We propose a polar coding scheme for block fading channels, aiming to achieve maximum diversity while maintaining the performance under additive white Gaussian noise channels (AWGNC). The key idea isdiversity alignment, which aligns maximum diversity with information bits through proper codeword bit to fading block mapping. We first develop a low-complexity diversity analysis tool based on Boolean approximation under genie-aided successive cancellation decoding which can be used for arbitrary bit-to-block mapping. Second, we propose a tractable optimal block mapping forM= 2 fading blocks and develop fast search algorithms for generalM≥ 2, achieving optimal low-rate diversity coding (dc=MatR= 1/M) for a wide range ofM. Moreover, our scheme can be applied to achieve optimal high-rate diversity-2 coding (dc= 2 atR= (M−1)/M) with good AWGNC codes. We show that appropriate block-mapping can achieve both low-rate and high-rate optimal diversity simultaneously. Numerical results demonstrate that our framework provides accurate diversity analysis and the proposed DA scheme outperforms conventional interleavers under block fading channels while preserving AWGNC performance. The proposed can play a crucial role in supporting the URLLC communication in future systems, where channel is quickly changing but high reliability is required.

In this paper, we propose a hybrid precoding/combining framework for communication-centric integrated sensing and full-duplex (FD) communication operating at mmWave bands. The designed precoders and combiners enable multiuser (MU) FD communication while simultaneously supporting monostatic sensing in a frequency-selective setting. The joint design of precoders and combiners involves the mitigation of self-interference (SI) caused by simultaneous transmission and reception at the FD base station (BS). Additionally, MU interference needs to be handled by the precoder/combiner design. The resulting optimization problem involves non-convex constraints since hybrid analog/digital architectures utilize networks of phase shifters. To solve the proposed problem, we separate the optimization of each precoder/combiner, and design each one of them while fixing the others. The precoders at the FD BS are designed by reformulating the communication and sensing constraints as signal-to-leakage-plus-noise ratio (SLNR) maximization problems that consider SI and MU interference as leakage. Furthermore, we design the frequency-flat analog combiner such that the residual SI at the FD BS is minimized under communication and sensing gain constraints. Finally, we design an interference-aware digital combining stage that separates MU signals and target reflections. The communication performance and sensing results show that the proposed framework efficiently supports both functionalities simultaneously.

With the advent of extreme massive multiple-input multiple-output (X-MIMO) systems and the emerging Frequency Range 3 (FR3, 7.125–24.25 GHz) for 6G networks, the dimensionality of channel state information (CSI) has increased significantly. However, to ensure scalable operation and avoid an excessive increase in overhead, the feedback size is expected to increase only marginally. This constraint necessitates an extremely high compression ratio during CSI feedback, which in turn severely degrades the reconstruction performance of standardized codebooks. Moreover, even when employing AI methods, training models from scratch under such severe compression conditions leads to vanishing or exploding gradients, resulting in unstable training and diminished performance. In this paper, we propose a novel hierarchical transfer learning with progressive compression (HTL-PC) method to address these challenges. Our approach leverages a pretrained autoencoder extraction block from a large-feedback model and progressively transfers it to models with smaller latent spaces, thereby enabling stable training under severe compression conditions. Extensive system-level simulations demonstrate that the proposed HTL-PC method achieves remarkable improvements in squared generalized cosine similarity and downlink cell throughput over benchmark techniques.

This paper introduces a novel hybrid analog/digital transceiver design for full-duplex (FD) integrated sensing and communication (ISAC) systems operating at mmWave band. The proposed scheme simultaneously supports downlink (DL) and uplink (UL) multiuser communication along with monostatic sensing, while suppressing the self-interference (SI). Considering that high SI levels may lead to saturation at low-noise amplifiers (LNAs), our design incorporates two SI mitigation constraints: one imposed at the receiver (RX) antennas before LNAs and another after the analog combining stage before analog-to-digital converters (ADCs). By formulating optimization problems that balance the trade-offs between spectral efficiency and beam-pattern error, we leverage a projected gradient ascent (PGA) algorithm with penalty-based methods to iteratively design hybrid precoders and combiners. Simulation results show that the proposed architecture strikes a balance between communication and sensing performance while effectively mitigating SI.

Cellular network traffic prediction plays a critical role in ensuring efficient network operations and management including network resource allocation, quality of service (QoS) assurance, energy saving, and service level agreement (SLA) guarantee. Accurately predicting traffic in a cellular network is a challenging task due to its highly dynamic temporal and spatial traffic patterns. Most existing studies on network traffic prediction either overlook or do not properly consider the interdependence of cells within a cellular network. This paper proposes a novel graph neural network (GNN) based traffic prediction solution that utilizes the adjacency relationship constructed using handover information to reflect the interdependent nature of cells’ interactions. A two-layer system is developed to capture both temporal and spatial traffic dynamics as well as local and global impacts. Initial results using field data show more than 15% prediction accuracy improvement in terms of root mean square error and mean absolute error.

Knowledge understanding is a foundational part of envisioned 6G networks to advance network intelligence and AI-native network architectures. In this paradigm, information extraction plays a pivotal role in transforming fragmented telecom knowledge into well-structured formats, empowering diverse AI models to better understand network terminologies. This work proposes a novel language model-based information extraction technique, aiming to extract structured entities from the telecom context. The proposed telecom structured entity extraction (TeleSEE) technique applies a token-efficient representation method to predict entity types and attribute keys, aiming to save the number of output tokens and improve prediction accuracy. Meanwhile, TeleSEE involves a hierarchical parallel decoding method, improving the standard encoder-decoder architecture by integrating additional prompting and decoding strategies into entity extraction tasks. In addition, to better evaluate the performance of the proposed technique in the telecom domain, we further designed a dataset named 6GTech, including 2390 sentences and 23747 words from more than 100 6G-related technical publications. Finally, the experiment shows that the proposed TeleSEE method achieves higher accuracy than other baseline techniques, and also presents 5 to 9 times higher sample processing speed.

