Beamspace channel estimation is crucial for unlocking the potential of millimeter-wave (mmWave) communications in low Earth orbit (LEO) satellite networks. Effective channel estimation tailored to the unique characteristics of LEO channels, including hybrid beamforming architectures, is imperative. This paper presents a novel joint design framework integrating transmit pilot waveforming and receive compressive sensing (CS) for downlink beamspace channel estimation in LEO satellite communications. By exploiting channel sparsity and deep learning with an autoencoder (AE), the joint design is formulated as a sensing matrix design problem. We explore a simple AE training method utilizing 1-sparse channel patterns, enabling efficient decoding with orthogonal matching pursuit (OMP) and its variants. Subsequently, a low-rank approximation method is employed to extract the precoder and compressor from the trained sensing matrix. We further propose a machine learning (ML)-OMP method using a customized multilayer perceptron (MLP) network for iterative support selection. Simulation results demonstrate that the proposed transmit pilot waveforming and receive CS methods significantly enhance beamspace channel estimation performance with fewer pilot time slots. Furthermore, the proposed ML-OMP method outperforms conventional OMP, particularly in scenarios with non-customized precoders and compressors.

Chain-based routing protocols such as PEGASIS reduce redundant transmissions in Wireless Sensor Networks (WSNs), yet their static link structures and greedy neighbor selection often result in long communication links, uneven energy depletion, and premature network failure. Achieving both globally optimized chain construction and energy-balanced routing under diverse network deployments therefore remains a significant research challenge. To address these limitations, this paper proposes GWO-PEGASIS, a scalable two-stage optimization framework that integrates K-means clustering for spatial partitioning with the Grey Wolf Optimizer (GWO) to construct globally optimized intra- and inter-cluster chains under connectivity constraints. In the first stage, K-means forms spatially compact clusters based on node density, initial energy, and spatial distribution. In the second stage, GWO enables adaptive chainhead selection, balanced multihop forwarding, and improved spatial load distribution. Extensive simulations validate the effectiveness of the proposed method: GWO-PEGASIS shortens total chain length by 10.46% relative to PEGASIS, more than doubles the network lifetime, and further improves lifetime by 11.2% and 14.6% over EB-PEGASIS-SCL and MC-CRITIC-KM, respectively. It also achieves the lowest per-round energy consumption, the most balanced residual-energy distribution, and the highest throughput among all evaluated protocols. These results demonstrate that GWO-PEGASIS offers a robust, energy-efficient, and scalable routing solution for WSNs, highlighting the strong potential of swarm-intelligence-driven optimization for addressing complex topology design challenges in resource-constrained environments.

This work addresses the problem of joint robust transmission, reflection, and reception strategy design in an active reconfigurable intelligent surface (ARIS)-assisted multiuser multiple-input multiple-output (MIMO) system. Specifically, a signal-to-interference-noise (SINR) maximization problem has been formulated by jointly optimizing the transmit beamforming matrix at the base station (BS), the linear reception filters at the users, and the reflection coefficient matrix at the ARIS. The optimization has been performed under constraints on the BS transmit power, the maximum amplification power of the ARIS, and the maximum amplitude coefficients of the ARIS. To jointly optimize RIS-assisted systems, this paper proposes an efficient deep learning (DL) model. Specifically, a multi-layer perceptron (MLP)-based deep neural network (DNN) has been designed to effectively approximate the optimal solution. Further, to handle channel state information (CSI) uncertainties arising from estimation errors and environmental variations, a novel uncertainty injection scheme has been proposed for training DL models. The output of the solution is perturbed through uncertainty injection. The model learns a robust beamforming matrix, linear reception filters, and reflection configurations that maintain high SINR under worst-case channel conditions. Simulation results demonstrate that, for the optimized phase and ARIS configuration, the proposed DL trained with the UI scheme achieves a 26.23% SINR improvement compared to the model trained without UI (WUI). In addition to the ARIS, the performance of the passive reconfigurable intelligent surface has also been analyzed. Further, the time complexity and robustness of the proposed model have been evaluated.

This research studies transmit pre-processing designs using dual millimeter wave (mmWave) active arrays with multiple input multiple output (MIMO) software defined radio (SDR) platform for a low Earth orbit (LEO) satellite communication emulator. It involves the fast Doppler, line-ofsight (LOS) with small delay effect, and MIMO array interference effects. In this emulator, we propose spatial nulling and MIMO pre-processing cancellation techniques to eliminate the MIMO streams interference. First, differential training sequences are utilized to acquire and compensate for the LOS fast Doppler offset. Then, a maximum power beam scanning scheme via the phase shifters of mmWave arrays is employed to estimate the arrival angles between MIMO array transmitter and receiver stations. Next, the steering vectors of the desired and interference signals via these estimated angles are adopted by the phase-only optimization algorithms to calculate the spatial beamforming weights of the dual mmWave array, which can null the interference stream and retain the desired stream. After the spatial nulling processing with the interference mitigation, the residual MIMO interference still exists in the MIMO receiver. We further propose MIMO pre-processing filters using the least square method to cancel the residual MIMO interference. Finally, the measurement results of the dual mmWave array SDR platform with the fast Doppler and the over-the-air (OTA) scenarios confirm that the proposed joint spatial nulling and MIMO pre-cancellation techniques can eliminate the MIMO interference and provide the high-quality error vector magnitude (EVM) performance.

Accurate and reliable localization is a prerequisite for unmanned aerial vehicle (UAV) swarm applications. However, conventional GPS or RF-based localization systems often do not function effectively in highly dynamic and unstable mobile ad-hoc environments. This paper proposes a new approach to localize UAVs accurately in unknown communication environments with anomalous GPS reception, based on the received signal strength (RSS) between UAVs. The proposed approach is non-trivial, given the combinational nature of the considered problem and the requirement of high localization accuracy in the UAV application scenario. The key idea of the proposed approach is to solve the position mapping problem by refining a convex relaxation formulation that considers whether the target to be localized is inside or outside the convex hull formed by the anchors. In addition, a variational graph autoencoder is utilized to learn the latent representations for the undirected graph formed from the estimated position, which is then used to calculate the anomaly score. The optimal anchor node selection is obtained by solving a fractional knapsack problem that takes into account the anomaly score of different anchor combinations. Simulation results demonstrate that the proposed approach achieves higher detection and localization accuracy and is more robust to RSS measurement errors compared to the baseline schemes.

