Partial Channel State Information Case Research Articles

Downlink Connection Density Maximization for NB-IoT Networks Using NOMA With Perfect and Partial CSI

We address the issue of maximizing the number of connected devices in a Narrowband Internet-of-Things (NB-IoT) network using nonorthogonal multiple access (NOMA) in the downlink. We first propose an optimal joint subcarrier and power allocation strategy assuming perfect channel state information (CSI) called stratified device allocation (SDA), which maximizes the connectivity under data rate, power, and bandwidth constraints. Then, we generalize the connectivity maximization problem to the case of partial CSI, where only the distance-dependent path-loss component of the channel gain is available at the base station (BS). We introduce a novel framework called the stochastic connectivity optimization (SCO) framework. In this framework, we propose a heuristic improvement to SDA, namely, SDA with excess power (SDA-EP) algorithm for operation under partial CSI. Furthermore, we derive a concave approximation (SCO-CA) algorithm of near-optimal performance to SCO given the same amount of CSI. Through computer simulations, we show that SDA-EP and SCO-CA outperform conventional NOMA and OMA schemes in the presence of partial CSI over a wide range of service scenarios.

Estimation in Wireless Sensor Networks With Security Constraints

In this paper, we investigate the performance of distributed estimation schemes in a wireless sensor network in the presence of an eavesdropper. The sensors transmit observations to the fusion center (FC), which at the same time are overheard by the eavesdropper. Both the FC and the eavesdropper reconstruct a minimum mean-squared error estimate of the physical quantity observed. We address the problem of transmit power allocation for system performance optimization subject to a total average power constraint on the sensor(s), and a security/secrecy constraint on the eavesdropper. We mainly focus on two scenarios: 1) a single sensor with multiple transmit antennas and 2) multiple sensors with each sensor having a single transmit antenna. For each scenario, given perfect channel state information (CSI) of the FC and full or partial CSI of the eavesdropper, we derive the transmission policies for short-term and long-term cases. For the long-term power allocation case, when the sensor is equipped with multiple antennas, we can achieve zero information leakage in the full CSI case, and dramatically enhance the system performance by deploying the artificial noise technique for the partial CSI case. Asymptotic expressions are derived for the long-term distortion at the FC as the number of sensors or the number of antennas becomes large. In addition, we also consider multiple-sensor multiple-antenna scenario, and simulations show that given the same total number of transmitting antennas the multiple-antenna sensor network is superior to the performance of the multiple-sensor single-antenna network.

On the Secrecy Rate Maximization with Uncoordinated Cooperative Jamming by Single-Antenna Helpers

A practical uncoordinated cooperative jamming (UCJ) scheme is proposed in this paper to enhance the physical layer security of the single-input-single-output (SISO) wiretap channel. Differing from the existing works, all the helpers in this UCJ scheme are uncoordinated single-antenna uplink users, and each helper transmits a jamming signal independently to confound the eavesdropper. The intended receiver plays the role of base station in the cellular systems as well as a control center to properly allocate the jamming power of each helper to maximize the secrecy rate. Two cases are addressed in solving the secrecy rate maximization problem, namely, the global channel state information (CSI) case and the partial CSI case. The optimal solution is obtained in the global CSI case, and a near-optimal solution is obtained under the secrecy outage probability constraint in the partial CSI case. Numerical results show that the performance of the proposed scheme is comparable with the existing works.

Distortion Outage Minimization in Distributed Estimation With Estimation Secrecy Outage Constraints

In this paper, we investigate a class of distortion outage minimization problems for a wireless sensor network in the presence of an eavesdropper. The observation signals transmitted from the sensors to the fusion center (FC) are overheard by the eavesdropper. Both the FC and the eavesdropper reconstruct minimum mean squared error estimates of the physical quantity observed. We address the problem of transmit power allocation to minimize the distortion outage at the FC, subject to an expected total transmit power constraint across the sensor(s) and a secrecy outage constraint at the eavesdropper. Applying a rigorous probabilistic power allocation technique, we derive power policies for the full channel state information (CSI) case. Suboptimal power control policies are studied for the partial CSI case in order to reduce the high computational cost associated with large numbers of sensors or receive antennas. Numerical results show that significantly improved performance can be achieved by adding multiple receive antennas at the FC. In the case of multiple transmit antennas, the distortion outage at the FC can be dramatically reduced and in some cases completely eliminated, by transmitting the observations on the null space of the eavesdropper's channel or deploying an artificial noise technique, in the full CSI and partial CSI cases, respectively.

