This paper presents omnidirectional small-scale fading measurements and analysis at millimeter-wave (mmWave) frequency bands −73 and 81 GHz -in an airport environment. More than 825 power delay profiles (PDPs) were measured at each mm Wave band in an outdoor airport and an indoor campus environment in Boise. These measurements yielded small-scale omnidirectional models and angular power profiles for both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Various angular combinations of PDPs were obtained by controlling the beam directions of the transmitter (TX) and receiver (RX) antennas using a gimbal. A linear track was employed at each receiver to move the antenna in half-wavelength increments. Measured data indicate that small-scale fading of received signal amplitudes follow both a Ricean and a log-normal distribution in the outdoor airport LOS and NLOS scenarios at 73 and 81 GHz bands. Results reveal that the cumulative distribution function curve is a Ricean distribution with$K$-factor ranging from 3 – 13 dB, and 21 – 27 dB and a log-normal distribution bounded by$\sigma= 0.2\text{dB}$and$\sigma=0.35\text{dB}$for the LOS 81 GHz outdoor channel. Similar results can be seen at each band for the indoor campus environment. The indoor measurements show that the measured LOS curve is bounded by the Ricean distribution of$K$factor ranging from 8 to 13 dB, and log-normal distribution with$\sigma=0.2$to 0.25 dB, respectively in the indoor 73 GHz channel. The angular power profile results depict that main lobe power over all directions is higher at 81 GHz channel in comparison to the 73 GHz channel in both LOS and NLOS scenarios, as expected.

The use of directional transmission in millimeter-Wave (mmWave) frequencies results in limited channel coherence time. In this paper, we take the limited channel coherence time into account for non-orthogonal multiple access (NOMA) in mmWave hybrid beamforming systems. Due to the limited coherence time, the beamwidth of the hybrid beamformer affects the beam-training time, which in turn directly impacts the data transmission rate. To investigate this trade-off, we utilize a combined beam-training algorithm. Then, we formulate a sum-rate expression which considers the channel coherence time and beam-training time as well as users' power and other system parameters. Further, a joint power and beamwidth optimization problem is solved by iterating between the power allocation and the beamwidth optimization. When allocating the power, we use the log-exponential reformulation and the sequential parametric convex approximation (SPCA) methods to solve the non-convex problem. Since beamwidth optimization involves too many variables, we propose an algorithm which iterates between clusters of users. Numerical results show that the optimized mmWave hybrid beamforming-NOMA system can achieve much higher sum-rates compared to NOMA with analog beamforming and traditional multiple access techniques.

System level simulations of large 5G networks are essential to evaluate and design algorithms related to network issues such as scheduling, mobility management, interference management, and cell planning. In this paper, we look back to the idea of spatial indexing and its advantages, applications, and future potentials in accelerating large 5G network simulations. We introduce a multi-level inheritance based architecture which is used to index all elements of a heterogeneous network (HetNet) on a single geometry tree. Then, we define spatial queries to accelerate searches in distance, azimuth, and elevation. We demonstrate that spatial indexing can accelerate location-based searches by 3 orders of magnitude. Further, the proposed design is implemented as an open source platform freely available to all.

In this paper, we propose a generalized millimeter-Wave (mmWave) reconfigurable antenna multiple-input multiple-output (RA-MIMO) architecture that takes advantage of lens antennas. The considered antennas can generate multiple independent beams simultaneously using a single RF chain. This property, together with RA-MIMO, is used to combat small-scale fading and shadowing in mmWave bands. To this end, first, we derive a channel matrix for RA-MIMO. Then, we use rate-one space-time block codes (STBCs), together with phase-shifters at the receive reconfigurable antennas, to suppress the effect of small-scale fading. We consider two kinds of phase shifters: i) ideal which is error-free and ii) digital which adds quantization error. The goal of phase-shifters is to convert a complex-valued channel matrix into real-valued. Hence, it is possible to use rate-one STBCs for any dimension of RA-MIMO. We investigate diversity gain and derive an upper bound for symbol error rate in cases of ideal and digital phase-shifters. We show that RA-MIMO achieves the full-diversity gain with ideal phase-shifters and the full-diversity gain for digital phase-shifters when the number of quantization bits is higher than one. We investigate RA-MIMO in the presence of shadowing. Our analysis demonstrates that, by increasing the dimension of RA-MIMO, the outage probability decreases which means the effect of shadowing decreases. Numerical results verify our theoretical derivations.

