To account for nonstationarity, channel characterization and system design methods that employ the non-widesense stationary uncorrelated scattering (non-WSSUS) assumption are desirable. Furthermore, the inadequacy of the Doppler shift operator to properly account for the frequency shift in wideband channel implies that the timefrequency characterization methods that employ the Doppler shift operator are not appropriate for most wideband channels. In this article, the statistical time-scale domain characterization of the non-WSSUS wideband channel is presented. This approach employs the time scaling operator in order to account for frequency spreading, and also emphasizes on the nonstationarity of the wideband channel. The non-WSSUS statistical assumption termed localsense stationary uncorrelated scattering (LSSUS) is presented and employed in characterizing the nonstationary property of the time-varying wideband channel. The LSSUS channel model is then parameterized to provide useful coherence and stationarity/nonstationarity parameters for optimal system design. Some application relevance of the developed model in terms of channel capacity and diversity techniques are discussed. Measurement and simulation results show that the assumption of ergodic capacity and the performance of various diversity techniques depend on the degree of channel stationarity/nonstationarity. It is shown that the quantification of this degree of stationarity through the channel parameters can provide a way of tracking channel variation and allowing for adaptive application of diversity techniques and the channel capacity.

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Abstract-In this paper, we propose a new approach to generate quadrupling-frequency optical millimeter-wave (mm-wave) signal with carrier suppression by using two parallel Mach-Zehnder modulators (MZMs) in Radio-over-fiber (RoF) system. Among the numerous properties of this approach, the most important is that a filterless optical mm-wave at 60 GHz with an optical sideband suppression ratio (OSSR) as high as 40 dB can be obtained when the extinction ratio of the MZM is 25 dB. Simplicity and cost-effectiveness have made this approach a compelling candidate for future wave-division-multiplexing RoF systems.Theoretical analysis is conducted to suppress the undesired optical sidebands for the high-quality generation of frequency quadrupling mm-wave signal. The simulation results show that a 60 GHz mm-wave is generated from a 15 GHz radio frequency (RF) oscillator with an OSSR as high as 40 dB and an radio frequency spurious suppression ratio (RFSSR) exceeding 35 dB without any optical or electrical filter when the extinction ratio of the MZM is 25 dB. Furthermore, the effect of the non-ideal RF-driven voltage as well as the phase difference of RF-driven signals applied to the two MZMs on OSSR and RFSSR is discussed and analyzed. Finally, we establish a RoF system through simulation to verify the transmission performance of the proposed scheme. The Q-factor performance and eye patterns are given.

Device to Device (D2D) communication was first considered in out-band to manage energy issues in the wireless sensor networks. The primary target was to secure information about system topology for successive communication. Now the D2D communication has been legitimated in in-band by the 3rd Generation Partnership Project (3GPP). To initiate D2D communication, Device Discovery (DD) is a primary task and every D2D application benefits from DD as an end to end link maintenance and data relay when the direct path is obstructed. The DD is facing new difficulties because of the mobility of the devices over static systems, and the mobility makes it more challenging for D2D communication. For in-band D2D, DD in a single cell and multi-cell, and dense area is not legitimated properly, causing latency, inaccuracy, and energy consumption. Among extensive studies on limiting energy consumption and latency, DD is one of the essential parts concentrating on access and communication. In this paper, a comprehensive survey on DD challenges, for example single cell/multi-cell and dense area DD, energy consumption during discovery, discovery delay, and discovery security, etc., has been presented to accomplish an effective paradigm of D2D networks. In order to undertake the device (user) needs, an architecture has been projected, which promises to overwhelm the various implementation challenges of DD. The paper mainly focuses on DD taxonomy and classification with an emphasis on discovery procedures and algorithms, a summary of advances and issues, and ways for potential enhancements. For ensuring a secure DD and D2D, auspicious research directions have been proposed, based on taxonomy.

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices. The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections. Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes. This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices. The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form. The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes. The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.INDEX TERMS Internet-of-things, federated learning, energy consumption, edge nodes, central processing unit.