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THz band is envisioned to be used in 6G systems to meet the ever-increasing demand for data rate. However, before an eventual system design and deployment can proceed, detailed channel sounding measurements are required to understand key channel characteristics. In this paper, we present a first extensive set of channel measurements for urban outdoor environments that are ultra-wideband (1 GHz 3dB bandwidth), and double-directional where both the transmitter and receiver are at the same height. In all, we present measurements at 38 Tx/Rx location pairs, consisting of a total of nearly 50,000 impulse responses, at both line-of-sight (LoS) and non-line-of-sight (NLoS) cases in the 1-100 m range. We provide modeling for path loss, shadowing, delay spread, angular spread and multipath component (MPC) power distribution. We find, among other things, that outdoor communication over tens of meters is feasible in this frequency range even in NLoS scenarios, that omni-directional delay spreads of up to 100 ns, and directional delay spreads of up to 10 ns are observed, while angular spreads are also quite significant, and a surprisingly large number of MPCs are observed for 1 GHz bandwidth and 13 degree beamwidth. These results constitute an important first step towards better understanding the wireless channel in the THz band.

In this paper, we investigate online learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) – reservoir computing (RC). We first introduce the Time-Frequency RC to take advantage of the structural information inherent in OFDM signals. Using the time domain RC and the time-frequency RC as building blocks, we provide two extensions of the shallow RC to RCNet: 1) Stacking multiple time domain RCs; 2) Stacking multiple time-frequency RCs into a deep structure. The combination of RNN dynamics, the time-frequency structure of MIMO-OFDM signals, and the deep network enables RCNet to handle the interference and nonlinear distortion of MIMO-OFDM signals to outperform existing methods. Unlike most existing NN-based detection strategies, RCNet is also shown to provide a good generalization performance even with a limited online training set (i.e, similar amount of reference signals/training as standard model-based approaches). Numerical experiments demonstrate that the introduced RCNet can offer a faster learning convergence and as much as 20% gain in bit error rate over a shallow RC structure by compensating for the nonlinear distortion of the MIMO-OFDM signal, such as due to power amplifier compression in the transmitter or due to finite quantization resolution in the receiver.

Spatial spectrum sensing (SSS) enables mobile devices to sense the spatial spectrum holes and reuse the scarce spectrum opportunistically. In this paper, we model and analyze the SSS in uplink two-tier user-centric deployed heterogeneous networks (HetNets) where secondary users (SUs) sense the spectrum holes of cellular users. In the two-tier user-centric deployed HetNets, small cell base stations (SBSs) are deployed in hotspots with high user density, and macro base stations (MBSs) are deployed uniformly. Based on the semi-static power control mechanism, the average transmit power of cellular users associated with MBS and SBS are derived, respectively. Furthermore, the spatial false alarm probability and the spatial miss detection probability of a typical SU are obtained, respectively. Moreover, we characterize the coverage probability and the area spectral efficiency (ASE) of SU and cellular networks. The SUs’ optimal SSS radius is obtained to maximize the ASE of the entire network while guaranteeing the ASE of cellular networks above a certain threshold. Simulation results show that when the density of SUs is small, a decrease in SUs’ SSS radius reduces the coverage probability of SUs. However, it improves the ASE of SUs networks, although the inter-SU interference increases.

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users’ distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on users’ distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

This paper investigates the capability of millimeter-wave (mmWave) channel sounders with phased arrays to perform super-resolution parameter estimation, i.e., determine the parameters of multipath components (MPC), such as direction of arrival and delay, with resolution better than the Fourier resolution of the setup. We analyze the question both generally, and with respect to a particular novel multi-beam mmWave channel sounder that is capable of performing multiple-input-multiple-output (MIMO) measurements in dynamic environments. We firstly propose a novel two-step calibration procedure that provides higher-accuracy calibration data that are required for Rimax or SAGE. Secondly, we investigate the impact of center misalignment and residual phase noise on the performance of the parameter estimator. Finally we experimentally verify the calibration results and demonstrate the capability of our sounder to perform super-resolution parameter estimation.

