Primary Rate Maximization in Movable Antennas Empowered Symbiotic Radio Communications

In this paper, we propose a movable antenna (MA) empowered scheme for symbiotic radio (SR) communication systems. Specifically, multiple antennas at the primary transmitter (PT) can be flexibly moved to favorable locations to boost the channel conditions of the primary and secondary transmissions. The primary transmission is achieved by the active transmission from the PT to the primary user (PU), while the backscatter device (BD) takes a ride over the incident signal from the PT to passively send the secondary signal to the PU. Under this setup, we consider a primary rate maximization problem by jointly optimizing the transmit beamforming and the positions of MAs at the PT under a practical bit error rate constraint on the secondary transmission. Then, an alternating optimization framework with the utilization of the successive convex approximation, semi-definite processing and simulated annealing (SA) modified particle swarm optimization (SA-PSO) methods is proposed to find the solution of the transmit beamforming and MAs' positions. Finally, numerical results are provided to demonstrate the performance improvement provided by the proposed MA empowered scheme and the proposed algorithm.

STAR-RIS Assisted Wireless-Powered and Backscattering Mobile Edge Computing Networks

Wireless powered and backscattering mobile edge computing (WPB-MEC) network is a novel network paradigm to supply energy supplies and computing resource to wireless sensors (WSs). However, its performance is seriously affected by severe attenuations and inappropriate assumptions of infinite computing capability at the hybrid access point (HAP). To address the above issues, in this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided scheme for boosting the performance of WPB-MEC network under the constraint of finite computing capability. Specifically, energy-constrained WSs are able to offload tasks actively or passively from them to the HAP. In this process, the STAR-RIS is utilized to improve the quantity of harvested energy and strengthen the offloading efficiency by adapting its operating protocols. We then maximize the sum computational bits (SCBs) under the finite computing capability constraint. To handle the solving challenges, we first present interesting results in closed-form and then design a block coordinate descent (BCD) based algorithm, ensuring a near-optimal solution. Finally, simulation results are provided to confirm that our proposed scheme can improve the SCBs by 9.9 times compared to the local computing only scheme.

Transmit Power Minimization for STAR-RIS Empowered Symbiotic Radio Communications

In this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered transmission scheme for symbiotic radio (SR) systems to make more flexibility for network deployment and enhance system performance. The STAR-RIS is utilized to not only beam the primary signals from the base station (BS) towards multiple primary users on the same side of the STAR-RIS, but also achieve the secondary transmission to the secondary users on another side. We consider both the broadcasting signal model and unicasting signal model at the BS. For each model, we aim for minimizing the transmit power of the BS by designing the active beamforming and simultaneous reflection and transmission coefficients under the practical phase correlation constraint. To address the challenge of solving the formulated problem, we propose a block coordinate descent based algorithm with the semidefinite relaxation, penalty dual decomposition and successive convex approximation methods, which decomposes the original problem into one sub-problem about active beamforming and the other sub-problem about simultaneous reflection and transmission coefficients, and iteratively solve them until the convergence is achieved. Numerical results indicate that the proposed scheme can reduce up to 150.6% transmit power compared to the backscattering device enabled scheme.

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm the effectiveness of the algorithm.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper, we propose an active reconfigurable intelligent surface (RIS) enabled hybrid relaying scheme for a multi-antenna wireless powered communication network (WPCN), where the active RIS is employed to assist both wireless energy transfer (WET) from the power station (PS) to energy-constrained users and wireless information transmission (WIT) from users to the receiving station (RS). For further performance enhancement, we propose to employ both transmit beamforming at the PS and receive beamforming at the RS. We formulate a sum-rate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT, transmit and receive beamforming vectors, and network resource allocation. To solve this non-convex problem, we propose an efficient alternating optimization algorithm with linear minimum mean squared error criterion, semi-definite relaxation (SDR) and successive convex approximation techniques. Specifically, the tightness of applying the SDR is proved. Simulation results demonstrate that our proposed scheme with 10 reflecting elements (REs) and 4 antennas can achieve 17.78% and 415.48% performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs, respectively.

EM-Type Algorithms for DOA Estimation in Unknown Nonuniform Noise

The expectation--maximization (EM) algorithm updates all of the parameter estimates simultaneously, which is not applicable to direction of arrival (DOA) estimation in unknown nonuniform noise. In this work, we present several efficient EM-type algorithms, which update the parameter estimates sequentially, for solving both the deterministic and stochastic maximum--likelihood (ML) direction finding problems in unknown nonuniform noise. Specifically, we design a generalized EM (GEM) algorithm and a space-alternating generalized EM (SAGE) algorithm for computing the deterministic ML estimator. Simulation results show that the SAGE algorithm outperforms the GEM algorithm. Moreover, we design two SAGE algorithms for computing the stochastic ML estimator, in which the first updates the DOA estimates simultaneously while the second updates the DOA estimates sequentially. Simulation results show that the second SAGE algorithm outperforms the first one.

EM and SAGE algorithms for DOA Estimation in the Presence of Unknown Uniform Noise

The expectation-maximization (EM) and space-alternating generalized EM (SAGE) algorithms have been applied to direction of arrival (DOA) estimation in known noise. In this work, the two algorithms are proposed for DOA estimation in unknown uniform noise. Both the deterministic and stochastic signal models are considered. Moreover, a modified EM (MEM) algorithm applicable to the noise assumption is also proposed. These proposed algorithms are improved to ensure the stability when the powers of sources are unequal. After being improved, numerical results illustrate that the EM algorithm has similar convergence with the MEM algorithm and the SAGE algorithm outperforms the EM and MEM algorithms for the deterministic signal model. Furthermore, numerical results show that processing the same samples from the stochastic signal model, the SAGE algorithm for the deterministic signal model requires the fewest iterations.