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Intelligent reflecting surface (IRS) has received widespread attention because it can significantly improve the performance of wireless networks. The X-duplex is a more flexible duplex mode, which can decrease a small sum rate in exchange for a higher quality-of-service performance. In this article, an IRS transmission scheme based on X-duplex is investigated to enhance the flexibility and freedom of communication design. We present the X-duplex IRS with the simultaneous wireless information and power transfer, and establish a power consumption model based on real application scenarios. An alternating optimization algorithm is proposed to maximize the energy efficiency (EE) of a multi-input single-output (MIMO) network assisted by the X-duplex IRS. As for the X-duplex relay, we explore an algorithm based on the golden section to optimize the EE. Simulation results comprehensively evaluate the performance of the two auxiliary transmission methods. The X-duplex IRS has higher EE and weighted sum rate (WSR) than the X-duplex relay when sufficient transmission power is available. However, the IRS cannot entirely replace the traditional relay. The X-duplex relay with self-interference energy harvesting achieves a higher WSR and EE when the access point energy is limited or the transmission distance is short.

In this article, the effects of multiantenna users and low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) on multigroup multicast downlink cell-free massive multi-input multi-output (MIMO) systems are explored. Leveraging on the distributed conjugate beamforming receiver and the generalized additive quantization noise model, we first derive a closed-form expression for the achievable sum spectral efficiency (SE). The resultant expression enables us to precisely quantify the effects of key parameters, such as the transmit power of access points (APs), the numbers of groups, users’ antennas, APs’ antennas, and quantization bits. Due to the joint influence of channel hardening property, multiantenna gain, and intergroup interference, the research becomes sophisticated particularly. The result shows that when the sum-SE is small, the multicast is better than the unicast and vice versa. Typically, it reveals that the full-resolution DAC can be replaced by 5-bit low-resolution DAC to achieve the same performance, and the additional antenna at users can effectively enhance the system performance. Then, according to a generated energy cost model, the formula of the energy efficiency (EE) is obtained. When the DAC quantization is equal to 2-bit, it can achieve the maximum sum EE. Finally, the simulation results are provided to verify the derived results.

In this paper, we mathematically analyze bit-error rate (BER) and diversity order of physical-layer network coding (PNC) scheme in the two-way relay channel (TWRC) under the assumption of multiple antennas at relay node. We assume the quadrature phase shift keying (QPSK) modulation for simple data transmission in IoT applications, and max–min transmit antenna selection (TAS) scheme in the broadcast (BC) phase for achieving full diversity gain. In particular, we obtain the upper bound of BER at the multiple access (MA) phase, while deriving the exact BER performance at the BC phase. To the best our knowledge, the mathematical analysis in this paper is the first analytical results in the literature. Through computer simulations, the analytical results are validated.

In this paper, we propose a time-synchronization method for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) systems. NC-OFDM is a modulation and multiplexing technique that is often used for cognitive radio (CR) systems. In OFDM-based CR systems, time synchronization is limited by a number of challenges: multipath channels with a high delay spread, a narrowband interference (NBI), and minimal number of subcarriers. Indeed, the proposed method addresses these problems since it does not require information on subcarriers nor the NBI center frequency; moreover, it works well on the multipath channel with a high delay spread.

For a multiple-input multiple-output (MIMO) system, or a multiuser MIMO system, it is important to acquire the accurate channel state information. In this article, a superimposed-pilot-based channel estimation scheme is proposed for multicell multiuser MIMO uplink systems. In this scheme, the pilot sequences and data sequences are superimposed, but the space generated by the pilot sequences and the space spanned by data sequences are separated far enough in order to reduce interference. Based on this scheme, the minimum mean-square error channel estimation is deduced, and the optimal power allocation between the pilots and the data signals is derived to maximize throughput. These results show that our proposed scheme can overcome the pilot contamination and achieve better performance than the traditional time-multiplexing scheme with mutually orthogonal pilot sequences for different users when the coherent time slot is limited.

Analog beamforming (ABF) architectures for both large-scale antennas at base station (BS) and small-scale antennas at user side in millimetre wave (mmWave) channel are constructed and investigated in this paper with the aid of deep learning (DL) techniques. Transmit and receive beamformers are selected through offline training of ABF network that accepts input as channel. The joint optimization of both beamformers based on DL for maximization of spectral efficiency (SE) for massive multiple input multiple output (M-MIMO) system has been employed. This design procedure is carried out under imperfect channel state information (CSI) conditions and the proposed design of precoders and combiners shows robustness to imperfect CSI. The simulation results verify the superiority in terms of SE of deep neutral network (DNN) enabled beamforming (BF) design of mmWave massive MIMO system compared with the conventional BF algorithms while lessening the computational complexity.

In this article, the achievable sum rate and the energy efficiency (EE) are investigated for a multiuser relay-aided massive multiple-input–multiple-output (MIMO) downlink. Low-resolution digital-to-analog converters (DACs) are equipped at both the base station (BS) and the relay station (RS), and the amplify-and-forward protocol is adopted at the RS. Under the Rician fading channel, closed-form approximate expressions for the achievable sum rate are derived with perfect and imperfect channel state information. A more general power law is extracted to save transmit power without reducing the achievable sum rate, and a local optimal power allocation scheme is proposed to improve the channel capacity of active users. Then, the tradeoff between the achievable sum rate and the EE is discussed. The numerical results show that, due to the quantization noise, the transmit power has a limited increase in the achievable rate. In addition, it is more valuable to improve the DAC quantization bit at the RS when the number of BS antennas is larger than that of RS antennas. In addition, under strong line-of-sight channels or high pilot transmit power conditions, the channel estimation accuracy is higher, and the best tradeoff between the achievable sum rate and the EE can be obtained when the DAC quantization bit is 4.

Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) is presented to enhance the spectral and energy efficiency over the conventional cyclic prefix OFDM and zero padding OFDM, but the interbloc interference (IBI) between the data block and adjacent pilot symbols deteriorates its performance. The IBI is considered as the main TDS-OFDM drawback in the harsh long-delay underwater acoustic (UWA) multipath channel. This article proposes a new scheme called X-transform time-domain synchronous index modulation (IM) OFDM to eliminate the TDS-OFDM IBI, explore the multipath diversity, and reduce the peak-to-average power ratio (PAPR). Particularly, X-transform, which merges the discrete Hartley transform and discrete Fourier transform in a very low-complexity unitary matrix, and IM techniques have been employed to remove the effects of IBI existing in underwater acoustic (UWA) TDS-OFDM. Moreover, compressive sensing has been adopted to guarantee a reliable UWA channel estimation based on a guard interval IBI-free region. The proposed scheme is evaluated over simulated and experimental UWA channels. The computer simulation and the real experiment show the outperformance of the proposed scheme in terms of bit error rate, computational complexity, PAPR, energy efficiency, and spectral efficiency over its conventional benchmarks.

The Ultra-WideBand (UWB) technique, which offers good energy efficiency, flexible data rate, and high ranging accuracy, has recently been recognized as a revived wireless technology for short distance communication. This paper presents a brief overview of two UWB techniques, covering Impulse-Radio UWB (IR-UWB) and Frequency-Modulation UWB (FM-UWB) methods. The link margin enhancement technique, Very-WideBand (VWB), and power consumption reducing technique, chirp UWB, are also introduced. Then, several potential applications of IR-UWB with transceiver architectures are addressed, including high data rate proximity communication and secure wireless connectivity. With fine-ranging and energy-efficient communication features, the UWB wireless technology is highly promising for secure mobile Internet of Things (IoT) applications.

Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices' positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology., Comment: 12 pages, 10 figures. Accepted for publication in IEEE IoT Journal

This paper presents a method for the theoretical analysis of the radiative-wireless power transmission (R-WPT) in lossy environments. We identified the efficiency bounds for specific receiver antennas when the available transmission area was limited. In addition, the optimal transmission currents were obtained and used to determine the proper location and polarization of the transmitting array. It was found that the fields emitted from the receiver in the transmitting mode are an important factor for determining the efficiency bound. To understand the R-WPT with lossy media better, the proposed theoretical framework was applied to two examples. The efficiency bounds for the examples were compared with the results obtained by using previous works to assess the usability of the proposed theory. This study could serve as a basis for the development of R-WPT systems for practical microwave transmission applications with lossy objects.

The Device-to-Device (D2D) communication underlaying cellular networks is considered in this study. The D2D transmitter in the D2D mode can directly transmit messages to a receiver, but it may interfere with the transmission of another cellular user who shares the same uplink channel. The transmitter can also operate in a cellular mode in which no interference to another cellular user occurs. We propose a mode selection scheme that aims to minimize the transmission power of the D2D transmitter subject to constraints on the minimum required data rate and maximum interference to other cellular users. The proposed scheme is based on bounds for transmission power and is less complex than the optimal scheme. Furthermore, it requires only a few statistics and does not need a fading channel distribution. The performance of the scheme is close to optimum when the number of Base Station (BS) antennas is large and the mean absolute deviation of the fading terms is small. We verify this with numerical results of the Rician and Rayleigh fading channels by assuming that the BS antennas are independent. The simulation results for the two correlated BS antennas are presented herein.

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) at the secondary user (SU) transmitter (ST) and phase shifts at the IRS to minimize the ST' s total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. The successive convex approximation (SCA) method, Schur's complement, General sign-definiteness principle, inverse Chi-square distribution and penalty convex-concave procedure are invoked for dealing with these intricate constraints. The non-convex optimization problems are transformed into several convex subproblems and efficient algorithms are proposed. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems. Simulation results also show that the number of transmit antennas at the ST and the number of phase shifts at the IRS should be carefully chosen to balance the channel realization feasibility rate and the total transmit power., Accepted by IEEE Transactions on Cognitive Communications and Networking. Keywords: Reconfigurable Intelligent Surface, Intelligent Reflecting Surface, Cognitive Radio Networks, Robust Design, Imperfect CSI

Massive multiple-input multiple-output (MIMO) systems can support a large number of industrial Internet of things (IIoT) devices using many service antennas that are equipped at a base station (BS). Even though Massive MIMO can increase both spectral efficiency (SE) and energy efficiency (EE), to support numerous IIoT devices, a drastic amount of downlink power consumption can be required. We investigate the performance of downlink signal transmission for massive IIoT networks using Massive MIMO, and propose a downlink signal transmission scheme that can significantly reduce the transmission power consumption with little SE loss. We consider a system in which the number of IIoT devices is much larger than the number of service antennas, and in order to simultaneously support a large number of IIoT devices, every orthogonal uplink reference signal (RS) may be reused by a few IIoT devices. In this situation, we derive simple SE approximations, and based on the approximation, a target data rate is determined. The target data rate is used to determine a target signal-to-interference and noise ratio (SINR), and the corresponding signal power. In addition, the SE approximation can be used as an upper bound and the corresponding signal power can be determined by setting an adjustable parameter which is multiplied to the upper bound. The maximum allowable uplink/downlink signal power is pre-defined to prevent unexpected high power requirements. The simulation results are provided to show the effectiveness of the proposed schemes.