Outdoor to indoor penetration loss is one of the crucial challenges faced at millimeter-wave frequencies. This paper presents the results from 28-GHz channel sounding campaigns performed to investigate the impact of this phenomenon on the wireless propagation channel characteristics in small cell and fixed wireless access scenarios. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beam-forming and electronic beam steering for directionally resolved measurements. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We compare the measured path loss, delay spread, and angular spread for indoor and outdoor receiver locations for two different types of buildings. We find that the penetration loss strongly depends on the angle of incidence, and the scatterers on the outside of the building strongly impact how much power is coupled into the building. Based on the results, we provide statistical models for path loss, delay spread, and angular spread.

Multidimensional characterization of outdoor urban macrocellular propagation channels is essential for the analysis and design of next-generation (5G and beyond) cellular massive MIMO (multiple-input-multiple-output) systems. Since most massive MIMO arrays will extend in two or three dimensions, an understanding of 3-D parameters (i.e., azimuth and elevation) of the multipath components (MPCs) is required. This paper presents an extensive measurement campaign for 3-D outdoor propagation channels in an urban macrocellular environment. Measurements were performed with a 20 MHz wideband polarimetric MIMO channel sounder centered at 2.53 GHz and MPCs were extracted using RIMAX—an iterative maximum likelihood algorithm. The physical propagation mechanisms of the observed discrete MPCs are explained in terms of waveguiding, over-the-rooftop propagation, and scattering by far-away objects. MPCs exhibit clustering in the temporal and spatial domains; both intra- and inter- cluster parameters and their relevant statistics are provided. We also extract diffuse MPCs, show that they can comprise a moderate portion of the overall energy, and provide a statistical characterization.

This paper considers a fundamental issue of path loss (PL) modeling in urban micro cell (UMi) environments, namely the spatial consistency of the model as the mobile station moves along a trajectory through street canyons. This paper is motivated by the observed non-stationarity of the PL. We show that the traditional model of power law PL plus log-normally distributed variations can provide misleading results that can have serious implications for system simulations. Rather, the PL parameters have to be modeled as random variables that change from street to street and also as a function of the street orientation. Variations of the PL, taken over the ensemble of the whole cell (or multiple cells), thus consist of the compound effect of these PL parameter variations together with the traditional shadowing variations along the trajectory of movement. Ray-tracing results demonstrate that ignoring this effect can lead to a severe overestimation of the local standard deviation in a given area. Then, a spatially consistent stochastic street-by-street PL model is established, and a parameterization for 28-GHz UMi cells is given. The model correctly describes the PL as a function of the street orientation as well as the large variance observed for all the PL model parameters.

Initial access is the process which allows a mobile user to first connect to a cellular network. It consists of two main steps: cell search (CS) on the downlink and random access (RA) on the uplink. Millimeter wave (mmWave) cellular systems typically must rely on directional beamforming (BF) in order to create a viable connection. The beamforming direction must therefore be learned-as well as used-in the initial access process for mmWave cellular networks. This paper considers four simple but representative initial access protocols that use various combinations of directional beamforming and omnidirectional transmission and reception at the mobile and the BS, during the CS and RA phases. We provide a system-level analysis of the success probability for CS and RA for each one, as well as of the initial access delay and user-perceived downlink throughput (UPT). For a baseline exhaustive search protocol, we find the optimal BS beamwidth and observe that in terms of initial access delay it is decreasing as blockage becomes more severe, but is relatively constant (about $\pi/12$) for UPT. Of the considered protocols, the best trade-off between initial access delay and UPT is achieved under a fast cell search protocol., Comment: Submitted to IEEE Transactions on Wireless Communications

In order to meet the challenge of increasing data-rate demand as well as the form factor limitation of the base station (BS), 3-D massive multiple-input multiple-output (MIMO) technology has been introduced as one of the enabling technologies for fifth generation mobile cellular systems. In 3-D massive MIMO systems, a BS will rely on the uplink sounding signals from mobile stations to figure out the spatial information for downlink MIMO operations. Accordingly, multi-dimensional parameter estimation of a MIMO channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study the angle and delay estimation for 3-D massive MIMO systems under a parametric channel modeling. To be specific, we first introduce separate low complexity time delay and angle estimation algorithms based on unitary transformation, and analytically characterize the mean squared errors (MSEs) of these estimations for massive MIMO systems. Then, a matrix-based estimation of signal parameters via rotational invariance technique algorithm is applied to jointly estimate the delay and the angles where the MSEs are also analytically characterized. Our results show that the antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance. Simulation results suggest that the characterized MSEs match well with the simulated ones.