In this paper, we present millimeter-wave (mmWave) penetration loss measurements and analysis at E- bands-73 GHz and 81 GHz. Penetration loss was measured for common building materials such as clear glass, metal, tinted glass, wood, and drywall on the campus of Boise State University in the city of Boise. A horn antenna with a gain of 24 dBi was used at the transmitter and receiver at both bands, and both antennas were boresight-aligned with respect to the test material. A total of twelve locations were selected to test five materials. We tested two indoor materials (clear glass and wood) in at least two locations to determine the effect of penetration loss of materials in similar compositions. The average penetration loss and standard deviation were estimated for these indoor materials. We measured an average penetration loss of 2 to 9 dB for wood and glass, respectively. Furthermore, we measured the penetration loss of common indoor and outdoor building materials. We studied that outdoor materials had larger penetration losses, e.g., we obtained a penetration loss of 22.69 dB for outdoor metal, where this value dropped to 16.04 dB for indoor metal at 73 GHz. Similar results were also obtained for the 81 GHz channel, where the largest penetration loss was measured to be 26.5 dB through a tinted glass door in an outdoor setting.

In this paper, two large-scale fading path loss models are presented based on indoor and outdoor channel measurements at 73 GHz. The line-of-sight millimeter-wave propagation measurement campaigns were uniquely conducted within the indoor and outdoor environments at an airport setting, i.e., the Boise Airport. The channel measurements were made with directional transmit and receive antennas with a 24 dBi gain at different receive antenna heights. From the measured data, we obtained the parameters of two path loss models, i.e., the close-in reference distance model (CIM) and the floating-intercept model (FIM). Results show that the path loss exponents estimated from the CIM are very close to that of the free- space path loss model, while the FIM provides a better fit to the measurement data.

Localization in indoor environments is essential to further support automation in many scenarios such as warehouses and factories. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate localization in indoor environments, however, has proved a challenging task due to multi- path fading methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with other sensors used for odometry to track the location of a mobile robot. This paper presents significant real world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to enhance the localization accuracy by reducing the localization error for a mobile robot.

The effect of beam misalignment on rate performance of the downlink hybrid beamforming-based non-orthogonal multiple access (HB-NOMA) systems is analyzed in this paper. First, an HB-NOMA framework is introduced in the context of multiuser millimeter wave (mmWave) communications, a sum-rate maximization problem is formulated for that, and an algorithm is introduced to design digital and analog precoders and power allocation. Then, a lower bound for the achievable rate of the perfectly aligned line-of-sight (LoS) channels is derived. Subsequently, the impact of beam misalignment is evaluated when the users experience misaligned LoS or non-LoS channels. To this end, a misalignment factor is modeled and each misaligned effective channel is described in terms of the perfectly aligned effective channel parameters and the misalignment factor. Furthermore, a lower bound for the achievable rate, and an upper bound for the rate gap expression between the aligned and misaligned HB-NOMA systems are established. The analyses reveal that a large misalignment can remarkably degrade the rate. Extensive numerical simulations are conducted to verify the findings.

Self-organizing networks (SONs) can help to manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on the multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we introduce Q-learning-based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables the ongoing transmit power adaptation as new small cells are added to the network. Furthermore, the sample complexity of the Q-DPA algorithm to achieve ϵ-optimality with high probability is provided. We demonstrate, at the density of several thousands femtocells per km2, the required quality of service of a macrocell user can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.

