This paper proposes a novel Particle Swarm Optimization-Aided Reinforcement Learning (PSO-RL) framework for efficient beam and power management in reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) networks. To optimize dynamic beam coordination and resource allocation, we introduce a reinforcement learning (RL)-based approach that integrates value and policy networks, with the policy network enhanced by a target policy network (TPN). By employing PSO, we refine TPN parameters to accelerate convergence and enhance solution exploration. Simulation results validate the effectiveness of the proposed PSO-RL framework, demonstrating significant performance improvements in RISassisted network deployments. The method enhances system capacity by 29.75 % and improves edge user capacity by 36.65 %. These results highlight the potential of PSO-RL for optimizing beam allocation in dynamic urban network environments.

Reconfigurable Intelligent Surfaces (RIS) are perceived as cost-effective entities capable of expanding network coverage and enhancing system performance in 6G networks. Considering an RIS-assisted 6G millimeter network, this paper aims to find low-complexity beam and power allocations that maximize the network throughput. To resolve the complexity of such a mixed-integer non-convex programming problem, an iterative power decremental method is developed to solve the beam and power allocation problem alternatively under the system design constraints, making use of the concept of difference of convex functions and the zeroth-order gradient ascent method. Simulation results verify the superiority of the proposed method and its effectiveness in RIS-assisted millimeter networks.

Three-dimensional (3D) positioning technology plays an important role in millimeter wave (mmWave) non-terrestrial or integrated sensing and communication (ISAC) networks in sixth-generation (6G) systems. However, the complexity of joint range and orientation estimation in mmWave hybrid beamforming (HBF) systems forms a technical hurdle to its practical realization. In view of the recent advancement in green learning (GL) technology, a low-complexity GL architecture is developed herein for 3D positioning in mmWave HBF systems. The entire architecture only consists of one layer of unsupervised representation learning, followed by a supervised feature learning stage and a regression layer for parameter estimation. Compared to the typical deep learning method, the complexity of the proposed method is at least 3 order lower, while the performance is comparable to that of the maximum likelihood estimations of individual parameters given perfect knowledge of the other parameters. This presents the potential of GL in ISAC for future$6G$systems.

In this paper, we consider a prospective receiving hybrid beamforming structure consisting of several radio frequency (RF) chains and abundant antenna elements in multi-input multi-output (MIMO) systems. Due to conventional costly full connections, we design an enhanced partially connected beamformer employing a low-density parity-check (LDPC)-based structure. As a benefit of the LDPC-based structure, information can be exchanged among clustered RF/antenna groups, which results in a low computational complexity order. Advanced message passing (MP) capable of inferring and transferring information among different paths is designed to support the LDPC-based hybrid beamformer. We propose a message-passing enhanced antenna and RF chain selection (MARS) scheme for minimizing the operational power of antennas and RF chains of the receiver as well as hybrid beamforming. Furthermore, sequential and parallel MP schemes for MARS are designed, namely, MARS-S and MARS-P, respectively, to address the convergence speed issue. A heuristic genetic algorithm is designed for receiving hybrid beamforming, comprising gene generation initialization, elite selection, crossover, and mutation. Simulations validate the convergence of both the MARS-P and the MARS-S algorithms. Due to the asynchronous information transfer of MARS-P, it requires higher power than MARS-S, which strikes a compelling balance among power consumption, convergence, and computational complexity. It is also demonstrated that the proposed MARS scheme outperforms the existing benchmarks using the heuristic method of fully/partially connected architectures in the open literature by requiring the lowest power and realizing the highest energy efficiency.

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km$^{2}$show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services.

A streaming-based Indoor Positioning System (IPS) is developed for beam-based cellular networks that apply directional beam patterns for communications and sensing. The proposed IPS is designed to provide a real-time positioning service by leveraging the computing framework and the streaming modules of Apache Spark streaming. Under the Spark Streaming framework, a beam and model-based location tracking method is designed and optimized for positioning precision and resource utilization. Experiments show that the average positioning error of the proposed user tracking method is about 0.4 meters, which achieves 56.2% improvements over static locating methods. More importantly, a static timing analysis is carried out to evaluate the throughput and bottlenecks of the proposed IPS system. Based on the analysis, our proposed architecture is capable and promising for low-latency IPS provisioning under the O-RAN framework.

For millimeter or Tera-Hertz communications, channel capacity is often found limited by its channel diversity due to the sparse or line-of-sight transmission conditions in these frequency bands. This diversity problem exacerbates particularly in future non-terrestrial communications. Wireless communication that exploits the diversity of orbital angular momentum (OAM) in electro-magnetic waves is considered one of the potential technologies to push the limit despite the skepticism for its long-rang transmissions in free space. Inspired by the recent advances on hybrid beamforming, we propose a novel OAM beam focusing method to explore the feasibility of multiple input multiple output (MIMO) OAM wireless transmissions. With the proposed optimal beam focusing and power allocation scheme and the use of a concentric uniform circular array (CUCA) of diameter 1.2 meters (m), we show that signals of up to three OAM modes can be delivered to a receive uniform circular array of diameter 20 cm at a distance of 500 m, which presents the potential of MIMO OAM for future 6th generation communication systems.

Low Earth orbit (LEO) satellite constellation is capable of providing global coverage area with high-rate services in the next sixth-generation (6G) non-terrestrial network (NTN). Due to limited onboard resources of operating power, beams, and channels, resilient and efficient resource management has become compellingly imperative under complex interference cases. However, different from conventional terrestrial base stations, LEO is deployed at considerable height and under high mobility, inducing substantially long delay and interference during transmission. As a result, acquiring the accurate channel state information between LEOs and ground users is challenging. Therefore, we construct a framework with a two-way transmission under unknown channel information and no data collected at long-delay ground gateway. In this paper, we propose hierarchical multi-agent multi-armed bandit resource allocation for LEO constellation (mmRAL) by appropriately assigning available radio resources. LEOs are considered as collaborative multiple macro-agents attempting unknown trials of various actions of micro-agents of respective resources, asymptotically achieving suitable allocation with only throughput information. In simulations, we evaluate mmRAL in various cases of LEO deployment, serving numbers of users and LEOs, hardware cost and outage probability. Benefited by efficient and resilient allocation, the proposed mmRAL system is capable of operating in homogeneous or heterogeneous orbital planes or constellations, achieving the highest throughput performance compared to the existing benchmarks in open literature.