To suppress the co-channel interference in a multi-cell multi-user multiple-input-multiple-output downlink cellular network, a novel interference alignment transceiver beam-forming design along with a low complexity iterative coordinated beam-forming scheme is introduced. While the latter combats the intra-cell interference, the former is utilized to mitigate the inter-cell interference. The proposed schemes consider the codebook-based feedback, which is adopted in the LTE/LTE-advanced systems. Optimal downlink user-specific and cell-specific beam-forming matrices are characterized to maximize the lower bound of expected signal-to-leakage-plus-noise ratio and to minimize the residual inter-cell interference, respectively. Moreover, closed-form expressions for these beam-forming matrices under limited channel state information feedback and in the presence of the quantization error are identified. Simulations are conducted to investigate the performance of the proposed strategy. The results indicate that our scheme can significantly improve the average spectral-efficiency of the underlying network when compared with existing ones where the quantization error is neglected. Furthermore, for a fixed payload size of the codebook, unlike zero-forcing beam-forming in which the sum throughput is bounded as the signal-to-noise ratio (SNR) increases, in our scheme, the performance gap between rank 2 feedback and perfect feedback remains approximately constant as SNR increases.

Ultra-densification is one of the main features of 5G networks. In an ultra-dense network, how to conduct interference management and spectrum allocation is a challenging issue. Spectrum sensing in cognitive radio networks is a distributed and efficient way to resolve this issue in ultra-dense networks. However, most of the studies on spectrum sensing only focus on sensing temporal spectrum opportunities where one or multiple primary users are active, which does not make full use of spectrum opportunities in the spatial location domain. To overcome the shortcomings of conventional temporal spectrum sensing, we study the problem of spatial spectrum sensing, which senses spatial spectrum opportunities in wireless networks. In this paper, the performance of spatial spectrum sensing and its application in sensing-based device-to-device (D2D) cellular networks are analyzed using stochastic geometry. Specifically, by modeling the locations of active transmitters as a Poisson point process, the spatial spectrum sensing problem is formulated using the framework of a detection theory. Closed-form expressions are obtained for the sensing threshold, probabilities of spatial detection, and false alarm. Furthermore, analytical throughput for D2D users and cellular users under both channel inversion and constant power allocation cases are derived. The optimal sensing radius that maximizes the defined network metric is obtained numerically. Finally, the simulation and numerical results are presented to verify our theoretical analysis.

With the promise of meeting future capacity demands, 3-D massive-MIMO/full dimension multiple-input-multiple-output (FD-MIMO) systems have gained much interest in recent years. Apart from the huge spectral efficiency gain, 3-D massive-MIMO/FD-MIMO systems can also lead to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of the system, accurate channel state information is critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for 3-D millimeter wave massive-MIMO orthogonal frequency division multiplexing (OFDM) systems. To be specific, the DoA is estimated using estimation of signal parameter via rotational invariance technique method, and the root mean square error of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system. An ergodic capacity analysis of the system in the presence of DoA estimation error is also conducted, and an optimum power allocation algorithm is derived. Furthermore, it is shown that the DoA-based channel estimation achieves a better performance than the traditional linear minimum mean squared error estimation in terms of ergodic throughput and minimum chordal distance between the subspaces of the downlink precoders obtained from the underlying channel and the estimated channel.