In this article, the problem of joint transceiver design for network-assisted full-duplex (NAFD) systems under cell-free massive multiple-input multiple-output (MIMO) network with simultaneous wireless information and power transfer is investigated. The uplink users’ transmit power, the uplink and downlink beamforming vectors, the fronthaul compression ratio, and the power-splitting ratio are jointly optimized to minimize the total transmission power under the constraints of signal-to-noise ratio, quality of service (QoS), energy harvesting, fronthaul capacity and transmit power of users, and remote antenna units. Applying the block-coordinate-descent method and the successive convex approximation, an algorithm is proposed to handle the nonconvex optimization problem. Finally, the simulation results validate the effectiveness of the proposed algorithm and show that NAFD systems can achieve lower transmission power than cloud radio access network co-frequency co-time full-duplex systems.

For a downlink communication system, we propose a nonorthogonal multiple access scheme with two energy harvesting relays. The base station (BS) transmits the data of both near and far users simultaneously using the superposition coding technique. Two relays alternately amplify and forward the data to the far-user. Each relay can harvest wireless energy from the signals of both the BS and the other transmitting relay, so each relay can achieve more opportunities to accumulate more energy for a higher-quality cooperative relaying. The near-user performs adaptive interference cancelation to retrieve its desired data. Numerical results show that the nonorthogonal scheme greatly outperforms the orthogonal scheme in terms of throughput.

As a time-frequency doubly selective channel, severe multipath, Doppler as well as the large time delay characteristics of underwater acoustic (UWA) channel pose significant challenge to the research and design of UWA communication and network systems. To mitigate these negative factors, inherent sparsity contained in UWA channel has been extensively investigated to improve UWA communication via sparsity exploitation receiver. While the performance of UWA sensor network is highly dependent on that of physical layer, there is few investigations reported on exploiting channel sparsity from the viewpoint of UWA networking. In this paper, a UWA sensor network adopting sparsity exploitation physical layer is evaluated based on Network Simulator 3 (NS-3) simulation tool. The simulation time-varying channel is generated by incorporating Bellhop channel model with the statistical characteristics extracted from experimental shallow water channels. Three types of physical layer, i.e., that does not adopting sparse exploitation, adopting compressed sensing (CS) as well as dynamic compressed sensing (DCS) technique, are employed for evaluation and comparison of network behavior under different media access control (MAC) protocols. The evaluation results verify the effectiveness of sparsity exploitation in improving UWA sensor network performance at the presence of time variations, while giving quantitative comparison between enhancement achieved by the CS and DCS sparsity exploitation.

A highly secure and reliable optical orthogonal frequency division multiplexing passive optical network (OFDM-PON) based on the chaos encryption inside polar code scheme is proposed. The 3-dimensional Chua's circuit model is adopted to realize chaotic encryption during polar encoding with low complexity. The three chaotic sequences generated by Chua's circuit model are used to encrypt information index bit matrix, frozen bits (during polar encoding) and subcarrier sequence, respectively. The BER and security performance of the optical transmission system are enhanced by our method. A 70 Gb/s polar encoded encrypted 16 quadrature amplitude modulation OFDM signal transmission over a 2 km seven-core fiber is further experimentally demonstrated. The experimental results show that the polar encoded encrypted 16 QAM-OFDM obtains a 2.1 dB gain in receiver sensitivity at a BER of 10−3 under the same bit rate compared to the normal 16 QAM-OFDM without FEC. Moreover, the key space of the proposed scheme is 1084, which is large enough to guard against any malicious attacks from illegal ONUs effectively. The combined superiority of BER and security performance enable such OFDM-PON based on chaos encryption inside polar code, to have a high prospect of application in low-cost and reliable optical access system.

In this letter, a simultaneous wireless information and power transfer (SWIPT) enabled generalized frequency division multiplexing based non-orthogonal multiple access (GFDM-NOMA) cooperative communication model is proposed to guaranteed the transmission performance of the source while saving the power consumption of the relay. The GFDM subsymbols in two phases are both divided into two sets. In the first phase, the relay utilizes two different subsymbol sets to decode the source information and harvest the source energy, respectively. In the second phase, the relay uses the paired subsymbols to transmit the source information to the destination and the relay information to the relay’s receiver simultaneously by NOMA, and utilizes the remaining subsymbols for the transmissions between the relay and the relay’s receiver. The sum system rate is maximized through a joint optimization algorithm for subsymbol and power allocations. Simulation results show that the proposed scheme has better performance.

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the access point's beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies dynamically. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relaying parameters by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning and achieve a higher reward performance, up to nearly 20%, compared to the conventional DDPG approach., Comment: 29 pages, 7 figures, 2 tables, submitted to IEEE Transactions on Wireless Communications

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.