An efficient beam and channel acquisition and tracking scheme is developed under a reconfigurable hybrid beamforming (R-HBF) architecture for wideband millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Under such an R-HBF, a hierarchical multi-resolution beam scanning and space-frequency compressive sensing method is proposed to achieve fast beam and channel acquisitions in mmWave MIMO orthogonal frequency division multiplexing (OFDM) systems. With the initial frequency-domain beam space channel estimates, Kalman-based algorithms are further developed to track the transmit and receive beamforming directions and the channel coefficients of the sparse propagation paths in mmWave mobile channels. Simulations show that the proposed method can greatly reduce the beam training time, and that the channel capacity evaluated with the estimated channels is only 2 dB inferior to the one with ideal channels. Moreover, the beam selection error rates are less than 10−3in typical urban mmWave mobile channels and traveling speeds even if the transmit power is only 10 dBm.

A framework that incorporates channel modeling, position estimation, and error analysis methods is developed for network-based indoor positioning with radio signal strength (RSS) measurements of WiFi access points (APs). To construct an accurate channel model with RSS measurements severely influenced by propagation attenuations, multipath reflections, and shadowing effects, a novel sparse Bayesian learning algorithm is developed to model the radio power map (RPM) in indoor space. Based on the proposed RPM model, a 2-stage positioning method is further developed. In the first stage for coarse positioning, the location is determined up to a room-scale indoor space. Then, in the second stage for fine positioning, the RPMs of the given indoor space are used for location estimation in the space with a Bayesian estimator. The mean squared positioning errors are verified with the Bayesian Cramer-Rao lower bound. Extensive experiments show that the average positioning error of the proposed RPM-based approach is 1.98 meters which achieve 22 percent improvements over the state-of-the-art RSS-based indoor positioning methods. More importantly, the proposed modeling and positioning method can effectively exploit the spatial relationship in the RSS samples to improve positioning accuracy.

To improve angular resolutions and precision in radar object detection and estimation, designs of radar systems tend to employ more antenna elements or modules, for instance the millimeter wave (mmWave) radar for autonomous driving. However, as the number of antennas increases, the computational complexity for radar signal processing also increases drastically. An effective way to resolve such a dilemma is to select optimal antenna subarrays for individual objects under detection and estimation, which not only can constrain the computational complexity for subsequent radar signal processing, but also can maintain the precision of angular estimation. In view of the complexity of optimal subarray selections, existing results typically use greedy approaches to search for suboptimal solutions. More recently, deep learning (DL) methods are proposed to improve the correctness of subarray selection. Motivated by the recent advancements in DL, we propose herein a novel DL method of subarray selection for radar multi-target detection and angles estimations based on a one-dimensional Saak transformation. Compared to the existing DL method based on convolutional neural networks (CNN), the proposed method not only has a higher correctness in subarray selection, but also has a lower computational complexity. Simulation results also show that signal collections for subarray selection can be limited to two chirp signal durations, which significantly reduce the time delays for subsequent radar signal processing.

The throughput performance of smart antenna systems (SASs) in WiFi networks is studied in this work. Considering a WiFi network whose access points (APs) support the switched-beam SAS, a beam switching strategy which is compatible with the legacy WiFi protocol is proposed to enhance more concurrent links by allowing APs directionally sensing and accessing the wireless channel. The throughput advantage is verified by the simulation results in random deployment scenarios. More importantly, the proposed beam switching strategy provides extra design factors to adjust or balance the downlink and uplink throughput per WiFi user to meet the requirements of various applications and scenarios.

This paper studies the deployment problem of Bluetooth Low Energy (BLE) gateways, and proposes an effective algorithm for it to support e-Healthcare services in hospitals or daycare centers. With the acceleration of aging, demands for elder care services have increased dramatically. Gateway deployments for Internet of Things (IoT) play an important role to support such services over remote intelligent healthcare platforms, in particular when allowing patients wearing wireless medical devices to walk freely in wards or indoor spaces. The gateway deployment problem becomes much more complicated under this condition due to obstacles and human bodies in the way between the gateways and the patients. In addition, the hardware specification of a gateway and the signal shadowing by patients themselves also limit the connectivity capacity of a gateway. These motivate us to study a design that takes all the factors of indoor floor plan, radio propagation attenuation, user densities, and human body signal shadowing into the consideration of gateway deployment. Particular features of our proposed algorithm include: 1) guaranteeing a full signal coverage; 2) accounting for patients' self-shadowing effects; and 3) adapting to non-uniform user densities in different spaces of indoor environments. The average user service continuity degree can reach up to 0.99 in indoors with various sizes of room spaces and user densities. Moreover, performances of the average gateway serving load implies the selected locations to deploy gateways are adequate.

An efficient beam and channel tracking method is developed under a reconfigurable hybrid beamforming (R-HBF) architecture for wideband millimeter wave (mmWave) multiple input multiple output (MIMO) systems. With the R-HFB architecture, a multi-resolution compressive sensing (CS) method is proposed for initial beam and channel acquisitions in mmWave MIMO orthogonal frequency division multiplexing (OFDM) systems. With the initial beam and channel estimates, recursive schemes are further developed to track the angles of arrivals and departures and the channel coefficients of time-varying multiple channel paths. Simulation results show that the proposed method can significantly reduce the beam training time with a beam selection error rate less than $10^{-3}$ at typical vehicular speeds when the transmit power is greater than 10 dBm.

The throughput performance of a heterogeneous dense small cell network (HetNet) with collocated WiFi and LTE interfaces is studied herein. From a geometrical statistical viewpoint, the average throughput for LTE with the function of further enhanced inter-cell interference coordination (FeICIC) is investigated in an attempt to find a proper operation setting for FeICIC to benefit users on cell edges. Furthermore, the optimal setting for WiFi offloading is also studied to maximize the worse user experience in such a HetNet. The proposed analysis method allows us to further study a proper strategy that provides fair services to users under the severe interferences in HetNet.

