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Presents information and current topics of interest to the global communications industry. [ABSTRACT FROM AUTHOR]

In wireless power transfer (WPT)-assisted relaying systems, spectral efficiency (SE) of source-relay link plays a dominant role in system SE performance due to the limited transmission power at the WPT-aided relay. In this paper, we propose a novel protocol for a downlink orthogonal frequency division multiple access (OFDMA) system with a WPT-aided relay operating in full-duplex (FD) decode-and-forward (DF) mode, where the time slot durations of the source-relay and relay-users hops are designed to be dynamic, to enhance the utilization of degrees of freedom and hence the system SE. In particular, a multiple-input and signal-output (MISO) source-relay channel is considered to satisfy the stringent sensitivity of the energy harvesting (EH) circuit at the relay, while a single-input and single-output (SISO) relay-user channel is considered to alleviate the power consumption at the relay node. Taking into account the non-linearity of EH efficiency, a near-optimal iteration-based dynamic WPT-aided FD relaying (A-FR) algorithm is developed by jointly optimizing the time slot durations, subcarriers, and transmission power at the source and the relay. Furthermore, self-interference generated at the relay is utilized as a vital energy source rather than being canceled, which increases substantially the total energy harvested at the FD relay. We also reveal some implicit characteristics of the considered WPT-aided FD relaying system through intensive discussions. Simulation results confirm that the proposed A-FR achieves a significant enhancement in terms of SE with different relay's locations and the number of users, compared to the conventional symmetric WPT-aided FD relaying (S-FR) and the time-switching-based WPT-aided FD relaying (TS-FR) benchmarks. [ABSTRACT FROM AUTHOR]

We propose a bidirectional distributed antenna (DA) system where DAs are capable of working dynamically in hybrid duplex modes: full duplex (FD), half duplex, and sleep, which enables higher degree of freedom and hence much higher energy efficiency (EE) than DA sole-FD systems, with just marginal loss in spectral efficiency (SE). The proposed system also demonstrates significant EE and SE enhancement over FD colocated antenna systems. Compared to the above two benchmark systems, the proposed system also requires much simpler self-interference cancelation design for FD mode due to less cross talks between antennas. A low-complexity EE maximization scheme is proposed for the bidirectional DA system. A channel gain based DA clustering algorithm is first performed to activate/deactivate transmit/receive chains, which highlights the characteristics of DA deployment, and then a distributed hybrid duplexing algorithm is performed to optimize the downlink beamformer and the uplink transmission power. Various practical aspects are taken into account for system design, such as self-interference at DAs in FD mode, cochannel interference from uplink users to downlink users, and multiuser interference in both uplink and downlink. The effectiveness of the proposed system is verified by simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the power allocation issue for cooperative-sensing-based code-division multiple access (CDMA) cognitive radio (CR) networks. We consider a network consisting of multiple secondary users (SUs) and a secondary base station (BS) implementing a two-phase protocol. In the first phase, censor-based cooperative spectrum sensing is carried out to detect the PU's presence. When the channel is estimated to be free, SUs transmit data in the uplink to the BS in the second phase by using CDMA. We optimize the sensing parameters and transmit power of SUs jointly to minimize the total energy consumption with the constraints on SUs' quality of service (QoS) and detection probability of the PU. This is a nonconvex problem, which we represent as a monotonic optimization problem and solve by means of monotonic programming. Furthermore, we study the separate optimization problem in which sensing parameters and transmit power are optimized to minimize the energy consumption of the first phase and the second phase disjointly. Numerical results show that the proposed joint optimization method saves energy consumption significantly in lower signal-to-noise ratios (SNRs). It can introduce as high as 3.9 and 8.7 dB joule energy saving per time slot in zero-sensing SNR under the same data rate constraints of 0.5 and 1 bit/s/Hz, respectively. [ABSTRACT FROM PUBLISHER]