Different from conventional wired line connections, industrial control through wireless transmission is widely regarded as a promising solution due to its reduced cost, increased long-term reliability, and enhanced reliability. However, mission-critical applications impose stringent quality of service (QoS) requirements that entail ultra-reliability low-latency communications (URLLC). The primary feature of URLLC is that the blocklength of channel codes is short, and the conventional Shannon's Capacity is not applicable. In this paper, we consider the URLLC in a factory automation (FA) scenario. Due to densely deployed equipment in FA, wireless signal are easily blocked by the obstacles. To address this issue, we propose to deploy intelligent reflecting surface (IRS) to create an alternative transmission link, which can enhance the transmission reliability. In this paper, we focus on the performance analysis for IRS-aided URLLC-enabled communications in a FA scenario. Both the average data rate (ADR) and the average decoding error probability (ADEP) are derived under finite channel blocklength for seven cases: 1) Rayleigh fading channel; 2) With direct channel link; 3) Nakagami-m fading channel; 4) Imperfect phase alignment; 5) Multiple-IRS case; 6) Rician fading channel; 7) Correlated channels. Extensive numerical results are provided to verify the accuracy of our derived results., Comment: Accepted by IEEE TCOM

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as O-OFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

This paper shows how to enhance the bandwidth and bit error rate (BER) performance of modern communications system in multicarrier modulation systems by including a compression technique in frequency dimensional after multicarrier modulation. The proposed system design for multi-carrier systems was implemented using a standard system based on Fast Fourier Transform (FFT). This proposed idea of the multicarrier modulation system done by adding the Discrete Wavelet Transform (DWT) as compression stage to multicarrier coefficients. This operation will be done by sending the approximation coefficients of DWT and discard the details coefficients. The details coefficients in receiver side will compensate with zeros in IDWT stage. The simulation results were examined using additive white Gaussian noise (AWGN), flat Rayleigh fading and selective fading channels showing that the proposed additional compression scheme after DWT can double the data rate and reduce the bit error rate (BER).

To expedite research progress on terahertz (THz) communications, we analyze the outage performance of THz communication systems by a compound channel model in this paper. Different from existing models, the compound channel model incorporates the effects of spreading loss, molecular absorption loss, shadowing, and multi-path fading via a composite distribution. By using this model, we maintain an equilibrium of the outage performance analysis between mathematical tractability and the fidelity of realistic THz channels. Specifically, by utilizing the compound channel model, outage performance analysis can get rid of sophisticated case-specific channel modeling relying on field measurement and the ray-tracing assessment. To facilitate the application of the proposed channel model, we also design a maximum likelihood estimation (MLE) based channel parameter estimation approach for the compound channel model. The analytical results of outage performance by using the compound channel model are given in closed form and verified by numerical results.

Author(s): Yuan, Bofeng; Ismailoglu, Nilab; Jafar, Syed A | Abstract: Blind Interference Alignment (BIA) schemes create and exploit channel coherence patterns without the knowledge of channel realizations at transmitters, while beamforming schemes rely primarily on channel knowledge available to the transmit- ters without regard to channel coherence patterns. In order to explore the compatibility of these disparate ideas and the possibility of synergistic gains, this work studies the Degrees of Freedom (DoF) of the 2-user (?1 × ?1)(?2 × ?2) Multiple- Input Multiple-Output (MIMO) Interference Channel (IC) where Transmitter 1 is equipped with reconfigurable antennas and has no channel knowledge, while Transmitter 2 has partial channel knowledge but no reconfigurable antennas. Taking a fundamental dimensional analysis perspective, the main question is to identify which antenna configurations allow synergistic DoF gains. The main results of this work are two-fold. The first result identifies antenna configurations where both reconfigurable antennas and partial channel knowledge are individually beneficial, as those where ?1 l ?1 l min(?2,?2). The second result shows that synergistic gains exist in each of these settings, over the best known solutions that rely on either reconfigurable antennas or partial channel knowledge alone. Coding schemes that jointly exploit partial channel knowledge and reconfigurable antennas emerge as a byproduct of the analysis.

The most significant challenge of underwater optical wireless communication (UOWC) system is to overcome its limited coverage. To expand the achievable communication range, we investigate the performance of the dual-hop UOWC system with simultaneous lightwave information and power transfer (SLIPT). The time splitting (TS) method is adopted for wireless power transfer in the proposed system, where the information and energy are transmitted in different phases. A suitable transmission strategy is designed for the model without additional power supply, which contains three phases, i.e. information transmission, energy transmission, and forwarding process. The expressions of the average bit error rate (BER) at the target node and the energy harvested by the relay node are derived over underwater attenuation channel. Then, the effects of the TS factor and the distances on the system performance are investigated in two sub-problems, which minimize the average BER while satisfying the energy harvesting and transmitting rate constraints. Numerical results indicate the performance improvement by adopting the relay node with SLIPT.