As the proliferation of Internet of Things (IoT) devices accelerates, next-generation wireless networks face unprecedented demands for secure, efficient, and scalable communication frameworks. This paper investigates the integration of non-orthogonal multiple access (NOMA) with reconfigurable intelligent surfaces (RIS) and full-duplex (FD) operations to address these challenges while mitigating the adverse effects of residual hardware impairments (HWI) that cause signal distortion. The proposed RIS-aided FD-NOMA system is designed to enhance resilience and secrecy in IoT communication networks, optimizing the secrecy rate while adhering to power constraints for active beamforming at the base node (BN) and unit-modulus requirements for passive beamforming via RIS. Employing an alternate optimization (AO) framework, the complex joint optimization problem is divided into tractable subproblems solved through generalized convex approximations to achieve near-optimal solutions. Numerical results validate the superiority of the proposed model, demonstrating substantial performance improvements over conventional IoT systems without RIS support or with half-duplex NOMA protocols. The findings underscore the transformative potential of RIS-aided FD-NOMA systems in securing IoT networks against hardware imperfections while meeting their stringent connectivity and security demands.

In this work, we investigate the performance of a multi-active reconfigurable intelligent surface (ARIS)-aided full-duplex (FD) secure Internet of Things (IoT) network with imperfect Channel State Information (iCSI) in the presence of an eavesdropper (Eve). We formulate a power minimization problem while ensuring the minimum Quality of Service (QoS) of all the nodes within available resource constraints considering the norm-bounded iCSI. To tackle the nonconvex nature of the formulated problem, we adopt analytical methods, such as semidefinite programming, S-procedure, and general sign-definiteness, and propose an alternating optimization (AO)-based algorithm that jointly optimizes the receive and transmit beamforming at Alice, power allocation at each uplink user, and active beamforming at ARIS. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of ARISs, compared to its passive RIS (PRIS) counterpart, toward achieving a robust and secure FD system is demonstrated. Finally, we discuss the impact of key parameters, such as maximum amplification factor, RIS, and CSI error on the performance of the considered system.

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

In this article, we consider multiple solar-powered wireless nodes (WNs) which utilize the harvested solar energy to transmit collected data to multiple unmanned aerial vehicles (UAVs) in the uplink. In this context, we jointly design UAV flight trajectories, UAV-node user association, and uplink power control to effectively utilize the harvested energy and manage co-channel interference within a finite time horizon. The design goal is to ensure the fairness of WNs by maximizing the worst user rate. The joint design problem is highly nonconvex and requires causal (future) knowledge of the instantaneous energy state information (ESI) and channel state information (CSI), which are difficult to predict in reality. To overcome these challenges, we propose an offline method based on convex optimization that only utilizes the average ESI and CSI, where line-of-sight (LOS) and non-LOS (NLOS) channels are considered. The problem is solved by three convex subproblems with successive convex approximation (SCA) and alternative optimization. We further design an online convex-assisted reinforcement learning (CARL) method based on real-time environmental information. An idea of multi-UAV regulated flight corridors, based on the optimal offline UAV trajectories, is proposed to avoid unnecessary flight exploration by UAVs and enables us to improve the learning efficiency and system performance, as compared with the conventional reinforcement learning (RL) method. Computer simulations are used to verify the effectiveness of the proposed methods. The proposed CARL method provides 25% and 12% improvement on the worst user rate over the offline and conventional RL methods.

As a revolutionary paradigm for green ultra-reliable low-latency communication (URLLC), reconfigurable intelligent surfaces (RISs) have been considered as a prominent architecture for enabling next-generation communication systems. Recently, a novel RIS framework, called simultaneous transmitting and reflecting (STAR-RIS), has been proposed to facilitate both transmission and reflection through the meta-material surface, leading to full-space coverage and even better beamforming flexibility than conventional RIS. This paper investigates an energy-efficient resource allocation design scheme for a STAR-RIS-aided downlink system under various STAR-RIS modes to deliver energy-efficient URLLC services by jointly optimizing the beamforming at the base station (BS) and STAR-RIS, subject to the given requirements on the rate, packet-error probability, and latency. Owing to the non-convex and NP-hard nature of the formulated problem, we propose an alternating optimization framework that obtains suboptimal solutions to the problems of beamforming design at the BS and STAR-RIS, respectively, in an iterative manner by exploiting fractional programming and successive convex approximation approaches. Simulation results confirm that the TS, ES, and MS modes of STAR-RIS achieve approximately$30\%-50\%$,$20\%-40\%$, and$10\%-15\%$, respectively better performance than a conventional reflecting-only RIS while guaranteeing strict reliability and latency requirements of URLLC. Specifically, among all the possible modes of STAR-RIS, the time-splitting mode renders an effective solution due to its better interference management.

The conventional routing protocol considers several local transmission factors in a single node for the many-to-one packet transmission. These factors lead to rapid energy consumption in some specific nodes, thereby generating energy holes to reduce network lifetime. In this article, a novel energy-balanced power-efficient gathering in sensor information systems (EB-PEGASISs) is proposed to improve energy efficiency and utilization for wireless sensor networks (WSNs). In the original power-efficient gathering in sensor information system (PEGASIS), the protocol constructs a chain-based network with the locally shortest distance rather than the globally optimal network length. To improve this, two construction algorithms are proposed to achieve the shortest network length in the EB-PEGASIS, including centralized formation and distributed construction schemes. In the centralized algorithm, the chain length of each starting node is computed, and the minimum chain length can be subsequently determined in the following packet transmission phase. In order to reduce formation complexity, a distributed method uses the$K$-means clustering algorithm to partition the network into clusters. In particular, it forms a subchain in each cluster and connects each subchain into the final shortest chain. Additionally, to balance the energy consumption among all nodes in the packet transmission phase, Jain’s fairness index for residual battery capacity is designed to minimize the fluctuation of energy consumption among nodes in a network. As a result, not only energy efficiency is achieved with the shortest chain length but also energy utilization is balanced for packet transmission. Ultimately, simulation results validate that the network lifetime of the EB-PEGASIS is about 2.46 times the original PEGASIS and 3.36 times the random projection-polar coordinate-chain (RPC) protocol for WSNs.

In traditional wireless sensor networks (WSNs), packets are mainly transmitted in a multihop routing manner. The multihop transmission, however, leads to a hotspot problem in the sink connectivity area (SCA), and the overall network efficiency is reduced due to the quick battery power exhaustion of nodes in that area. This article proposes a novel balanced routing algorithm with transmission range adjustment (BRATRA) to address the network efficiency problem, including the energy efficiency and utilization issues. First, a balanced routing strategy is designed to deal with the SCA load imbalance problem. With the shortest balanced path, the amounts of forwarding packets for the nodes in the SCA and all the other intralayers become more even. From the perspective of power equilibrium in each routing path, each node then determines its accurate transmission radius according to the derived formula and performs power control to realize the even power utilization between interlayers, thereby prolonging the overall network lifetime. Performance evaluation validates that the proposed BRATRA strategy can achieve efficient power utilization in each intralayer and double the network lifetime as compared to the Dijkstra routing strategy. Additionally, it yields better power utilization fairness among nodes, and on average only 5% of battery power is unused for all network nodes, resulting in a network lifespan ten times larger than that using a conventional strategy.

