The impacts of channel estimation, beamforming adjustment, phase shift adjustment, and computation costs on an intelligent reflecting surface (IRS)-assisted secure wireless communication system are severe in practice but are usually ignored for simplicity. In this paper, we consider a multi-antenna BS serving a single-antenna legitimate user with the help of a multi-element IRS in the presence of an eavesdropper. To maximally reduce the implementation cost, we investigate the no-instantaneous channel state information (ICSI) case with the legitimate user's and eavesdropper's statistical CSI (SCSI). First, we present a SCSI-adaptive (quasi-static) beamforming and phase shift design, also referred to as a quasi-static design, which has low channel estimation, beamforming adjustment, and phase shift adjustment costs. Then, we formulate the maximization of the achievable ergodic secrecy rate with respect to the quasi-static design as a challenging stochastic non-convex problem. Next, we propose two parallel iterative algorithms to obtain a stationary point and an approximate stationary point and present their respective quasi-static designs. Furthermore, we show that the quasi-static designs derived from the stationary point and approximate stationary point achieve lower implementation costs than existing designs. Finally, we numerically verify the analytical results and demonstrate notable gains of the two proposed quasi-static designs over existing designs.
We study a distributed algorithm for adjusting beamforming vectors in a peer-to-peer wireless network with multiple-input multiple-output (MIMO) channels. Each transmitter precoding matrix has rank one, and a linear minimum mean squared error (MMSE) filter is applied at each receiver. Our objective is to maximize the total utility summed over all users, where each user's utility is a function of the received signal-to-interference-plus-noise ratio (SINR). Given all users' beamforming vectors and receive filters, each receiver announces an interference price, representing the marginal cost of interference from other users. A particular transmitter updates its beamforming vector to maximize its utility minus the interference cost to other users. We show that if the utility functions satisfy certain concavity conditions, then the total utility is non-decreasing with each update. We also present numerical results that illustrate the effect of ignoring interference prices from all but the closest users, and relaxing requirements on the frequency of beam and price updates.
This paper investigates a fluid antenna (FA) enhanced integrated sensing and communication (ISAC) system consisting of a base station (BS), multiple single-antenna communication users, and one point target, where the BS is equipped with FAs to enhance both the communication and sensing performance. First, we formulate a problem that maximizes the radar signal-to-noise ratio (SNR) by jointly optimizing the FAs' positions and transmit beamforming matrix. Then, to tackle this highly non-convex problem, we present efficient algorithms by using alternating optimization (AO), successive convex approximation (SCA), and semi-definite relaxation (SDR). Numerical results demonstrate the convergence behavior and effectiveness of the proposed algorithm.
We study distributed algorithms for adjusting beamforming vectors and receiver filters in multiple-input multiple-output (MIMO) interference networks, with the assumption that each user uses a single beam and a linear filter at the receiver. In such a setting there have been several distributed algorithms studied for maximizing the sum-rate or sum-utility assuming perfect channel state information (CSI) at the transmitters and receivers. The focus of this paper is to study adaptive algorithms for time-varying channels, without assuming any CSI at the transmitters or receivers. Specifically, we consider an adaptive version of the recent Max-SINR algorithm for a time-division duplex system. This algorithm uses a period of bi-directional training followed by a block of data transmission. Training in the forward direction is sent using the current beam-formers and used to adapt the receive filters. Training in the reverse direction is sent using the current receive filters as beams and used to adapt the transmit beamformers. The adaptation of both receive filters and beamformers is done using a least-squares objective for the current block. In order to improve the performance when the training data is limited, we also consider using exponentially weighted data from previous blocks. Numerical results are presented that compare the performance of the algorithms in different settings.
Interference mitigation approaches in the presence of multiple receive antennas in the uplink of a multi-cell wireless communications system are studied in this paper. A formulation based on interference pricing is proposed, where it is shown that a single price per base-station can be computed and exchanged, in order to set the mobile transmit powers per cell. The work is premised on a decentralized network architecture where schedulers make decisions on users connected to their cell, and there is a low-rate inter-cell communication link to enable distributed interference mitigation. The proposed utility maximization approach provides a general framework for multi-user multiple-input multiple-output (MIMO) systems on the uplink and accommodates both optimal (MMSE) and sub-optimal (MRC) multi-antenna receivers.
Radiation hardness of a particle detector, double metal contact GaAs semiconductors has been investigated in 14 MeV neutron exposure. The leak current, the charge collection efficiency and the spectrum of MIPs are measured after 1012n/cm2 dose. The results are compared with 60Co 1.25MeV γ photons radiation. The mechanism of radiation damage and the effect on time performances of GaAs detectors are discussed. A hypothesis of the active layer distribution of the GaAs detectors based on experiment data is given. The computation agrees with test results.
This paper presents a comparative study of algorithms for jointly optimizing beamformers and receive filters in an interference network, where each node may have multiple antennas, each user transmits at most one data stream, and interference is treated as noise. We focus on techniques that seek good suboptimal solutions by means of iterative and distributed updates. Those include forward-backward iterative algorithms (max-signal-to-interference-plus-noise ratio (SINR) and interference leakage), weighted sum mean-squared error (MSE) algorithms, and interference pricing with incremental signal-to-noise ratio (SNR) adjustments. We compare their properties in terms of convergence and information exchange requirements, and then numerically evaluate their sum rate performance averaged over random (stationary) channel realizations. The numerical results show that the max-SINR algorithm achieves the maximum degrees of freedom (i.e., supports the maximum number of users with near-zero interference) and exhibits better convergence behavior at high SNRs than the weighted sum MSE algorithms. However, it assumes fixed power per user and achieves only a single point in the rate region whereas the weighted sum MSE criterion gives different points. In contrast, the incremental SNR algorithm adjusts the beam powers and deactivates users when interference alignment is infeasible. Furthermore, that algorithm can provide a slight increase in sum rate, relative to max-SINR, at the cost of additional iterations.
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