In order to reduce the downlink training overhead of mmWave communications, we propose a novel downlink beamforming strategy using the uplink sub-6GHz channel and downlink mmWave pilots that are sent from a few active antennas. Specifically, we design a novel dual-input neural network architecture, called FusionNet, to merge the sub-6GHz channel and the channel of a few active mmWave antennas. The proposed fusion model could intelligently adjust the attention paid (by the neural network) for sub-6GHz channel and mmWave channel by an attention mechanism. The output of the FusionNet represents the probability for each beam being the optimal one. We also propose an antenna selection model that can choose better active antennas to send the downlink pilots, in which the gradient of antenna selection vector is approximated by that of an antenna probability vector. Simulation results demonstrate the superior performance of the proposed strategy compared to the existing one that purely relies on the sub-6GHz information or compared to sthe shallow model that directly adds uniform pilots.

In this article, we consider the downlink of a cellular communication network, where dual-connectivity at the end-users is enabled with the assistance of a full-duplex (FD) massive-multiple-input-multiple-output (mMIMO) relay. In particular, the base station transmits separate data streams through the cochannel direct link as well as the FD relay channel, utilizing the successive-interference-cancellation (SuIC) capability at the receiver. As a result, the downlink communication data can be interchangeably loaded to separate subcarriers, employing an orthogonal multicarrier strategy, or to different available links, i.e., direct or FD relay link, employing the nonorthogonal SuIC at the receiver. In order to reliably model the SuIC operation at the receiver, the collective sources of impairments, including the nonlinear transmit and receiver chain distortions as well as the channel state information inaccuracy are incorporated. An optimization problem for joint subcarrier and power allocation is then devised in order to maximize system sum-rate, which belongs to the class of smooth difference-of-convex problems. An iterative optimization solution is then proposed, utilizing successive inner approximation framework, which converges to the point that satisfies Karush–Kuhn–Tucker optimality conditions. Numerical results show performance and robustness gain of proposed SuIC scheme in terms of sum-rate compared to previously proposed single-connectivity and half-duplex relaying schemes.

It is widely believed that the channel reciprocity holds in time division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems, so the uplink channel feedback is unnecessary. This is actually true when the equal number of transmitting and receiving antennas are used by users. However, due to the constraint of transmission power and hardware complexity, users may use more receiving antennas than transmitting antennas in practical wireless communication systems, which is common for current 5G users, e.g., category 20 users in 5G can support at most 8 receiving antennas in the downlink but only at most 2 transmitting antennas in the uplink. In this practical yet important scenario, only partial channel reciprocity holds, and thus channel feedback is still required in TDD massive MIMO systems. Unfortunately, this important topic has not been investigated in the literature. To fill in this gap, in this paper, we propose an angle-domain support based channel feedback scheme for TDD massive MIMO systems with unequal number of receiving and transmitting antennas. Firstly, by utilizing the angle-domain channel sparsity of massive MIMO channel, we illustrate that a small number of elements in angle-domain channel can capture the most power of the channel, and define a set including the indexes of these elements as the angle-domain support. Based on the angle-domain support, we propose to decompose the channel feedback into two parts: the angle-domain support and the low-dimensional angle-domain representation indexed by the angle-domain support. In the proposed scheme, due to the partial channel reciprocity, the angle-domain support is obtained by an angle-domain windows based support detection method directly at the base station (BS) without channel feedback at first. Then, the low-dimensional angle-domain representation indexed by the angle-domain support is quantized and fed back through limited feedback schemes. Finally, the base station can recover the downlink channel according to the angle-domain support and the low-dimensional angle-domain representation. Theoretical analysis reveals that the channel feedback overhead of the proposed scheme is proportional to the size of the angle-domain support, and thus is much smaller than the channel feedback overhead of the traditional scheme. Simulation results demonstrate that the proposed channel feedback scheme can achieve the near-optimal achievable sum-rate performance with low channel feedback overhead.

Multiband operation in millimeter wave (mmWave) will obtain lots of performance gain by offering larger bandwidth. However, the prerequisite is the acquisition of accurate channel state information (CSI), which is a knotty task with hybrid analog/digital architecture. This study devotes to efficiently reconstruct and track the time drifting multiband channel to keep CSI precise in time division duplex (TDD) mmWave multiple-input-multiple-output system with hybrid analog/digital architecture. Utilizing spatial reciprocity, an efficient multiband channel reconstruction scheme is introduced, which elaborately estimates the central sub-band channel and then reconstructs side sub-band channels from the central one. To this end, a beam training-based Newtonized orthogonal matching pursuit (BT-NOMP) algorithm is proposed to estimate the central sub-band channel. With the help of frequency-independent parameters extracted from BT-NOMP, side sub-bands channel can be well reconstructed with additional low-complexity path gains estimation process. Furthermore, to avoid frequent channel reconstruction in a slightly drifting channel meanwhile keep the CSI accurate, a rotated beam-based channel tracking algorithm is developed using historical observations of channel parameters. Numerical results prove the efficiency of the proposed multiband channel reconstruction scheme and the accuracy of the channel tracking algorithm.

In this letter, we investigate a multi-relay assisted over-the-air computation network for multi-modal sensing with direct links, where each node is equipped with multiple antennas and all the relays are operated in an amplify-and-forward mode. Specifically, the whole transmission is divided into two phases and all the sensors transmit symbols during both two phases. In particular, we are interested in minimizing the computation distortion measured by the mean-squared error (MSE) via jointly optimizing beamforming matrices at all nodes, subject to individual power constraints at the sensors and relays. The major difficulty lies in the strong coupling of beamforming matrices in the objective function and the non-convex transmit power constraints at the relays. To tackle this problem, a low-complexity locally optimal method based on alternating optimization is proposed, where closed-form expressions are obtained in each iteration. Furthermore, simulation results show that our proposed beamforming design can substantially enhance the computation MSE performance, as compared to other benchmark schemes.