To mitigate the growing overhead resulting from the increasing number of antennas in massive multiple-input multiple-output (MIMO) systems, various autoencoder-based schemes employing two-sided artificial intelligence (AI) models for channel state information (CSI) feedback-specifically, feedback of the precoding matrix-have been proposed. In these twosided AI models, the encoder and decoder are separately deployed at the user equipment (UE) and the base station (BS), posing significant challenges in maintaining synchronization between them in real-world scenarios with multiple UEs and BSs. In this paper, we propose a novel transformer-based temporaldomain multi-length optimized (TMO) decoder that effectively leverages temporal correlations in MIMO channels to enhance CSI reconstruction. Unlike existing schemes that require different models or additional signaling overhead for re-synchronization between the encoder and decoder under varying conditions, the transformer-based TMO decoder can robustly recover CSI by utilizing feedback of the precoding matrix with temporal information using a single fixed model for inputs of varying sequential time steps. Performance evaluations demonstrate that the proposed transformer-based TMO decoder achieves significantly higher squared generalized cosine similarity and average downlink throughput compared to state-of-the-art techniques.

We propose a machine learning (ML) based end-to-end framework for pilotless communications that consists of two key components. The first component is an asymmetric modulation constellation that enables pilotless communications under channel impairments. The second component is a neural network (NN) receiver featuring an architecture that has a core of several serially-connected ResNet-like blocks. The transmitter only sends data symbols (without any pilots), and the NN receiver enables pilotless communications by using the received data symbols from the asymmetric constellation to perform implicit channel estimation/compensation and generate log-likelihood ratios (LLRs) for the bits comprising the data symbols. The combination of the asymmetric modulation constellation and the NN receiver achieves similar or superior performance to a traditional zero-forcing (ZF) receiver that relies on pilot symbols for channel estimation for 64-ary and 256-ary modulations for channels with limited time and frequency selectivity.

This paper presents a puncturing pattern design for polar codes in hybrid automatic repeat request (HARQ) systems. We divide the encoder output bit sequence into sub-blocks. The sub-block indices are then permuted to generate a set of equivalent puncturing patterns (EPPs), each of which guarantees high reliability for message bits. A puncturing pattern for each HARQ transmission is selected from the generated set of EPPs. A Gaussian-approximation based method is proposed to optimize the selection, ensuring that additional redundancy bits are sent in HARQ transmissions. The proposed design has low complexity due to the limited number of sub-blocks and can be implemented offline to generate a lookup table of puncturing patterns. Up to 0.5 dB improvements in the error-correcting performance are obtained compared with state-of-the-art techniques.

Network slicing plays a critical role in enabling multiple virtualized and independent network services to be created on top of a common physical network infrastructure. In this paper, we introduce a deep reinforcement learning (DRL)-based radio resource management (RRM) solution for radio access network (RAN) slicing under service-level agreement (SLA) guarantees. The objective of this solution is to minimize the SLA violation. Our method is designed with a two-level scheduling structure that works seamlessly under Open Radio Access Network (O-RAN) architecture. Specifically, at an upper level, a DRL-based inter-slice scheduler is working on a coarse time granularity to allocate resources to network slices. And at a lower level, an existing intra-slice scheduler such as proportional fair (PF) is working on a fine time granularity to allocate slice dedicated resources to slice users. This setting makes our solution O-RAN compliant and ready to be deployed as an ‘xApp’ on the RAN Intelligent Controller (RIC). For performance evaluation and proof of concept purposes, we develop two platforms, one industry-level simulator and one O-RAN compliant testbed; evaluation on both platforms demonstrates our solution’s superior performance over conventional methods.

We introduce two classes of measurement-based wireless schedulers. The Opportunistic Guaranteed Rate Scheduler (OGRS) meets a user’s delay constraints by opportunistically allocating the user the equivalent of a fixed service rate, which for a leaky-bucket constrained traffic ensures the delay requirements are met. By contrast, the Opportunistic Guaranteed Delay Schedulers (OGDS) schedules data transmissions when the current channel is better than what is expected in the time window before packet deadlines expire. Meeting such delay requirements requires a complementary admission control policy. We exhibit a simple measurement based policy, that indirectly accounts for heterogeneity in traffic, channel, and delay constraints by monitoring the statistics of user’s aggregate resource usage. We show that the spectral efficiency of our proposed approach is stochastically better than a wireless guaranteed rate scheduler. We bound spectral efficiency by considering an optimal offline policy with access to future channel rates and show via extensive simulations that OGRS can be within 10%-40% of the bound whereas OGDS is within 10% of the bound for a range of delay constraints. Additionally, we demonstrate that OGDS can exhibit better spectral efficiency at higher delay deadlines than schedulers leveraging neural network based predictions for future channel rates.