The large spectrum available in the millimeter- Wave (mmWave) band has emerged as a promising solution for meeting the huge capacity requirements of the 5th generation (5G) wireless networks. However, to fully harness the potential of mmWave communications, obstacles such as severe path loss, channel sparsity and hardware complexity should be overcome. In this paper, we introduce a generalized reconfigurable antenna multiple-input multiple-output (MIMO) architecture that takes advantage of lens-based reconfigurable antennas. The considered antennas can support multiple radiation patterns simultaneously by using a single RF chain. The degrees of freedom provided by the reconfigurable antennas are used to, first, combat channel sparsity in MIMO mmWave systems. Further, to suppress high path loss and shadowing at mmWave frequencies, we use a rate- one space-time block code. Our analysis and simulations show that the proposed reconfigurable MIMO architecture achieves full-diversity gain by using linear receivers and without requiring channel state information at the transmitter. Moreover, simulations show that the proposed architecture outperforms traditional MIMO transmission schemes in mmWave channel settings.

There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problem in such complex networks. By defining each base station as an agent, a cellular network is modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.

This paper aims to study the utilization of non-orthogonal multiple access (NOMA) in hybrid beamforming (HB) multiuser systems, called HB-NOMA, to serve a large number of mobile users. First, a sum-rate expression for the HB-NOMA problem is formulated. Second, an effective algorithm is proposed to maximize the sum-rate. Then, a lower bound is derived under two cases: i) the angle between the effective channel vectors of the MU with the highest channel gain and other MUs located inside a cluster is zero (or close to it) which is denoted by perfect correlation, and ii) this angle is non-zero, which we denote by imperfect correlation. For the second case, the relationship between the effective channels of two HB-NOMA users is modeled. The lower bound for the first case indicates that only the presence of inter-cluster interference and the use of an analog precoder negatively impact the sum-rate. However, in the case of imperfect correlation, which is more realistic, an inefficient MU cluster, can cause severe intra-cluster interference in the network. To verify our findings, numerical simulations have been conducted.

Dense deployment of small base stations (SBSs) is one of the main methods to meet the 5G data rate requirements. However, high density of independent SBSs will increase the interference within the network. To circumvent this interference, there is a need to develop self-organizing methods to manage the resources of the network. In this paper, we present a distributed power allocation algorithm based on multi-agent Q-learning in an interference-limited network. The proposed method leverages coordination through simple message passing between SBSs to achieve an optimal joint power allocation. Simulation results show the optimality of the proposed method for a two-user case.

This paper considers the unavailability of complete channel state information (CSI) in ultra-dense cloud radio access networks. The user-centric cluster is adopted to reduce the computational complexity, while the incomplete CSI is considered to reduce the heavy channel training overhead, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. The exact data rate is difficult to obtain in closed form, and instead we conservatively replace it with its lower-bound. The resulting problem is non-convex, combinatorial, and even infeasible. A practical algorithm, based on UE selection, successive convex approximation and semi-definite relaxation approach, is proposed to solve this problem with guaranteed convergence. We strictly prove that the semidefinite relaxation is tight with probability 1. Finally, extensive simulation results are presented to show the fast convergence of our proposed algorithm and demonstrate its superiority over the existing algorithms.

This paper deals with the unavailability of full CSI in ultra-dense user-centric TDD C-RAN. To reduce the channel training overhead, we consider the incomplete CSI case, where only large-scale inter-cluster CSI is available. Channel estimation for intra-cluster CSI is also considered, where we formulate a joint pilot allocation and user equipment (UE) selection problem to maximize the number of admitted UEs with fixed number of pilots. A novel pilot allocation algorithm is proposed by considering the multi-UE pilot interference. Then, we consider robust beam-vector optimization problem subject to UEs' data rate requirements and fronthaul capacity constraints, where the channel estimation error and incomplete inter-cluster CSI are considered. Simulation results demonstrate its superiority over the existing algorithms.

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual selfinterference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.

Millimeter wave (mmWave) technology is gaining momentum because of its ability to provide high data rates. However, in addition to other challenges in the operation of mmWave systems, developing cell search algorithms is a challenge due to high path loss, directional transmission, and excessive sensitivity to blockage at mmWave frequencies. Thus, the cell search schemes of long term evolution (LTE) cannot be used with mmWave networks. Exhaustive and iterative search algorithms have been proposed in literature for carrying out cell search in mmWave systems. The exhaustive search offers high probability of detection with high discovery delay while the iterative approach offers low probability of detection with low discovery delay. In this paper, we propose a hybrid algorithm that combines the strengths of exhaustive and iterative methods. We compare the three algorithms in terms of misdetection probability and discovery delay and show that hybrid search is a smarter algorithm that achieves a desired balance between probability of detection performance and discovery delay.