We proposed two methods for the localization of drone controllers based on received signal strength indicator (RSSI) ratios: the RSSI ratio fingerprint method and the model-based RSSI ratio algorithm. To evaluate the performance of our proposed algorithms, we conducted both simulations and field trials. The simulation results show that our two proposed RSSI-ratio-based localization methods outperformed the distance mapping algorithm proposed in literature when tested in a WLAN channel. Moreover, increasing the number of sensors improved the localization performance. Averaging a number of RSSI ratio samples also improved the performance in propagation channels that did not exhibit location-dependent fading effects. However, in channels with location-dependent fading effects, averaging a number of RSSI ratio samples did not significantly improve the localization performance. Additionally, reducing the grid size improved the performance in channels with small shadowing factor values, but this only resulted in marginal gains in channels with larger shadowing factors. Our field trial results align with the simulation results in a two-ray ground reflection (TRGR) channel. Our methods provide a robust and effective solution for the localization of drone controllers using RSSI ratios. [ABSTRACT FROM AUTHOR]

Resource allocation in orthogonal frequency division multiple access (OFDMA) relay cellular networks (RCN) has been investigated. We introduce an orthogonal frequency-and-time transmission (OFTT) protocol, in which orthogonal frequency and time resources are allocated to different communication modes and phases, respectively, and propose a simple algorithm for resource allocation. Communication modes (one- and two-hop modes), subchannels, and relay transmit power are sequentially allocated to enhance the power efficiency of the OFDMA RCN. We show an achievable quality-of-service tradeoff between one- and two-hop users. Furthermore, we show that the relays consume proportional power to their own second hop channel gains, whereas a single selected relay uses its full available power. Network power and system throughput are evaluated to confirm that the proposed OFTT protocol with the sequential resource allocation is power efficient in OFDMA RCN. [ABSTRACT FROM AUTHOR]

This paper studies the problem of signal detection for multiple-input multiple-output (MIMO) channels with large signal dimensions. We propose a block-iterative generalized decision feedback equalization (BI-GDFE) receiver to recover the transmitted symbols in a block-iterative manner. By exploiting the input-decision correlation, a measure for the reliability of the earlier-made decisions, we design the feed-forward equalizers (FFEs) and feedback equalizers (FBEs) in such a way that maximized signal-to-interference-plus-noise ratio (SINR) is achieved for each of the iterations. Novel implementations are also introduced to simplify the complexity of the receiver, which requires only one-tap filters for FFE and FBE. The proposed receiver also works when the signal dimension is greater than the observation dimension. The asymptotic performance of the proposed receiver is analyzed and its convergence has been confirmed through numerical evaluations for various parameters. Computer simulations are presented to illustrate the capability of the proposed receiver to achieve single user matched-filter bound (MFB) for large random MIMO channels when the received SNR is high enough. [ABSTRACT FROM AUTHOR]

In this article, we study the radar angular resolution, the capability of distinguishing multiple targets in different directions of arrivals (DoA). We present a deep learning-based super resolution DoA estimator for multiple input multiple output (MIMO) radar with single snapshot data. The estimator consists of a deep learning DoA classifier (DLDC) in the central bearing angle zone, which can simultaneously detect up to 11 targets in $[ { - 2^\circ,\ 2^\circ } ]$ with 32 virtual antenna elements, and a spatial filtering pre-rotator (SFPR) that makes the DLDC supporting any bearing angle location in a wider radar field of view (FoV). We present the DLDC-SFPR structure and provide detailed parameter setting. In the performance evaluation, the resolution of proposed method is first compared with the theoretical angular resolution limit (ARL). It is shown that the proposed estimator reaches the Chernoff ARL. The probability of resolution (PoR) is then investigated and compared with typical and state-of-the-art DoA estimators in various conditions. The numerical results show that the DLDC-SFPR achieves 0.4° resolution with 90% PoR to distinguish 2 targets at signal to noise ratio (SNR) of 7 dB if the targets are on-gird. In the case of any-angle, the same resolution and probability are achieved at SNR = 12 dB. The proposed method is robust to multi-target concurrent cases, and outperforms the existing reported approaches. Two procedures, uniform scanning and progressive scanning, are proposed for wider FoV applications. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel unified framework to protect multi-function wireless systems from jamming attacks. Examples of such multi-function system include joint radar and communication (JRC) systems and simultaneous wireless information and power transfer (SWIPT) systems. By abstracting the system functionalities as a joint optimization problem of multiple queues, we achieve effective resistance against jammers for the multi-functions simultaneously. We incorporate different anti-jamming techniques into one framework. Deception mechanism is adopted to lure the jammer to attack and make its actions more predictable, and ambient backscatter technology is used to leverage the jamming signals. Since conventional Markov decision process (MDP) has only one decision epoch at every time slot, it cannot be used to model the deception strategy which needs two decision epochs to leverage the jamming signals. We therefore formulate the problem using an advanced two-step MDP. After that, a deep reinforcement learning algorithm with a prioritized double deep Q-Learning architecture is proposed to learn optimal strategies in different system states. We show that by jointly considering the multi-functions of the system with potential jamming attacks during design phase, significant improvement can be achieved for both of the system functionalities. [ABSTRACT FROM AUTHOR]