Nonlinear self-interference (SI) cancellation is essential for mitigating the impact of transmitter-side nonlinearity on overall SI cancellation performance in flexible duplex systems, including in-band full-duplex (IBFD) and sub-band full-duplex (SBFD). Digital SI cancellation (SIC) must address the nonlinearity in the power amplifier (PA) and the in-phase/quadrature-phase (IQ) imbalance from up/down converters at the base station (BS), in addition to analog SIC. In environments with rich signal reflection paths, however, the required number of delayed taps for time-domain nonlinear SI cancellation increases exponentially with the number of multipaths, leading to excessive complexity. This paper introduces a novel, low-complexity, frequency domain nonlinear SIC, suitable for flexible duplex systems with multiple-input and multiple-output (MIMO) configurations. The key approach involves decomposing nonlinear SI into a nonlinear basis and categorizing them based on their effectiveness across any flexible duplex setting. The proposed algorithm is founded on our analytical results of intermodulation distortion (IMD) in the frequency domain and utilizes a specialized pilot sequence. This algorithm is directly applicable to orthogonal frequency division multiplexing (OFDM) multi-carrier systems and offers lower complexity than conventional digital SIC methods. Additionally, we assess the impact of the proposed SIC on flexible duplex systems through system-level simulation (SLS) using 3D ray-tracing and proof-of-concept (PoC) measurement.

Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback is often overlooked. This issue, which arises from low density pilot placement in current standards, results in significant aliasing effects in outdoor channels and consequently limits CSI recovery performance. The main objective of this work is to solve this issue by introducing a new CSI upsampling framework at the gNB as a post-processing solution to address the gaps caused by undersampling. Leveraging the physical principles of discrete Fourier transform shifting theorem and multipath reciprocity, our framework effectively uses uplink CSI to mitigate aliasing effects. We further develop a learning-based method that integrates the proposed algorithm with the Iterative Shrinkage-Thresholding Algorithm Net (ISTA-Net) architecture, enhancing our approach for non-uniform sampling recovery. Our numerical results show that both our rule-based and deep learning upsampling methods significantly outperform traditional interpolation techniques or multiple state-of-the-art approaches by 8-13 dB and 2-10 dB, respectively, in terms of normalized mean square error.

Human respiration rate (RR) estimation is essential for various health care applications, such as sleep apnea detection and chronic obstructive pulmonary disease early diagnose. Recently, radio frequency based RR estimation has achieved high accuracy for single-person RR detection. However, multi-person RR estimation is still the obstacle blocking the wide commercialization of RF sensing based RR solution. In this paper, a novel multi-person RR estimation algorithm that can overcome the frequency resolution limit is present. The proposed algorithm is not only analytically justified but also verified in a real test-bed involving commercial off-the-shelf WiFi devices. Extensive experiment results show a 98% accuracy in people-counting and a root mean square error (RMSE) of 0.13 breath per minute (bpm) on RR detection. To the best of our knowledge, this is the first WiFi sensing work that can detect different people who share the same RR by only using a single pair of transmit and receive antenna.

We propose machine learning (ML) based optimization methods for modulation and digital predistortion (DPD), that overcome the signal distortion due to power amplifier (PA) non-linearity and memory effects. The proposed methods generate and exploit an adjusted modulation constellation to compensate for a given PA non-linearity, whereas conventional and widely-employed methods mainly rely on DPD to that end. This potentially removes the need for DPD if memory effects are not detrimental and enables the transmitter to operate close to the power saturation region, increasing the PA power efficiency. The AI/ML framework to learn an adjusted constellation is trained to produce a target square quadrature amplitude modulation (QAM) signal at the PA output. We also present a DPD learning architecture for the adjusted constellations. The proposed methods outperform square 16-ary/64-ary QAMs with DPD by more than 1 dB and are within 0.2~10.3 dB of the theoretical performance at a symbol error rate of 0.01, when the PA operates in its saturation region.

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms and reduced feedback overhead. Common codebook-based approaches include Type II and eType II precoding methods defined in the 3GPP standards. Feedback in these systems is typically standardized per subband (SB), allowing user equipment (UE) to select the optimal precoder from the codebook for each SB, thereby reducing feedback overhead. However, this subband-level feedback resolution may not suffice for frequency-selective channels. This paper addresses this issue by introducing an uplink CSI-assisted precoder upsampling module deployed at the gNodeB. This module upsamples SB-level precoders to resource block (RB)-level precoders, acting as a plug-in compatible with existing gNodeB or base stations.

High-frequency wideband cellular communications over mmWave and sub-THz offer the opportunity for high data rates. However, it also presents high path loss, resulting in limited coverage. High-gain beamforming from the antenna array is essential to mitigate the coverage limitations. The conventional phased antenna arrays (PAA) cause high scheduling latency owing to analog beam constraints, i.e., only one frequency-flat beam is generated. Recently introduced joint phase-time array (JPTA) architecture, which utilizes both true-time-delay (TTD) units and phase shifters (PSs), alleviates analog beam constraints by creating multiple frequency-dependent beams for scheduling multiple users at different directions in a frequency-division manner. One class of previous studies offered solutions with "rainbow" beams, which tend to allocate a small bandwidth per beam direction. Another class focused on uniform linear array (ULA) antenna architecture, whose frequency-dependent beams were designed along a single axis of either azimuth or elevation direction. This paper presents a novel 3D beamforming design that maximizes beamforming gain toward desired azimuth and elevation directions and across sub-bands partitioned according to scheduled users’ bandwidth requirements. We provide analytical solutions and iterative algorithms to design the PSs and TTD units for a desired subband beam pattern. Through simulations of the beamforming gain, we observe that our proposed solutions outperform the state-of-the-art solutions reported elsewhere.

Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of ~1 m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of$\approx 1$mm in channels with a Rician-factor$\geq 7$(a$100 \times $improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of <1 mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.