In this paper, we investigate the performance of downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) aided vehicular communications systems. To prepare the background for MISO NOMA-based vehicular systems, we first derive the exact outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC) expressions for the single-input single-output (SISO) NOMA-based vehicular systems over independent but not necessarily identically distributed (i.ni.d.) double Nakagami-m fading channels. Furthermore, we propose two transmit antenna selection schemes at multiple-antenna source vehicle for both far user and near user. Based on these antenna selection strategies, we derive the exact expressions for the OP, ABER, and EC, of the MISO NOMA-based vehicular systems over i.ni.d. double Nakagami-m fading channels. The derived ABER expressions for both SISO and MISO NOMA-enabled vehicular systems can be applicable for various modulation schemes. Moreover, to reveal better insights into the diversity orders of SISO and MISO NOMA-enabled vehicular systems, we present the asymptotic OP analysis in the high signal-to-noise ratio (SNR) regime. The numerical and simulation results corroborate our theoretical and analytical findings, and demonstrate the effects of transmit power, power allocation factor, fading severity parameters, and number of antennas, on the systems performance. We further compare the performance of both systems, and show that the multiple antennas can improve the performance of vehicular systems, which can be further improved significantly with the simultaneous improvement in fading severity parameters.

Quantum key distribution (QKD) provides information-theoretic security based on quantum mechanics. Integrating QKD with classical data traffic by using wavelength division multiplexing (WDM) techniques in a single fibre is a cost-efficient way to improve security in legacy infrastructure. In such a system, the main noise source to the quantum channel is spontaneous Raman scattering (SRS) caused by the classical channels. In this letter we introduce a channel allocation strategy for both quantum and classical signals to minimize the SRS noise. A use case that quantum and classical channels co-exist in a dense WDM system is investigated. The results show >26% increase of achievable transmission distance for the QKD system when implementing the introduced channel allocation strategy. Moreover, a network updating plan is proposed, which provides a guideline to light the new wavelengths for classical communications while minimizing the SRS noise to quantum channels.

This paper proposes a novel non-Hermitian symmetry ${2 \times 2}$ MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system based on fast Hartley transform (FHT) with unipolar encoding (referred to as HU-OFDM) for visible light communications. The new scheme uses FHT instead of FFT (fast Fourier transform) to process the complex-valued signals reducing the computational complexity and hardware cost and employs two LEDs (light-emitting diodes) to transmit the real and imaginary parts of a complex-valued OFDM signal respectively. Unipolar encoding is applied to the transmitted signal from each LED to ensure non-negative. The restrictions of Hermitian symmetry in traditional optical OFDM systems and real constellation mapping in FHT based optical OFDM systems can be removed simultaneously in the proposed system. Compared with MIMO-ACO/DCO-OFDM system, HU-OFDM has significant performance improvement along with a tremendous decrease in hardware cost and computational complexity. Compared with other non-Hermitian symmetry MIMO-OFDM systems, HU-OFDM has significant advantages in terms of power efficiency, system design flexibility, computational complexity, or hardware cost without losing reliability.

A compact wideband multiple-input multiple-output (MIMO) antenna for body-centric communication is presented in this article. Initially, an electric field-driven LC metamaterial resonator inspired ...

This paper presents a novel energy-efficient Radio Frequency (RF) Energy Harvesting (EH) scheme for secrecy performance in Internet of Things (IoT) network using cooperative communication approach under dissimilar faded channel environments. We have combined the concept of Destination Based Jamming (DBJ) with power beacon aided EH in our model. This paper provides three scenarios when the links undergo rayleigh fading, weibull fading, and nakagami-q (hoyt) fading where the effect of fading on the secrecy performance has been examined. Closed-form expressions for Secrecy Rate (SR) and System Energy Efficiency (SEE) are derived for the given scenarios. Analytical expressions have been well substantiated through extensive simulation results. The comparative analysis of system performance in terms of SR with respect to some important system parameters has been shown for various conventional protocols i.e., Decode-and-Forward (DF), Amplify-and-Forward (AF) and Hybrid-Decode-Amplify-Forward (HDAF). Furthermore, our scheme has been compared with benchmark techniques such as Cooperative Relaying (CR) and Cooperative Jamming (CJ) to quantify the performance improvement. Theoretical analysis and simulated results reveal the improvement of our proposed Signal-to-Noise (SNR) based hybrid decode-amplify forward Cooperative IoT (CIoT) scheme over existing relaying schemes which are further validated through statistical analysis. Results also depict the impact of eavesdropper position on secrecy performance at different channel mean power values. Finally, the SEE has been addressed and the impact of power splitting factor and transmit beacon power on the secrecy performance on the proposed model has also been analyzed with respect to change in fading parameters.

