The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

In this paper, we study the outage probability minimizing problem in a two-hop cooperative relay network. To reduce outage probability, existing studies propose many schemes for relay selection and power allocation, which are usually based on the assumption of exact channel state information (CSI). However, it is difficult to obtain perfect instantaneous CSI in practical situations where channel states change rapidly, and thus traditional methods would not perform well. Considering these factors, we turn to the emerging reinforcement learning (RL) methods for solutions. RL methods do not need any prior knowledge of CSI, but use neural network for approximation and decision after interacting with communication environment. Nevertheless, conventional RL methods, including most deep reinforcement learning (DRL) methods, cannot perform well when the search space is too large. In addition, non-stationarity is a common problem when using hierarchical reinforcement learning (HRL), which is caused by the changing behavior in different hierarchies. Therefore, we first propose a DRL framework with an outage-based reward function, which is then used as a baseline. Then, we further design an HRL framework and training algorithm. By decomposing relay selection and power allocation into two hierarchical optimization objectives, and combining on- policy and off-policy methods in the HRL framework, our method successfully address the sparse reward and non-stationary problem. Simulation results reveal that compared with traditional DRL method, the proposed HRL training algorithm can converge faster and reduce the outage probability by 8% in two-hop relay network with the same outage threshold.

With the development of precise positioning technology, a growing number of location-based services (LBSs) facilitate people’s life. Most LBSs require proof of location (PoL) to prove that the user satisfies the service requirement, which exposes the user’s privacy. In this paper, we propose a zero-knowledge proof of location (zk-PoL) protocol to better protect the user’s privacy. With the zk-PoL protocol, the user can choose necessary information to expose to the server, so that hierarchical privacy protection can be achieved. The evaluation shows that the zk-PoL has excellent security to resist main attacks, moreover the computational efficiency is independent of input parameters and the zk-PoL is appropriate to delay-tolerant LBSs.

Existing analytical work on the proportional fair scheduling (PFS) algorithm has some limitations: 1) it requires independent identically distributed (i.i.d) relationship among users, 2) it uses a modified version of preference metric different from the original one, and 3) it is only valid for single-input-single-output (SISO) communications. This letter presents an analytical expression for the expected throughput of PFS over Rayleigh fading channels without the limitations above. The derived expression can be easily evaluated, and applies to both SISO and multiple-input-multiple-output (MIMO) cases. In addition, existing analytic result can be derived by using our expression in corresponding scenarios.

This paper provides analytical expressions to evaluate the performance of a random access wireless network in terms of user throughput and network throughput, subject to the constraint of proportional fairness amongst users. The proportional fair scheduling (PFS) algorithm is considered an attractive bandwidth allocation criterion in wireless networks for supporting high resource utilization while maintaining good fairness among network flows. The most challenge of a PFS problem is the lack of analytic expression. Though the PFS algorithm has been a research focus for some time, the results are mainly obtained from computer simulations. It is known that a PFS problem is NP-hard and, until recently, there are very few papers which give analytic insights into the PFS algorithm. Typically, existing works use simplified form of the PF preference metric and assume simple linear model, or the given analytic expression is valid only for very limited cases. In this research, we give analytical results of the PFS algorithm by providing closed-form expressions for the throughput in Rayleigh fading networks. We use Gaussian approximation method to model the feasible data rate in Rayleigh fading environments. Results obtained from the simulation and numerical analysis verifies the high accuracy of the closed-form expressions given in the paper. In particular, the analytic expressions given here will provide great help for the system design of a PFS-enable network, not only in that it is obtained from more realistic rate model, but also it applies to various kinds of network scenarios.

In this paper, we study linear transceiver designs for indoor visible light communications (VLCs) with multiple light emitting diodes (LEDs). Specifically, we investigate VLCs including white emitting diodes and VLCs including red/green/blue (RGB) LEDs. The transmitter precoding and the offset are jointly designed by considering certain key practical lighting constraints, such as optical power, non-negativeness, and color illumination. Various non-convex transceiver design problems are formulated aiming to minimize total mean-square-error to improve transmission reliability. We show that for multi-input single-output white VLCs, the optimal precoding reduces to a simple LED selection strategy. For multi-input multi-output (MIMO) white VLCs, we prove that the optimization problem with multiple constraints can be equivalently simplified to a problem with single constraint, which enables us to propose efficient algorithms to search local optimal solutions. For MIMO RGB VLCs, by using certain useful transformations, we show that the precoding design is equivalent to covariance matrix design of transmit signals, which can be further transformed to a convex optimization problem. To develop an algorithm to find the optimal solution, we derive the optimal structure of the covariance matrix and show that the optimal solution can be obtained via a water-filling approach. Extensive simulation results are provided to verify the performance of the proposed designs.