The THz band (0.1-10 THz) has attracted considerable attention for next-generation wireless communications due to the large amount of available bandwidth that may be key to meet the rapidly increasing data rate requirements. Before deploying a system in this band, a detailed wireless channel analysis is required as the basis for proper design and testing of system implementations. One of the most important deployment scenarios of this band is the outdoor microcellular environment, where the Transmitter (Tx) and the Receiver (Rx) have a significant height difference (typically ≥ 10 m). In this paper, we present double-directional (i.e., directionally resolved at both link ends) channel measurements in such a microcellular scenario encompassing street canyons and an open square. Measurements are done for a 1 GHz bandwidth between 145-146 GHz and an antenna beamwidth of 13 degree; distances between Tx and Rx are up to 85 m and the Tx is at a height of 11.5 m from the ground. The measurements are analyzed to estimate path loss, shadowing, delay spread, angular spread, and multipath component (MPC) power distribution. These results allow the development of more realistic and detailed THz system performance assessment.

Condensed channel parameters, such as Root Mean Square (RMS) delay spread and angular spread, are essential ways of representing results from the measurement of wireless propagation channels. However, real-world measurements are invariably affected by noise, which is not inherently reduced by the Fourier processing - in contrast to high-resolution parameter estimation. Even when the signal-to-noise ratio is relatively high, e.g., 20 dB, the impact of the noise on the condensed parameters can be significant and lead to erroneous estimates. This can be explained, e.g., for the computation of the RMS delay spread, where long delays (which often carry noise only) are "up-weighted" by the square of the delay. While techniques like noise thresholding are often applied, the reasons for choosing thresholds are usually not described in detail. This paper systematically analyzes noise thresholding for joint delay-angle measurements and derives equations for the impact of thresholds on the computation of delay and angle RMS spreads and window parameters under various measurement circumstances. The paper provides results based on closed-form equations, as well as MonteCarlo (MC) simulations, and application to real-world measurements in the sub-THz band.

Due to its ubiquitous and contact-free nature, the use of WiFi infrastructure for performing sensing tasks has tremendous potential. However, the channel state information (CSI) measured by a WiFi receiver suffers from errors in both its gain and phase, which can significantly hinder sensing tasks. By analyzing these errors from different WiFi receivers, a mathematical model for these gain and phase errors is developed in this work. Based on these models, several theoretically justified preprocessing algorithms for correcting such errors at a receiver and, thus, obtaining clean CSI are presented. Simulation results show that at typical system parameters, the developed algorithms for cleaning CSI can reduce noise by 40% and 200%, respectively, compared to baseline methods for gain correction and phase correction, without significantly impacting computational cost. The superiority of the proposed methods is also validated in a real-world test bed for respiration rate monitoring (an example sensing task), where they improve the estimation signal-to-noise ratio by 20% compared to baseline methods.

To deal with notorious delays in communication systems, it is crucial to forecast key system characteristics, such as the communication load. Most existing studies aggregate data from multiple edge nodes for improving the forecasting accuracy. However, the bandwidth cost of such data aggregation could be unacceptably high from the perspective of system operators. To achieve both the high forecasting accuracy and bandwidth efficiency, this paper proposes an Adaptive Multi-Teacher Weighting in Teacher-Student Learning approach, namely AdaTeacher, for communication load forecasting of multiple edge nodes. Each edge node trains a local model on its own data. A target node collects multiple models from its neighbor nodes and treats these models as teachers. Then, the target node trains a student model from teachers via Teacher-Student (T-S) learning. Unlike most existing T-S learning approaches that treat teachers evenly, resulting in a limited performance, AdaTeacher introduces a bilevel optimization algorithm to dynamically learn an importance weight for each teacher toward a more effective and accurate T-S learning process. Compared to the state-of-the-art methods, Ada Teacher not only reduces the bandwidth cost by 53.85%, but also improves the load forecasting accuracy by 21.56% and 24.24% on two real-world datasets.

The association of mobile devices with network resources (e.g., base stations, frequency bands/channels), known as load balancing, is critical to reduce communication traffic congestion and network performance. Reinforcement learning (RL) has shown to be effective for communication load balancing and achieves better performance than currently used rule-based methods, especially when the traffic load changes quickly. However, RL-based methods usually need to interact with the environment for a large number of time steps to learn an effective policy and can be difficult to tune. In this work, we aim to improve the data efficiency of RL-based solutions to make them more suitable and applicable for real-world applications. Specifically, we propose a simple, yet efficient and effective deep RL-based wireless network load balancing framework. In this solution, a set of good initialization values for control actions are selected with some cost-efficient approach to center the training of the RL agent. Then, a deep RL-based agent is trained to find offsets from the initialization values that optimize the load balancing problem. Experimental evaluation on a set of dynamic traffic scenarios demonstrates the effectiveness and efficiency of the proposed method.

The availability of large bandwidths in the terahertz (THz) band will be a crucial enabler of high data rate applications in next-generation wireless communication systems. The urban microcellular scenario is an essential deployment scenario where the base station (BS) is significantly higher than the user equipment (UE). Under practical operating conditions, moving objects (i.e., blockers) can intermittently obstruct various parts of the BS- UE link. Therefore, in the current paper, we analyze the effect of such blockers. We assume a blockage of the strongest beam pair and investigate the availability and extent of angular diversity, i.e., alternative beampairs that can sustain communication when the strongest is blocked. The analysis uses double-directional channel measurements in urban microcellular scenarios for 145– 146 GHz with BS-UE distances between 18 to 83 m. We relate the communication-system quantities of beam diversity and capacity to the wireless propagation conditions. We show that the SNR loss due to blockage depends on the blocked angular range and the specific location, and we find mean blockage loss to be on the order of 10–20 dB in line-of-sight (LOS) and 5–12 dB in NLOS (non-LOS). This analysis can contribute to the design of intelligent algorithms or devices (e.g., beamforming, intelligent reflective surfaces) to overcome the impact of the blockage.