The future wireless communication requires a reliable transmission at high data rates, so the transmission over frequency-selective fading Multiple-Input–Multiple-Output MIMO channels become interesting since the capacity of "MIMO" channels expressions enormous gains above that of their essential single-input–single-output "SISO" channels. This paper examines the performance of the Low Complexity Zero Forcing "LCZF" equalizer for both systems single-input–single-output-Orthogonal Frequency Division Multiplexing" SISO-OFDM" and spatially multiplexed-Multiple-Input–Multiple-Output "SM-MIMO-OFDM" with different "QAM" modulations. It is exploring a new algorithm to improve the performance of the "BER", spectral efficiency, and power efficiency and to reduce the complexity of the "RF" communication system under the effect of the Additive White Gaussian Noise "AWGN" and multipath fading channel. It is also improves an efficient channel by developing a Low Complexity Zero Forcing "LCZF" equalizer for both "SISO-OFDM" and "SM-MIMO-OFDM" wireless Communication systems. This is done by proposing a new algorithm at the receiver side to covert the Linear Convolution in to Cyclic Convolution by adding Zero Padding "ZP" to the channel impulse response in such a way to be the same length to the transmitted signal in the time domain which is of length N, where N is the length of "IFFT".

Group-blind multiuser detectors for uplink code-division multiple-access (CDMA) were developed by Wang and Host-Madsen. These detectors make use of the spreading sequences of known users to construct a group constraint to suppress the intracell interference. However, such techniques demand the estimation of the multipath channels and the delays of the known users. In the present paper, the blind generalized receiver is de-veloped for CDMA in fading multipath channels. The proposed generalized receiver utilizes the correlation in-formation between consecutively received signals to generate the corresponding group constraint. It is shown that by incorporating this group constraint, the proposed generalized receiver can provide different performance gains in both the uplink and downlink environments. Compared with the well-known group-blind detectors, our new methods only need to estimate the multipath channel of the desired user and do not require the channel es-timation of other users. Simulation results demonstrate that the proposed generalized receiver outperforms the conventional blind linear multiuser detectors.

Phase Modulation Orthogonal Frequency Division Multiplexing (PM-OFDM) and Code Division Multiple Access (CDMA) have distinctive advantages. On one hand, PM-OFDM is an attractive technique to combat multipath fading and reduce high PAPR in conventional OFDM. On the other hand, CDMA has the ability to serve multiple users at the same time and/or frequency. In this article, a new PM-OFDM-CDMA system that combines PM-OFDM and CDMA is proposed. The idea behind the proposed technique is to combine the advantages of these techniques in order to enhance the performance of the 5G system by serving multiple users simultaneously. The performance of the proposed system is analysed in terms of BER under different channel conditions. A Minimum Mean Square Equaliser (MMSE) scheme is used in order to avoid the effect of multipath effect and noise simultaneously. From the simulation results, we conclude that the proposed system has good performance that can improve 5G wireless communication networks, especially for battery-powered mobile devices. This amounts to the constant envelope of the proposed waveform which generates constant instantaneous power and therefore reduced PAPR.

In this paper, a robust transmit power control scheme is proposed for multi-channel underlay device-to-device (D2D) communications with imperfect channel state information (CSI). The transmit power of the D2D user equipment (DUE) on each channel is optimized to maximize the average spectral efficiency (SE) whilst maintaining the quality-of-service (QoS) of the cellular user equipment (CUE) in the presence of errors in the CSI. To this end, we propose a novel deep neural network (DNN) structure and training methodology, in which artificially distorted CSI is used to compensate for the effect of imperfect CSI, such that a robust transmit power control strategy against channel error can be derived. Our simulation results show that even when the CSI is inaccurate, in our proposed scheme the degradation of the average SE can be kept low whilst maintaining negligible QoS violation, thereby confirming its effectiveness and robustness.

Channel estimation plays a significant role in the Inter-carrier interference (ISI) mitigation and symbol detection in Orthogonal frequency division multiplexing (OFDM) systems. As the speed of mobile terminal increases, the channel changes rapidly, causing fast fading which will degrade the system performance. In this paper, a low complexity fast fading channel estimation method suited for scattered pilot OFDM systems is proposed. This method is characterized by estimating channel matrix directly in the frequency domain, which can significantly reduce the channel estimation complexity with good system performance. Simulation results verify its superiority over the existing estimation methods.

Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. To improve the channel estimation accuracy and reduce the pilot overhead, multi-input multi-output (MIMO) radar is deployed to assist millimeter wave (mmWave) channel estimation. In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. To allocate pilot resources reasonably, we design a transmission frame structure of joint radar module and communication module, which divides the estimation scheme into two stages, i.e., the arrival/departure (AoA/AoDs) estimation stage and the gain estimation stage. In view of the imperfections of array elements in practice, we propose an AoA/AoDs estimation algorithm based on subspace reconstruction in the AoA/AoDs estimation stage named two-step angle estimation (TSAE) algorithm. In the gain estimation stage, a DL based channel gain estimator is designed. An autoencoder combined with residual structure named residual denoising autoencoder (RDAE) is proposed to eliminate the noise on wireless signals, which is passed into the least square (LS) estimation module to obtain gains. Simulation results demonstrate that the MIMO radar aided and DL-based channel estimator provides the efficient estimation performance of the high-mobility mmWave channel with fewer training resources.