Selective and opportunistic amplified-and-forward (SOAF) relaying has been shown more effective than the typical opportunistic AF relaying for cooperative automatic repeat request (ARQ). The key to exploit the diversity potential of SOAF ARQ lies in a proper quality control mechanism for the relayed signals. Although quality control methods have been proposed from the viewpoint of capacity outage probability, the methods are shown herein not enough for SOAF ARQ to exploit the full diversity in packet error rates (PERs). Reinvestigating the quality control problem from the perspective of maximum likelihood sequence detection, our analysis shows that the PERs can attain the full spatial diversity offered by relays in every ARQ round only if the thresholds to control the received signal qualities of the forwarding relays increase with the signal-to-noise ratio (SNR). Sufficient conditions on the thresholds settings are proposed, which explicitly characterize the relationship among the threshold requirements, the SNR, and the minimum code-word distance of the employed channel code. An effective threshold searching algorithm is further developed for SOAF ARQ to exploit both the diversity and the SNR gains in PER. Simulations with trellis codes also verify our analysis and the effectiveness of the proposed quality control method.

A practical and effective service deployment method is proposed for coordinated multi-point (CoMP) transmissions in ultra-dense networks (UDN). Despite the potential of UDN for throughput enhancement, the throughput of edge users is still considerably deteriorated by interferences from adjacent cells. Applying the CoMP transmissions among cells appears to be a feasible solution to resolve this issue. However, the nature of the almost randomly deployed locations of small cell base stations (BSs) and their limited numbers of antennas make it difficult to design an effective resource coordination mechanism for the CoMP services in UDN. Reconsidering this problem from an antenna allocation viewpoint, we present a simple yet effective approach to improve the cell-edge throughput under a centralized radio access network architecture. The first step is to partition the entire service area into multiple subareas, and the second step is to allocate the antenna degrees of freedom to each subarea. To improve the scalability of this service method, a two-tier service deployment method is further introduced based on the BS clustering and service area partitioning of the entire network. The simulation results show that the proposed service architecture can provide a significant improvement on the cell-edge throughput while still maintaining a good average throughput in the UDN.

A reconfigurable hybrid beamforming (HBF) system is presented to dynamically exploit the channel diversities and antenna array directivity in millimeter wave (mmWave) channels. To develop an effective method for HBF architecture adaptation in wideband mmWave channels, the performance of an architecture is evaluated based on its average frequency-domain channel capacity. According to our analysis and simulations, the spatial covariance of the frequency-domain channels of using HBF can still be approximately modeled in a Kronecker form. Furthermore, beamforming directivity and channel spatial diversity have different advantages on capacity improvement in different transceiving conditions. Based on the results of channel modeling, system analysis, and simulations, a reconfigurable HBF architecture is thus proposed to enable flexible adjustments of the active subarrays, the antennas and polarizations of each subarray, and the active analog front-end and radio frequency chains linked to the active subarrays. A training procedure is meanwhile developed for the HBF system to learn proper architectures according to the system constraints, the channel statistics, the baseband precoder structure, and the channel capacity between the transmitter and the receiver. Compared with a fixed HBF system, such a reconfigurable system provides 3-15-dB power gains in channel capacity under various simulated channel conditions.

A cross-layer analysis framework is developed for the design and performance evaluations of cooperative automatic repeat request (ARQ) with opportunistic multi-point relaying (OMPR) in mobile networks. In particular, three exemplar OMPR functions and their combinations are studied for opportunistic distributed space-time coded (ODSTC) retransmissions in time-division duplex long-term evolution advanced systems. According to the complexities and coordination delays of the OMPR functions, the influences of channel fading on the spatial and temporal diversities offered by ODSTC-based ARQ and their impacts on the system throughput, transmission energy consumption, and relays' queuing delays can be evaluated effectively under this framework. Based on the analytical results, a rate adaptation strategy is also proposed to maximize the system throughput subject to constraints on queuing delays at relay stations (RSs) and packet error rates at mobile stations (MSs). Analysis and simulation results show that in typical vehicular speeds, using maximally two active RSs for ODSTC is a very robust and effective choice to provide near best system throughput with less energy consumptions and lower protocol complexities. The throughput gains over a system without using RS retransmissions range from 16 ~ 80% for cell-edge MSs, with only 25 ~ 45% of the transmission energy consumption.

A joint beam training and rate adaptation mechanism for downlink transmissions is presented for multiple input multiple output systems with switched beam antennas. The proposed method only depends on the beam patterns and the acknowledgement (ACK) replied by the receiver, and is compatible with the legacy WiFi standards. This feature makes not only the complexity of the beam training scheme extremely low, but also the feasibility relatively high. Due to the practical limitations of antenna designs and manufacturing, beam patterns may not have distinctive directivity for certain directions. Therefore, beam pattern scaling is utilized to make the resultant beam patterns match well with the ideal user directions and channel conditions.Simulation results with real switched beam patterns show that the precision of the training scheme is very high.

Coordinated multipoint (CoMP) transmission has been considered an important technology to manage the serious inter-cell interference in ultra-dense small cell networks (UDN). In this paper, a scheduling framework and a two-tier spectrum resource allocation method for CoMP transmissions in UDNs are proposed. For a number of clusters, the tier-one allocation divides the resource blocks (RBs) into two sets: one is for center users and the other is for edge users. Tier-two resource allocation is performed at a central scheduler to allocate spectrum resources to edge users on a cluster basis. Applying the two-tier scheduling method to a simulated UDN on OpenStack and OpenAirInterface (OAI) software platforms, the UE throughput performances are presented and discussed. We further analyze the computational complexity of each step for performing 1-slot CoMP transmission. The numerical simulation results showed that, upon deploying the small cells in a Poisson Point Process (PPP) with mean density 0.0003/km2, and the ratio of eNBs to UEs is (3:1), more than 50% UEs have 15Mbps throughput performance.

