To achieve long-range communication, a single-hop long-range wide area network (LoRaWAN) must sacrifice throughput. In contrast, multi-hop LoRaWAN can extend the communication range while maintaining high throughput by introducing relay nodes between a transmitter and a receiver. However, several challenges arise in multi-hop LoRaWAN. First, the hidden-node problem can lead to packet collisions because relay nodes operate autonomously and may not detect other nodes’ transmissions. Second, the acknowledgment (ACK)-based retransmission control process (ACK-RCP) reduces the throughput due to the duty cycle constraint imposed on unlicensed bands. To address these challenges, this paper proposesa monitoring resource activity-based retransmission control process (MRA-RCP)that determines whether retransmission is necessary byoverhearing signal transmission in the next hop. Thus, it does not require explicit feedback from the receiving node. The simulation results show that the proposed MRA-RCP can increase the number of successfully delivered packets to 187 % of that of the system with the conventional ACK-RCP. Furthermore, experimental evaluations with commercially available LoRaWAN nodes show that the proposed MRA-RCP achieves 192 % of the packets successfully delivered, compared to the conventional ACK-RCP in a real LoRaWAN environment.

Wireless communication systems under high mobility and multipath conditions suffer from severe channel variations in time and frequency. To overcome this issue, this study extends a differential space-time line coding (DSTLC) scheme to an orthogonal frequency-division multiplexing framework, i.e., a differential space-time-frequency line-coded (DSTFLC) system. In the DSTFLC system, communication performance depends on how the DSTFLC symbols are arranged on the time-frequency resource grid. To address this issue, three resource grid mapping (RGM) schemes are proposed: time-first RGM, frequency-first RGM, and Gilbert (generalized Hilbert) RGM schemes. As verified by the numerical results, three RGM schemes exhibit distinct benefits under different channel conditions, namely, time-varying channels with short delay spreads, frequency-selective channels in low-mobility scenarios, and time-varying frequency-selective channels. The proposed RGM schemes would provide practical design guidelines for deploying various Internet of Things applications under dynamic channel conditions.

Ubiquitous connectivity is the most significant goal of sixth-generation (6G) wireless networks. To this end, integrating non-terrestrial networks (NTNs) with terrestrial networks (TN) can be an inevitable path to 6G. To achieve cost-effectiveness and bandwidth efficiency in NTN design, the open radio access network (O-RAN) and integrated access and backhaul (IAB) approaches, which prevail in TN, are adopted in the proposed NTN architecture. To maximize the uplink rate under bandwidth and power constraints, we jointly optimize i) the power allocation to pilot and data symbols, ii) the compression rates for inter-satellite link (ISL) transmission of pilot and data, and iii) the bandwidth partitioning between the access link and the xHaul link. Because the original optimization problem is challenging to solve directly, it is decomposed into three subproblems: power, compression rate, and bandwidth optimization, while the other parameters being kept fixed. The convergent solution is obtained via an iterative optimization approach that alternates between optimization and a gradient-based search. The effectiveness of the proposed approach is verified by numerical simulations.

This study considers covert and secure communications in which a transmitter, Alice, sends information to a legitimate receiver, Bob, using a differential space-time line code (Diff-STLC) scheme. A warden, Willie, attempts to detect Alice’s transmission based on signal strength. Upon successful detection, Willie becomes an eavesdropper (Eve) and starts to eavesdrop on the information. Willie and Eve are adversaries against covert and secure communications, respectively. To enhance the limited secrecy of a conventional Diff-STLC system, we propose a code-hopping strategy by randomly switching between two Diff-STLC structures. As a result, only Bob, aware of the hopping pattern, can decode the signals. To quantify both covertness and secrecy, we define a covert secrecy rate (CSR) as the difference between the achievable rates at Bob and Eve. Using the detection probability of Willie and the bit error rates of Eve and Bob, we derive an analytical lower bound for CSR. Both analytical and Monte Carlo results confirm that the proposed Diff-STLC hopping method significantly enhances the performance of CSR, thus improving the overall covertness and secrecy in communications.

