Exploiting Full-duplex Receivers for Achieving Secret Communications in Multiuser MISO Networks

We consider a broadcast channel, in which a multi-antenna transmitter (Alice) sends $K$ confidential information signals to $K$ legitimate users (Bobs) in the presence of $L$ eavesdroppers (Eves). Alice uses MIMO precoding to generate the information signals along with her own (Tx-based) friendly jamming. Interference at each Bob is removed by MIMO zero-forcing. This, however, leaves a "vulnerability region" around each Bob, which can be exploited by a nearby Eve. We address this problem by augmenting Tx-based friendly jamming (TxFJ) with Rx-based friendly jamming (RxFJ), generated by each Bob. Specifically, each Bob uses self-interference suppression (SIS) to transmit a friendly jamming signal while simultaneously receiving an information signal over the same channel. We minimize the powers allocated to the information, TxFJ, and RxFJ signals under given guarantees on the individual secrecy rate for each Bob. The problem is solved for the cases when the eavesdropper's channel state information is known/unknown. Simulations show the effectiveness of the proposed solution. Furthermore, we discuss how to schedule transmissions when the rate requirements need to be satisfied on average rather than instantaneously. Under special cases, a scheduling algorithm that serves only the strongest receivers is shown to outperform the one that schedules all receivers.Comment: IEEE Transactions on Communication

Ganoderma boninense Pat. from basal stem rot of oil palm (Elaeis guineensis) in Peninsular Malaysia

Several hundred sporophores of Ganoderma were collected from 5 - 40 years old palm trees infected with basal stem rot in 5 oil palm estates in Peninsular Malaysia. Based on the morphometric studies of the pores, dessepiments and basidiospores dimensions and other morphological characteristics, the sporophores were identified as belonging to a single species, G. boninense Pat

Throughput-efficient sequential channel sensing and probing in cognitive radio networks under sensing errors

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic trans-mission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all chan-nels and then pick the best among them, due to the poten-tially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid colli-sions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR’s average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that ad-dresses all the above considerations is derived by formulat-ing the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to con-duct a “second-round ” optimization over the operational pa-rameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone

RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverage estimation. Conducting site surveys to obtain RF maps is labor-intensive and sometimes not feasible. In this paper, we propose radio-frequency adversarial deep-learning inference for automated network coverage estimation (RADIANCE), a generative adversarial network (GAN) based approach for synthesizing RF maps in indoor scenarios. RADIANCE utilizes a semantic map, a high-level representation of the indoor environment to encode spatial relationships and attributes of objects within the environment and guide the RF map generation process. We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment. RADIANCE incorporates this loss function along with the antenna pattern to capture signal propagation within a given indoor configuration and generate new patterns under new configuration, antenna (beam) pattern, and center frequency. Extensive simulations are conducted to compare RADIANCE with ray-tracing simulations of RF maps. Our results show that RADIANCE achieves a mean average error (MAE) of 0.09, root-mean-squared error (RMSE) of 0.29, peak signal-to-noise ratio (PSNR) of 10.78, and multi-scale structural similarity index (MS-SSIM) of 0.80.Comment: 6 pages, 6 figure