This paper deals with crosstalk distortion (or coupling) between the multi-channel RF components in the wideband multi-input-multi-output (MIMO) transceiver systems. We study the feasible crosstalk parameter estimation, compensation techniques, and calibration procedures in the coupling scenario of the joint transmitter (TX) and receiver (RX). For the MIMO wideband crosstalk transceiver, the proposed calibration rule includes the wideband crosstalk estimation, followed by the compensation procedures of “RX calibration” and “TX calibration”. Next, the printed circuit board (PCB) with crosstalk effect is adopted to verify the performance of the proposed crosstalk distortion compensation. Then, it can be realized in the transmission of single-carrier and multi-carrier orthogonal frequency division multiplexing (OFDM) signals with decoupling processing. Furthermore, to achieve the above purpose, this paper uses the commercial AD9371 wideband RF module integrated with the wideband actual crosstalk PCB module. Finally, the measurement results show that the wideband crosstalk estimation and compensation techniques can suppress crosstalk about 30dB when applied to single-carrier signals, and the error vector measurement (EVM) performance can be improved about 20dB when applied to OFDM signals. Therefore, the proposed design methodology is useful and can provide high-quality performance for wideband MIMO communication.

In this paper, we study the cascaded pre-calibration techniques to overcome the coexisting problems of RF IQ imbalance (IQI) and RF spectrum distortion, which can improve the performance of a single package mmWave IC. The calibration techniques are designed separately. We first pre-compensate the IQI effect by using a single-tone signal with a blind algorithm to estimate the impairment parameters and calculate the precalibration weighting coefficients. After that, we adopt quadrature wideband training sequences to estimate the RF distorted responses and calculate the pre-calibration filters via the deconvolution techniques. The above calibration schemes are realized in the digital pre-processing domain and can perform better than the conventional RF calibration circuit design. Both the computer simulation results and the mmWave software defined radio (SDR) platform measurement results confirm that the proposed techniques can pre-calibrate the cascaded RF IQI and RF spectrum distortion problems.

In this article, a balanced multipath routing and hybrid transmission approach (BMRHTA) is proposed to effectively alleviate the imbalance of the forwarding load in a sink connection area (SCA) and prolong the network lifecycle for wireless sensor networks (WSNs). To achieve the energy efficient and balanced WSNs, three design issues, including the multipath, multihop, and single-hop transmissions, are jointly optimized to maximize the overall network lifecycle. First, the path load aggregation phenomenon in the SCA, which makes the forwarding packet load unevenly distributed among hotspots, is examined. In order to achieve the load balance in SCA, multiple shortest balanced paths are generated in the BMRHTA model. In the first stage, two uncorrelated shortest paths are discovered from each node to the sink and the optimal path selection cycle can be determined to achieve the SCA load balance. Afterward, a network equilibrium policy is offered to resolve the optimal transmission period of energy balance via hybrid transmission. As a result, the balanced shortest paths, the path selection cycle and the transmission period can be determined in the network formation phase to avoid the excessive load concentration in the subsequent maintenance phase. Simulation results show that the joint two uncorrelated balanced routing and the proposed network equilibrium policy can nearly quadruple the network lifecycle extension, as compared to a conventional node power policy. Also, the proposed BMRHTA achieves better performance than the current state-of-the-art competitive approaches in terms of energy efficiency and lifecycle.

In this article, a distributed topology control approach with hierarchical self-routing (DTC-HSR) is presented for Bluetooth low-energy (BLE) networks. First, the conventional star piconet is replaced by the designed mesh-ring subnet with better throughput and lower delay. To achieve the goal of load balancing design, two phases, including the leader selection and the topology construction are executed in the proposed approach. In the leader selection phase, each master node discovers its adjacent slave nodes to determine a leader master as a coordinator. In the topology construction phase, the local mesh-ring subnet is first formed and then the global mesh-ring subnets are interconnected into the desired DTC-HSR topology. To form the local mesh-ring subnet, each leader master computes the desired number of piconets with even link connectivity and distributes the piconet connection information for each node to form a mesh-ring subnet. In addition, each master node connects with the other local mesh-ring subnets via its associated bridge nodes, including slave nodes, intrabridges, and interbridges to create the definitive DTC-HSR scatternet. Afterward, a hierarchical self-routing strategy is jointly employed for the DTC-HSR to efficiently deliver routed packets through different mesh-ring subnets. Simulation results demonstrate that the DTC-HSR topology with the even connectivity feature outperforms the dual-ring tree (DRT) and cluster-based mesh (CBM) approaches in terms of network transmission and energy efficiency performances. The DTC-HSR configuration thus achieves efficient topology construction and hierarchical self-routing for load balancing in BLE networks.

Ultra-reliable low-latency communication (URLLC) is one of the most important components in the fifth generation (5G) cellular networks for realizing mission-critical applications. In this paper, we jointly optimize the transceiver design and decoding error probability (DEP) of a full-duplex (FD) URLLC system, where the base station (BS) operates in FD mode, while the uplink (UL) and downlink (DL) users work in half-duplex (HD) mode. Accordingly, an optimization problem is formulated to maximize the achievable total (UL plus DL) rate for an FD URLLC system under finite blocklength, subject to the end-to-end (E2E) reliability constraint from the UL user to each DL user and the total transmission power constraint at the UL user and at the BS. We analyze the problem structure and convexify the problem by approximating the channel dispersion in scenarios of high and mid-to-high signal-to-interference plus noise ratio (SINR) regimes, respectively. Next, efficient iterative algorithms are proposed to find the near-optimal power allocation for the UL user and transceiver weights for the BS. Furthermore, closed-form expressions of the transceiver weights are derived, and the convergence of the proposed algorithms is proved. Simulation examples demonstrate the impact of the code blocklength, number of DL users, transmitter/receiver distortion and DEP threshold on the system performance.

In this article, we design neural network (NN)-based transmit power control prediction for solar-powered energy harvesting (EH) communications under single-user (SU) and multiuser (MU) scenarios with real solar data. Although the directional water filling (DWF) is known as the optimal scheme for the SU case, it necessitates the full (past and future) channel state information (CSI) and energy state information (ESI) in realizing the optimal solution over a time period. To conquer this impracticality, an SU-GreenPCNet, which only requires the past short-term CSI and ESI for predicting the SU transmit power, is proposed and trained with the historical solar data. For the MU case, two iterative algorithms, namely, weighted-sum minimum mean-square error (WMMSE) and iterative DWF (IDWF), are investigated to tackle the original nonconvex power control problem when the full state knowledge is assumed to be known in advance. The solutions with the historical solar data are then served as benchmarks for designing two MU-GreenPCNets. As an extension of the SU-GreenPCNet, a centralized scheme is proposed at the central controller for jointly determining the MU transmit power values based on the past short-term state knowledge of all users. A distributed scheme is further investigated to reduce the signaling overhead, in which each transmitter merely utilizes the past short-term CSI, ESI, and MU interference (MUI) knowledge associated with its user pair. The simulation results show that the proposed power control prediction schemes can pragmatically achieve satisfied sum rate performance in both SU and MU scenarios, as compared with the benchmark schemes.

