This paper proposes a new artificial noise (AN) scheme that combines with the relay selection techniques to improve the physical layer security for a wiretap full-duplex decode-and-forward cooperative network. The communication consists of two phases. In the first phase, the source transmits a message to the selected full-duplex relay, which simultaneously injects AN to mitigate eavesdropping attacks. In the second phase, the selected relay decodes the source message, XORs the source message and AN, and then broadcasts the mixed-signal to the destination. Different form existing schemes, the proposed AN scheme makes the eavesdropper fail in obtaining any information about the source message directly in the second phase. We take into account the correlation of the channel coefficients for the same link during different phases in the performance analysis of AN-aided max-min (MM) and partial (Par) relay selection schemes, and characterize the security-reliability trade-off (SRT) of the proposed schemes by the closed-form expressions of outage probability and intercept probability. Numerical results show that the proposed AN-aided MM/Par relay selection achieves a better SRT performance than the corresponding full-duplex mode assisted AN-aided relay selection scheme in existing literature. Furthermore, the MM scheme outperforms the Par scheme in terms of the SRT, and the SRT of the MM scheme can be substantially improved by increasing the number of relays, while the improvement of the Par scheme is limited.
This paper considers the legitimate proactive eavesdropping in the underlaid device-to-device (D2D) communications networks. In such network, a dedicated node working in the full-duplex mode monitors a pair of suspicious D2D users, and transmits its own information to the base station at the same time. We formulate a beamforming design problem for the monitoring node to maximize its transmitting rate while guaranteeing the success of surveillance. As the problem is non-convex, we first transform it into a semi-definite problem via semi-definite relaxation and obtain an optimal solution to the transformed problem. For low-complexity implementation, we propose another scheme based on zero-forcing and max-ratio-transmitting beamforming methods. For simulation results, we show that compared to the passive eavesdropping, the two proposed beamforming schemes can improve the achievable legitimate transmitting rate and guarantee the non-outage probability of surveillance.
We investigate a new deployment form of reflective intelligent surface (RIS), which aims at enhancing the quality of service of a main communication system in a target region, while without the need of changing its transmission protocol and scheme (i.e., the RIS is "transparent" to the main system). To this end, we mathematically formulate a coverage enhancement problem, where a RIS is used transparently in the sense that the BS can be unaware of its existence, while the minimum channel link strength, measured from every BS antenna to any point in the target region, can be maximized. The formulated problem is non-convex with mixed discrete-continuous variables. To tackle this challenge, we recast it into a convex feasibility problem via spatial sampling and semi-definite relaxation. Based on a derived analytical upper bound on the link strength difference between any two location points, we further characterize the coverage-similarity region of a given location, and accordingly propose an improved spatial sampling scheme for efficient implementation. Simulation results show that the proposed transparent RIS design achieves better coverage performance than benchmark schemes. More importantly, it can effectively improve the communication performance without affecting the transmission scheme originally adopted by the main communication system.
Non-terrestrial networks (NTNs) which often contain lossy links with long propagation delay play an important role in the Future 6G network. The transport-layer performance on NTNs links will directly impact user experience. The Transmission Control Protocol (TCP), the dominant transport protocol of the past decades, exhibits poor performance on NTN links due to the low link utilization and the high end-to-end in-order delivery. In this paper, we propose an enhanced framework for the transport layer protocol which is based on reinforcement learning (RL) for congestion control and utilizes a proactive packet-level forward erasure correction (FEC) approach called streaming coding to provide low-delay loss recovery without retransmission at all. By validating in a popular QUIC open-source implementation, namely quic-go, we have demonstrated that the enhanced framework is feasible and it can achieve much shorter end-to-end in-order delivery delay.
In this paper, joint physical-MAC layer optimization in OFDM systems using adaptive modulation and subcarrier allocation is investigated based on utility theory. Since utility qualifies the level of users' satisfaction derived from the radio resources they occupy, it is ideal for a network optimization metric. We formulate the cross-layer optimization problem as one that maximizes the sum of the utilities over all active users through rate adaptation and dynamic subcarrier allocation with the limited radio resource and time-varying wireless channel constraints. Two effective subcarrier allocation algorithms with low complexity are proposed to solve the constrained optimization problem. Simulation results demonstrate the significant improvement of utility-based cross-layer optimization in OFDM.
In this paper, we consider network transmissions over a single or multiple parallel two-hop lossy paths. These scenarios occur in applications such as sensor networks or WiFi offloading. Random linear network coding (RLNC), where previously received packets are re-encoded at intermediate nodes and forwarded, is known to be a capacity-achieving approach for these networks. However, a major drawback of RLNC is its high encoding and decoding complexity. In this work, a systematic network coding method is proposed. We show through both analysis and simulation that the proposed method achieves higher end-to-end rate as well as lower computational cost than RLNC for finite field sizes and finite-sized packet transmissions.
In this paper, we investigate throughput improvement of hot-spot wireless access with distributed antennas. To exploit shadowing diversity of multiple distributed antennas, we propose an antenna selection approach, which takes both desired signal strength and interference strength into consideration. We also develop a power allocation approach to further enhance the throughput of wireless access. We then use wireless stadium as an example to demonstrate the effectiveness of the proposed approaches.
In Transparent Interconnection of Lots of Links (TRILL) active-active
access, a Reverse Path Forwarding (RPF) check failure issue may occur
when using the pseudo-nickname mechanism specified in RFC 7781. This
document describes a solution to resolve this RPF check failure issue
through centralized replication. All ingress Routing Bridges
(RBridges) send Broadcast, Unknown Unicast, and Multicast (BUM)
traffic to a centralized node with unicast TRILL encapsulation. When
the centralized node receives the BUM traffic, it decapsulates the
packets and forwards them to their destination RBridges using a
distribution tree established per the TRILL base protocol (RFC 6325).
To avoid RPF check failure on an RBridge sitting between the ingress
RBridge and the centralized replication node, some change in the RPF
calculation algorithm is required. RPF checks on each RBridge MUST be
calculated as if the centralized node was the ingress RBridge, instead
of being calculated using the actual ingress RBridge. This document
updates RFC 6325.
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