This paper proposes a data-aided approach to address the insufficient number of channel state information (CSI) samples in conventional passive WiFi sensing methods using beacon frames to enable high WiFi sensing performance. It selectively uses pilot symbols or all symbols based on error detection to increase the available CSI for WiFi sensing, resembling IEEE 802.11bf's null data packet approach, where all the received symbols are used for CSI measurements. Reusing correctly detected data symbols increases CSI samples, improving signal-to-noise ratio and detection sensitivity. Experimental results using a software-defined radio show enhanced sensing in human presence detection, with potential applications in tasks like respiration rate estimation, fall detection, and people counting.

In this paper, a conventional multiuser space-time line code (MSTLC) transmission scheme is enhanced to a new enhanced MSTLC (EMSTLC). By introducing the balancing factors at receivers’ antennas to adjust the achievable rates among users, the EMSTLC precoder and balancing factors are designed in a unified minimum mean square error framework to maximize either sum rate or rate balancing. Numerical results verify that the proposed EMSTLC systems achieve a higher sum rate and better minimum user rate than baseline multiuser transmission methods, including the conventional MSTLC scheme. Furthermore, the designed low-complexity algorithms can significantly reduce the computational complexity of the EMSTLC system.

In this study, a novel single-tone-based channel estimation method is proposed for orthogonal time-frequency-space (OTFS) systems. The channel parameters, including the delay taps, Doppler taps, and channel gains, are sequentially estimated in a delay-Doppler domain. Concretely, the delay taps are estimated using a threshold method that provides a constant probability of false alarms (Neyman-Pearson criterion). The integer and fractional Doppler taps and the channel gains of each separable multipath component are obtained using a single-tone parameter estimation method. The estimated parameters are then used to reconstruct the channel matrix. The simulation results demonstrate that the proposed method outperforms conventional low-complexity channel estimation methods in terms of the normalized mean squared error of the channel estimation and the bit error rate (BER) of the communications at the cost of a marginal increase in computational complexity. Further, the BER performance of the proposed method is almost identical to that of perfect channel estimation.

There has been a growing interest in using unmanned-aerial-vehicles (UAVs) for high-rate wireless communications due to their flexibility in deployment and relatively low costs. In this article, we consider an UAV-assisted wireless network, where a multiantenna ground base station provides communication services to a group of users through a set of UAVs using the in-band wireless backhaul. We propose a novel framework for optimizing the multi-UAV-assisted wireless network and consider the following two problems: one minimizes the total system cost and the other maximizes the system utility. In contrast to the previous studies, we consider the deployment cost of UAVs and the impact of the UAV locations on the backhaul capacity for both the problems. The two problems are complicated mixed-integer nonlinear programming (MINLP) problems, which involve the joint optimization of the UAV selection and their locations, the UAV-user association, and the wireless resource allocations subject to the Quality of Service requirements of users and the wireless backhaul capacity constraints. Therefore, we propose the block coordinate descent-based algorithms combined with successive convex approximation to solve the two problems. Simulation results show that the proposed methods are adaptable to the wireless backhaul constraints and outperform the other benchmark methods.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) enables full-space manipulation of signal propagation. In this work, we consider a multi-STAR-RIS-assisted multiple-input multiple-output (MIMO) system for multi-users and investigate the impact of different STAR-RIS schemes on the system performance. We jointly optimize the beamforming matrix of the base station and the transmitting and reflecting coefficient (TRC) matrix of each STAR-RIS to maximize the sum rate of users. We propose a block coordinate descent (BCD) algorithm to optimize the beamforming matrix and the TRC matrices. We reformulate the optimization problem and optimize the beamforming matrix using the Lagrange duality method to reduce the computational complexity and then employ the constrained concave-convex procedure to tackle the optimization problem for the TRC matrices. When the energy splitting (ES) scheme is applied, our proposed method achieves a remarkable improvement of up to 17.97% in the user sum rate compared to the mode switching and the equal ES schemes in the multi-STAR-RIS-assisted system. Furthermore, when compared with systems assisted by multiple conventional RISs, our system can achieve a substantial gain of 38.99%. The improvement is even more pronounced compared to systems with a single STAR-RIS or a single conventional RIS.