The potential of the millimeter wave (mmWave) band in meeting the ever growing demand for high data rate and capacity in emerging fifth-generation (5G) wireless networks is well-established. Since mmWave systems are expected to use highly directional antennas with very focused beams to overcome severe pathloss and shadowing in this band, the nature of signal propagation in mmWave wireless networks may differ from current networks. One factor that is influenced by such propagation characteristics is the interference behavior, which is also impacted by the simultaneous use of the unlicensed portion of the spectrum by multiple users. Therefore, considering the propagation characteristics in the mmWave band, we propose a spatial-spectral interference model for 5G mmWave applications, in the presence of Poisson field of blockages and interferers operating in licensed and unlicensed mmWave spectrum. Consequently, the average bit error rate of the network is calculated. Simulation is also carried out to verify the outcomes of the paper.

Impairments such as time varying phase noise (PHN) and carrier frequency offset (CFO) result in loss of synchronization and poor performance of multi-relay communication systems. Joint estimation of these impairments is necessary in order to correctly decode the received signal at the destination. In this paper, we address spectrally efficient multi-relay transmission scenarios where all the relays simultaneously communicate with the destination. We propose an iterative pilot-aided algorithm based on the expectation conditional maximization for joint estimation of multipath channels, Wiener PHNs, and CFOs in decode-and-forward-based multi-relay orthogonal frequency division multiplexing systems. Next, a new expression of the hybrid Cramér-Rao lower bound (HCRB) for the multi-parameter estimation problem is derived. Finally, an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking is employed. Numerical results show that the proposed estimator outperforms existing algorithms and its mean square error performance is close to the derived HCRB at different signal-to-noise ratios for different PHN variances. In addition, the combined estimation algorithm and the iterative receiver can significantly improve average bit-error rate (BER) performance compared with existing algorithms. In addition, the BER performance of the proposed system is close to the ideal case of perfect channel impulse responses, PHNs, and CFOs estimation.

Millimeter-wave (mm-wave) systems suffer from an assortment of propagation and hardware challenges such as extremely high pathloss/shadowing and amplifier non-linearity/phase noise, respectively. In this paper, we demonstrate via simulations that non-coherent frequency shift keying (FSK) can utilize the vast bandwidth at mm-wave frequencies to combat significant pathloss and shadowing in this band, while being robust to amplifier non-linearity and phase noise. To support our findings, we establish a comprehensive simulation setup and set of parameters that consider the impact of pathloss, shadowing, amplifier non-linearity, and phase noise, at mm-wave frequencies. Our results indicate that non-coherent FSK outperforms other modulation schemes such as phase shift keying and quadrature amplitude modulation. This outcome combined with the low detection complexity of non-coherent FSK make it an attractive modulation for achieving multi Gbps wireless links at mm-wave frequencies. The proposed comprehensive simulation setup can also be applied to investigate and validate the performance of various mm-wave systems in practical settings.

Energy harvesting (EH) provides a means of greatly enhancing the lifetime of wireless sensor nodes. However, the randomness inherent in the EH process may cause significant delay for performing sensing operations and transmitting sensed information to the sink. Unlike most existing studies on the delay performance of EH sensor networks, where only the energy consumption of transmission is considered, we consider the energy costs of both sensing and transmission. Specifically, we consider an EH sensor that monitors some status property and adopts a harvest-then-use protocol to perform sensing and transmission. To comprehensively study the delay performance, we consider two complementary metrics and analytically derive their statistics: 1) update age-measuring the time taken from when information is obtained by the sensor to when the sensed information is successfully transmitted to the sink, i.e., how timely the updated information at the sink is, and 2) update cycle-measuring the time duration between two consecutive successful transmissions, i.e., how frequently the information at the sink is updated. Our results show that the consideration of sensing energy cost leads to an important tradeoff between the two metrics: more frequent updates result in less timely information available at the sink.