This study proposes a multiple mesh-ring (MMR) topology construction with a hybrid routing strategy for Bluetooth low energy (BLE) networks. To achieve the load-balancing network, three design phases, including leader selection, role decision, and scatternet formation, are executed to generate even MMR configurations. First, each master discovers its adjacent slaves to determine a coordinator. Second, the coordinator computes the desired number of piconets with even scatternet link connectivity and distributes the scatternet connection information for each master. Finally, each designated master connects with its associated nodes, including slaves, intrabridges, and interbridges to create the link balanced MMR scatternet. To jointly design an energy-efficient routing protocol for the MMR topology, a hybrid routing strategy is deployed to perform the shortest path routing inside the mesh-ring subnet, and self-routing through different mesh-ring layers. Simulation results demonstrate that the balanced MMR topology leads to a significant gain in terms of network transmission performance and network lifetime when compared to the conventional dual-ring tree (DRT) and cluster-based mesh (CBM) approaches for BLE networks.

Ultra-reliable and low-latency communication (URLLC) is one of the key applications in fifth generation (5G) cellular networks, which requires extremely high reliability (~99.9999%) and low latency (<; 1 ms). In this paper, the energy efficiency (EE) of multi-user multicarrier amplify-and-forward (AF) networks is maximized under short packet transmission. Accordingly, we formulate an energy-efficient resource allocation problem to jointly optimize the transmit power, subcarrier pairing and allocation, and error probability with finite block-length codes subject to the constraints of the decoding error probability of each user pair, subcarrier pairing and allocation and total transmission power. The formulated problem is non-convex and hence difficult to solve. We analyze the structure of the problem and hence convert it into a convex problem which is approximately equivalent to the original one. An efficient algorithm is also proposed which is capable of producing a near-optimal solution. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC, by showing the impact of various system parameters on EE.

One of the key applications in the fifth generation (5G) communication systems is to support extremely high reliability (~ 99.999%) and low latency (<; 1 ms), namely ultra-reliable and low-latency communication. In this paper, we consider the problem of maximizing energy efficiency (EE) for downlink multi-user multiple-input single-output (MISO) networks under short packet transmission. An optimization problem is formulated to jointly optimize the precoders at the base station (BS) for serving multiple downlink users and the error probability with finite blocklength (FBL) codes, subject to the constraints on decoding error probability per URLLC user and on the BS transmit power. Since the formulated problem is non-convex, we convert this problem into a convex one by analyzing the structure of the EE objective. We then propose an algorithm to find a near-optimal solution for maximizing the EE. Simulation results validate the effectiveness of the proposed algorithm that supports energy-efficient URLLC.

In this paper, we jointly optimize the transceiver design and decoding error probability of a full-duplex (FD) ultrareliable low-latency communication (URLLC) system, where the base-station (BS) operates in an FD mode while the uplink (UL) and downlink (DL) users work in a half-duplex (HD) mode. Accordingly, an optimization problem is formulated for an FD URLLC system under the finite blocklength (FBL) to maximize the achievable total (UL plus DL) rate subject to the reliability (i.e., the decoding error probability) of each link and total transmission power constraints at the UL user and the BS. We convexify the formulated non-convex problem by analyzing the problem structure. Next, an efficient iterative algorithm is proposed to find the near-optimal power allocation for the UL user and the transceiver weights for the BS. Simulation examples show the impact of the blocklength and decoding error probability on the system performance.

This paper investigates the problem of cache node placement and selection with the coexistence of unmanned aerial vehicles (UAVs) cache and device- to-device (D2D) cache in mobile networks. In recent years, caching popular content in UAV base stations has received growing interests as a promising solution to improve communication performances. With the agility and mobility features, the dynamic movement of cache-enabled UAV should be further designed to increase the cache-aided throughput. Different from the conventional caching approaches assuming ground users remain static, we consider the dynamic movement design of UAV to maximize the cache- aided throughput taking into account the movement of ground users. As the formulated optimization problem is NP-hard, we propose a mobility-aware probabilistic caching algorithm in which K-means clustering is utilized to obtain the partition of ground users. Simulation results show that the proposed algorithm notably outperforms the pure D2D cache scheme (without UAV caching) in different cases.

In this paper, subcarrier pairing and power allocation are jointly investigated to maximize the energy efficiency (EE) of a dual-hop multicarrier decode-and-forward (DF) relay network. The optimization problem is formulated as a ratio of the spectrum efficiency (SE) over the entire power consumption of the network subject to total power and subcarrier pairing constraints. A near-optimal iterative scheme is proposed to perform the subcarrier pairing and power allocation for achieving the maximum EE of the network. A two-step suboptimal resource allocation scheme is further proposed to reduce the complexity, in which the subcarrier pairing is first performed by considering the channel quality of the source-to-relay (SR) and relay-to-destination (RD) links, followed by an energy-efficient power allocation scheme to maximize the EE. Numerical results are presented to confirm the effectiveness of the proposed schemes and to demonstrate the tradeoff between EE and SE performances.

In this paper, the outage performance is studied for a full-duplex (FD) relay network that adopts an amplify-and-forward (AF) scheme. The residual self- interference (RSI) generated by a relay node is treated as a useful signal, rather than noise as in the existing works, at a destination node. We provide a new approximate closed-form expression for the outage probability with the knowledge of the instantaneous source-relay (SR) channel gain, and an optimal power allocation (OPA) scheme to further improve the performance by minimizing the derived outage probability in hand. The analytical results are validated through numerical simulations, which reveals that the proposed FD scheme can outperform the conventional half-duplex (HD) and FD schemes in the AF relay networks.

The authors design a transceiver for a multiuser (MU) multiple‐input multiple‐output communication system with a full‐duplex relay (FDR) station. Such a system contains several types of interference, such as MU interference, parallel streams, and self‐interference. To cancel this interference and achieve reliable MU communication, the authors use an equivalent FDR station model, in which the block diagonalisation (BD) scheme, which includes a base station, a relay, and a mobile station, can be used to derive the linear precoders and postprocessing filter. This work also studies link quality enhancement for the full‐link system, including MU direct link and relay links communications. More interference will exist in a full‐link system. They proposed a novel separable precoder and postprocessing algorithms to cancel all interference over the downlink FDR system. Simulation results demonstrate that the proposed transceiver design can provide a reliable sum rate with linear growth performance under the MU scenario with multiple interference and channel estimation errors and obtain diversity gain to improve the full system link quality.