• SnS 2 with different morphology are synthesized by adjusting additives. • Photocatalytic reduction of CO 2 to CH 4 using SnS 2 is first reported. • Flower-like morphology benefits rapid transfer and aggregation of electrons. • Flower-like morphology favors the process of photocatalytic reduction of CO 2. • The CH 4 yield of flower-like SnS 2 is 5.7 times higher than that of tablet-like SnS 2. Artificial photosynthesis has attracted a lot of attention because it can tackle both global environmental problems and energy crisis. In this paper, SnS 2 with different morphologies were synthesized to study their activity and selectivity of photocatalytic reduction of carbon dioxide (CO 2). The size of tablet-like SnS 2 is around 80–120 nm while the flower-like SnS 2 is composed of nanosheets with a thickness of 10 nm. The reduction products of the as-obtained samples are both CO and CH 4. The flower-like SnS 2 sample processes more efficacious separation of photogenerated carriers compared to tablet-like SnS 2 and shows higher photocatalytic reduction efficiency with CH 4 yield of 97.5 μmol g−1, which is approximately 5.7 times higher than that of tablet-like SnS 2 , while the tablet-like SnS 2 shows high selectivity (79%) for CO production. The results reveal that the morphology plays an important role in the activity and selectivity of photocatalytic reduction of CO 2 over SnS 2. [ABSTRACT FROM AUTHOR]

In recent years, software defined radio and digital signal processing have been widely used in communication and radar. As a result, the hardware and RF front-end for radar and wireless communication tends to be similar. Thus, using the same RF and hardware platform for joint radar-communication becomes viable. Joint radar-communication would bring more efficient plan and usage for the radio spectral resource. Furthermore, it could enable new applications that require information exchange and precise localization at the same time. In this paper, cyclic prefixed single carrier (CP-SC) and its variations are chosen as the waveforms for joint radar-communication. CP-SC waveform and its variations are popular in wireless communication and have been chosen by a few standards like IEEE 802.11ad and LTE-advanced. Efficient algorithms are proposed to use such waveforms for range and speed detection/estimation of targets. The proposed algorithms are derived from the maximum likelihood (ML) principle and have low computational complexity. Simulations show that the estimation performance of the proposed method is almost the same as that of ML and is much better than that of the channel estimation based method. [ABSTRACT FROM AUTHOR]