This study explores the security of the existing space-time line code (STLC) scheme. A novel STLC-hopping scheme is proposed to ensure physical-layer secure communication of the STLC transceiver, overcoming the limitations of the conventional STLC systems posed by correlated legitimate and eavesdropping channels. The transmitter randomly selects an STLC encoding matrix for every two symbols, and the legitimate receiver decodes them based on the transmitter’s chosen matrix under the assumption that the transceiver shares a pseudorandom sequence generator for the STLC-hopping pattern. The proposed STLC-hopping system is scalable for the number of transmit antennas and compatible with a conventional STLC scheme. Even with highly correlated eavesdropping channels, the proposed STLC-hopping method prevents an eavesdropper from decoding STLC signals. Numerical results confirm the effectiveness of the proposed STLC-hopping method by showing it can almost sustain the secrecy rate lower bound, regardless of the eavesdropping channel correlations, and offer enhanced physical-layer security for STLC systems. Further, bit-error-rate performance results verify that hopping between merely two STLCs can provide sufficient secure communications.

This article investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA)-based multiple unmanned-aerial-vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in 3-D to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in IoT during an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimize the UL-and-DL 3-D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight and non-line-of-sight channel models are studied depending on the position of sensors and UAVs. The influence of potential limited number of channels in IoT is also considered. Guidelines on the 3-D placement of UAVs in the DL charging and the UL communications are also given. Finally, simulation results show that the proposed optimal time allocation OFDMA-UAV scheme achieves significant throughput gains compared with conventional schemes.

In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.

In this study, we consider a space-time line code (STLC) system with N transmit antennas and two receive antennas. Although the STLC system is scalable to an arbitrary number of transmit antennas, a large number of them will increase the hardware cost and complicate the implementation of the system. To resolve this practical limitation, we propose a transmit antenna selection (TAS) scheme for the N-by-2 STLC system. If S ≤ N antennas are used for the transmission, the STLC transmitter selects the S antennas such that the instantaneous signal-to-noise ratio (SNR) is maximized. For a comprehensive performance analysis, the instantaneous SNR of the proposed TAS is characterized in terms of the statistical mean and moment-generating function (MGF). To investigate the performance of the proposed TAS approach, the approximated expression of error rate is derived based on the MGF method. Moreover, using asymptotic analysis, we show that the proposed TAS-STLC system achieves full spatial diversity of 2N, while the coding gain is affected by N, S, and the modulation order. As a special case, we also provide a mathematical analysis of the error rate of the conventional STLC system when N = S.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted two-way relay network, in which two users exchange information through the base station (BS) with the help of an RIS. By jointly designing the phase shifts at the RIS and beamforming matrix at the BS, our objective is to maximize the minimum signal-to-noise ratio (SNR) of the two users, under the transmit power constraint at the BS. We first consider the single-antenna BS case, and propose two algorithms to design the RIS phase shifts and the BS power amplification parameter, namely the SNR-upper-bound-maximization (SUM) method, and genetic-SNR-maximization (GSM) method. When there are multiple antennas at the BS, the optimization problem can be approximately addressed by successively solving two decoupled subproblems, one to optimize the RIS phase shifts, the other to optimize the BS beamforming matrix. The first subproblem can be solved by using SUM or GSM method, while the second subproblem can be solved by using optimized beamforming or maximum-ratio-beamforming method. The proposed algorithms have been verified through numerical results with computational complexity analysis.

In this paper, the dimension of artificial noise (AN) and corresponding transmit power in multiple-input multiple-output single-eavesdropper channels are investigated. It is widely known that AN is transmitted in the null space of the channel matrix of a legitimate link between a source and a destination. Here, the secrecy outage probabilities are derived for various dimensions of AN. Recognizing the fact that the secrecy outage performance depends on the dimension of the AN, an optimization problem is formulated to maximize the secrecy rate under a secrecy outage constraint. The optimal power splitting factor between the information and AN signals is then derived. Furthermore, the asymptotic behaviors of the optimal power splitting factor and the resulting maximum secrecy rate are examined. Through asymptotic analyses and numerical verification, it is formally shown that the full-dimension AN maximizes the secrecy rate under a secrecy outage constraint. Because the complexity of AN design increases as the dimension of the AN increases, we can achieve a tradeoff between secrecy performance and AN design complexity by determining the dimension of the AN. This study can provide a guideline of AN design specifically pertaining to the dimension of the AN.