A new approach for indoor positioning is presented, aimed at designing a WiFi positioning system that is feasible and convenient for both service providers and end users. In the proposed approach, only access points (APs) need to collect the received signal strengthes (RSS) of mobile devices, and use these RSS samples to jointly estimate the devices' locations. To enhance the accuracy of positioning, the relationship between the RSS samples and their geometrical locations is explored, leading to a sparse Bayesian model for the radio power map of the RSS observations of each AP. With more than 20 training anchors, the accuracy of the proposed model-based positioning method can be lower than 3.4 meters in an indoor space with only 4 randomly deployed APs, which outperforms the fingerprinting method by 0.4 meter. Extensive experimental results also verify that the proposed positioning service can offer considerable accuracy with only limited efforts in training, suggesting that the prototype is realistic for randomly deployed dense WiFi networks.

Despite that network capacity can increase with cell densification, interferences from adjacent cells may deteriorate the throughput of users close to cell edges. To alleviate this problem and to offer a service quality with no significant cell edge degradation, we propose a feasible coordinated multi-point (CoMP) transmission architecture from a new look on antenna allocation in dense small cell networks (DSN). The CoMP idea is based on a two-step mechanism; the first step partitions the entire service region into multiple areas, and the second step adjusts the number of active users for each area. We then develop an antenna allocation algorithm to enhance the cell-edge user throughput, making use of the spatial multiplexing (SM) gain, array gain and cooperative diversity gain. Simulation results show that applying SM to DSN plays a crucial role to enhance the user throughput. And one of our proposed cooperative schemes is shown to have a great potential to improve the throughput for a large portion of users or the cell service area.

A reconfigurable hybrid beamforming (HBF) system is proposed to flexibly exploit the channel diversities and antenna array directivity in millimeter wave channels. Sub-arrays of antennas can be reconfigured in the proposed HBF system to adjust the spatial and polarization diversities and the array gains according to the channel environments and the relative locations between the transmitter and the receiver. Based on the reconfigurable HBF system, a training procedure is also proposed for the transmitter and the receiver to learn the best operating mode according to channel quality and characteristics. Compared to a fixed HBF system, the reconfigurable HBF system has 3 to 10 dB power gains in channel capacity in various simulated channel conditions.

A hybrid architecture of radio frequency (RF) beamforming (BF) and baseband (BB) signal processing is presented for downlink (DL) multi-user (MU) multiple input multiple output (MIMO) systems operating in millimeter wave (mmWave) channels. To avoid the strong spatial correlation and the transmission or reception dead zones caused by the use of single-polarized antenna arrays in mmWave radio, the hybrid architecture is implemented on dual-polarized (DP) planar antenna arrays (PAA). To balance the quality of each data stream of each user under a limited power consumption, an iterative algorithmfor jointly designing the RF beamformers (BFer), BB precoder, BB equalizers, and the power allocation is proposed for the DL MU-MIMO system of using the hybrid architecture. Simulation results show that the signal to interference-plus-noise ratio (SINR) achieved with the MU-MIMO system that uses the proposed hybrid architecture is only 5~6 dB lower than that of the conventional one which uses 8 times more BB modules for the same 4 × 4 DP-PAA. Furthermore, the RF BFers obtained by the proposed iterative algorithm provide a better SINR than that of directly steering the RF BFs towards the line-of-sight transmission path.

The exponential growth of wireless data traffic has pushed wireless network vendors and researchers to seek for new paradigms to revolutionize the traditional communication technologies to support 100 times the network capacity of the current communication systems. Therefore, cognitive radio (CR) has been one of the most promising technologies for the next generation communication system, such as 802.11af and the ongoing efforts to define CR for LTE-A and beyond systems. In view of this development trend, in this paper, we propose a control channel design for heterogeneous CR networks (CRNs) that also incorporate the WiFi air interface. Based on the proposed control channel, an analytical model is further developed to analyze the throughput for a certain kind of these heterogeneous CRNs. Both the analytical and simulation results are presented and discussed.

The efficacy of channel modeling and training for multiple-input multiple-output (MIMO) time-varying flat faded Rayleigh channels is studied herein from the information-theoretical perspective. To characterize the channel dynamics in wide-sense stationary uncorrelated scattering wireless environments, proper autoregressive (AR) channel models for different fading speeds are discussed from the viewpoints of the mean squared error (MSE) and Bayesian Cramér-Rao lower bound (BCRB) of channel estimation. Furthermore, the training efficacy is examined with the achievable capacity when the MSE of channel estimation attains the BCRB. Our numerical simulations show that neither is the first-order AR model enough, nor is a large-order AR model needed for modeling time-varying fading channels. The analysis on BCRB also shows that the influence of the multiple access interference among transmit antennas is not negligible for channel estimation in time-varying fading channels even if using orthogonal sequences for training. As channel tracking can utilize the current and all past training symbols, the optimal training lengths for each data packet may be less than the number of transmit antennas. These results help re-examine the efficacy of model complexity and training overhead, and characterize the achievable rate for MIMO systems that use more practical methods in estimating Rayleigh fading channels.

A cooperative spectrum sharing, power allocation and user scheduling method is studied in this work under the framework of LTE self-organizing femtocell networks. Making use of dynamic channel and power allocation, and interference control, all femto access points (APs) in a self-organizing network (SON) can share the channels available to the SON with guaranteed quality of service. Simulation results show that the effective channel usage ratio can reach 75% in average even with no explicit cellular planning inside the service area of an SON. This result supports the feasibility of randomly deploying large numbers of femto APs in metropolitan areas based on the concept of SON.

A new signal synthesis and tracking method is developed for a 3-Lead wireless electrocardiography (ECG) system. Exploiting the temporal correlations in ECG signals, the standard 12-lead ECG signals can be synthesized with high precision by using only three differential pairs of electrodes. The correlations between the original and synthesized ECG leads can be as high as 0.98. And the separation between the two electrodes of a pair can be drastically reduced to around 10 cm, which is about the size of a medium adhesive tape. Experiment results also show that the proposed ECG synthesis method is much more robust to variations in acquisition positions when taking into account the spatial correlations among the ECG leads in syntheses. This greatly improves the feasibility of using fully wireless ECG systems on the long-term monitoring and diagnosis of heart diseases.