The full-duplex (FD) cooperative nonorthogonal multiple access (NOMA) achieves a superior throughput over the conventional half-duplex (HD) cooperative NOMA, wherein the strong users (SUs) with good channel conditions can act as an FD relay node for the weak users with poor channel conditions. However, the energy efficiency (EE) of a cooperative NOMA may be degraded due to the additional power consumption incurred at the SUs. We are, therefore, motivated to investigate the EE maximization problem of an FD cooperative NOMA system. More importantly, we investigate the “signal-to-inference-noise ratio gap reversal” problem of cooperative NOMA systems, which imposes successive interference cancellation (SIC) performance degradation at the SUs. This problem has not been documented in the existing cooperative NOMA literature. A low-complexity algorithm is proposed for maximizing the system's EE while guaranteeing a successful SIC operation. Our numerical results show that the proposed algorithm achieves both a higher EE and throughput over the existing HD cooperative NOMA and nonadaptive FD cooperative NOMA. More importantly, the proposed scheme guarantees a successful SIC operation at the SUs. [ABSTRACT FROM AUTHOR]

In this study, we investigate distributed antenna systems (DASs) with partial channel state information (CSI). In the DAS considered here, one control unit coordinates multiple baseband units (BBUs), and each BBU with multiple distributed antennas (DAs) knows only the direct channels from its DAs to its associated users and supports them simultaneously. For fairness among users, a distributed precoder structure is designed to maximize the upper bound of the minimum user rate, under the assumption that the direct channel dominates the intra-BBU interfering links. We then propose a distributed general precoder that allows multiple BBUs to serve multiple users simultaneously. Numerical results verify that, in terms of the minimum and average achievable rates, the proposed scheme significantly outperforms an existing scheme in which each BBU supports a single user. The results also demonstrate that the proposed scheme is comparable to a full-CSI based scheme. [ABSTRACT FROM PUBLISHER]

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate. [ABSTRACT FROM AUTHOR]

In this paper, we study a novel reconfigurable intelligent surface (RIS) enabled symbiotic radio system, where a RIS is used to enhance the communication between the primary transmitter (PTx) and the primary receiver (PRx), and concurrently transmit its information (e.g., environmental monitoring information) to the PRx by varying the phase shifts. The objective is to cooperatively optimize the active transmit beamforming at the PTx and passive reflecting beamforming at the RIS to minimize the PTx's transmit power, subject to the signal-to-noise ratio constraints of primary and RIS transmissions. A new optimization problem is formulated where the RIS phase shifts are not only related to the channel state information (CSI), but also related to its message. First, we consider the perfect CSI setup to draw useful insights into the cooperative beamforming design between the PTx and RIS. Then, the worst-case robust beamforming design is carried out under the imperfect CSI setup. In particular, we take into account the imperfect successive interference cancellation at the PRx. Finally, simulation results show the effectiveness of the RIS information transfer and the integration of RIS into a symbiotic radio system can significantly improve the performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a battery-aided demand response strategy to minimize the energy expenditure incurred by grid-tied systems such as domestic and industrial loads. Unlike existing algorithms in the literature, our proposed strategy takes into account the non-linear behavior of the rechargeable battery. In addition, continuous-time block tariffing is adopted as a pricing strategy that generalizes current schemes such as time-of-use and consumption-based block pricing. To find the optimal demand response strategy, we formulate a nonconvex optimization problem, for which we obtain an approximate solution by introducing linearization and discretization in time. To gain further insight, we derive an analytical solution by introducing some simplifying approximations that allow us to use calculus of variations to obtain closed-form analytical results for the optimal charging and discharging schedules. Through simulations, we show that the strategy based on calculus of variations is able to achieve better performance and incur smaller computational costs than the solution based on discretisation in time. Finally, we discuss how the energy expenditure is related to pricing parameters, and specifications of the battery such as size and Peukert exponent. [ABSTRACT FROM AUTHOR]

