In this paper, we consider a wireless powered communication network with an energy harvesting (EH) jammer where eavesdroppers try to wiretap the communication between users and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH users and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, each user sends information to the H-AP in a time division multiple access manner, while the jammer generates jamming signals to interfere the eavesdroppers. We adopt two different secrecy performance measurements according to the level of channel state information (CSI) of the eavesdroppers. First, with a single user, we maximize the secrecy rate by optimizing the time allocation between the ET and the IT phase when perfect CSI of the eavesdroppers is available at all nodes. In contrast, when the instantaneous CSI of the eavesdroppers is not available at legitimate nodes, we analyze and minimize the secrecy outage probability. We also extend the single user analysis to a more general multi-user situation with an additional consideration of the transmit power allocation at the jammer. Finally, we evaluate the performance of our proposed solutions through simulations and demonstrate that a performance gain compared to conventional schemes becomes more pronounced with the increased number of eavesdroppers and users.

In this paper, we consider a proactive eavesdropping scenario, where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full-duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts jamming signals to maintain the data rate of the suspicious users below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the receive combining vector at the central monitor, the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate. First, we examine the case of a single eavesdropping relay equipped with a single antenna and provide some useful insights. Also, an effective two-layer optimization method is proposed to obtain the globally optimal solution. Then, we study a general case of multiple eavesdropping relays with multiple antennas, and solve a semi-definite relaxation problem. Numerical results verify the efficiency of our proposed solutions for both single and multiple relay cases and show considerable performance gains over conventional schemes.

In this paper, we consider a legitimate proactive eavesdropping scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several multi-antenna full-duplex spoofing relays and a multi-antenna cooperative jammer. Assuming an adaptive transmission policy at the suspicious users, the spoofing relays not only intercept but also forward the manipulated information to control the data rate of the suspicious users in collaboration with the jammer. We provide a technique which jointly optimizes the receive combining vector at the central monitor, the precoders at the relays, and the transmit covariance matrix at the jammer for maximizing the eavesdropping rate. To reveal some fundamental properties of the optimal operation, we first study a single-relay system. It is shown that when the central monitor experiences a poor eavesdropping channel link, the spoofing relay and the jammer should transmit destructive signals and jamming signals, respectively. In this case, the suspicious users are forced to decrease their data rate, while the intercepted information can be successfully decoded by the central monitor. On the other hand, when the eavesdropping channel condition is favorable, the spoofing relay forwards constructive signals to further increase the data rate at the suspicious users in a way that more information can be intercepted from the suspicious users. We then formulate a general eavesdropping rate maximization problem for multiple relays and present a semi-definite relaxation approach. A low-complexity design is also proposed based on our analysis for the single-relay system. Simulation results verify the efficiency of the proposed solutions compared to other baseline schemes in various practical setups.

This letter studies a joint design of fronthaul compression and precoding for full-duplex (FD) cloud radio access networks (C-RAN). In this system, each radio unit (RU) takes the role of FD transmission and a control unit connected to all of the RUs via finite-capacity links performs co-operative interference management by centralized baseband processing. There exists non-negligible self-interference (SI) due to FD transmission even after analog SI cancellation (SIC) is applied at the RU. To efficiently alleviate the residual SI, we propose the implementation of additional digital SIC at each RU, which mitigates the SI in baseband and thus reduces the burden of the finite uplink fronthaul capacity. We then consider the problem of maximizing the sum rate of uplink and downlink users while satisfying the per-RU fronthaul capacity and power constraints. The problem is solved by the majorization minimization approach under a difference of convex structure. Through simulation results, we confirm that the proposed FD C-RAN system with SIC implemented at the RUs can achieve substantial gains compared to conventional schemes.

We consider a Fog Radio Access Network (F-RAN) with a Base Band Unit (BBU) in the cloud and multiple cache-enabled enhanced Remote Radio Heads (eRRHs). The system aims at delivering contents on demand with minimal average latency from a time-varying library of popular contents. Uncached requested files can be transferred from the cloud to the eRRHs by following either backhaul or fronthaul modes. The backhaul mode transfers fractions of the requested files, while the fronthaul mode transmits quantized baseband samples as in Cloud-RAN (C-RAN). The backhaul mode allows the caches of the eRRHs to be updated, which may lower future delivery latencies. In contrast, the fronthaul mode enables cooperative C-RAN transmissions that may reduce the current delivery latency. Taking into account the trade-off between current and future delivery performance, this letter proposes an adaptive selection method between the two delivery modes to minimize the long-term delivery latency. Assuming an unknown and time-varying popularity model, the method is based on model-free Reinforcement Learning (RL). Numerical results confirm the effectiveness of the proposed RL.