We propose machine learning (ML) based optimization methods and new high order modulations for reliable and high-capacity communications. The widely adopted square quadrature amplitude modulations (QAM) fundamentally exhibit a shaping loss of up to 1.53 dB to the Shannon capacity bound. The proposed modulations obtained through the ML based optimization outperform the square QAMs and other state of-the-art ones by about 1.2 dB and 0.3 dB, respectively, for 1024-ary modulation with LDPC coding. We construct the neural network architecture and training methods to reflect the desired properties of well-performing modulations. This significantly helps in the training convergence of the ML models to a desired optimal state and leads to the modulation constellation and bit to-symbol mapping that reduces the shaping loss to the Shannon capacity bound to a large extent. Moreover, the ML methods enable the development of new optimal modulations for a wide range of target SNR and modulation orders.

Next generation wireless schedulers will support increasingly heterogeneous devices/applications in terms of their traffic characteristics and service requirements. Particularly challenging is the need to deliver traffic subject to delay and reliability constraints in a spectrally efficient manner. We propose a new measurement-based Opportunistic Guaranteed Deadline Scheduler (OGDS) that meets strict delay deadlines on users’ packets. This is achieved by scheduling packet transmissions when the current channel rate is better than that expected in the time window before packet deadlines expire. In order to meet such requirements one must have a complementary admission control policy. We exhibit a simple, once again measurement based policy, that indirectly accounts for heterogeneity in traffic, channel and delay constraints by monitoring statistics of OGDS’s resource usage. We show via extensive synthetic and trace driven simulations that OGDS requires at most 10−25% more resources compared to an optimal offline scheduling policy with complete knowledge of future channel rates, and performs much better than standard baselines including the state-of-the-art MLWDF scheduler. Finally, we propose a modification to OGDS that enables one to control the jitter at a possible loss in spectral efficiency.

Next generation wireless schedulers will support increasingly heterogeneous users/devices in terms of their traffic characteristics and service requirements. Particularly challenging is the need to deliver low latency traffic with strict deadlines in a spectrally efficient manner. We introduce a class of wireless schedulers, Opportunistic Guaranteed Rate (OGRS) that exploits the temporal variability in users' channel capacity with a view on maintaining delay guarantees. OGRS meets the user's delay constraints by opportunistically allocating the user the equivalent of a fixed service rate, which given a dual leaky bucket constraint on its traffic will ensure the delay requirements are met. We consider offline policies with access to future channel rates, which establishes a bound to the wireless spectral efficiency. We show via extensive simulations that OGRS can be within 10%-40 % of this bound for a range of delays that were considered. These gains translate to more than a two fold enhancement in eMBB users' throughput, when URLLC and eMBB traffic share resources. Finally, we propose a measurement based admission control strategy for latency constrained URLLC users, so that the network can guarantee QoS to all its users - existing as well as newly admitted ones.

Mobility load balancing (MLB) aims to solve the problem of uneven resource utilization in cellular networks. Since network dynamics are usually complicated and non-stationary, conventional model-based MLB methods fail to cover all scenarios of cellular networks. On the other hand, deep reinforcement learning (DRL) can provide a flexible framework to learn to distribute cell load evenly without explicit modeling of the underlying network dynamics. In this paper, we introduce a novel decentralized DRL-based MLB method where each cell has a DRL agent to learn its handover parameters and antenna tilt angle. As the number of cells increases, the decentralized framework is more computationally efficient than its centralized counterpart by dividing the action space. Furthermore, our designed decentralized DRL architecture only requires readily known information defined in existing cellular standards, and it can achieve a more balanced cell load distribution than the centralized DRL one by using individual reward functions. To provide realistic performance evaluation, a network simulator is introduced strictly following the Third Generation Partnership Project (3GPP) specifications. Furthermore, field data is used to construct the underlying cellular environment. Extensive evaluations have been conducted to demonstrate the fact that the introduced decentralized DRL-based MLB method can achieve a more balanced cell load distribution and a better performance of edge users than the state-of-the-art MLB methods.

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, and double-directional where both transmitter and receiver are at the same height. In all, we present measurements at 38 locations, consisting of nearly 50,000 impulse responses, representing both line-of-sight (LoS) and non-line-of-sight (NLoS) cases in the 1-100 m range between 145-146 GHz. We provide modeling for path loss, shadowing, delay spread, angular spread, multipath component (MPC) power distribution and Qtapnumber. 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° beamwidth. These results constitute an important first step towards better understanding the wireless channel in the THz band.

THz communication has attracted a great deal of attention due to the large bandwidth available in this band. However, system development and deployment requires detailed knowledge of the wireless channel, which must be provided by measurements (channel sounding) and subsequent modeling in key scenarios of interest. One important scenario in this regard is the urban outdoor microcellular scenario where the Transmitter (Tx) and the Receiver (Rx) have a significant height difference. In this paper, we present ultra-wideband double-directional channel measurements in a microcellular scenario for the band between 145-146 GHz in a street canyon with Tx-Rx of distances up to 67 m. The results are analyzed in the delay and angular domain, and the impact of the street canyon on the power delay profiles and angular power spectra is detailed. These measurements thus contribute towards the creation of a more realistic and detailed THz channel model in these scenarios.