In this paper, we investigate unmanned aerial vehicle (UAV) assisted communication systems that require quasi-balanced data rates in uplink (UL) and downlink (DL), as well as users’ heterogeneous traffic. To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements. A hybrid-mode multiple access (HMMA) scheme is proposed toward heterogeneous traffic, where non-orthogonal multiple access (NOMA) targets high average data rate, while orthogonal multiple access (OMA) aims to meet users’ instantaneous rate demands by compensating for their rates. HMMA enables a higher degree of freedom in multiple access and achieves a superior minimum average rate among users than the UAV assisted NOMA or OMA schemes. Under HMMA, a joint UL-DL resource allocation algorithm is proposed with a closed-form optimal solution for UL/DL power allocation to achieve quasi-balanced average rates for UL and DL. Furthermore, considering the error propagation in successive interference cancellation (SIC) of NOMA, an enhanced-HMMA scheme is proposed, which demonstrates high robustness against SIC error and a higher minimum average rate than the HMMA scheme. [ABSTRACT FROM AUTHOR]

The rate maximization for the $K$-user interference channels (ICs) has been investigated extensively in the literature. However, the practical problem of minimizing the error probability with given signal modulations and/or data rates of the users is less studied. In this paper, by utilizing additional degrees of freedom from the improper signaling (versus the conventional proper signaling) , we seek to optimize the precoding matrices for the $K$-user single-input single-output (SISO) ICs to minimize pair-wise error probability (PEP) and symbol error rate (SER) with two proposed algorithms, respectively. Compared with conventional proper signaling and other state-of-the-art improper signaling designs, our proposed improper signaling schemes achieve notable error rate improvement in SISO-ICs under both the additive white Gaussian noise (AWGN) and cellular system setups with or without channel coding. Our study provides another viewpoint for optimizing transmissions in ICs and further justifies the practical benefit of improper signaling in interference-limited communication systems. [ABSTRACT FROM PUBLISHER]

Due to the scarcity of channel availability in IEEE 802.11p standard, applications related to infotainment and multimedia services are vulnerable to suffer from delays and connectivity. Cognitive radio (CR) has the potential to overcome the issue of spectrum scarcity. By making use of the CR technology, secondary users (SU) can access a radio frequency spectrum other than 802.11p through spectrum sensing. Majority of low complex existing algorithms are based on energy detection that are considering the static environment of SUs. In practice, the algorithms, which considers mobility are mainly focused on low vehicle speed. However, the combined effect of primary user (PU) activity statistics and SU mobility has not been tackled while evaluating the performance of opportunistic spectrum access. In particular, this work considers the joint impact of PU activity and vehicle mobility on spectrum sensing performance by considering single lane and double lane vehicular scenarios. We also evaluate the impact of traffic densities on sensing performance. The main contribution of this work is to show the impact of PU activity statistics on sensing performance by deriving analytical expressions for miss-detection probability. These expressions are used for analysis and simulation of cognitive radio enabled vehicular communications. From our observations, it is noted that the probability of vehicle connectivity is improved approximately by 50 $\%$ for initial distance of SU when spectrum sensing is employed under PU activity. [ABSTRACT FROM AUTHOR]

Both information age and delay are critical performance metrics of emerging time-sensitive applications. However, their inherent correlation in the finite block length (FBL) regime has remained uninvestigated as it is affected by block length and update rate in a complex manner. In this paper, closed-form expressions for average age of information (AoI), peak AoI (PAoI) and delay are derived for an FBL Last-Come First-Served system with retransmission and non-preemption policies, based on which a comprehensive analysis of the relationship among the three metrics in the FBL regime is presented. It is proved that there exists a strong tradeoff between delay and AoI/PAoI given a block length, and that AoI, PAoI and delay have positive correlation given an update rate, regardless of the weight. With the goal of minimizing delay and AoI simultaneously, the weighted sum of delay and PAoI (upper bound on AoI) is formulated and proved to be convex with respect to block length and update rate. A low-complexity optimization algorithm is developed with a closed-form expression of the optimal update rate, whose performance approaches the Pareto boundaries of the PAoI-delay and the AoI-delay regions, at much lower complexity than exhaustive search. [ABSTRACT FROM AUTHOR]

