Fast switching speed, low power consumption, and good stability are some of the important properties of spin transfer torque assisted voltage controlled magnetic anisotropy magnetic tunnel junction (STT-assisted VCMA-MTJ) which makes the non-volatile full adder (NV-FA) based on it attractive for Internet of Things. However, the effects of process variations on the performances of STT-assisted VCMA-MTJ and NV-FA will be more and more obvious with the downscaling of STT-assisted VCMA-MTJ and the improvement of chip integration. In this paper, a more accurate electrical model of STT-assisted VCMA-MTJ is established on the basis of the magnetization dynamics and the process variations in film growth process and etching process. In particular, the write voltage is reduced to 0.7 V as the film thickness is reduced to 0.9 nm. The effects of free layer thickness variation (γtf) and oxide layer thickness variation (γtox) on the state switching as well as the effect of tunnel magnetoresistance ratio variation (β) on the sensing margin (SM) are studied in detail. Considering that the above process variations follow Gaussian distribution, Monte Carlo simulation is used to study the effects of the process variations on the writing and output operations of NV-FA. The result shows that the state of STT-assisted VCMA-MTJ can be switched under -0.3%≤γtf≤6% or -23%≤γtox≤0.2%. SM is reduced by 16.0% with β increases from 0 to 30%. The error rates of writing ‘0’ in the NV-FA can be reduced by increasing Vb1 or increasing positive Vb2. The error rates of writing ‘1’ can be reduced by increasing Vb1 or decreasing negative Vb2. The reduction of the output error rates can be realized effectively by increasing the driving voltage (Vdd).

Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have received considerable interest recently. On space-time coding, some algorithms with perfect channel state information (CSI) have been proposed. In certain fast varying situation, however, it may be difficult to estimate the channel accurately and it is natural to study the blind detection algorithm without CSI. In this paper, based on subspace, a new blind detection algorithm without CSI is proposed. Using singular value decomposition (SVD) on output signal, noise subspace and signal subspace, which keep orthogonal to each other, are obtained. By searching the intersection of the signal subspace and the limited symbol vector set, symbol detection is achieved. The simulations illustrate that the proposed algorithm significantly improves system performance by receiving more output signals relative to transmit symbols. Furthermore, the presented algorithm is robust to the fading channel that changes between two successive blocks.

For the performance deficiency of the pilot symbol aided channel estimation in orthogonal frequency division multiplexing (OFDM) systems, the wavelets network interpolation channel estimator is proposed. By contrast with conventional methods, wavelets network interpolation channel estimator can guarantee the high transmission rate and lower Bit error rates (BER). Computer simulation results demonstrate that the proposed channel estimation method exhibit an improved performance compared to the conventional linear channel estimation methods and is robust to fading rate, especially in fast fading channels.

This paper investigates the resource allocation problem for the downlink of non-orthogonal multiple access (NOMA) networks. A novel resource allocation method is proposed to deal with the problem of maximizing the system capacity while taking into account user fairness. Since the optimization problem is nonconvex and intractable, we adopt the idea of step-by-step optimization, decomposing it into user pairing, subchannel and power allocation subproblems. First, all users are paired according to their different channel gains. Then, the subchannel allocation is executed by the proposed subchannel selection algorithm (SSA) based on channel priority. Once the subchannel allocation is fixed, to further improve the system capacity, the subchannel power allocation is implemented by the successive convex approximation (SCA) approach where the nonconvex optimization problem is transformed into the approximated convex optimization problem in each iteration. To ensure user fairness, the upper and lower bounds of the power allocation coefficients are derived and combined by introducing the tuning coefficients. The power allocation coefficients are dynamically adjustable by adjusting the tuning coefficients, thus the diversified quality of service (QoS) requirements can be satisfied. Finally, simulation results demonstrate the superiority of the proposed method over the existing methods in terms of system performance, furthermore, a good tradeoff between the system capacity and user fairness can be achieved.

Ad hoc networks are becoming an interesting research area, as they inherently support unique network applications for the wireless communications in a rugged environment, which requires rapid deployment and is difficult to be provided by an infrastructure network. Many issues need to be addressed for the ad hoc networks. In this paper, we propose an efficient distributed coordination function on the media access control protocol to enhance the power conservation of mobile hosts by using a power control algorithm and the network throughput of an ad hoc network by using an algorithm for simultaneous frame transmissions. Extensive simulation is studied to evaluate the improvement of the proposed method. The results of the simulation exhibit significant improvement to the standard access control protocol. With slight improvement of network throughput, up to 85% of the consumed energy was able to be saved in compared to the standard protocol and up to 7 times of the energy efficiency was enhanced with the proposed method.

Survivable virtual network embedding (SVNE) is one of major challenges of network virtualization. In order to improve the utilization rate of the substrate network (SN) resources with virtual network (VN) topology connectivity guarantee under link failure in SN, we first establishes an Integer Linear Programming (ILP) model for that under SN supports path splitting. Then we designs a novel survivable VN topology protection method based on particle swarm optimization (VNE-PSO), which redefines the parameters and related operations of particles with the embedding overhead as the fitness function. Simulation results show that the solution significantly improves the long-term average revenue of the SN, the acceptance rate of VN requests, and reduces the embedding time compared with the existing research results.

Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially in the Internet of Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which is capable of resisting simple power analysis attacks (SPA) and can be configured to support all of point operations and modular operations on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) is the most complex and time-consuming operation of ECC, while modular multiplication (MM) and modular division (MD) have high computational complexity among modular operations. For decreasing power dissipation and increasing reconfigurable capability, a Reconfigurable Modular Multiplication Algorithm and Reconfigurable Modular Division Algorithm are proposed, and MM and MD are implemented by two adder units. Combining with the optimization of operation scheduling of PM, on 55 nm CMOS ASIC platform, the proposed architecture takes 0.96, 1.37, 1.87, 2.44 ms and consumes 8.29, 11.86, 16.20, 21.13 uJ to perform one PM on 160-bit, 192-bit, 224-bit, 256-bit field orders. It occupies 56.03 k gate area and has a power of 8.66 mW. The implementation results demonstrate that the proposed architecture outperforms the other contemporary designs reported in the literature in terms of area and configurability.

To overcome the false lock or detection missing problems caused by the multiple peaks of the auto-correlation function (ACF) of Binary Offset Carrier (BOC) modulated signal, an acquisition algorithm based on unit correlation for BOC(n,n) signal is proposed in this paper. The local BOC signal is separated into two unit signals, an odd one and an even one. Then a reconstruction of the unit correlation functions between the unit signals and the received BOC signal is performed and M sections of reconstructed correlation function are accumulated according to the non-coherent method, so that this novel acquisition algorithm can not only eliminate the multiple secondary peaks, but also retain the advantage of the narrow correlation main peak. Simulation results show that the acquisition sensitivity of the proposed algorithm is increased 3dBHz compared with the ASPeCT method, and the computation cost is only 41.46% of the ASPeCT method when M=2.

Multi-rate capabilities are supported by the physical layers of most 802.11 devices. To enhance the network throughput of MANETs, transfer rate adaptation schemes at MAC layer should employ the multi-rate capability at physical and the information of previous transmissions provided by MAC and physical layers. In this paper, we propose a transfer rate adaptation scheme plus back-to-back frame transmissions, and fragmentation at MAC layer, named TRAF. TRAF adopts a bi-direction-based approach with an extended option to select an appropriate rate for frame transmission under fast changing channel conditions. Consecutive back-to-back frame transmissions to fully utilize good channel quality during a coherent time interval and fragmentation algorithm to maintain high throughput under worse channel conditions are recommended in TRAF. Extensive simulation is experimented to evaluate the performance of TRAF. Regarding simulation results, frame delivery ratio, network throughput, and fairness of TRAF are significantly improved by comparing to that of fix rate, ARF, RBAR, OAR, and AAR protocols.

How to restore virtual network against substrate network failure (e.g. link cut) is one of the key challenges of network virtualization. The traditional virtual network recovery (VNR) methods are mostly based on the idea of centralized control. However, if multiple virtual networks fail at the same time, their recovery processes are usually queued according to a specific priority, which may increase the average waiting time of users. In this letter, we study distributed virtual network recovery (DVNR) method to improve the virtual network recovery efficiency. We establish exclusive virtual machine (VM) for each virtual network and process recovery requests of multiple virtual networks in parallel. Simulation results show that the proposed DVNR method can obtain recovery success rate closely to centralized VNR method while yield ~70% less average recovery time.

The multi-step prediction model based on partial least squares (PLS) is established to predict short-term load series with high embedding dimension in this paper, which refrains from cumulative error with local single-step linear model, and can cope with the multi-collinearity in the reconstructed phase space. In the model, PLS is used to model the dynamic evolution between the phase points and the corresponding future points. With research on the PLS theory, the model algorithm is put forward. Finally, the actual load series are used to test this model, and the results show that the model plays well in chaotic time series prediction, even if the embedding dimension is selected a big value.

When new users join a recommender system, traditional approaches encounter challenges in accurately understanding their interests due to the absence of historical user behavior data, thus making it difficult to provide personalized recommendations. Currently, two main methods are employed to address this issue from different perspectives. One approach is centered on meta-learning, enabling models to adapt faster to new tasks by sharing knowledge and experiences across multiple tasks. However, these methods often overlook potential improvements based on cross-domain information. The other method involves cross-domain recommender systems, which transfer learned knowledge to different domains using shared models and transfer learning techniques. Nonetheless, this approach has certain limitations, as it necessitates a substantial amount of labeled data for training and may not accurately capture users’ latent preferences when dealing with a limited number of samples. Therefore, a crucial need arises to devise a novel method that amalgamates cross-domain information and latent preference extraction to address this challenge. To accomplish this objective, we propose a Cross-domain Recommender System based on Domain Knowledge Transferor and Latent Preference Extractor (TECDR).  In TECDR, we have designed a Latent Preference Extractor that transforms user behaviors into representations of their latent interests in items. Additionally, we have introduced a Domain Knowledge Transfer mechanism for transferring knowledge and patterns between domains. Moreover, we leverage meta-learning-based optimization methods to assist the model in adapting to new tasks. The experimental results from three cross-domain scenarios demonstrate that TECDR exhibits outstanding performance across various cross-domain recommender scenarios.