The rapid growth of energy consumption due to the strong demands of wireless multimedia services, has become a major concern from the environmental perspective. In this paper, we investigate a novel energy-efficient resource allocation scheme for relay-assisted multiuser networks to maximize the energy efficiency (EE) of the network by jointly optimizing the subcarrier pairing permutation formed in one-to-many/many-to-one manner, subcarrier allocation, as well as the power allocation altogether. By analyzing the properties of the complex mixed-integer nonlinear programming problem, which is generally very difficult to solve in its original form, we transform the problem into an equivalent convex problem by relaxing the integer variables using the concept of subcarrier time sharing, and by applying a successive convex approximation approach. Based on the dual decomposition method, we derive an optimal solution to the joint optimization problem. The impact of different network parameters, namely number of subcarriers and number of users, on the attainable EE and spectral efficiency (SE) performance of the proposed design framework is also investigated. The numerical results are provided to validate the theoretical findings and to demonstrate the effectiveness of the proposed algorithm for achieving higher EE and SE than the existing schemes.

In this paper, we study an amplify-and-forward relay network with energy harvesting (EH) source and relay nodes. Both nodes can continuously harvest energy from the environment and store it in batteries with finite capacity. Additionally, the source node is capable of transferring a portion of its energy to the relay node through a dedicated channel. The network performance depends on not only the energy arrival profiles at EH nodes but also the energy cooperation between them. We jointly design power control and transfer for maximizing the sum rate over finite time duration, subject to energy causality and battery storage constraints. By introducing auxiliary variables to confine the accumulated power expenditure, this non-convex problem is solved via a successive convex approximation approach, and the local optimum solutions are obtained through dual decomposition. Also, when channels are quasi-static and the power control values of the source (relay) node are preset to a constant, a monotonically increasing power control structure with the time is revealed for the relay (source) node with infinite battery capacity. Computer simulations are used to validate the theoretical findings and to quantify the impact of various factors, such as EH intensity at nodes and relay position on the sum rate performance.

The uplink spectrum of cellular networks is often underutilized due to the asymmetric behavior of wireless data services. To enhance the efficiency, a full-duplex (FD) cellular system is proposed to reuse the uplink spectrum for concurrent uplink and downlink transmissions. By jointly designing transmit beamforming and power control, a weighted sum-interference power minimization problem is formulated for downlink users to manage the self- interference and multiuser interference with the guaranteed QoS of uplink users. An iterative joint transmit beamforming and power control (TB-PC) algorithm is then proposed to solve the joint design problem based on the dual decomposition. The performance is validated by computer simulation, and the proposed FD scheme can potentially improve the spectrum efficiency when a small serving cell is considered for downlink users, as compared with the conventional half- duplex scheme.

A perturbation-based power allocation and multirelay selection approach is proposed for multiple-input multiple-output relay networks in multipath fading channels. In this approach, the relays are partitioned into two groups according to Lagrangian multipliers of power constraints. The power allocation for the relays is perturbed by increasing the power for the potential relay's group, while decreasing the power of the relays in the other group. An optimization framework is then formulated as a tradeoff between the relay selection and the mean square error performance degradation. Computer simulations are used to demonstrate the performance.

In this paper, we develop wireless-powered device-to-device (D2D) communications underlaying a time-division duplex cellular network, where D2D users (DUs) coexist with cellular users (CUs) and harvest energy from a base station during the downlink time for sustaining communications during the uplink time. Two spectrum access modes, coexistence and hybrid, are considered for the DUs. Our goal is to maximize the sum rate of the DUs by jointly designing beamforming and time allocation as well as DU transmit power, while maintaining the quality-of-service for the CUs. In a single DU scenario, the joint design problems in the downlink and uplink are decoupled and solved in sequence. By doing so, the optimal downlink beamforming is found via a semi-definite relaxation (SDR) approach. From a DU power control perspective, a scheme is proposed for obtaining the optimal solution of the remaining uplink design in the coexistence and hybrid modes. For a scenario with multiple DUs, a converted SDR problem is considered to attain the optimal solution of the original problem when the uplink receive beamforming is appropriately predetermined to null out the DU interference. We present simulation results to quantify the impact of various network parameters on the performance of the proposed schemes.

In this paper, we consider an energy harvesting (EH) two-way (TW) dual-relay network, including one non-EH relay and one EH relay equipped with a finite-sized battery. In the network, a space-time transmission protocol with space-time network coding is designed, and an optimal transmission policy for the EH relay is proposed by using a stochastic solar EH model. In this optimal policy, the long-term paired-wise error probability (PEP) of the system is minimized by adapting the EH relay's transmission power to the knowledge of its current battery energy, channel fading status, and causal solar EH information. The designed problem is formulated as a Markov decision process framework, and the conditional capability of the contribution to PEP by the EH relay is adopted as the reward function. We uncover a monotonic and limited difference structure for the expected total discounted reward. Furthermore, a non-conservative property and a monotonic structure of the optimal policy are revealed. Based on the optimal policy and its special structures, the expectation, lower and upper bounds, and asymptotic approximation of the PEP are computed and an interesting result on the system diversity performance is revealed, i.e., the full diversity order can be achieved only if the EH capability index, a metric to quantify the EH node's capability of harvesting and storing energy, approaches to infinity; otherwise, the EH diversity order is only equal to one, and the coding gain of the network is increasing with the EH capability index at this time. Furthermore, a full diversity criterion for the EH TW dual-relay network is proposed. Finally, computer simulations confirm our theoretical analysis and show that our proposed optimal policy outperforms other compared policies.

In this paper, energy-efficient resource allocation algorithms are investigated to improve the energy efficiency (EE) in two-way multi- carrier amplify-and-forward (AF) relay networks by ensuring the quality-of-service (QoS) and balancing the EE of the user links. We formulate an EE-balancing optimization problem that maximizes the ratio of the spectral efficiency (SE) and the total network power consumption by jointly designing the subcarrier pairing at the relay node and power allocation at all nodes under the total transmit power and QoS constraints, thereby leading to a mixed integer programming problem which turns out to be non-convex. Further, we resolve the problem by a series of convex transformations and propose an iterative EE algorithm to determine the solution through Lagrangian dual decomposition. Moreover, a suboptimal EE algorithm is also investigated with reduced complexity at the cost of acceptable performance degradation. Simulation results validate the performance gain of the proposed algorithms and show the performance tradeoff between EE and SE.

Energy harvesting and wireless energy transfer are capable of relieving the battery limitation of wireless devices. In this paper, an amplify-and-forward relay network (AF-RN) is considered, where an energy harvesting source node communicates with a destination node through an energy harvesting relay node. To further improve the performance, the relay is allowed to harvest energy from the radio frequency (RF) signals sent by the source node through a dedicated energy control channel. A joint power control and energy transfer scheme is investigated with the goal of maximizing the achievable sum rate by a deadline subject to energy causality constraints. The problem is challenging in that the objective function is non-convex, and a successive convex approximation approach is proposed to achieve the optimal power control and energy transfer solution. Finally, numerical examples are given to demonstrate the effectiveness of our proposed algorithm.