Due to its time-varying nature, oscillator phase noise can significantly degrade the performance of the channel estimation, carrier recovery, and data detection blocks in high-speed wireless communication systems. In this paper, we propose a new data-aided joint channel, carrier frequency offset (CFO) and phase noise estimator for orthogonal frequency division multiplexing (OFDM) relay systems. For the data transmission phase, we propose a new iterative receiver that tracks phase noise and detects the transmitted symbols. Additionally, we derive the hybrid Cramér-Rao lower bound for evaluating the performance of channel estimation and carrier recovery algorithms in OFDM relay networks. Extensive simulations demonstrate that the application of the proposed estimation and receiver blocks significantly improves the performance of OFDM relay networks in the presence of phase noise and CFO.

Realistic modeling of energy consumption is crucial for accurate performance analysis of wireless-powered sensor nodes. In this paper, we analyze the performance of wireless-powered sensor transmissions taking into account both the energy cost of sensing and transmission. We consider a sensor that is harvesting energy from an ambient radio-frequency (RF) signal and using this energy to perform sensing and transmission. Since energy harvesting is time-varying in nature, it introduces a delay in the sensor transmissions. We study two delay-related metrics, one measuring how frequent the sensed information is updated at the sink and the other measuring the time taken from the sensing operation to successful transmission of sensed information. We analytically characterize the statistical behavior of both metrics and find an important tradeoff between them. In particular, our results illustrate that more frequent update of sensed information at the sink increases the time taken from the sensing operation to successful transmission of sensed information.

In this paper, the channel estimation problem in full duplex (FD) point-to-point wireless communication systems is investigated. Because of the existence of two interfering and communication channels, this problem is fundamentally different from the conventional channel estimation problems in half duplex (HD) communication systems. Here, we propose a blind channel estimator that simultaneously estimates the channel parameters of the FD system without requiring time division duplex. A major problem for blind estimators is the identifiability of the parameters. Therefore, thorough analysis is presented to determine the identifiable parameters in this estimation problem. It will be shown that a completely blind estimator utilizing a symmetric modulation set suffers from phase ambiguity. For this reason, only the channel magnitudes can be estimated blindly. It is further presented that if the modulation set is not centered around the origin then this phase ambiguity can be resolved.

In this paper, we propose a high-rate space-time block code (STBC) for millimeter-wave wireless communication systems that are equipped with reconfigurable multiple-input multiple-output (MIMO) antennas. We assume that each reconfigurable antenna element has the capability of forming its beam and can independently change the characteristics of its radiation pattern1. We exploit this feature of the antenna elements to construct the proposed space-time block code where each coded symbol is sent over independent beams. As a result, the received coded symbols at different receive antennas will have different power levels, a desired property that can be used in data detection. We show that the proposed code achieves a coding rate of two and requires a low-complexity maximum likelihood (ML) detector. We carry out computer simulations that demonstrate the performance of the proposed code and shows its superiority in compared to previous rate-2 STBC counterparts. We also comment on the generalization of the proposed STBC for higher MIMO dimensions.

In this paper, while considering the impact of the antenna correlation and the interference from neighboring users, we analyze channel estimation and training sequence design for multi-input multi-output (MIMO) two-way relay systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) phase and the broadcast (BC) phase. By considering the Kronecker-structured channel model, we derive the optimal linear minimum mean-square-error (LMMSE) channel estimators. The corresponding training designs for the MAC phase and the BC phase are then formulated and solved to improve channel estimation accuracy. For the general scenario of the training sequence design for both phases, two iterative training design algorithms are proposed that are verified to produce training sequences achieving near optimal channel estimation performance. Furthermore, for specific practical scenarios, where the covariance matrices of the channel or disturbances are of particular structures, the optimal training sequence design guidelines are obtained. The minimum required training lengths for channel estimation in both the MAC phase and the BC phase are also analyzed. Comprehensive simulations are carried out to demonstrate the effectiveness of the proposed training designs.