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

Required throughput for a fronthaul in a cloud-radio access network tremendously increases and becomes a bottleneck in a communication network with wide bandwidth and a large number of antennas. To reduce the fronthaul capacity requirement, more digital function blocks are shifted from a central unit to a distributed unit. In this study, we consider separated delivery of data and pilot signals with different compression rates under time-varying channels to reduce fronthaul burden. By analyzing channel prediction error based on a Kalman filter, an achievable rate for uplink data is derived. The fronthaul rates for data and pilots are derived using rate-distortion theory. Both uplink rate maximization and fronthaul rate minimization are simultaneously designed by considering the data and pilot signal distortions as well as the frame structure. First, the optimization of signal distortion with a fixed pilot interval is investigated. Next, a sub-optimal algorithm to find the pilot interval with the fixed signal distortion is developed. Finally, an iterative algorithm to obtain the sub-optimal solution, which is the joint solution of signal distortion and pilot interval, is proposed. The numerical results demonstrate that the proposed algorithm can provide convergent solutions.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Recently, unmanned aerial vehicles (UAVs) have been the subject of much attention, and UAV swarms are a promising solution to resolve the limitations of a single UAV. In terms of aerodynamics, fixed-wing UAV swarms fly in a V-formation to reduce their energy usage via upwash. UAVs in the V-formation are typically in close proximity, reducing power consumption aerodynamically. However, these formations lack spatial separability, degrading the performance of communication employing non-orthogonal multiple access schemes. In this paper, to tackle this problem, the formation of UAV swarms considering both aerodynamics and communications employing the beam division multiple access (BDMA) scheme is examined. Problems related to the minimization of power consumption of UAVs and the ground control station (GCS) are formulated separately, after which we optimize the beamforming vector, power, and formation of the UAV swarm and propose a novel UAV swarm formation, termed the O-formation, that is power-efficient for communications. Numerical results show that formation of the UAV swarm affects the power consumption related to communications and that the proposed O-formation improves the power efficiency compared to the V-formation and a random formation.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions.

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.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex wireless-powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely-distributed energy-constrained IoT sensors. The UAV broadcasts energy when flying and hovering, and collects information only when hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT network. Under these practical assumptions, we formulate three optimization problems: a sum-throughput maximization (STM) problem, a total-time minimization (TTM) problem, and a total-energy minimization (TEM) problem. For the TEM problem, we further take into consideration that the power needed for hovering, flying, and transmitting are different. For the STM, TTM and TEM problems, optimal solutions are obtained. Finally, numerical results show that the performance achieved by the proposed optimal time allocation schemes outperform existing time allocation schemes. It is also observed that i) the time allocation between hovering and flying time has different trends for different goals; ii) there is an optimal UAV transmit power range that minimizes the energy consumed by the UAV during the entire cycle.

This study investigates time-division duplex (TDD) orthogonal-frequency-division multiple access (OFDMA) unmanned aerial vehicles (UAVs)-aided wireless-powered Internet-of-Things (IoT) networks. Here, a swarm of UAVs simultaneously charge all IoT devices with constant power during a downlink (DL) phase. Using the harvested energy, each IoT device transmits data to an UAV during an uplink (UL) phase via OFDMA. We propose a novel framework to maximize the UL throughput by formulating and solving a joint optimization problem to find the optimal DL and UL time portions, device-UAV association, and the 3D placement of the UAVs. Using our proposed framework, it is shown that the 3D position of the UAVs will have different trends during the UL communications and the DL charging. The proposed TDD-OFDMA UAVs- aided can significantly improve the sum throughput of the IoT devices compare to the fixed base stations schemes.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-aided full-duplex wireless powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely distributed energy constrained IoT sensors. The UAV broadcasts energy while flying and hovering. On the other hand, the UAV collects information while hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT networks. Thus, the energy broadcasted from the UAV is only available for the adjacent sensor. Here, we propose a new line model for UAV-aided IoT networks. With the proposed line model, we investigate the optimal time allocation to maximize the network throughput subject to a total time constant and a UAV maximum flight speed. The formulated throughput maximization problem is proved to be a convex optimization problem and the optimal solution is obtained by the mutual coupling of the convex optimization conditions. We further propose a simple algorithm under a specific condition. Finally, the numerical results verify that the performance achieved by the proposed optimal time allocation scheme outperforms the existing time allocation schemes. The maximum communication distance of the UAV at different heights and different transmission powers can be obtained through the comparison of algorithms.