In this paper, we propose an optimal relay transmission policy by using a stochastic energy harvesting (EH) model for the EH two-way relay network, wherein the relay is solar-powered and equipped with a finite-sized battery. In this policy, the long-term average outage probability is minimized by adapting the relay transmission power to the wireless channel states, battery energy amount, and causal solar energy states. The designed problem is formulated as a Markov decision process (MDP) framework, and conditional outage probabilities for both decode-and-forward (DF) and amplify-and-forward (AF) cooperation protocols are adopted as the reward functions. We uncover a monotonic and bounded differential structure for the expected total discounted reward, and prove that such an optimal transmission policy has a threshold structure with respect to the battery energy amount in sufficiently high SNRs. Finally, the outage probability performance is analyzed and an interesting saturated structure for the outage performance is revealed, i.e., the expected outage probability converges to the battery empty probability in high SNR regimes, instead of going to zero. Furthermore, we propose a saturation-free condition that can guarantee a zero outage probability in high SNRs. Computer simulations confirm our theoretical analysis and show that our proposed optimal transmission policy outperforms other compared policies.

In this paper, we focus on designing energy-efficient power allocation schemes to improve the energy efficiency (EE) in multiuser multi-carrier two-way relay networks which are able to not only balance the EE of the two-way links but also ensure the quality-of-service (QoS). Specifically, the proposed design framework attempts to maximize a ratio of the spectral efficiency (SE) over the total network power consumption under a total power constraint as well as a signal-to-interference plus noise power ratio (SINR) constraint. The original problem is indeed non-convex, and we prove the convexity of the problem after a series of convex transformation. An iterative approach is proposed to find the local optimal solution of the original problem for achieving the maximum EE. Simulation results are provided to demonstrate the tradeoff between the EE and the SE.

Energy harvesting from the surroundings is a promising solution to perpetually power-up wireless sensor communications. This paper presents a data-driven approach of finding optimal transmission policies for a solar-powered sensor node that attempts to maximize net bit rates by adapting its transmission parameters, power levels and modulation types, to the changes of channel fading and battery recharge. We formulate this problem as a discounted Markov decision process (MDP) framework, whereby the energy harvesting process is stochastically quantized into several representative solar states with distinct energy arrivals and is totally driven by historical data records at a sensor node. With the observed solar irradiance at each time epoch, a mixed strategy is developed to compute the belief information of the underlying solar states for the choice of transmission parameters. In addition, a theoretical analysis is conducted for a simple on-off policy, in which a predetermined transmission parameter is utilized whenever a sensor node is active. We prove that such an optimal policy has a threshold structure with respect to battery states and evaluate the performance of an energy harvesting node by analyzing the expected net bit rate. The design framework is exemplified with real solar data records, and the results are useful in characterizing the interplay that occurs between energy harvesting and expenditure under various system configurations. Computer simulations show that the proposed policies significantly outperform other schemes with or without the knowledge of short-term energy harvesting and channel fading patterns.

The use of energy harvesting cooperative relays is a promising solution to battery-limited wireless networks. In this paper, we consider a cooperative system in which one source node transmits data to one destination with the assistance of an energy harvesting decode-and-forward (DF) relay node. Our objective is to minimize the long-term average symbol error rate (SER) performance through a Markov decision process (MDP) framework. By doing so, we find the optimal stochastic power control at the relay that adapts the transmission power to the changes of energy harvesting, battery, channel, and decoding states. We derive a finite-integral expression for the exact average SER of the cooperative system. Further insights are gained by analyzing the asymptotic average SER and its lower and upper bounds at high signal-to-noise ratio (SNR), and the performance is eventually characterized by the occurrence probability of the relay's actions at the worst channel states in the MDP. We also show that the optimal cooperative policy at asymptotically high SNR follows a threshold-type structure, i.e., the relay spends the harvested energy only when the signal is successfully decoded and the source is faced with the worst channel condition in its direct link. Using these observations to quantify the diversity gain and the energy harvesting gain, we reveal that full diversity is guaranteed if and only if the probability of harvesting zero energy quantum is zero, which can be achieved by reducing the energy quantum size or increasing the energy harvesting capability. Finally, we present several numerical examples to validate the analytical findings.

Cognitive radio (CR) is concretely embodied in hierarchical cellular systems by deploying an underlying microcellular system to reuse the underutilized spectrum of a macrocellular system. One of the key challenges to the success of hierarchical cellular systems is to manage the intercell interference between the macrocell and the microcell and to maximize the spectrum efficiency. In this paper, antenna beamforming, power allocation, and multiuser scheduling are jointly designed to opportunistically utilize the macrocell's uplink spectrum in serving multiple secondary microcellular users concurrently. The joint design of antenna beamforming, power allocation, and scheduling with the objective of maximizing the sum rate is indeed a mixed-integer nonlinear programming NP-hard problem. The proposed simpler iterative subgradient projection and semidefinite programming approach can obtain better performance than the conventional zero-forcing beamforming. Furthermore, unlike the optimal singular value decomposition (SVD) beamforming that requires all users to have channel knowledge at the receiver for cooperation, the proposed joint design methodology requires no channel knowledge at the receiver and can outperform the SVD beamforming without user scheduling. When considering both implementation complexity and performance enhancement issues, the proposed joint power allocation, multiuser scheduling, and antenna beamforming technique can help provide important insights into the design of interference management techniques for hierarchical CR systems.

An optimal transmission policy is considered for energy harvesting (EH) wireless point-to-point communications, wherein the source node is solar-powered and equipped with a finite-sized battery. The long-term outage probability is minimized by adapting the transmission power to the causal energy arrival information, battery energy amount and channel fading through a Markov decision process (MDP) framework. We reveal an interesting saturated structure of the expected outage probability for which it eventually converges to a battery empty probability in high signal-to-noise power ratio (SNR). This phenomenon that links outage probability with EH capability is derived based on a monotonic and bounded differential structure of the long-term reward and a threshold structure of the optimal policy. Furthermore, a saturation-free condition on the outage performance is presented as well. Simulations confirm the theoretical analysis and the superiority of the proposed policy.

Green communications have emerged as a demanding concept for improving the network energy efficiency (EE). In this paper, a pricing-based approach is investigated to achieve energy-efficient power allocation in relay-assisted multiuser networks. We introduce a network price to the power consumption as a penalty for the achievable sum rate, and study its impact on the tradeoff between the EE and the spectral efficiency (SE). It is hard to directly solve the problem as it is non-convex, and thus a concave lower bound on the pricing-based utility is applied to transform the problem into a convex one. Through dual decomposition, a q-price algorithm is proposed for iteratively tightening the lower bound and finding the optimal solution. In addition, an optimal price that enables green power allocation is defined and found from the viewpoint of maximizing EE. We further analyze the optimal power allocation strategies of the pricing-based approach in a two-user case under different noise operating regimes, yielding on-off, water-filling, and channel-reversal approaches, etc. Finally, the performance of the proposed approach is evaluated by computer simulations, and we characterize the interaction between the EE and SE for various network parameters when the network is designed from the energy-efficient perspective.