In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user needs to send the CSI to the BS. Often the large overhead associated with this CSI feedback in FDD systems becomes the bottleneck in improving the system performance. In this paper, we propose an AI-based CSI feedback based on an auto-encoder architecture that encodes the CSI at UE into a low-dimensional latent space and decodes it back at the BS by effectively reducing the feedback overhead while minimizing the loss during recovery. Our simulation results show that the AI-based proposed architecture outperforms the state-of-the-art high-resolution linear combination codebook using the DFT basis adopted in the 5G New Radio (NR) system.

As the pace of global 5G network deployments accelerates, the telecommunications industry strives to move from a conventional vertical stack, closed, hardware-based ecosystem to an open, interoperable, modular, cloud-based ecosystem that leverages software-based implementations of various network entities – including core network functions and base stations. Such an open ecosystem could facilitate the implementation of AI techniques and could be a critical advancement in realizing the vision of “zero-touch” wireless networks that support end-to-end automation– stretching from the core network to end-user devices. In this paper, we provide an overview of ongoing discussions along these lines in the context of the O-RAN Alliance along with a comprehensive discussion on various challenges. We then briefly present our vision of the evolution of intelligent networks beyond current 4G/5G networks.

Channel information is essential to unleash the benefits of 5G New Radio (NR) by enabling network intelligence that adapts transmissions to users’ channels. In this paper, we propose model-driven feature design and use support vector machine (SVM) to classify users’ speed range. Our model-driven features are designed based on stochastic channel modeling. Multiple features are derived from time-domain cross-correlation and time-domain auto-correlation function of the sounding reference signals. The classifier is trained and verified with extensive standard compliant simulation channels at different SNR levels and speeds, and attains greater than 90% accuracy.

Wireless communications in the THz frequency range can allow data rates of hundreds of Gbit/s, and will thus be an important part of 6G. A first important step for system design is an understanding of the underlying propagation channel. In this paper, we present results from one of the first measurement campaigns for medium-distance (up to 35 m) outdoor channels in urban environments where both the directions and delays of multipath are measured with good resolution. The results show a surprisingly rich multipath environment, leading to significant dispersion in both delay and angular domains. We also find that metallic-covered surfaces lead to a considerable enhancement of multipath, which indicates an important impact of building materials. Overall, the results indicate that relying on pronounced sparsity for THz system design might not always be valid in this type of environment.

As 360-degree videos and virtual reality (VR) applications become popular for consumer and enterprise use cases, the desire to enable truly mobile experiences also increases. Delivering 360-degree videos and cloud/edge-based VR applications require ultra-high bandwidth and ultra-low latency[1], challenging to achieve with mobile networks. A common approach to reduce bandwidth is streaming only the field of view (FOV). However, extracting and transmitting the FOV in response to user head motion can add high latency, adversely affecting user experience. In this paper, we propose a predictive adaptive streaming approach, where the predicted view with high predictive probability is adaptively encoded in relatively high quality according to bandwidth conditions and transmitted in advance, leading to a simultaneous reduction in bandwidth and latency. The predictive adaptive streaming method is based on a deep-learning-based viewpoint prediction model we develop, which uses past head motions to predict where a user will be looking in the 360-degree view. Using a very large dataset consisting of head motion traces from over 36,000 viewers for nineteen 360-degree/VR videos, we validate the ability of our predictive adaptive streaming method to offer high-quality view while simultaneously significantly reducing bandwidth.

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.

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.

Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high dimensional hidden dynamics and one trainable output layer. It has the nice feature of requiring limited training which is critical for certain applications where training data is extremely limited and costly to obtain. In this paper, we consider two ways to extend the shallow architecture to deep RC to improve the performance without sacrificing the underlying benefit: (1) Extend the output layer to a three layer structure which promotes a joint time-frequency processing to neuron states; (2) Sequentially stack RCs to form a deep neural network. Using the new structure of the deep RC we redesign the physical layer receiver for multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) signals since MIMO-OFDM is a key enabling technology in the 5th generation (5G) cellular network. The combination of RNN dynamics and the time-frequency structure of MIMO-OFDM signals allows deep RC to handle miscellaneous interference in nonlinear MIMO-OFDM channels to achieve improved performance compared to existing techniques. Meanwhile, rather than deep feedforward neural networks which rely on a massive amount of training, our introduced deep RC framework can provide a decent generalization performance using the same amount of pilots as conventional model-based methods in 5G systems. Numerical experiments show that the deep RC based receiver can offer a faster learning convergence and effectively mitigate unknown non-linear radio frequency (RF) distortion yielding twenty percent gain in terms of bit error rate (BER) over the shallow RC structure.

Next-generation virtual radio access networks (vRAN) will benefit from the flexibility provided by virtualization in proposed Cloud-RAN configurations. These systems for 5G and beyond may consist of commodity hardware such as GPUs in data centers with multiple connected base stations (gNBs) flexibly receiving allocated resources depending on time-varying, real-time demands. In this paper, parallel reconfigurable algorithms and architectures for channel decoding are proposed. In particular, flexible rate and block length LDPC decoders for the new radio (NR) physical layer on GPU are characterized. We implement these GPU decoders using reduced word lengths of 8-bits to represent the log-likelihood ratios during decoding, and we utilize multiple GPU streams to process multiple blocks of codewords in parallel. These techniques allow our implementation to reduce the device transfer overhead and achieve the low-latency or high-throughput targets for 5G and beyond. Moreover, we integrate our decoder into the Open Air Interface (OAI) NR software stack to investigate virtualization capabilities when containerizing vRAN functionality such as the LDPC decoder.