A hybrid architecture is presented for downlink beamforming (BF) with phased antenna arrays (PAA) in indoor 60 GHz spatial division multiple access (SDMA) channels. To manage the multiple access and inter-symbol interferences (MAI/ISI) encountered in SDMA with limited feedbacks, a cost-effective time-domain hybrid BF (HBF) method is presented to exploit the directivity provided by PAA in radio frequency (RF) beam patterns and the spatial diversity offered by multiple baseband processing modules. To maintain signal qualities under unpredictable MAI/ISI in wireless multimedia streaming to which indoor 60 GHz radio mainly applies, robust beamformers are designed to maintain the signal to interference-plus-noise ratio (SINR) for each user with minimum total transmit power. The percentages in which the target SINRs can be satisfied with the proposed HBF schemes are found sensitive to uncertainties in the phase shifters of PAA. Two kinds of robust formulations are thus proposed to jointly combat the MAI, ISI and phase uncertainties. Robust beamformers with semi closed-form expressions can be obtained with a nonlinear kind of them, whose SINR satisfaction ratio can attain 80% or more by extensive simulations in an indoor two-user 60 GHz environment if RF beam patterns of the users do not highly overlap in space.

Robust beamforming methods are studied to support relay-assisted coordinated multi-point (CoMP) retransmissions in downlink multi-cell networks. Linear beamformers (BFers) for relay stations of different cells are jointly designed to maintain, in a CoMP amplify-and-forward (AF) relaying manner, the target signal to interference-plus-noise ratios (SINR) at the cellular boundaries of this type of networks. Considering the feasibility in realizations and deployments, BFer designs are only allowed to use the channel state information (CSI) feedbacks of the wireless links inside a network. This kind of designs turns out to be a challenging optimization problem when attempting to maintain the SINR under the estimation and quantization errors in CSI. A conservative criterion and solution method is proposed for this robust design problem. Despite the conservativeness, the proposed method appears to provide an effective BFer design for CoMP AF relaying, either from the perspective of power consumption or from the viewpoints of BFers' complexity and feasibility in syntheses. Simulations also show that when applying the proposed CoMP AF relaying method in Automatic Retransmission reQuest (ARQ), data throughput can be efficiently increased for users close to the joint cellular boundaries inside a multi-cell network.

In this paper, we study the achievable rate of the training-based multi-input multi-output (MIMO) systems over time-varying flat fading channels modeled with the L-th order autoregressive, AR(L), process. Using the Bayesian Cramer-Rao lower bound (BCRB) to characterize the mean squared error of channel estimation, the achievable rate of the MIMO systems is investigated from the information-theoretical perspective. The optimum lengths for the training and the data blocks are determined to maximize the achievable rate. Besides, by modeling the time-varying wireless fading channel with a proper AR(L) model for the specific normalized Doppler frequency, fdTs, the aforementioned results can be extended to characterize the achievable rate in a more realistic time-varying wireless fading channel.

In contrast to the outage analysis, the packet error rates (PERs) of two types of cooperative automatic retransmission request (ARQ) protocols with opportunistic selective amplified-and-forward (OSAF) relaying are investigated from the perspective of the maximum likelihood sequence detection (MLSD). The results show that PERs with the threshold requirements derived from the outage analysis cannot benefit from the diversity via opportunistic retransmissions. Based on the PER analysis, effective threshold setting methods for the two types of ARQs are developed to avoid the diversity losses during ARQs, and verified via simulations using convolutional codes. In contrast to the ARQ protocols with the typical opportunistic AF relaying, the new threshold setting methods allow the OSAF ARQ protocols to provide for practical systems a spatial and temporal diversity comparable to what predicts with the outage analysis.

A cost-effective architecture is studied for a joint radio frequency (RF) and baseband hybrid beamforming (HBF) method that uses planar antenna arrays (PAA) in 60GHz radio transmission. Under the hybrid architecture, a space-time precoded HBF scheme is presented to exploit the spatial diversity for non line-of-sight transmission in 60GHz radio. Furthermore, an efficient one-bit feedback (OBF) beamforming (BF) scheme is proposed to search for the pointing directions of each RF beamformer (BFer) of the HBF architecture. Simulation results show that the proposed HBF architecture can provide a 5~10dB gain in the signal to noise ratio compared to an architecture that uses the RF BF only. In addition, the OBF BF scheme converges close to the LOS transmission in 15 iterations, which is much more efficient than exhaustively searching for the BF directions.

A body area network is developed for a 3-Lead wireless electrocardiography (ECG) monitoring system. To measure the 3-Lead ECG signals without using wires for electrodes to connect to a common ground, we study a new ECG sensing method that directly and synchronously measures the voltage differences across the nearby electrodes of distributed ECG leads. To verify the effectiveness of the proposed method, the qualities of the ECG signals acquired from various placements and distances between the electrodes are compared with the ones obtained with the standard wired ECG measurement method. The highest correlation coefficient between the wired and wireless ones is shown by experiments to be as high as 0.946. And the separation between the two electrodes of an ECG lead can be drastically reduced to around 10cm which is about the size of a medium adhesive tape. This shows that a 3-Lead wireless ECG monitoring system is in fact feasible. More advanced approaches may be developed based on the results obtained herein to further construct a 12-Lead wireless ECG system.

This paper addresses the analysis of diversity-multiplexing tradeoff (DMT) for a decode-and-forward (DF)-based cooperative network coding (CNC) system. The exact outage probability is also provided. Our results show that network coding can assist the relay node to improve multiplexing and diversity gain.

Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource management in cognitive radio. Furthermore, the proposed algorithm can still roughly reconstruct the power propagation map of the primary transmitters even when the measurement rate is below the lower bound for which compressive sensing (CS) can reconstruct signals with the l1-norm optimization method. Compared with the typical CS and Bayesian CS algorithms, simulation results show that average mean squared errors (MSE) of the estimated power propagation map are lower with the proposed algorithm. Besides, the computational complexity is also lower owing to bases pruning. The MSE of the location estimation are also shown to demonstrate the capability of the proposed algorithm.