Coordinated multi-point (CoMP) transmission and reception are introduced as a promising technology in the 3GPP LTE-advanced standard, to manage interference, improve the overall system performance, and enhance system reliability. In this paper, a resource allocation problem is studied for downlink CoMP coordinated beamforming systems, where each base station (BS) serves its own mobile stations. Multiple-input-multiple-output (MIMO) transmit precoding and resource allocation are linked to the underlying proportional-fair scheduling to ensure a good trade-off between cell-average and cell-edge user spectral-efficiency. Due to the coupled interference among mobile stations, the resulting proportional-fair resource allocation optimization problem becomes nonconvex. To solve for optimal operating point for MIMO CoMP network, a parallel successive convex approximation-based algorithm is introduced. The introduced scheme enables all BSs to update their optimization variables in parallel by solving a sequence of strongly convex subproblems. Closed-form expressions of the locally optimal solution in both the high and low signal-to-noise regimes are characterized. The performance of the introduced scheme is also investigated through simulations. Numerical results show the efficiency of the introduced algorithm.

Future wireless networks will face the dual challenge of supporting large traffic volumes while providing reliable service for delay-sensitive traffic. To meet the challenge, relay network has been introduced as a new network architecture for the fourth generation (4G) LTE-Advanced (LTE-A) networks. In this paper, we investigate resource allocation including subcarrier and power allocation for LTE-A relay networks under statistical quality of service (QoS) constraints. By dual decomposition, we derive the optimal subcarrier and power allocation strategies to maximize the effective capacity (EC) of the underlying LTE-A relay systems. Characteristics of optimal resource allocation strategies are identified, and a low-complexity suboptimal scheme is developed through optimizing the subcarrier and power allocation individually. Our result suggests that the optimal subcarrier and power allocation strategies depend heavily on the underlying QoS constraint. For example, in the low signal-to-interference-plus-noise (SINR) regime, when there are less stringent QoS constraints, base stations and relay stations tend to allocate all the power to the best available subcarrier. However, as QoS requirements become more stringent, both base stations and relay stations will spread their power over available subcarriers. On the other hand, in the high SINR regime, regardless of the QoS constraints, base stations and relay stations tend to equally allocate power among available subcarriers.

In this paper we propose a joint reduced-state sequence detector (JRSSD) for a multiple-input multiple-output (MIMO) system. The proposed JRSSD incorporates the set-partitioning principle to obtain a reduced-state trellis and is the space-time extension of the RSSD proposed in for a single-input single-output (SISO) equalization problem. We show that two elements are essential in achieving the desired complexity and performance tradeoff for the proposed JRSSD algorithm: 1) a proper multisymbol set-partition and 2) an efficient space-time structure that effectively decouples the spatial and temporal processing. We propose a simple multisymbol uniform set-partition (USP) that retains certain geometric symmetry in the partitioned subsets. We also develop two suboptimal algorithms, namely the decorrelating (DC) and ordered successive (OS) algorithms, to decouple the spatial multisymbol detection problem inherent in the processing of parallel transitions, into single-symbol detection problems. The symmetry in USP, together with the DC or OS algorithm, guarantees efficient processing of parallel transitions and leads to a low-complexity JRSSD. Furthermore, numerical simulations show that the performance of JRSSD is near that of the more complex delayed decision feedback sequence estimator (DDFSE).

Selective broadcast schemes for point-to-multiple point transmission of identical information to several selected users are studied for a code-division multiple-access wireless system. The channel states for all selected users are assumed known at both the transmitter and the receivers. The goal is to minimize total transmit power while satisfying minimum received signal-to-noise ratio (SNR) requirements. Three designs, namely, time-only, space-time and space-only, are investigated. In the time-only design no spatial diversity is available, and we solve the optimal transmit signature code by developing iterative least distance programming (ILDP) and linear programming (LP) algorithms. In the space-time design, transmit antennas are exploited in addition to the temporal dimension, and we show the ILDP algorithm is still applicable. The LP algorithm can also be adapted with the integration of space-time block codes, which we term the space-time block coding LP (STC-LP) algorithm. In the space-only design, only the spatial dimension is available and we study the optimization of the transmit antenna weights to satisfy the users' SNR requirements. We show that the STC-LP algorithm applies in this case. We also propose an iterative spatial diagonalization algorithm to explore the unique structure of the space-only problem.