Joint admission control and beamforming design for the interference cognitive radio network with partial channel state information case

In order to effectively utilise the spectrum of wireless network, secondary users (SUs) are often authorised to share spectrum with primary users (PUs) in cognitive networks. To guarantee effective transmission of PUs and SUs, the interference from the SUs to the PUs is limited and only the SUs that have the information rates (rates) than the corresponding thresholds are allowed to transmit their data. In order to select proper SUs to transmit and to maximise their achievable weighted sum rate (WSR) under rate requirement and interference constraint, the authors propose a joint admission control and beamforming design algorithm for partial channel state information (CSI) case. The WSR maximisation problem is first constructed by using approximation and convex relaxation, according to the statistical property of the CSI. Then a penalty factor is introduced to check the feasibility of such problem and instruct the admission of the SUs. Beyond the feasible SUs set, the WSR is maximised by updating the achievable rate of each user with rate profile technique and polyblock outer approximation method. Simulation results show that the proposed beamforming can achieve higher WSR than the beamforming of the existing baseline method.

Enhancing the Efficiency of Constrained Dual-Hop Variable-Gain AF Relaying Under Nakagami-<formula formulatype="inline"> <tex Notation="TeX">$m$</tex></formula> Fading

This paper studies power allocation for performance constrained dual-hop variable-gain amplify-and-forward (AF) relay networks in Nakagami- m fading. In this context, the performance constraint is formulated as a constraint on the end-to-end signal-to-noise-ratio (SNR) and the overall power consumed is minimized while maintaining this constraint. This problem is considered under two different assumptions of the available channel state information (CSI) at the relays, namely full CSI at the relays and partial CSI at the relays. In addition to the power minimization problem, we also consider the end-to-end SNR maximization problem under a total power constraint for the partial CSI case. We provide closed-form solutions for all the problems which are easy to implement except in two cases, namely selective relaying with partial CSI for power minimization and SNR maximization, where we give the solution in the form of a one-variable equation which can be solved efficiently. Numerical results are then provided to characterize the performance of the proposed power allocation algorithms considering the effects of channel parameters and CSI availability.

Selective Subcarrier Pairing and Power Allocation for DF OFDM Relay Systems with Perfect and Partial CSI

This paper investigates a decode-and-forward two-hop relaying system consisting of one source, one relay and one destination, in which orthogonal frequency division multiplexing is used. The relay forwards the message received from the source on a subset of available subcarriers in the second time slot. Firstly, a subcarrier pairing and selection algorithm is proposed, assuming that perfect channel state information (CSI) is available at all nodes, then, power is allocated to both the source and relay stations under individual power constraints in order to maximize the capacity. Secondly, subcarrier selection and pairing, and power allocation (PA) under partial CSI assumption along with individual power constraints are addressed. The result is a novel distributed algorithm with low complexity maximizing the expected value of capacity at the source and relay nodes. Finally, the simulation results show that selective relaying combined with subcarrier pairing and PA improves the system capacity to a considerable extent in both perfect and partial CSI cases.

Optimality of beamforming in fading MIMO multiple access channels

We consider the sum capacity of a multi-input multi-output (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have either no or partial CSI. When the transmitters have partial CSI, it is in the form of either the covariance matrix of the channel or the mean matrix of the channel. For the covariance feedback case, we mainly consider physical models that result in single-sided correlation structures. For the mean feedback case, we consider physical models that result in in-phase received signals. Under these assumptions, we analyze the MIMO-MAC from three different viewpoints. First, we consider a finite-sized system. We show that the optimum transmit directions of each user are the eigenvectors of its own channel covariance and mean feedback matrices, in the covariance and mean feedback models, respectively. Also, we find the conditions under which beamforming is optimal for all users. Second, in the covariance feedback case, we prove that the region where beamforming is optimal for all users gets larger with the addition of new users into the system. In the mean feedback case, we show through simulations that this is not necessarily true. Third, we consider the asymptotic case where the number of users is large. We show that in both no and partial CSI cases, beamforming is asymptotically optimal. In particular, in the case of no CSI, we show that a simple form of beamforming, which may be characterized as an arbitrary antenna selection scheme, achieves the sum capacity. In the case of partial CSI, we show that beamforming in the direction of the strongest eigenvector of the channel feedback matrix achieves the sum capacity. Finally, we generalize our covariance feedback results to double-sided correlation structures in the Appendix.