In this paper, we propose joint cooperative-transmit/receive frequency-domain equalization (cooperative-Tx/Rx FDE) with incremental relaying (IR) for broadband single-carrier (SC) transmission under the total and individual transmit power constraint at a source node (\ssr S) and a relay node (\ssr R). We derive the optimum cooperative-Tx/Rx FDE weights to minimize the mean square error (MSE) after packet combining at a destination node (\ssr D). We show that the optimum cooperative-Tx FDE weights allocate the transmit power in the minimum MSE (MMSE) sense in the frequency domain and in the maximal ratio transmission (MRT) sense in the spatial domain. To relax the condition that the complete channel state information (CSI) needs to be shared among all nodes, selection-based suboptimum cooperative-Tx FDE weights are derived. Computer simulation verifies the effectiveness of the proposed schemes and shows that the 5%-outage throughput of the system can be increased by around 30% over the conventional IR with only Rx FDE. [ABSTRACT FROM PUBLISHER]

In unmanned aerial vehicle (UAV)-assisted networks, UAV acts as an aerial base station which acquires the requested data via backhaul link and then serves ground users (GUs) through an access network. In this paper, we investigate an energy minimization problem with a limited power supply for both backhaul and access links. The difficulties for solving such a non-convex and combinatorial problem lie at the high computational complexity/time. In solution development, we consider the approaches from both actor-critic deep reinforcement learning (AC-DRL) and optimization perspectives. First, two offline non-learning algorithms, i.e., an optimal and a heuristic algorithms, based on piecewise linear approximation and relaxation are developed as benchmarks. Second, toward real-time decision-making, we improve the conventional AC-DRL and propose two learning schemes: AC-based user group scheduling and backhaul power allocation (ACGP), and joint AC-based user group scheduling and optimization-based backhaul power allocation (ACGOP). Numerical results show that the computation time of both ACGP and ACGOP is reduced tenfold to hundredfold compared to the offline approaches, and ACGOP is better than ACGP in energy savings. The results also verify the superiority of proposed learning solutions in terms of guaranteeing the feasibility and minimizing the system energy compared to the conventional AC-DRL. [ABSTRACT FROM AUTHOR]

In contrast with the traditional cryptography, physical layer security has attracted the attention of many researchers having aim at reinforcing the security of communication systems. As far as vehicular communication is concerned, it is challenging to maintain secure and reliable communication between the connected vehicles due to the density, mobility, and dynamic network topology. This paper considers a vehicle to infrastructure communication in which a legitimately fixed transmitter equipped with a single antenna transmits a confidential message to a legitimate mobile receiver equipped with multiple antennas in the presence of a passive mobile eavesdropper. In such a single input multiple output wireless system, the receiver performs the maximal ratio combining technique assuming constant vehicle speed. We assume that the antennas are closely spaced and depending upon the imperfect channel state information (CSI), we derive the closed-form expressions for the average outage probability, secrecy outage probability, and average secrecy outage rate over the uniform, exponential, and arbitrary correlated Nakagami- ${m}$ channels for dual antenna branches. In order to gain insight we also perform the high SNR asymptotic analysis of the outage probability and secrecy outage probability. Simulations are conducted to validate the accuracy of our derived analytic expressions. The computation error analysis is carried out to provide the suitability of the correlation type at the legitimate receiver side. Our findings suggest that the performance of the case with exponential channel correlation is better than those for the uniform and arbitrary. Numerical results show the joint effect of vehicle mobility and the antenna correlation on secrecy performance. Moreover, we also observed that the imperfect knowledge of the CSI degrades the security of the confidential messages severely under the effect of mobility. [ABSTRACT FROM AUTHOR]

