay and the receiver UAVs in the presence of interference. Interestingly, in our study, we find that for both the point-to-point and relayed links, increasing the transmit power may not always be an optimal solution for delay minimization problems. Moreover, we prove that there exists an optimal height that minimizes the end-to-end transmission delay in the presence of interference. The proposed framework can be used in practice by a network controller as a system parameters selection criteria, where among a set of parameters, the parameters leading to the lowest transmission latency can be incorporated into the transmission. The based analysis further set the baseline assessment when applying Command and Control (C2) standards to mission-critical G2U and UAV-to-UAV(U2U) services.