In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM.

In this letter, we propose a new secrecy enhancement scheme for spatial modulation systems by exploiting the knowledge of the legitimate channel state information to rotate the indices of the transmit antennas and the constellation symbols, which are mapped into spatial and constellation bits. A secrecy rate analysis shows that the eavesdropper cannot recover any information carried by spatial and constellation bits. The numerical results on the secrecy rate and the bit error rate verify the effectiveness and robustness of the proposed scheme.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) transmits additional information bits through the indices of active subcarriers. In this paper, we explore the potential of an improvement of OFDM-IM by reusing inactive subcarriers to transmit signals for a secondary system. We consider a cognitive radio network assisted by OFDM-IM, where the primary user (PU) transmits its signal to the primary receiver, and the secondary user (SU) senses the idle spectrum and transmits its own signal to the secondary receiver via those idle subcarriers. By either combining the signal transmitted from SU or not, we propose two different detection strategies with different trade-offs between the computational complexity and system performance. An asymptotically tight upper bound on the bit error rate is derived to evaluate the error performance for PU and SU.

Recently, many encryption algorithms based on fractional-order chaotic system have been proposed to solve the problem of image encryption. In this paper, we propose a novel color image compression-encryption algorithm based on fractional-order hyperchaotic system and DNA coding. In the data compression stage, the data of R, G, and B channels of the color image are converted to the frequency domain by two-dimensional discrete cosine transform (DCT), and then the amount of encrypted data is reduced by the quantization process. We design a data processing algorithm to ensure that the data after DCT is compatible with DNA coding data format. In the encryption stage, the processes of DNA encoding and decoding, DNA operation, and pixel scrambling are all controlled by the corresponding chaotic sequences, which are generated by the chaotic system. The original image is used to calculate the initial state of the chaotic system, which improves the performance of the algorithm against the chosen-plaintext attack significantly. Experimental results and security analysis illustrate that the proposed algorithm has excellent compression and security performance. It can not only reconstruct the original image well under the condition of low compression ratio, but also provide high security to resist various attacks. Besides, experimental results also indicate that the algorithm proposed can be applied to the fields of color image compression, encryption, and transmission.

In this paper, we study resource allocation problems for a two-tier cognitive heterogeneous network in interweave spectrum sharing mode. Secondary users (SUs) in small cells (SCs) opportunistically access the licensed spectrum resources. Non-orthogonal multiple access (NOMA) is used to boost the number of accessible SUs sharing the limited and dynamic licensed spectrum holes. Practically, there exists a tradeoff: an SC can increase its instantaneous sum throughput by accessing more idle bandwidth, which creates higher liability due to the dynamics of licensed spectrum and contention among the multiple SCs. Aiming to maximize the sum throughput of second-tier SCs network, we formulate a mixed integer nonlinear programming problem with the constraints of the available idle bandwidth, the successive interference cancellation complexity, the transmission power budget, and the minimum data requirements. To efficiently solve this problem, we decompose the original optimization problem into bandwidth resource allocation subproblem, SUs clustering subproblem, and power allocation subproblem. Based on the scale of SCs network and the activities of licensed spectrum, we introduce an optimal bandwidth configuration to maximize the average sum throughput of SCs. By analyzing the derivation of the achievable rate expression of a NOMA-enabled SU, we develop a novel SUs clustering algorithm which can improve the throughput of a cluster by grouping SUs with more distinctive channel conditions. With the results of SUs clustering, we propose power allocation within a NOMA cluster by using Karush-Kuhn-Tucker optimality conditions. Furthermore, we perform power allocation across NOMA clusters by using the difference of convex programming. Simulation results validate the performance of the proposed resource allocation algorithms.

The separation of the forwarding and control planes of software-defined networking brings a lot of flexibility to network management. However, with the increase in network capacity, network structure becomes more and more complicated. The controller placement problem in a large-scale network is still a hard nut to crack because of high complexity and difficulty in the tradeoff between performance. In this paper, a novel approach named community detection controller deployment is proposed. With the aid of theory of complex network analysis, the network topology of the controller to be deployed is regarded as a network composed of multiple communities, and then a suitable position is selected in each community to place the controller, which is capable of avoiding the complexity of global deployment. In order to balance the number of switches managed by the controller in each community, the scale constraint factor is introduced into Louvain heuristic algorithm to limit the number of nodes in each community and balance the differences in the number of nodes among different communities. Distinct from the existing clustering-based approaches, this method can independently identify partitions with community attributes according to the network structure without manual intervention. On the other hand, it can adjust the number of nodes within communities on demand to achieve topological equilibrium partition. Experiments are performed on real network topologies, and corresponding results show that the proposed method is more suitable for networks with plenty of nodes and can effectively balance the controllers' load while keeping latency at a lower level.