The paper studies effective protocols and corresponding performance of automatic retransmission request (ARQ) via decode-and-forward (DF) opportunistic distributed space-time coding (ODSTC). Analysis shows that the diversity via ARQ can be drastically increased by allowing non-active relays to overhear the DSTC signal sent by the active relays. Nevertheless, simple schemes without overhearing may also achieve the same diversity when the link quality between the source-relay or the relay-destination is good, hence greatly alleviating the need for a complex protocol with overhearing. Moreover, using more relays opportunistically does provide a higher coding gain if the source-relay channel quality is extremely good, otherwise using two or three relays gives almost the same performance as if using all of them.

An effective joint radio-frequency (RF) and baseband (BB) hybrid beamforming (HBF) scheme is presented for two-user spatial division multiple access (SDMA) in 60GHz radio using planar antenna arrays (PAA). Taking into account the physical constraints of the phase shifters of PAA, we study the design of downlink HBF from two perspectives of conic optimization: transmit power minimization subject to a lower limit on the signal-to-interference plus noise ratio (SINR), and the maximization of the minimum SINR subject to an upper limit on the transmit power. For the power minimization problem, we obtain closed-form expressions for the BB beamforming (BF) weights. And for the SINR maximization, a simple iterative scheme is obtained based on the closed-form expressions for the first problem. Simulation results show that the bit error rates with HBF can significantly outperform the ones with RF BF only when the desired user's signal is severely jammed by the interfering user's signal.

The paper investigates effective protocols and the corresponding performance for cooperative automatic retransmission request (ARQ) that uses the decode-and-forward (DF) opportunistic distributed space-time coding (ODSTC). According to the analysis, the delay-limited throughput enjoys significant enhancement even using one or two active relays only for ARQs than using direct retransmissions. Besides, allowing the non-active relays to overhear the DSTC signal sent by active relays during ARQs also leads to a better throughput via ODSTC. Nevertheless, in the high or the low SNR regimes, simple schemes without overhearing may provide almost the same performance offered by a scheme with overhearing despite the much inferior diversities. These features may be further explored to enhance the overall system throughput for multiple access systems using only limited system resources.

A Round-Robin Timing Exchange (RRTE) protocol is proposed for the distributed synchronization of wireless sensor network. Compared to the Timing-sync Protocol for Sensor Network (TPSN) and the Pairwise Broadcasting Synchronization (PBS) scheme, the power consumption for each sensor node (SN) of RRTE is much smaller than that of TPSN and is evenly distributed among each SN in contrast to the PBS method. In addition, the synchronization accuracy of RRTE falls within that of TPSN and PBS, and can be adjusted by controlling the number of SNs in one cycle of synchronization. This makes the synchronization protocol particularly useful for small-scale wireless sensor networks. Furthermore, to improve the accuracy of synchronization, a recursive second-order regression method is also proposed to smooth the timing adjustment of each synchronization step without requiring complicated computations.

The modified Bayesian Cramer-Rao bound (MBCRB) to the mean squared error (MSE) of multi-input and multi-output (MIMO) channel estimation under discrete nuisance parameters are studied in this work. A recursive formula is provided for the computation of the MBCRB in time-varying MIMO Rayleigh fading channels. Under the assumption that channel coefficients of the MIMO system are independent and identically distributed, the MBCRB for MIMO channel estimation is shown to be the same to the single-input and single-output (SISO) system. Based on the MBCRB, the MSE of Kalman channel tracking is investigated for various MIMO channel configurations with different orders of the channel model.

Simple cooperative automatic retransmission request (ARQ) protocols are presented based on the selective amplified-and-forward (SAF) relaying method. Analysis shows that the temporal diversity of ARQs can be exploited with AF relaying only if the channel quality to the relay exceeds a threshold that depends on the source data rate requirement. Based on this analysis, an effective ARQ protocol is first developed from the concept of selective relaying to attain the full temporal diversity. Moreover, the notion of SAF relaying is further extended to systems with multiple relays to exploit the spatial and temporal diversities, incorporating the mechanism of opportunistic relaying. Two types of opportunistic-selective AF (OSAF) relaying methods are thus proposed for cooperative ARQs. Analysis shows that both OSAF protocols can offer much higher diversities than ARQ schemes with the typical AF relaying method. And the throughput of ARQs with OSAF is more robust to the variations of channel qualities and is close to their decode-and-forward counterparts.

A radio-frequency and baseband hybrid beam-forming (HBF) scheme is presented for spatial division multiple access (SDMA) of 60 GHz applications using planar antenna arrays (PAA). PAA with phase shifters for each element antenna is partitioned into sub-blocks and each block is applied a common baseband beamforming weight. To suppress the grating lobes of beam pattern and to maintain the signal to interference plus noise ratio (SINR) of users in the SDMA system, we study the design of this HBF using convex optimization. Two design criteria are considered herein: one minimizes the transmit power subject to a SINR constraint, and the other maximizes the worst case SINR subject to a total power constraint. Simulation results show that both the SINR and directivity of HBF can be significantly improved by making use of the convex optimization technique.

Based on the expectation maximization (EM) algorithm, a joint channel tracking and maximal a posteriori symbol detection method is proposed for orthogonal frequency division multiple access systems employing multiple transmit and receive antennas (MIMO). Using the Forney-style factor graph (FFG), a forward-and-backward signal processing method is developed to recursively solve the EM problem for joint estimation and detection (JED) in MIMO fading channels. By proper message scheduling and passing over the FFG, simulations show that the proposed algorithm is effective for joint MIMO channel tracking and symbol detection in fast fading channels.

A radio-frequency and baseband beamforming scheme is presented for spatial division multiple access (SDMA) of 60 GHz applications using planar antenna arrays (PAA). PAA with phase shifters for each element antenna is partitioned into sub-blocks and each block or multiple blocks are applied to a user in the SDMA system. According to the analysis for the signal to interference plus noise ratios (SINRs) of the users in the SDMA system, it is shown that different users may have different permissible operating regions in order to maintain the SINRs for all users in the system. A procedure is proposed to identify the permissible operating regions for each user in the SDMA system. Based on these results, more flexible hybrid beamforming schemes are currently studied to support SDMA in a more efficient way for 60 GHz millimeter wave applications.