In this paper, we analyze joint channel, carrier frequency offset (CFO), and phase noise estimation in orthogonal frequency division multiplexing (OFDM) relaying networks. To achieve this goal, a detailed transmission framework involving both training and data symbols is first presented. Next, a novel algorithm that applies the training symbols to jointly estimate the channel responses, CFO, and phase noise parameters based on the maximum a posteriori criterion is proposed. Additionally, to evaluate the performance of the proposed channel estimation and carrier recovery algorithms, we analyze the ambiguities among the estimated parameters. Based on this analysis, a new Hybrid Cramér-Rao Lower Bound (HCRLB) is derived, which can effectively avoid such ambiguities. The simulation results show that the proposed estimation algorithm can achieve a performance close to the derived HCRLB.

In this paper, while considering the impact of antenna correlation and the interference from neighboring users, we study the problem of channel estimation and training sequence design in multi-input multi-output (MIMO) two-way relaying (TWR) systems. To this end, we propose to decompose the bidirectional transmission links into two phases, i.e., the multiple access (MAC) and the broadcast (BC) phases. By deriving the optimal linear minimum mean-square-error estimators, the corresponding training design problems for the MAC and BC phases are formulated and solved. Subsequently, algorithms and, in some special cases, closed-form solutions for obtaining the optimal training sequences for channel estimation in TWR systems are derived. Moreover, to further reduce channel estimation overhead, the minimum required length of the training sequences are determined. Simulation results verify the effectiveness of the proposed training designs in improving channel estimation performance in TWR systems.

The market trends towards the use of smaller dish antennas for TV satellite receivers, as well as the growing density of broadcasting satellites in orbit require the application of robust adjacent satellite interference (ASI) cancellation algorithms at the receivers. The wider beamwidth of a small size dish and the growing number of satellites in orbit impose an overloaded scenario, i.e., a scenario where the number of transmitting satellites exceeds the number of receiving antennas. For such a scenario, we present a two stage receiver to enhance signal detection from the satellite of interest, i.e., the satellite that the dish is pointing to, while reducing interference from neighboring satellites. Towards this objective, we propose an enhanced List-based Group-wise Search Detection (LGSD) receiver architecture that takes into account the spatially correlated additive noise and uses the signal-to-interference-plus-noise ratio (SINR) maximization criterion to improve detection performance. Simulations show that the proposed receiver structure enhances the performance of satellite systems in the presence of ASI when compared to existing methods.

The problem of joint oscillator phase noise (PHN) estimation and data detection for multi‐input multi‐output (MIMO) systems using bit‐interleaved‐coded modulation is analysed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation‐maximisation (EM) framework, is proposed. It is shown that at high signal‐to‐noise ratios, a maximum a posteriori (MAP) estimator can be used to carry out the maximisation step of the EM algorithm. Moreover, to reduce the computational complexity of the proposed EM algorithm, a soft decision‐directed extended Kalman filter‐smoother (EKFS) is applied instead of the MAP estimator to track the PHN parameters. The numerical results show that by combining the proposed EKFS‐based approach with an iterative detector that employs low‐density parity check codes, PHN can be accurately tracked. The simulations also demonstrate that compared to the existing algorithms, the proposed iterative receiver can significantly enhance the performance of MIMO systems in the presence of PHN.

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramér-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance.

This paper addresses training-based channel estimation in distributed amplify-and-forward (AF) multi-input multi-output (MIMO) multi-relay networks. To reduce channel estimation overhead and delay, a training algorithm that allows for simultaneous estimation of the entire MIMO cooperative network's channel parameters at the destination node is proposed. The exact Cramér-Rao lower bound (CRLB) for the problem is presented in closed-form. Channel estimators that are capable of estimating the overall source-relay-destination channel parameters at the destination are also derived. Numerical results show that while reducing delay, the proposed channel estimators are close to the derived CRLB over a wide range of signal-to-noise ratio values and outperform existing channel estimation methods. Finally, extensive simulations demonstrate that the proposed training method and channel estimators can be effectively deployed in combination with cooperative optimization algorithms to significantly enhance the performance of AF relaying MIMO systems in terms of average-bit-error-rate.

