This work proposes a jamming-resistant frequency hopping (FH) system that utilizes local channel observations for secret key generation (SKG). FH is a spread spectrum technique used in both military and consumer wireless applications to avoid jamming attacks, but requires pre-shared secret keys among communicating terminals, say Alice and Bob, to ensure that the same FH sequence is used at both sides. In our scheme, Alice and Bob utilize local observations of the channel between them as the source of common randomness to generate the shared secret key. By gathering multiple time slots into a frame, the sequence of channels observed in each frame can be used to determine the FH sequence in the next frame. In this case, the key generation rate must be high enough to identify the FH sequence in the next frame and, thus, to sustain the operation over time. However, by further considering the data transmission, an interesting tradeoff exists between the power allocated for SKG and channel estimation in the training phase and that for communication in the data transmission phase. Given the number of FH channels and the number of channels that the adversary can jam at once, we derive the minimum pilot signal power required for sustainability and also determine the optimal power allocation between pilot and data signals that maximizes the ergodic rate between the two users. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

Power and on-off control problems are examined for renewable energy enabled base-stations (BSs) and relay nodes (RNs) in cooperative multicast networks. Renewable energy is utilized at BSs and RNs to reduce the overall grid energy cost. By considering a practical energy consumption model and the statistics of the renewable energy arrival, the optimal transmit powers are first determined by minimizing the expected grid energy consumption subject to an average outage probability constraint at MUs. The optimal solution is found via line search in the general case and is obtained in closed-form at high SNR. In addition, an on-off control policy is also proposed to further reduce the basic operational energy costs. The joint on-off and power control problems are solved approximately using two sequential deflation techniques, namely, the subset-search and the convex-relaxation-based approaches. The power control problem is also extended to the multicarrier scenario with unequal transmit powers and is solved using successive convex approximation. Simulations using the photovoltaic energy arrival model are provided to demonstrate the effectiveness of the proposed schemes. The results show that expending more power at RNs allows for more efficient use of renewable energy and, thus, increases energy-savings.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission scheme in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as generalized SM (GSM), can use the maximum-likelihood (ML) detector jointly detecting the antenna-subset (AS) index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity can be prohibitively high when the dimension of the GSM system is large. In this paper, we propose new methods to solve the problem. The main idea is to split the detection into two stages, one for the AS index and the other for the symbol vector. For the detection of the AS index, we develop two methods, referred to as Gaussian approximation and QR projection. Once the AS index is detected, conventional low-complexity ML detectors can be applied for the detection of the symbol vector. The diversity order for the proposed methods are further analyzed and an enhanced method is also proposed to achieve near-optimum performance. Finally, the proposed methods are extended to conduct soft detection of GSM systems. Simulations show that our methods significantly outperform existing ones while the detection complexity remains similar.

Effective recognition of objects calls for the appropriate selection of feature descriptor. In this paper, we generalize the "extended local ternary patterns" (ELTP) to form a novel and compact set of features named center-symmetric extended local ternary patterns (CS-ELTP). The newly defined CS-ELTP follows a simplified encoding procedure and has a lower dimension for a fixed neighborhood region. It achieves good balances among feature dimension, recognition rate and noise resistance according to our comparative experimental analysis. In addition, we combine binary and ternary patterns to create a class of hybrid descriptor that possesses the characteristics of both types of descriptor. Experimental results indicate that the hybrid descriptor can improve the performance in noisy conditions while maintaining a reasonable feature dimension.

Spatial modulation (SM) combined with spatial multiplexing is a newly developed transmission technique in multiple-input multiple-output (MIMO) systems. The resultant system, referred to as SM-MIMO, can use the maximum-likelihood (ML) detector jointly detecting the antenna index and symbol vector. As known, the ML detector can achieve optimum performance; however, its computational complexity may become prohibitively high when the dimension of MIMO system or symbol constellation is large. In this paper, we propose a new method to solve the problem. The main idea is to conduct a two-stage detection, antenna index and symbol. We develop a Gaussian approximation method such that the antenna-index can be first reliably detected. An enhanced method is also proposed so that the near-optimum performance can be obtained. Simulations show that our methods outperform existing methods while the detection complexity remains low.