In this research, we present a joint channel tracking and maximal a posteriori (MAP) symbol detection method for orthogonal frequency division multiple access (OFDMA) systems in time-varying fading channels. In contrast to some existing joint estimation and detection schemes that perform channel tracking in time-domain, we propose an algorithm that do both channel tracking and symbol detection in frequency domain, thus preventing the performance degradation due to the multiple access interference present in the time domain channel estimation. In conjunction with appropriate frequency allocation for each user, it is shown that a low-complexity algorithm can be obtained and, in the mean time, provides good performance in channel tracking.

The performance analysis and transceiver design of an adaptive bit-interleaved-coded multi-input multi-output orthogonal frequency division multiplexing (BIC-MIMO-OFDM) system without channel station information (CSI) in priori are investigated in this research. First, the noncoherent capacity of a MIMO-OFDM system with TV subcarriers, nttransmit antennas and nrreceive antennas is analyzed. It is shown that the lower bound of noncoherent capacity in the high signal-to-noise ratio (SNR) region is asymptotically equal to n*(1 - n*L/N) log rho, where n* = min(nl,nr), L is the number of channel taps and rho is the SNR value. Then, an adaptive BIC-MIMO-OFDM system based on transmitter beamforming and variable-rate-variable-power (VRVP) QAM is proposed. The throughput of adaptive scheme can achieve the noncoherent capacity, however, the gap to the coherent capacity cannot be totally compensated due to the overhead of training. The effect of imperfect channel state information (CSI) feedback is studied. It is demonstrated by numerical simulation that the proposed BIC-MIMO-OFDM scheme has better performance than adaptive trellis coded modulation (TCM) when outdated channel estimation is used.

We investigate the channel capacity of noncoherent communications over fiat fading channels. The lower bound of the noncoherent channel capacity as well as the upper bound of the capacity penalty using noncoherent communications are characterized and contrasted with the coherent capacity. Analytical results show that the coherent channel capacity is achievable via noncoherent communications under certain signal to noise ratios (SNRs) and Doppler frequency shifts. The thresholds of SNRs and Doppler frequency shifts are provided, under which noncoherent communications can perform virtually at no capacity penalty.

Abstract — A complete solution to joint channel tracking and symbol detection for bit-interleaved MIMO-OFDM systems in a time-varying frequency-selective fading environment is proposed in this research. In the training mode, the Rao-Blackwellised particle filter (RBPF) is used to estimate the phase offset (PO) and the carrier frequency offset (CFO). In the data mode, based on a recursive EM procedure in conjunction with soft decoding, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. The soft decoding module is composed of the list sphere decoder (LSD) and the soft-in-soft-out (SISO) channel decoder based on the BCJR algorithm. The performance of the proposed algorithm is evaluated via simulation. It is demonstrated that the new algorithm has robust performance in the presence of phase noise and the carrier frequency offset. I.

An expectation maximization (EM) algorithm for joint channel tracking and symbol detection in a multi-input multi-output (MIMO) time-varying frequency-selective fading environment is proposed in this research. Based on the recursive EM procedure in conjunction with soft decoding, we develop an iterative algorithm that performs the minimum mean squared error (MMSE) channel estimation and the maximum a posterior (MAP) probability symbol detection jointly. Two soft decoders are examined; namely, the BCJR algorithm and the soft sphere decoder. The performance of the proposed algorithm is evaluated via simulation and compared with that of Kalman filtering with hard decision feedback. It is demonstrated by numerical simulation that the proposed algorithm has robust performance in the presence of a severe channel model mismatch.

The exact expression of pairwise error probabilities (PEP) for space-time orthogonal codes is derived for spatially and temporally correlated Rayleigh fading channels in this research. We adopt a moment generating function approach in developing a unified framework to facilitate the evaluation of PEP for various types of channel correlations. Under this new framework, the complexity for evaluating PEP is greatly simplified. Moreover, the simple expression provides valuable insights into the performance of space-time codes in the presence of correlated fading channels.

Multistage (MS) implementation of the minimum mean-square error (MMSE), minimum output energy (MOE), best linear unbiased estimation (BLUE), and maximum-likelihood (ML) filter banks (FBs) is developed based on the concept of the MS Wiener filtering (MSWF) introduced by Goldstein et al. These FBs are shown to share a common MS structure for interference suppression, modulo a distinctive scaling matrix at each filter's output. Based on this finding, a framework is proposed for joint channel estimation and multiuser detection (MUD) in frequency-selective fading channels. Adaptive reduced-rank equal gain combining (EGC) schemes for this family of FBs (MMSE, MOE, BLUE, and ML) are proposed for noncoherent blind MUD of direct-sequence code-division multiple-access systems, and contrasted with the maximal ratio combining counterparts that are also formed with the proposed common structure under the assumption of known channel-state information. The bit-error rate, steady-state output signal-to-interference plus noise ratio (SINR), and convergence of the output SINRs are investigated via computer simulation. Simulation results indicate that the output SINRs attain full-rank performance with much lower rank for a highly loaded system, and that the adaptive reduced-rank EGC BLUE/ML FBs outperform the EGC MMSE/MOE FBs, due to the unbiased nature of the implicit BLUE channel estimators employed in the EGC BLUE/ML schemes.

The performance of a set of linear reduced-rank multistage filter banks is studied in the context of multiuser detection for direct-sequence (DS) code-division multiple-access (CDMA) systems. The set of filter banks under consideration is comprised of the minimum mean-square error (MMSE), the minimum output energy (MOE), the best linear unbiased estimator (BLUE), and the maximum-likelihood (ML) detector. Based on a common framework for the multistage implementations of the aforementioned filter banks, the signal-to-interference plus noise ratios (SINRs) and bit-error rates (BERs) of these reduced-rank filter banks are studied for multipath Rayleigh-fading channels. A generic BER formula is provided for coherent detection and noncoherent differential detection schemes constructed under this common framework. Analysis shows that all of these performance measures are characterized by a kernel matrix K/sub mmse/ whose trace forms the output SINR of the MMSE filter bank. Through investigating the recursive structure of K/sub mmse/, the output SINRs are proven to be monotonically increasing with the number of stages and upper-bounded by a number equal to the paths of the desired user's channel. The condition for asymptotically achieving this upper bound is also provided, which leads to the notion of effective user capacity of linear reduced-rank multiuser detection as well as serves as a test for the existence of a BER floor for coherent detection. In addition, the channel mismatch due to differential detection is also shown to yield a BER floor for noncoherent detection. Based on this analysis, a simple yet effective rule for choosing the number of stages is provided for both coherent and noncoherent linear multistage multiuser detection.