In this paper, we investigate the performance of uplink non- orthogonal multiple access (NOMA) systems for internet- of-things (IoT) applications, which exploits a space-time line code (STLC) and a binary phase shift keying (BPSK) modulation at each sensor (transmitter). A single transmit antenna is assumed to be equipped at the sensor and two receive antennas are assumed to be equipped at a fusion center (FC). The STLC enables the FC to decode the transmitted symbols from multiple sensors without channel state information (CSI) of each sensor. In particular, we mathematically analyze the symbol error rate (SER) of the super-imposed symbol received at the FC and obtain the diversity order of the uplink NOMA-IoT network. We also propose a phase steering (PS)-based STLC technique to improve the SER performance, in which certain sensors rotate its constellation. The proposed PS-STLC technique significantly outperforms the conventional STLC scheme in the uplink NOMA-IoT network.

In this paper, we propose a novel uplink non-orthogonal multiple access (NOMA) for massive internet-of-things (IoT) networks where each device exploits a space-time line code (STLC) to send a small data packet to fusion center (FC) and the FC decodes the received packet without channel state information (CSI). In particular, we mathematically analyze the bit error ratio (BER) performance of the NOMA-based massive IoT network with the STLC by assuming the optimal joint maximum-likelihood (JML) detector. In addition, we propose a phase-steering based STLC (PS-STLC) technique to further improve the BER performance. Through extensive simulations, it is shown that the proposed PS-STLC technique achieves the optimal diversity and significantly improves the BER performance of the NOMA-based massive IoT network.

In this paper, we propose a two-step algorithm that can achieve any tradeoff point between antenna selection complexity and performance for a massive multiple-input multiple-output (M-MIMO) system. The first and second steps target low complexity and high performance, respectively, in the antenna selection. For the first-step antenna selection, we propose a correlation-based best-first selection algorithm that selects the least spatially correlated antennas. For the second-step selection, we consider a performance-aware algorithm that maximizes the singular values of a selected channel matrix. By adjusting the number of selected antennas in each step, we can actively balance the complexity and performance of the M-MIMO system. The computational complexity and the bit-error-rate performance of various antenna selection algorithms have been analyzed and compared. The investigation in the paper provides a good reference for further study for an antenna selection-based M-MIMO system.

In this paper, we will first motivate the heterogeneous network as an evolutionary path to the fifth generation (5G) communications. We then present an agile software defined heterogeneous network architecture which virtualizes the various radio access networks such as cellular basestations and Wi-Fi access points and the various carrier frequencies from both licensed and unlicensed bands. The software defined heterogeneous network architecture can therefore support much more efficient resource utilization and meet the quality of service requirement. We will also share our work in context-aware Wi-Fi-cellular network traffic steering and mobility management as two use cases in the software defined heterogeneous network.

Massive multiple input multiple output is a promising technology to keep up with the explosive demand of wireless data traffic. The benefits of having a large number of antennas, however, depend on the availability of channel state information (CSI), especially at the transmitter. In frequency division duplex systems, this CSI has to be sent back via the uplink channel, hence incurring a large overhead and degrading the spectral efficiency. Mobility of the users and the large number of antennas exacerbate the problem with frequent tracking of the many timevarying CSI coefficients. This paper presents one approach to address this issue. By tracking only the principal components of the channel gain, and exploiting the wide-sense stationarity of the channel, the amount of required feedback can be reduced significantly. Simulation study shows that the proposed technique is able to achieve high sum-rate with good tracking capability using only limited feedback.