Energy efficiency is a growing concern for the future wireless networks as energy consumption becomes a global environment problem. In this paper, an energy-efficient power control scheme is investigated for achieving the maximum energy efficiency in multiuser two-way balancing relay networks. We formulate the design problem as a ratio of the spectral efficiency over the entire energy consumption of the network under a total power constraint. An optimal power control scheme is proposed to iteratively improve the efficiency and finally reach the globally optimal solution. Compared with a heuristic scheme where the total available power is equally distributed among all nodes, the proposed scheme can dramatically improve not only the energy efficiency but also the spectral efficiency.

Cognitive users are expected to be capable of exploring spectrum holes over a wide range of frequencies. Motivated by the sparse characteristic of underutilized spectrum, we consider sparse spectrum sensing using compressive sensing techniques for cognitive orthogonal frequency division multiplexing (OFDM) systems. The spectrum sensing problem is formulated as a multi-subcarrier detection problem, solved via the composite hypothesis testing and Neyman-Pearson criterion. Considering the availability of channel state information (CSI) at the cognitive device, two sparse spectrum sensing approaches are proposed for detecting the compressive received signals in time domain. For the purpose of complexity reduction, we further incorporate a sub-block partition scheme into the proposed approaches to leverage the spareness of the spectrum occupancy. The proposed approaches enable a flexible tradeoff between the implementation complexity and the sensing accuracy for wideband cognitive radios.

The rapid growth of diversified applications has led to significant increase in data traffic and energy consumption in wireless networks. Hence, the energy efficiency becomes one of critical performance indices for designing next-generation wireless networks. In this paper, an optimization framework of power allocation is investigated for maximizing energy efficiency in multiuser relay networks. Under a total power constraint, we formulate the design problem with the objective as the ratio of the spectral efficiency over the entire power consumption of the network. An optimal energy-efficient power allocation algorithm is proposed for the source and the relay nodes to approach the maximum efficiency in an iterative manner. Compared with a heuristic scheme where the total available power is equally allocated to all nodes, the proposed optimal power allocation algorithm can dramatically improve energy efficiency with a slight loss in spectral efficiency.

The deployment of relays has been regarded as an effective means to improve the performance of the conventional wireless networks. The achievable performance generally depends on the availability of channel state information (CSI) at the relay and the destination nodes. The requirement of the dual-hop CSIs at the destination node, however, results in high computational complexity and unrealistic assumption for mobile relay networks, especially when the source-to-relay channel fluctuates rapidly due to the mobility of the source node. In this paper, we design an amplify-and-forward (AF) mobile relay network in which the instantaneous CSI of the source-to-relay link is unknown to the destination node. A composite hypothesis testing problem is formulated to derive the optimal detector. Unlike the existing approaches, the designed mobile relay network does not require excessive signalling overhead for obtaining the instantaneous CSI of the source-to-relay link at the destination while offering a significant performance improvement over the conventional wireless networks without the help from relays. The obtained performance even approaches that of the relay networks with full CSIs. When considering both implementation complexity issues and performance enhancement, the proposed detection scheme provides an alternative way of developing a low-complexity mobile relay networks.

This paper jointly designs power allocation and two-dimensional (2-D) equalizers for multiple relay nodes in a distributed multiple-input multiple-output (MIMO) relay network. Based on the minimum mean-square error (MMSE) criterion, 2-D temporal-and-spatial equalizers are investigated at relays with a total equalizer power constraint for equalizing-and-forwarding the signals from the source to the destination in multipath fading channels, which has not been addressed in the existing literature. A bisection algorithm is proposed to obtain the optimal solution by utilizing the Karush-Kuhn-Tucker (K.K.T.) conditions. With the proposed 2-D equalizers, the distributed MIMO relay network can not only effectively mitigate the inter-symbol interference (ISI) and multiple-antenna interference (MAI) but also achieve the spatial and multipath diversity gains. Simulation results show that the proposed scheme can provide a substantial performance gain in terms of the bit error rate (BER) as compared with the conventional one-tap equalizer scheme for distributed MIMO relay systems.

This paper considers an equalize-and-forward (EF) strategy for two-hop multiple-input multiple-output (MIMO) relay networks in multipath fading channels, where the relay nodes and the destination only know its respective backward channel state information (CSI) knowledge, and each node is equipped with multiple antennas for transmitting, receiving or forwarding signals. For such a relay network, the inter-symbol interference (ISI) and multiple-antenna interference (MAI) are two detrimental effect to degrade the bit error rate (BER) performance. In order to compensate for the interference problem, we design temporal-and-spatial (TS) equalizers to assist in forwarding and decoding the signals at the relay nodes and the destination node, respectively. Based on the minimum mean square error (MMSE) criterion with a total power constraint, an optimization framework is formulated to find the optimal TS equalizers with the backward CSI knowledge, and an iterative algorithm using the Karush-Kuhn-Tucker (K.K.T.) conditions is investigated to achieve the optimal solution. With these optimal TS equalizers, the MIMO relay network can not only effectively mitigate the interference but also provide both the spatial and multipath diversity gains. Simulation results indicate the effectiveness of the proposed algorithm, yielding a significant improvement on the BER performance.

The idea of cognitive radio (CR) has embodied concretely in hierarchical cellular systems by deploying an underlying microcellular system to reuse the underutilized spectrum licensed by an macrocellular system. The fundamental challenges for successfully realizing such hierarchical systems are to manage the intercell interference between the macrocell and microcell and to pursue the goal of maximizing the spectrum recycling efficiency. In this paper, we jointly consider antenna beamforming, power allocation, and multiuser scheduling for the microcellular system to opportunistically utilize the uplink spectrum of the macrocell and to concurrently serve multiple secondary users in the downlink. With the objective of maximizing the sum rate, the three-dimensional joint design problem is often formulated as a mixed integer nonlinear programming (MINLP) which is an NP-hard problem and very complicated to solve. We resort to semidefinite relaxation (SDR) techniques to convert to the cumbersome optimization problem into a convex problem by introducing an interference-related auxiliary variable. An iterative algorithm based on semidefinite programming is proposed to achieve the optimal solution. The zero-forcing (ZF) beamforming and the singular value decomposition (SVD)-based beamforming with the best scheduling are simulated for performance comparisons, and our simulation shows that the proposed scheme is much superior to the ZF scheme and quite close to the SVD scheme with a slight performance gap of 1 bps/Hz.

