Most of the traditional cryptanalytic technologies often require a great amount of time, known plaintexts, and memory. This paper proposes a generic cryptanalysis model based on deep learning (DL), where the model tries to find the key of block ciphers from known plaintext-ciphertext pairs. We show the feasibility of the DL-based cryptanalysis by attacking on lightweight block ciphers such as simplified DES, Simon, and Speck. The results show that the DL-based cryptanalysis can successfully recover the key bits when the keyspace is restricted to 64 ASCII characters. The traditional cryptanalysis is generally performed without the keyspace restriction, but only reduced-round variants of Simon and Speck are successfully attacked. Although a text-based key is applied, the proposed DL-based cryptanalysis can successfully break the full rounds of Simon32/64 and Speck32/64. The results indicate that the DL technology can be a useful tool for the cryptanalysis of block ciphers when the keyspace is restricted.

No abstract

No abstract

As the number of sensing nodes increases, cooperative spectrum sensing increases the detection probability of primary users at the cost of increased bandwidth of feedback channels. However, the excess feedback overhead of reporting sensing results may degrade the channel utilization of cognitive radio networks. This letter proposes cooperative spectrum sensing with a fixed number of feedback channels, where each sensing node opportunistically reports its sensing result to a fusion center via shared feedback channels only if its sensing result is greater than a threshold. The simulation results show that the proposed cooperative spectrum sensing increases the channel utilization with limited feedback overhead compared with the conventional cooperative spectrum sensing.

In this paper, we consider a multiple-input multiple-output (MIMO)—non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).

No abstract

No abstract

In this paper, we consider an underlay cognitive radio network where the spectrum is shared with the primary network. Due to the coexistence of primary and secondary networks, primary users (PUs) are interfered with by the inter-network interference, at the same time secondary users (SUs) counteract the intra-network (inter-user) interference. Based on the cooperative feedback between the primary network and the secondary network, the secondary transmitter (ST) applies the cognitive beamforming to suppress the interference to PUs while improving the sum rate of SUs. We herein propose an adaptive feedback bits allocation among multiple PUs and SUs where the quantized channel direction information (CDI) for the interference channel is forwarded to the ST in order to utilize the beamforming. Moreover, based on the cognitive beamforming, we adjust the transmit power of the ST under the constraint of the average interference at PUs. To jointly solve the feedback bits allocation and the transmit power control problems, we formulate an optimization problem which requires a little iterations compared with the separated feedback bits allocation and the transmit power control problems. Numerical results show that the proposed scheme significantly improves the sum rate of SUs while satisfying the average interference constraint at PUs.