A distributed antenna system (DAS) consists of multiple baseband units (BBUs) connecting to distributed antennas (DAs) via dedicated access links. In this study, we investigate a DAS with limited channel state information (CSI) and consider an average rate of users as an objective, where the expectation is taken over the channel uncertainty. We propose two distributed precoder designs that are based on a rate lower bound and a rate upper bound, respectively. As a benchmark, coordinated precoder and cooperative dirty paper coding (DPC)-based precoder with full CSI are compared with our proposed algorithms. Numerical results verifies that the rate performance of our upper-bound based scheme with limited CSI approaches tightly the maximum rates of the full CSI schemes, while that of lower-bound based scheme is relatively worse.

We propose a opportunistic multicast scheduling scheme to exploit content reuse when there is asynchronicity in user requests. A unicast transmission setup is used for content delivery, while multicast transmission is employed opportunistically to reduce wireless resource usage. We then develop a multicast scheduling scheme for the downlink multiple-input multiple-output orthogonal-frequency division multiplexing system in IEEE 802.11 wireless local area network (WLAN). At each time slot, the scheduler serves the users by either unicast or multicast transmission. Out-sequence data received by a user is stored in user's cache for future use. Multicast precoding and user selection for multicast grouping are also considered and compliance with the IEEE 802.11 WLAN transmission protocol. The scheduling scheme is based on the Lyapunov optimization technique, which aims to maximize system rate. The resulting scheme has low complexity and requires no prior statistical information on the channels and queues. Furthermore, in the absence of channel error, the proposed scheme restricts the worst case of frame dropping deadline, which is useful for delivering real-time traffic. Simulation results show that our proposed algorithm outperforms existing techniques by 17 % to 35 % in term of user capacity.

In wireless communication systems, the nonlinear effect and inefficiency of power amplifier (PA) have posed practical challenges for system designs to achieve high spectral efficiency (SE) and energy efficiency (EE). In this paper, we analyze the impact of PA on the SE-EE tradeoff of orthogonal frequency division multiplex (OFDM) systems. An ideal PA that is always linear and incurs no additional power consumption can be shown to yield a decreasing convex function in the SE-EE tradeoff. In contrast, we show that a practical PA has an SE-EE tradeoff that has a turning point and decreases sharply after its maximum EE point. In other words, the Pareto-optimal tradeoff boundary of the SE-EE curve is very narrow. A wide range of SE-EE tradeoff, however, is desired for future wireless communications that have dynamic demand depending on the traffic loads, channel conditions, and system applications, e.g., high-SE-with-low-EE for rate-limited systems and high-EE-with-low-SE for energy-limited systems. For the SE-EE tradeoff improvement, we propose a PA switching (PAS) technique. In a PAS transmitter, one or more PAs are switched on intermittently to maximize the EE and deliver an overall required SE. As a consequence, a high EE over a wide range SE can be achieved, which is verified by numerical evaluations: with 15% SE reduction for low SE demand, the PAS between a low power PA and a high power PA can improve EE by 323%, while a single high power PA transmitter improves EE by only 68%.

In this paper, we consider a distributed transmitter (D-TX) system, in which each TX has a dissimilar power amplifier with different maximum output power, and different number of transmit antennas. To improve energy efficiency (EE) of the D-TX system, we design a multiuser multiple-input multiple-output (MU-MIMO) precoding matrix, a transmit antenna selection (AS) matrix, and a power control (PC) matrix. A conventional zero-forcing based MU-MIMO precoding is shown to be EE optimal for given AS and PC. Optimal and heuristic PC methods are proposed for given AS and MU-MIMO precoding. For the AS, we also propose heuristic algorithms. Average transmit power, outage probability, and EE performance are evaluated to compare three AS algorithms, and to observe the performance gap between the optimal and heuristic PC methods. From the numerical results, we discuss a tradeoff between AS complexity and EE performance and provide a useful guide for energy efficient D-TX system design.