We consider a homogeneous cellular network where a multi-antenna base station (BS) in each cell transmits messages to its intended user over a common frequency band. To improve the system capacity of this multi-cell multi-input single-output (MISO) interference channel, one of the state-of-the-art algorithms, namely, downlink-beamforming coordination, allows all BSs to cooperate with one another to mitigate the effect of inter-cell interference. However, most existing algorithms are suboptimal and impractical in a dynamic wireless environment, due to the high computational complexity and the overhead involved in collecting global channel state information (CSI). In this study, we exploit deep reinforcement learning (DRL) and propose a distributed dynamic downlink-beamforming coordination (DDBC) method with partial observability of the CSI. Each BS is able to train its own deep Q-network and employs appropriate beamformer depending on its environment, which is observed through a designed limited-information exchange protocol. The simulation results show that the proposed DRL-based DDBC method, with a considerably lower system overhead, achieves a system capacity that is very close to that of the fractional programming algorithm with global and instantaneous CSI measurements. In addition, this work demonstrates the potential of utilizing DRL to solve DDBC problems in a more practical manner. [ABSTRACT FROM AUTHOR]

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge and prove that its optimality gap can be decreased at the cost of increment in its convergence time. Next, we consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that provides fairness and guarantees the required QoS of each E-R. Finally, we study a WPCN where the E-Rs utilize their received energy to transmit information to the E-AP. We maximize a generic fair network utility under the E-Rs’ QoS constraints and the E-AP’s power constraints. Numerical results show a significant improvement of $O(\log {N})$ in the total throughput compared to the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

We propose an iterative channel estimation algorithm based on the least square estimation (LSE) and sparse message passing (SMP) algorithm for the millimeter wave (mmWave) MIMO systems. The channel coefficients of the mmWave MIMO are approximately modeled as a Bernoulli–Gaussian distribution and the channel matrix is sparse with only a few nonzero entries. By leveraging the advantage of sparseness, we propose an algorithm that iteratively detects the exact locations and values of nonzero entries of the sparse channel matrix. At each iteration, the locations are detected by the SMP, and values are estimated with the LSE. We also analyze the Cramér–Rao Lower Bound (CLRB), and show that the proposed algorithm is a minimum variance unbiased estimator under the assumption that we have the partial priori knowledge of the channel. Furthermore, we employ the Gaussian approximation for message densities under density evolution to simplify the analysis of the algorithm, which provides a simple method to predict the performance of the proposed algorithm. Numerical experiments show that the proposed algorithm has much better performance than the existing sparse estimators, especially when the channel is sparse. In addition, our proposed algorithm converges to the CRLB of the genie-aided estimation of sparse channels with only five turbo iterations. [ABSTRACT FROM AUTHOR]

The current induced by an incident electric field can convert from the antenna mode to differential mode at unbalanced terminals of transmission lines (TLs), causing severe interference to a number of applications such as broadband power line communication, especially in the very high frequency (VHF) band. The previous work on modeling the current conversion is not applicable to VHF. We present a new model of the VHF current conversion which includes a general formula for the antenna mode characteristic impedance and two solutions to the formulated problem: 1) a numerical solution referred to as the antenna theory numerical (ATN) approach, which gives the exact value of the characteristic impedance; 2) an analytical solution referred to as the enhanced TL approximation (ETLA) approach, which gives the mean of the characteristic impedance. This is the first reported work to obtain the antenna mode characteristic impedance by the antenna theory. Furthermore, the ETLA approach outperforms the previous frequency-independent solution and requires a reduced complexity over the ATN approach. Our model is general as it converges to the previous model at lower frequencies. Simulation results also show the relationship between the antenna mode characteristic impedance and the power of the interference caused by the current conversion. [ABSTRACT FROM AUTHOR]

In this paper, the ergodic sum rate of a fading cognitive multiple-access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under such a constraint and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the ergodic sum rate of a fading C-MAC without successive interference cancelation (SIC). In particular, this paper considers two types of constraints: 1) average TPC and average IPC and 2) peak TPC and peak IPC. For the first case, it is proved that the optimal power allocation is dynamic time-division multiple access (D-TDMA), which is exactly the same as the optimal power allocation to maximize the ergodic sum rate of the fading C-MAC with SIC under the same constraints. For the second case, it is proved that the optimal solution must be at the extreme points of the feasible region. It is shown that D-TDMA is optimal with high probability when the number of SUs is large. Moreover, we show that when the SUs can be sorted in a certain order, an algorithm with linear complexity can be used to find the optimal power allocation. [ABSTRACT FROM PUBLISHER]