For distributed multi-user and multi-relay cooperative networks, the received signal may be affected by multiple timing offsets (MTOs) and multiple channels that need to be jointly estimated for successful decoding at the receiver. This paper addresses the design of optimal training sequences for efficient estimation of MTOs and multiple channel parameters. A new hybrid Cramer-Rao lower bound (HCRB) for joint estimation of MTOs and channels is derived. Subsequently, by minimizing the derived HCRB as a function of training sequences, three training sequence design guidelines are derived and according to these guidelines, two training sequences are proposed. In order to show that the proposed design guidelines also improve estimation accuracy, the conditional Cramer-Rao lower bound (ECRB), which is a tighter lower bound on the estimation accuracy compared to the HCRB, is also derived. Numerical results show that the proposed training sequence design guidelines not only lower the HCRB, but they also lower the ECRB and the mean-square error of the proposed maximum a posteriori estimator. Moreover, extensive simulations demonstrate that application of the proposed training sequences significantly lowers the bit-error rate performance of multi-relay cooperative networks when compared to training sequences that violate these design guidelines.

In cooperative networks, multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs) originate due to multiple distributed nodes. In this paper, algorithms for joint estimation of these parameters and channels in amplify-and-forward (AF) relaying networks are proposed. A new training model and transceiver structure at the relays for achieving synchronization throughout the network is devised. New exact closed-form expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. An estimation method is proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination based on space-alternating generalized expectation maximization (SAGE) and compared to a computationally-intensive least squares (LS) approach. The proposed estimator's performance is shown to be close to the CRLB at mid-to-high signal-to-noise ratio (SNR) resulting in significant cooperative performance gains in the presence of practical impairments.

Time varying phase noise is a limiting factor in highspeed wireless communication systems, e.g., microwave backhaul links in cellular networks. This paper seeks to investigate the performance of circular M-ary quadrature amplitude modulations (M-QAMs) in the presence of time varying phase noise. A new approximate union bound expression for the symbol-error probability (SEP) of a specific circular M-QAM constellation is derived. Numerical results show that this expression is accurate at medium-to-high signal-to-noise ratios (SNRs) for different constellation orders, M. Next, exact closed-form expressions for the SEP of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) modulations in the presence of phase noise are derived. Extensive simulations are carried out to compare the performance of rectangular and circular QAM modulations in the presence of time varying phase noise, where it is demonstrated that circular QAM modulations can outperform their rectangular counterparts when considering the effect of time varying phase noise.

Previous results in the literature have shown that derivation of the optimum maximum-likelihood (ML) receiver for symbol-by-symbol (SBS) detection of an uncoded data sequence based on all received signals in the presence of random phase noise is an intractable problem. This is because it involves the computation of the conditional probability distribution function (PDF) of the phase noise process. In this paper, we seek to minimize symbol error probability (SEP), which is achieved by SBS detection of the sequence based on all received signals. We show that the ML detector for this problem can be formulated as a weighted sum of central moments of the conditional PDF of phase noise. Given that the central moments of the conditional PDF of phase noise can be estimated, this new optimal structure is tractable with respect to the previously known optimal ML receiver. Furthermore, based on the new receiver structure, we propose a simple approximate method for SBS detection and investigate its scope and applicability. Simulation results demonstrate that SEP performance close to optimality can be obtained through the proposed method for a wide range of phase noise variances and low signal-to-noise ratio (SNR).

Heterogeneous networks have the potential to improve coverage, throughput, and energy efficiency of wireless networks through the use of specialized cellular structures, in particular femtocells and macrocells. However, to reduce interference between different cells, ensure smooth hand-offs from cell to cell, and achieve seamless operation the overall network needs to be synchronized. In this paper a new distributed clock synchronization scheme for heterogeneous networks is proposed that employs the clock drift ratio (CDR) information available at user-equipments (UEs) to achieve synchronization between non-interacting femtocells and macrocells. Simulation results show that the proposed scheme can significantly reduce the clock drift between macrocells and femtocells and result in timing synchronization throughout the network without introducing significant overhead.