We propose a time slot based transmission strategy, referred to as adaptive coordinated napping (CoNap), for energy saving in cellular networks under time-varying traffic demand. In adaptive CoNap network, multiple neighboring base stations (BSs) form a cluster and each BS operates in either a transmit mode (TM) or a nap mode (NM) in each time slot. The dynamic assignment of TM and NM to each BS is implicitly coordinated among multiple BSs. This implicit coordination is realized by a binary general flickering pattern matrix (FPM) through adaptively selected mapping matrix (MM). To track the time-varying traffic demand, we develop an adaptive algorithm to dynamically select the appropriate MM from a predefined MM set by taking into account the network quality of service (QoS) requirement. Our numerical results based on a realistic energy consumption model in a cellular network show that as high as 40% saving can be achieved without compromising the network QoS.

Spectral efficiency (SE) and energy efficiency (EE) are key measures for wireless communication systems. In OFDM systems, the non-linear effects and inefficiencies of power amplifiers (PAs) have posed practical challenges for system design. In this paper, we analyze the impact of the PA on the SE and EE tradeoff of OFDM systems. We also propose a PA switching technique which achieves a better SE-EE tradeoff, e.g., the EE can be improved by 323% with 15% SE reduction.

We consider an energy efficient resource allocation method for orthogonal frequency division multiple access (OFDMA) systems consisting of M users and N subchannels. Transmit power assignment follows the optimal strategy, i.e., a water-filling strategy, for the given subchannels. For the subchannel allocation, we introduce a recently proposed algorithm called a sequential linear assignment algorithm (SLAA). The SLAA determines how many subchannels are allocated to each user and which subchannels are allocated to each user by solving the outer and inner problems, respectively, in an iterative manner; as a result, it requires O(MN2(N-M)2) complexity. To reduce the computational complexity of SLAA, we propose a simplified sequential linear assignment algorithm (SSLAA). Based on the observation that a user requiring the higher power would take the more subchannels, SSLAA uses each user's power consumption as a metric to determine who will take an additional subchannel in each iteration. Consequently, contrast to SLAA using network power consumption as a metric, the SSLAA incurs significant reduction of computational complexity, by O(N2(N-M)2). Computer simulations in compared with other existing heuristic algorithms verify that the proposed SSLAA results in high energy efficiency and low computational complexity.

General signal model for orthogonal-frequency-division multiple access cellular networks with multiple amplify-and-forward relays has been introduced. An optimization problem minimizing network power is formulated to allocate subchannels and to design relay processing, and it is simplified to achieve a suboptimal solution. Numerical results show that the proposed resource allocation method achieves network power reduction and performance improvement.

Network power minimization problem is considered for orthogonal frequency-division multiple access (OFDMA) relay systems. To devise a simple algorithm, we introduce a new cost that is an inverse-of-required-minimum power for a target rate. Using the new cost, we propose a greedy-search-based subchannel resource allocation algorithm with a communication mode selection and relay processing adaptation. The communication mode selection allows each user to employ multiple (hybrid) modes with direct, amplify-and-forward (AF), and decode-and-forward (DF) communications, and the relay processing adaptation controls relay transmit power, so that network power consumption can be significantly reduced as verified in simulation.

In this paper, we consider a two-phase cooperative relay transmission setup with relay's private information (CRT-RPI). A half-duplex decode-and-forward (DF) relay not only helps the source-to-destination transmission, but also transmits its own private information mRto the destination. Distributed space-time coding is employed across the source and the relay to obtain cooperative diversity gain for the source message, and superposition coding between the source and relay messages is used at the relay to transmit mR. We derive the achievable rate region of CRT-RPI and design i) the optimal time sharing factor between two phases and ii) the power allocation factors for the two messages, based on the rate region. Numerical results are also provided to verify the effectiveness of our proposed optimization schemes.

For multiple half-duplex amplify -and-forward (AF) relays in cellular communications, a statistical power allocation (control) method is designed to minimize network power under the relay-transmit-power and the signal-to-interference-plus-noise ratio constraints. Computer simulation corroborates that the designed relay processing yields significant performance enhancement and network power reduction compared to a system with plain AF relays and a direct communication system without relays.

