Space-Time Codes Technology

Space-time codes technology is a channel coding for wireless digital communications, where multiple antennas are employed. It improves the capacity of the transmission as well as reducing errors. Also, this technology does not require the expansion of bandwidth or time slots. In order to achieve the highest efficiency, we have to first investigate the maximum efficiency that can be achieved. Then, the code design criteria for obtaining the maximum efficiency have to be derived. Last, the code design approaches have to be proposed. The article discusses those procedures

Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization

International audienceIn digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal to noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed, and reduced complexity variations, including stopping criteria and sequential implementation, are developed

Optimal Data Transfer of SEH-WSN Node via MDP Based on Duty Cycle and Battery Energy

Applications in wireless sensor networks (WSNs) are rapidly spreading out over the world. The one critical point of WSNs is energy consumption, where the transmitted data is limited by battery energy. Solar energy is used to handle the depletion of the battery energy via photo voltaic (PV) panels. A solar energy harvesting WSN (SEH-WSN) node utilizes exponential decision-dynamic duty cycle scheduling based on prospective increase in energy (ED-DSP) to save battery energy by adjusting the duty cycle from an exponential curve and future solar energy. To estimate the prospective solar energy, a prediction technique is applied, but does not guarantee 100% accuracy. Hence, this paper proposes a Markov Decision Process (MDP) to schedule a duty cycle of an SEH-WSN node instead of the ED-DSP depending on the predicted energy. We evaluate its performance via MATLAB simulations with simple irradiance models and real annual irradiance data. The results show that the MDP policy outperforms the ED-DSP

Spectrum Occupancy Model Based on Empirical Data for FM Radio Broadcasting in Suburban Environments

It is well-known that the analog FM radio channels in suburban areas are underutilized. Before reallocating the unused channels for other applications, a regulator must analyze the spectrum occupancy. Many researchers proposed the spectrum occupancy models to find vacant spectrum. However, the existing models do not analyze each channel individually. This paper proposes an approach consisting (i) a spectrum measurement strategy, (ii) an appropriate decision threshold, and (iii) criteria for channel classification, to find the unused channels. The measurement strategy monitors each channel’s activity by capturing the power levels of the passband and the guardband separately. The decision threshold is selected depending on the monitored channel’s activity. The criteria classifies the channels based on the passband’s and guardband’s duty cycles. The results show that the proposed channel classification can identify 42 unused channels. If the power levels of wholebands (existing model) were analyzed instead of passband’s and guardband’s duty cycles, only 24 unoccupied channels were found. Furthermore, we propose the interference criteria, based on relative duty cycles across channels, to classify the abnormally used channels into interference sources and interference sinks, which have 16 and 15 channels, respectively. This information helps the dynamic spectrum sharing avoid or mitigate the interferences

Simultaneous Wireless Information and Power Transfer in Multi-User OFDMA Networks with Physical Secrecy

This paper considers simultaneous wireless information and power transfer (SWIPT) from a base station to multiple Internet of Things (IoT) nodes via orthogonal frequency-division multiple access (OFDMA), where every node can eavesdrop on the subcarriers allocated to other nodes. Application layer encryption is unsuitable for IoT nodes relying on energy harvesting, and physical layer secrecy should be deployed. The different channels among users on every subcarrier can be exploited to obtain physical layer secrecy without using artificial noise. We propose an algorithm to maximize the secrecy rate of IoT nodes by jointly optimizing the power splitting ratio and subcarrier allocation. For fairness, the lowest total secrecy rate among users is maximized. Through simulations, the proposed algorithm is compared with the minimum effort approach, which allocates each subcarrier to the strongest node and selects the minimum sufficient power splitting ratio. The obtained secrecy rate is 3 times (4.5 over 1.5 bps/Hz) higher than that of the minimum effort approach in every case of parameters: the base station’s transmit power, the minimum harvested energy requirement of an IoT node and the energy harvesting efficiency

Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization

In digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal-to-noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed and reduced complexity variations, including stopping criteria and sequential implementation, are developed