A unified structure is proposed for joint channel tracking and symbol detection in multipath fading channels. Based on the expectation maximization (EM) algorithm, a group of recursive stochastic filters is derived for blind channel estimation, using the maximum likelihood and minimum mean squared error criteria. In conjunction with the BCJR algorithm, it is shown that the recursive procedure for joint channel estimation and maximum a posteriori symbol detection can be represented as a message-passing scheme on a factor graph. The graphical model not only provides a generalized implementation architecture for existing training-based schemes, it also greatly reduces the complexity of existing algorithms for blind channel tracking.

The problem of joint channel estimation and multiple access interference suppression for an asynchronous code-division multiple-access system is studied. A low-complexity sliding-window scheme is proposed for channel tracking based on the expectation maximization (EM) algorithm and a noncoherent maximum a posteriori probability (MAP) multiuser detector. By exploiting the features of the discrete random phase ambiguities of channel estimates using the EM algorithm, the proposed scheme can track channel phase even if the channel is in a deep fade. In conjunction with iterative soft interference cancellation, the proposed detector can achieve a near single user system performance with polynomial complexity in the number of users.

The multistage (MS) Wiener filter proposed in J.S. Goldstein et al. (1998) is extended to blind maximum likelihood (ML) multiuser detection for direct-sequence (DS) code-division multiple access (CDMA) systems. This multistage ML detector is shown to share the same structure with the MS minimum-mean squared error (MMSE), minimum output energy (MOE) and best linear unbiased estimator (BLUE) filter banks (FBs) derived in S.-H. Wu et al. (2002) and S.-H. Wu et al. (2003), each with a distinctive output scaling matrix. Based on this result, a common framework is proposed for the implementation and analysis of coherent maximum ratio combining (MRC) and non-coherent differential equal gain combining (EGC) of these receivers. A generic bit error rate (BER) formula is provided for receivers constructed within this common framework. The BER floors for EGC receivers are analyzed and compared to those of the MRC schemes presented in S.-H. Wu et al. (2003). Based on simulation results, the heterogeneous EGC BLUE-ML receiver exhibits the best performance due to the fact that BLUE-FB is essentially the ML channel estimator and ML-FB is the ML symbol detector for the linear Gaussian system models of DS-CDMA systems.

The problem of joint blind channel estimation and multiple access interference (MAI) suppression for an asynchronous code-division multiple-access (CDMA) system is studied. A low-complexity sliding-window scheme based on the expectation maximization (EM) algorithm is developed for joint blind maximum a posteriori probability (MAP) multi-user detection (MUD) and stochastic maximum likelihood (ML) channel estimation in a dispersive fading channel. With multi-stage soft interference cancellation, MAI can be efficiently suppressed. Together with an initial value prediction method for algorithm initialization at each window, the proposed scheme can track fading channels on the fly with no phase ambiguities even when channel gains are close to zero.

The multistage (MS) Wiener filter proposed in [J.S. Goldstein et al., Nov. 1998] is extended to meet the best linear unbiased estimator (BLUE) criterion in this research. The MS BLUE filter is shown to share the same structure of the MS minimum-mean squared error (MMSE)/minimum output energy (MOE) filter bank proposed in S. H. Wu et al. [Nov. 2002] for multipath channels modulo a scaling matrix. The limiting performance and the relationship between the output SINRs of MS-BLUE, MS-MMSE and MS-MOE multiuser receivers in a multipath fading channel are derived and linked to the resultant bit rate error (BER). It is shown in our analysis the BERs and SINRs of the MMSE, MOE and BLUE receivers are, respectively in flat Rayleigh fading channels.

Performance of the output signal-to-interference-plus-noise ratios (SINRs) and bit error rates (BERs) of the maximum ratio combining (MRC) multistage minimum-mean squared error (MMSE), minimum output energy (MOE), best linear unbiased estimator (BLUE) and maximum likelihood (ML) receivers are analyzed for direct-sequence code-division multiple access (DS-CDMA) systems in multipath Rayleigh fading channels, based on the common multistage structure we proposed previously (Sau-Hsuan Wu et al., Proc. IEEE Globecom, 2002, 2003; Proc. IEEE ICC, 2003). The SINRs of these receivers are proved to be monotonically increasing with the number of applied stages. The upper bound of output SINRs and its achievability conditions are also provided, which are shown to form the condition for the occurrence of BER floors of the multistage MRC MMSE/MOE/BLUE/ML multiuser receivers. A rule for selecting the number of stages is thus provided to take full advantage of the multistage structure of these receivers.

A multi-stage minimum mean square error filter bank (MS-MMSE-FB) and a minimum output energy filter bank (MS-MOE-FB) for the detection of DS-CDMA (Direct-Sequence Code-Division Multi-Access) signals under a dynamic multi-path fading environment are presented. The output signal-to-interference plus noise ratios (SINRs) for both MS-MMSE-FB and MS-MOE-FB are derived. The analytical results reveal how the output SINRs of reduced-rank MS-MMSE-FB and MS-MOE-FB vary with the rank of filters. The findings coincide with results of Honig and Goldstein (see IEEE Trans. on Communications, 2000), namely, the rank needed to achieve a target performance does not scale with the system loading in an additive white Gaussian noise (AWGN) channel. The derivation shows that, with the aid of desired user's channel information, the rank of the receivers can be drastically dropped without affecting the steady state SINR, an important factor for interference suppression in a dynamic fading channel with adaptive MS-MMSE-FB/MS-MOE-FB detections.