In this paper, we design cooperative beamforming weights for source, relay and destination nodes based on a minimum means-quare-error (MMSE) formulation under network power constraints. We also propose a mode selection procedure based on the instantaneous system throughput. Simulation results indicate that the MMSE cooperative beamforming method with mode selection achieves better performance compared to other beamforming methods: direct beamforming, relay beamforming, and cooperative beamforming without mode selection.

In this paper, we propose a double space-time transmit diversity (DSTTD)-based two-path relay system with power allocation and link combining methods. The two-path relaying protocol can restore spectral efficiency decreased due to the half-duplexing at the relay nodes and the DSTTD method can appropriately achieve the diversity gain from the two paths. To circumvent severe performance degradation caused by error propagation through the relays, power allocation is considered by maximizing an achievable rate of the system. For the performance enhancement with low complexity, two estimates from the direct-and relay-links are combined at the destination node with combining weights derived from the effective receive signal-to-noise ratios (SNRs) of the links. The performance evaluation shows that the proposed system can provide significant performance enhancement compared to the conventional point-to-point and half-duplex relay communications in any SNR region.

In this paper, we design a multiuser two-way relay system using space division multiple access (SDMA) communications and devise an optimal scheduling method that maximizes the sum rate while ensuring fairness among users. To reduce the computational load at the relays, we propose rate- and angle-based suboptimal scheduling methods. The numerical results illustrate tradeoff between complexity and the performance. Specifically, when the relay has two antennas, we verify that the rate-based method can provide significant computational savings at the cost of a rate reduction of less than 4% when compared with the optimal scheduling method.

Under uncertain channel conditions, local and global power control factors for amplify-and-forward relay processing and source-destination beamforming are jointly and iteratively designed based on a minimum mean-square-error (MMSE) criterion. The influence of imperfect channel information on system performance is examined by computer simulation. As a result, it is verified that the proposed power control methods can relieve the performance degradation arising from channel uncertainties.

A pilot emitting amplify-and-forward relay and hop-by-hop beamforming (HBF) scheme are proposed for two-hop relaying systems. The conventional beamforming (CBF) is performed with respect to an overall channel from a source node (SN) to a destination node (DN) through a relay node (RN) while the proposed HBF performs two independent beamformings, from the SN to the RN and from the RN to the DN. For beamforming, the SN and the DN require the channel state information (CSI) from the SN to the RN and from the RN to the DN, respectively. From our analytic and numerical results, it is shown that the bit-error-rate (BER) performance of the proposed HBF is identical to that of CBF with the perfect CSI. However, the proposed HBF can achieve a lower BER than the CBF method since the channel estimation performance of the HBF system is better than that of CBF, and this is verified by deriving and comparing the mean square errors of the channel estimations of HBF and CBF.

A peak-to-average power ratio (PAPR) reduction technique for multi-input single-output (MISO) orthogonal frequency division multiplexing (OFDM) systems with beamforming is proposed. This technique consists of adaptive antenna selection and phase rotation. By using the proposed method, a large reduction in PAPR can be obtained at the expense of a small signal-to-noise ratio (SNR) loss. According to the simulation results, the PAPR is reduced by 10% in terms of complementary cumulative distribution function (CCDF) with negligible SNR loss. If a 1 dB SNR loss is allowed, a 33% reduction in PAPR can be achieved.

A simple algorithm to select user-pair and allocate frequency band is proposed for multiuser-MIMO OFDMA systems. For sake of comparison, various resource allocation algorithms, such as max-throughput, best-fit, first-fit, and random-fit algorithm, are examined with regard to the system throughput, computational complexity, and fairness among the users. Computer simulation results show that the first-fit algorithm can achieve a large reduction of computation for resource allocation as well as good fairness among users, with only a small reduction of throughput.

Presented in this paper is a study of the capacity evaluation of various multiuser MIMO schemes in cellular environments. The throughputs per user of the generalized zero-forcing with rank adaptation and vector perturbation schemes are compared with the capacity bound of the Gaussian MIMO broadcast channel, obtained by dirty paper coding under proportional fairness scheduling. The average cell throughputs of these schemes are also compared. From these comparisons, this study provides vital information for applying multiuser MIMO schemes in multicell environments