Dual-function radar-communication (DFRC) systems with Orthogonal Frequency Division Multiplexing (OFDM) make efficient use of spectrum, while maintaining reliable robust-ness against multipath fading for communication and improved estimation accuracy for radar. Nevertheless, it would be costly to use a fully digital beamforming architecture, where each antenna is equipped with a radio frequency (RF) chain. This motivates us to consider hybrid beamforming for OFDM-DFRC system in this work. More specifically, the analog beamformer for the whole bandwidth and digital beamformer for each subcarrier are simultaneously designed, by jointly optimizing the spectral efficiency (SE) of communication and spatial spectrum matching error (SSME) of radar. To tackle the resulting optimization problem, a consensus alternating direction method of multipliers (consensus-ADMM) approach is devised. Further, in a practical sense, we consider the issue of hybrid transmit beamforming design with finite-resolution phase shifters, to facilitate economical implementation of the analog beamformer. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.

Wireless communications and sensing at terahertz (THz) band are increasingly investigated as promising short-range technologies because of the availability of high operational bandwidth at THz. In order to address the extremely high attenuation at THz, ultra-massive multiple-input multiple-output (MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. However, the cost and power associated with fully digital beamformers of these huge antenna arrays are prohibitive. In this paper, we develop wideband hybrid beamformers based on both model-based and model-free techniques for a new group-of-subarrays (GoSA) ultra-massive MIMO structure in low-THz band. Further, driven by the recent developments to save the spectrum, we propose beamformers for a joint ultra-massive MIMO radar-communications system, wherein the base station serves multi-antenna user equipment (RX), and tracks radar targets by generating multiple beams toward both RX and the targets. We formulate the GoSA beamformer design as an optimization problem to provide a trade-off between the unconstrained communications beamformers and the desired radar beamformers. To mitigate the beam split effect at THz band arising from frequency-independent analog beamformers, we propose a phase correction technique to align the beams of multiple subcarriers toward a single physical direction. To further decrease the ultra-massive MIMO computational complexity and enhance robustness, we also implement deep learning solutions to the proposed model-based hybrid beamformers. Numerical experiments demonstrate that both techniques outperform the conventional approaches in terms of spectral efficiency and radar beampatterns, as well as exhibiting less hardware cost and computation time., 17pages14figures. Accepted in IEEE Journal of Selected Topics in Signal Processing

Reconfigurable intelligent surface (RIS) has recently received extensive research interest due to its capability to intelligently change the wireless propagation environment. For RIS-aided wireless communications in frequency division duplex (FDD) mode, channel feedback at the user equipment (UE) is essential for the base station (BS) to acquire the downlink channel state information (CSI). In this paper, a dimension reduced channel feedback scheme is proposed to reduce the channel feedback overhead by exploiting the single-structured sparsity of BS-RIS-UE cascaded channel. Since different UEs share the same sparse BS-RIS channel but have their respective RIS-UE channels, there are only limited non-zero column vectors in the BS-RIS-UE cascaded channel matrix, and different UEs share the same indexes of the non-zero columns. Thus, the downlink CSI can be decomposed into user-independent channel information (i.e., the indexes of non-zero columns) and user-specific channel information (i.e., the non-zero column vectors), where the former for all UEs can be fed back by only one UE, while the latter can be fed back with a fairly low overhead by different UEs, respectively. Simulation results show that, compared with the conventional method, the proposed scheme can reduce channel feedback overhead by more than 80% for RIS-aided wireless communications.

Network-assisted full-duplex enable simultaneous uplink (UL) and downlink (DL) communications by allocating remote antenna units (RAUs) to perform either UL reception of DL transmission at each time slot, which improve the spectral efficiency (SE) of cell-free massive multiple-input multiple-output (MIMO) systems. However, previous studies mainly focus on analyzing the system performance under given duplex mode of RAUs. In this letter, we propose to dynamically optimize the duplex mode of RAUs to further improve the SE. An algorithm based on parallel successive convex approximation is proposed to solve the non-convex duplex mode optimization problem. To reduce algorithm complexity and achieve higher SE, a reinforcement learning algorithm based on enhanced Q-learning is also proposed. Simulation results show that the proposed algorithms improve the SE of cell-free massive MIMO systems greatly with lower complexity.

Communications systems concerted over wireless channels depend on the environment. Communications system can be more reliable and efficient by properly analyzing wireless channels. Today's most important features are a high data rate and reliable performance to exploiting viable networks during this new information age. The channel is not time-invariant in wireless communication, so the received signal exhibits amplitude, phase, and angle variations due to multipath fading. Increasing data rates and reducing bandwidth make Orthogonal Frequency Division Multiplexing (OFDM) an important component of wireless communication systems. The OFDM technique uses many carriers very efficiently. With this scheme, interference is robustly reduced, and fading scenarios are easily accommodated. Analyzing digital modulation schemes requires evaluating link performance with fading channels. The paper compares channel performance over varying fading environments using a variety of modulation schemes. We study the BER and SNR properties of the AWGN, Rician fading and Rayleigh fading channels modulated with BPSK, QPSK, and M-ary QAM.

Orthogonal frequency division multiplexing (OFDM) is a popular multicarrier technique in communication system owing to its robustness against multipath fading and less complexity. Deep learning (DL...

In this paper, a new planar printed quasi-Yagi antenna with compact size and wide bandwidth is presented for a multiple-input-multiple-output (MIMO) antenna system. Antenna element consists of a re...