In this paper, we present a joint antenna beamforming and power allocation technique to maximize the multiuser sum rate in an underlying microcellular system which reuses the same spectrum of a macrocellular system. One challenge in this kind of hierarchical cognitive radio (HCR) systems is to manage the interference between the macrocell and the microcell. The key contribution of this paper is to develop an optimization technique for antenna beamforming that can maximize the achievable sum rate of the underlying cognitive radio (CR) microcellular system and control the interference between the macrocell and the microcell with a satisfaction level. The proposed technique optimizes the sum rate performance by maximizing its lower bound and transfers the original non-convex problem into a convex optimization problem by introducing auxiliary variables to confine the intra-user interference power among the secondary system. Next, an iterative sum rate maximization (ISM) algorithm is developed to find the beamforming weights and the allocated power for each secondary user to simultaneously maximize system sum rate, coverage, and concurrent multiuser transmission probability in the HCR system. The developed joint design methodology provides valuable insights into the design of an optimal HCR system for various numbers of users as well as cell coverage, and can quantitatively optimize the performance tradeoffs in the hierarchical multiuser CR systems for current and future wireless communication applications.

In this paper, we investigate the impact of a receiver amplifier on orthogonal frequency division multiplexing (OFDM) and single-carrier block transmission (SCBT) systems which are two potential air interfaces for future short-range wireless communications at 60GHz radio spectrum. We derive a generalized baseband equivalent model for the receiver amplifier by using a power series to capture the nonlinear distortion behavior with several parameters. Based on this model, we develop a simulation flow in the baseband domain for evaluating the system performance deterioration with the non-ideal receiver amplifier. Computer simulations are used to compare the performance of OFDM and SCBT systems under several radio frequency (RF) parameters, such as 1dB compression point, the 3rd-order intercept point, etc. The developed methodology across the baseband and RF can help us jointly optimize the system performance and implementation cost with the nonlinear amplifier at the receiver side.

In this paper, we investigate the service coverage for interweaving cognitive radio networks with cooperative relays in shadowed areas within the metropolitan region. We highlight the influence of relays on the primary and secondary systems in severe shadow fading channels. The outage probability is utilized as a comprehensive performance metric to characterize the coverage quality for the cognitive radio system with relays. We show that cooperative spectrum sensing among the secondary transmitter and relays can improve the vacant spectrum detection probability and the false alarm probability, thereby making the additionally introduced interference by relays to be constrained within a tolerable limit. Overall, our analysis unveils that both the spectrum utilization rate and the spatial coverage can be significantly improved through the use of the relays, and there exists the spatial selectivity phenomena for both the primary and secondary systems.

In this paper, we resort to the expectation-maximization (EM) algorithm to tackle the inter-carrier interference (ICI) problem, caused by time-variant multipath channels, for both orthogonal frequency division multiplexing (OFDM) systems and bit-interleaved coded modulation (BICM)/OFDM systems. We first analyze the ICI in frequency domain with a reduced set of parameters, and following this analysis, we derive an EM algorithm for maximum likelihood (ML) data detection. An ML-EM receiver for OFDM systems and a TURBO-EM receiver for BICM/OFDM systems are then developed to reduce computational complexity of the EM algorithm and to exploit temporal diversity, the main idea of which is to integrate the proposed EM algorithm with a groupwise ICI cancellation method. Compared with the ML-EM receiver, the TURBO-EM receiver further employs a soft-output Viterbi algorithm (SOVA) decoder to exchange information with a maximum a posteriori (MAP) EM detector through the turbo principle. Computer simulation demonstrates that the two proposed receivers clearly outperform the conventional one-tap equalizer, and the performance of the TURBO-EM receiver is close to the matched-filter bound even at a normalized maximum Doppler frequency (MDF) up to 0.2.

In this paper, we jointly design antenna beamforming and power allocation to maximize the sum rate for multiple users in a hierarchical cognitive radio (CR) system, where an underlying microcellular system adopts dynamic spectrum access techniques to reuse the same spectrum of a macrocellular system. One challenge in this kind of hierarchical CR systems is the interference management between the macrocell and the microcell. We suggest utilizing the antenna beamforming technique to overcome the interference between the macrocell and the microcell, while maximizing the achievable sum rate of the underlying CR microcellular system. We formulate an optimization problem to achieve the objective of maximizing sum rate subject to the constraints of the maximum allowable interference power to the primary system, the minimum required signal-to-interference plus noise power ratio (SINR) for the secondary users, and the maximum transmission power for the underlying CR base station (BS). An iterative rate maximization (IRM) algorithm is developed to find the optimal solution. This developed methodology provides an important insight into the design of an optimal hierarchical CR system for various numbers of users as well as cell coverage.

In this paper, we propose a modified DVB-T system architecture with a multi-carrier multi-code transmission format. The main techniques we use in the system include the orthogonal Walsh code spreading and soft multipath interference cancellation (soft MPIC). Both techniques are used to combat the system degradation caused by the multipath effect. With the spreading gain and the path diversity gain achievable from the used techniques, the proposed system architecture shows much better performance compared with the original DVB-T system.

In this paper, we derive the decision-feedback (DF) discrete Fourier transform (DFT)-based channel estimation method from Newton's method for space-time block code (STBC)/ orthogonal frequency division multiplexing (OFDM) systems. Through our derivation, the equivalence between Newton's method and the DF DFT-based method is established. Computer simulations are also used to demonstrate the equivalence of the two methods in terms of BER and normalized square error (NSE) performance. Finally, the results presented in this paper also hold for conventional OFDM systems.

In this paper, we investigate channel estimation for orthogonal frequency division multiplexing (OFDM) systems with space-time block code (STBC) in mobile wireless channels. Our proposed method consists of two-stage processing and is developed on the basis of the classical discrete Fourier transform (DFT)-based channel estimation method. In the initialization stage, we employ a multipath interference cancellation technique to estimate multipath delays and multipath complex gains. In the tracking stage, we develop a refined decision-feedback (DF) DFT-based channel estimation method in which a few pilot tones inserted in OFDM data symbols are applied to form an optimal gradient vector at the first iteration such that the error propagation effect is mitigated. In order to reduce computational complexity, an approximate weighting matrix is adopted to avoid matrix inversion. We demonstrate the proposed method through computer simulation of an STBC/OFDM system with two transmit antennas and a single receive antenna. The results show that our method outperforms the classical DFT-based method, the STBC-based minimum mean square error (MMSE) method, and the Kalman filtering method as well, and that significant signal-to-noise ratio (SNR) performance improvement can be achieved, especially when a high-level modulation scheme, e.g. 16QAM, is adopted in high-mobility environments.

This letter presents a complementary codes pilot-based space-time block code/orthogonal frequency division multiplex (STBC/OFDM) system. In this system, a pair of complementary codes transmitted in a pre-defined order with the OFDM data signals is used as the pilot signals in a two-antenna transmitter diversity system, and used to estimate the channels for optimal data detection at the receiver side. A complete receiver architecture has been designed and Monte Carlo simulations have been used to verify the performance of the system in mobile radio fading channels.