Designing beamforming techniques for the downlink (DL) of frequency division duplex (FDD) massive MIMO is known to be a challenging problem due to the difficulty of obtaining channel state information (CSI). Indeed, since the uplink-downlink bands are disjoint, the system cannot rely on channel reciprocity to estimate the channel from uplink (UL) pilots as in time division duplexing (TDD) system. Still, in this paper, we propose original designs for robust beamformers that do not require any feedback from the users and only rely on the transmission of UL pilots. The price to pay is that the beamformer is non-coherent in the sense that it does not leverage full knowledge of the phase of each multipath component. A large variety of novel designs are proposed under different criterion and partial phase knowledge.

Current studies that apply reinforcement learning (RL) to dynamic spectrum access (DSA) problems in wireless communications systems mainly focus on model-free RL (MFRL). However, in practice, MFRL requires a large number of samples to achieve good performance making it impractical in real-time applications such as DSA. Combining model-free and model-based RL can potentially reduce the sample complexity while achieving a similar level of performance as MFRL as long as the learned model is accurate enough. However, in a complex environment, the learned model is never perfect. In this article, we combine model-free and model-based RL, and introduce an algorithm that can work with an imperfectly learned model to accelerate the MFRL. Results show our algorithm achieves higher sample efficiency than the standard MFRL algorithm and the Dyna algorithm (a standard algorithm integrating model-based RL and MFRL) with much lower computation complexity than the Dyna algorithm. For the extreme case where the learned model is highly inaccurate, the Dyna algorithm performs even worse than the MFRL algorithm while our algorithm can still outperform the MFRL algorithm.

Channel estimation for the downlink of frequency division duplex (FDD) massive multiple-input-multiple output (MIMO) systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users. The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived. The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

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.

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.

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.

This paper studies the effect of the user hand grip on the design of beamforming codebooks for 5G millimeter-wave (mmWave) mobile handsets. The high-frequency structure simulator (HFSS) is used to characterize the radiation fields for fourteen possible handgrip profiles based on experiments we conducted. The loss from hand blockage on the antenna gains can be up to 20-25 dB, which implies that the possible hand grip profiles need to be taken into account while designing beam codebooks. Specifically, we consider three different codebook adaption schemes: a grip-aware scheme, where perfect knowledge of the hand grip is available; a semi-aware scheme, where just the application (voice call, messaging, etc.) and the orientation of the mobile handset is known; and a grip-agnostic scheme, where the codebook ignores hand blockage. Our results show that the ideal grip-aware scheme can provide more than 50% gain in terms of the spherical coverage over the agnostic scheme, depending on the grip and orientation. Encouragingly, the more practical semi-aware scheme we propose provides performance approaching the fully grip-aware scheme. Overall, we demonstrate that 5G mmWave handsets are different from pre-5G handsets: the user grip needs to be explicitly factored into the codebook design.

As virtual reality (VR) applications become popular, the desire to enable high-quality, lightweight and mobile VR leads to various edge/cloud-based techniques. This paper introduces a predictive pre-rendering approach to address the ultra-low latency challenge in edge/cloud-based six Degrees of Freedom (6DoF) VR. Compared to 360-degree videos and 3DoF (head motion only) VR, 6DoF VR supports both head and body motions, thus not only viewing direction, but also viewing position changes. In our approach, the predictive view is rendered in advance based on the predicted viewing direction and position, leading to a reduction in latency. The key to achieving this efficient predictive pre-rendering approach is to predict the head and body motion accurately using past head and body motion traces. We develop a deep learning-based model and validate its ability using a dataset of over 840,000 samples for head and body motion.

Current trends in spectrum regulation show that more and more unlicensed and shared spectrum bands are poised to be opened up for mobile communication. However, the question remains how to best utilize this spectrum and build efficient networks, and if the time has come for newer approaches to be considered for the next generation system. In this work, we propose a coordinated shared spectrum framework that can be considered for next generation cellular standardization. In designing the framework, we aim to improve on the current unlicensed access schemes toward increasing spectral efficiency in highly- dense networks. To this end, we demonstrate that with the proposed framework both throughput and access delay can be significantly improved over the state-of-the-art LAA system. We also show that large statistical multiplexing gains are possible through dynamic sharing instead of static, hard splitting of shared spectrum, as in the current CBRS system.

Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain.

This paper develops a novel framework for the modeling and analysis of spatial spectrum sensing (SSS) for device-to-device (D2D) communications in uplink two- tier user-centric deployed heterogeneous networks (HetNets), where small cell base stations (SBSs) are deployed in the places with high user density termed hotspots introduced by 3GPP. We study the average transmit power of uplink users, the probability of spatial false alarm and the probability of spatial miss detection of a typical D2D transmitter (D2D-Tx) during SSS. Based on the results, we further characterize the coverage probability of a typical D2D user and the area spectral efficiency (ASE) of D2D networks. Simulation results verify our analysis and demonstrate the advantages of SSS-based D2D communications in future wireless networks.

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal- to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

Application of massive multiple-input multipleoutput (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink training and feedback. Channel extrapolation, i.e., estimating the channel response at the downlink frequency band based on measurements in the disjoint uplink band, is a promising solution to overcome this bottleneck. This paper presents measurement campaigns obtained by using a wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64 element antenna array, in both an anechoic chamber and outdoor environment. The Space Alternating Generalized Expectation- Maximization (SAGE) algorithm was used to extract the parameters (amplitude, delay, and angular information) of the multipath components from the attained channel data within the â€training†(uplink) band. The channel in the downlink band is then reconstructed based on these path parameters. The performance of the extrapolated channel is evaluated in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in comparison to the â€ground truthâ€, i.e., the measured channel at the downlink frequency. We find strong sensitivity to calibration errors and model mismatch, and also find that performance depends on propagation conditions: LOS performs significantly better than NLOS.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: 1) avoiding causing harmful interference to primary users (PUs) and 2) conducting effective interference coordination with other SUs. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn “appropriate” spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network, called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

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.