A statistical analysis of walking passerby's effects on narrowband wireless body area network (WBAN) in outdoor and indoor environments at 2.48 GHz is presented. Experiments were conducted to measure the dynamic channel responses of various on-body links with four sets of pre-defined passerby movements while the user remains stationary in a standing posture. On-body links that experienced fades greater than 20 dB from the static path gain were identified. The effects of passerby movement types, in both outdoor and indoor environments, were analyzed and compared to the effects of user's motions such as walking and running. Parameters for the Ricean distribution, which was found to be a good fit for channel gain variation, are presented to model the channel up to the second-order statistics, such as level crossing rate and average fade duration. The parameters allow the use of fading channel simulators to reproduce the passerby's effects in the on-body channels. [ABSTRACT FROM PUBLISHER]

Joint channel and rate allocation with power minimization in orthogonal frequency-division multiple access (OFDMA) has attracted extensive attention. Most of the research has dealt with the development of suboptimal but low-complexity algorithms. In this paper, the contributions comprise new insights from revisiting tractability aspects of computing the optimum solution. Previous complexity analyses have been limited by assumptions of fixed power on each subcarrier or power-rate functions that locally grow arbitrarily fast. The analysis under the former assumption does not generalize to problem tractability with variable power, whereas the latter assumption prohibits the result from being applicable to well-behaved power-rate functions. As the first contribution, we overcome the previous limitations by rigorously proving the problem's NP-hardness for the representative logarithmic rate function. Next, we extend the proof to reach a much stronger result, namely, that the problem remains NP-hard, even if the channels allocated to each user are restricted to be a consecutive block with given size. We also prove that, under these restrictions, there is a special case with polynomial-time tractability. Then, we treat the problem class where the channels can be partitioned into an arbitrarily large but constant number of groups, each having uniform gain for every individual user. For this problem class, we present a polynomial-time algorithm and provide its optimality guarantee. In addition, we prove that the recognition of this class is polynomial-time solvable. [ABSTRACT FROM PUBLISHER]

We introduce a generalized two-way relay channel where two sources exchange information (not necessarily of the same rate) with help from a relay, and each source additionally sends private information to the relay. We consider the Gaussian setting where all point-to-point links are Gaussian channels. For this channel, we consider a two-phase protocol consisting of a multiple access channel (MAC) phase and a broadcast channel (BC) phase. We propose a general decode-and-forward (DF) scheme where the MAC phase is related to computation over MAC, while the BC phase is related to BC with receiver side information. In the MAC phase, we time share a capacity-achieving code for the MAC and a superposition code with a lattice code as its component code. We show that the proposed DF scheme is near optimal for any channel conditions, in that it achieves rates within half bit of the capacity region of the two-phase protocol. [ABSTRACT FROM AUTHOR]

Cooperative communications have received considerable attention for wireless multimedia applications as a technique to improve reliability and service coverage. To apply such cooperation over slow fading channels, we consider exploiting the spatial diversity in multiple relay networks. In this paper, we focus on amplify-and-forward relaying schemes, namely, orthogonal amplify-and-forward, selective relaying, and distributed beamforming, for the cooperative wireless multimedia transmission. Furthermore, the successive refinement source coding is exploited such that the multimedia signal is encoded into multiple layers and the quality of its reconstruction at the receiver is improved when more layers are received correctly. We study two strategies for the layered source transmission, namely, progressive transmission and superposition coding. With our developed framework, we propose suboptimal resource allocation algorithms to efficiently assign rate, power, and channel uses to different layers so as to maximize the quality of the multimedia signal reconstructed at the receiver. The proposed optimization methodology is simple and only requires the knowledge of the channel statistics compared to the existing algorithms. As a result, the receiver only requires to feedback the determined rate, power, and channel uses to the transmitter whenever the channel statistics or the layered source transmission strategy changes. [ABSTRACT FROM PUBLISHER]