The distributed nature of cooperative networks may result in multiple carrier frequency offsets (CFOs), which make the channels time varying and overshadow the diversity gains promised by collaborative communications. This paper seeks to address multiple CFO estimation using training sequences in space-division multiple access (SDMA) cooperative networks. The system model and CFO estimation problem for cases of both decode-and-forward (DF) and amplify-and-forward (AF) relaying are formulated and new closed-form expressions for the Cramer-Rao lower bound (CRLB) for both protocols are derived. The CRLBs are then applied in a novel way to formulate training sequence design guidelines and determine the effect of network protocol and topology on CFO estimation. Next, two computationally efficient iterative estimators are proposed that determine the CFOs from multiple simultaneously relaying nodes. The proposed algorithms reduce multiple CFO estimation complexity without sacrificing bandwidth and training performance. Unlike existing multiple CFO estimators, the proposed estimators are also accurate for both large and small CFO values. Numerical results show that the new methods outperform existing algorithms and reach or approach the CRLB at mid-to-high signal-to-noise ratio (SNR). When applied to system compensation, simulation results show that the proposed estimators significantly reduce average-bit-error-rate (ABER).

Successful collaboration in cooperative networks require accurate estimation of multiple timing offsets. When combined with signal processing algorithms the estimated timing offsets can be applied to mitigate the resulting inter-symbol interference (ISI). This paper seeks to address timing synchronization in distributed multi-relay amplify-and-forward (AF) and decode-and-forward (DF) relaying networks, where timing offset estimation using a training sequence is analyzed. First, training sequence design guidelines are presented that are shown to result in improved estimation performance. Next, two iterative estimators are derived that can determine multiple timing offsets at the destination. The proposed estimators have a considerably lower computational complexity while numerical results demonstrate that they are accurate and reach or approach the Cramer-Rao lower bound (CRLB).

The distributed nature of cooperative networks may result in multiple carrier frequency offsets (CFOs), which make the channel time-varying and overshadow the performance gains promised by collaborative communications. However, much of the analysis in the area of cooperative communications assumes perfect synchronization. This paper seeks to address frequency synchronization in cooperative communication systems, where CFO estimation using a training sequence is analyzed. The Cramer-Rao lower bound (CRLB) for various cooperative protocols is derived. In the next step, we propose two computationally efficient iterative estimators based on the MUltiple SIgnal Characterization (MUSIC) algorithm, that can estimate the CFOs from all the relays simultaneously. Unlike existing multiple CFO estimation algorithms, the proposed estimators are effective and accurate in the presence of both large and small CFO values and numerical and simulation results show that both methods outperform the existing algorithms and reach the CRLB at mid-to-high signal to noise ratio.

In this paper we propose a new low feedback and low complexity scheme that adjusts the phases at the relays, thus allowing for the coherent combining of signals from multiple relays in a cooperative multi-input-multi-output (MIMO) system using space division multiple access (SDMA). It is assumed that the channel state information (CSI) is not fed back to the source and the relays and the case of imperfect CSI at the destination is also taken into consideration. In determining the parameters, conventional convex optimization schemes cannot be applied due to the non-convex nature of the function. A low complexity method is used to optimize the system that utilizes bisection search. Numerical calculations and BER simulations demonstrate a significant performance gain over conventional AF.

Computationally scalable and accurate estimation, prediction, and simulation of wireless communication channels is critical to the development of more adaptive transceiver algorithms. Previously, the application of autoregressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This correspondence presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. Here, the problem is decomposed into autoregressive (AR) model matching followed by linear system identification. Performance is compared to that of AR, inverse discrete Fourier transform, and sum of sinusoids techniques. Also, for the first time, the finite-precision performance of different methods is compared.

Adaptive transceivers play an important role in wireless communications and the design of MIMO systems. Therefore models that enable simulation of dynamic and time varying channels in a computationally scalable fashion are extremely valuable. Previously, the application of auto regressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This paper presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. The resulting ARMA synthesis is then used in the generation of dynamic MIMO channel realizations with time varying Doppler at significantly lower computational complexities.