This paper presents results from channel sounding campaigns that investigate the impact of foliage blockage on the wireless propagation channel characteristics at 28 GHz. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beamforming 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 derive models for the distance-dependent foliage attenuation for different receiver heights. Additionally, we compare the angular spread and delay spread for links with and without foliage blockage to understand the effects of foliage on all channel characteristics.

This paper investigates the effects of a human body shadowing for a device-to-device (D2D) communications scenario at 28 GHz. The measurements are performed with a real-time channel sounder equipped with fast-switching phased antenna arrays, which enables the directionally resolved wideband measurement of dynamic effects. By exploiting the phase coherence of the setup, the multi-path components can be tracked over time, observing the temporal variations of the channel characteristics. This paper presents results of the human body shadowing in an outdoor (plaza) environment at two different link distances: 5 m and 10 m. We then analyze results corresponding to the assumption of fixed beams with different beamwidth, a 12° directional single beam and a 102° sectoral combined beam. More importantly, for the first time, the time-varying angular power spectrum and the temporal evolutions of the mean angle and the angular spread statistics in a dynamic channel of walking pedestrians are presented, which cannot be measured with traditional horn antenna channel sounders.

This paper presents the results from a 28 GHz channel sounding campaign performed to investigate the effects of outdoor to indoor penetration on the wireless propagation channel characteristics for an urban microcell in a fixed wireless access scenario. The measurements are performed with a real-time channel sounder, which can measure path loss up to 169 dB, and equipped with phased array antennas that allow electrical beam steering for directionally resolved measurements in dynamic environments. 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. For outdoor and indoor receiver locations, we compare path loss, delay spreads and angular spreads obtained for two different types of buildings.

While massive multiple-input multiple-output system (MIMO) promises to provide substantial increase in spectral efficiency-per- cell, due to high dimensionality, estimating its channel is considered as one of the major challenges towards extracting all the benefits of such large antenna systems. In this paper, based on parametric channel modeling, we present a direction of arrival (DoA) estimation method for multi-cell multi-user 3D massive-MIMO/Full Dimension (FD-MIMO) orthogonal frequency division multiplexing (OFDM) system using estimation of signal parameter via rotational invariance technique (ESPRIT). Furthermore, we analytically characterize the performance of ESPRIT-based DoA estimation in multiuser scenario, and investigate how pilot contamination, intra-cell, and inter-cell interference affect DoA estimation performance.

Spatial correlation across an antenna array is known to be detrimental to the terminal signal-to- interference-plus-noise-ratio (SINR) and system spectral efficiency. For a downlink multiuser multiple-input multiple-output system (MU-MIMO), we show that the widely used, yet overly simplified, correlation models which generate fixed correlation patterns for all terminals tend to underestimate the system performance. This is in contrast to more sophisticated, yet physically motivated, remote scattering models that generate variations in the correlation structure across multiple terminals. The remote scattering models are parameterized with measured data from a recent 2.53 GHz urban macrocellular channel measurement campaign in Cologne, Germany. Assuming spatially correlated Ricean fading, with maximum-ratio transmission precoding, tight closed-form approximations to the expected (average) SINR, and ergodic sum spectral efficiency are derived. The expressions provide clear insights into the impact of variable correlation patterns on the above performance metrics. Our results demonstrate the sensitivity of the MU-MIMO performance to different correlation models, and provide a cautionary tale of its impact.

This paper presents results from the (to our knowledge) first dynamic double-directionally resolved measurement campaign at mm-wave frequencies for an outdoor microcellular scenario. The measurements are performed with USC's real- time channel sounder equipped with phased array antennas that can steer beams electrically in microseconds, allowing directional measurements in dynamic environments. Exploiting the phase coherency of the setup, the multi-path components can be tracked over time to investigate the temporal dependencies of the channel characteristics. We present results for time-varying path-loss, delay spread, mean angles and angular spreads observed at the transmitter (TX) and receiver (RX) in the presence of moving vehicles and pedestrians. Additionally, we investigate excess losses observed due to blockage by vehicles and compare the cases when TX and RX are using fixed beams or when they are capable of adjusting beam directions dynamically.

Cell search is the process for a user to detect its neighboring base stations (BSs) and make a cell selection decision. Due to the importance of beamforming in 5G cellular networks including both the millimeter wave and sub-6 GHz networks, there is a need for a better understanding of the directional cell search delay performance. A cellular network with fixed BS and user locations is considered, so as to take into account the strong temporal correlations that exist for the SINR experienced by each BS and user in this context. For Poisson cellular networks with Rayleigh fading channels, a closed-form expression for the spatially averaged mean cell search delay of all users is derived. This mean cell search delay for a noise-limited network is proved to be infinite whenever the non-line-of-sight path loss exponent is larger than two. For interference-limited networks, a phase transition for the mean cell search delay is shown to exist in terms of the number of BS beams M: the mean cell search delay is infinite when M is smaller than a threshold and finite otherwise. Beam-sweeping is also demonstrated to be effective in decreasing the cell search delay, especially for cell edge users.

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 presents results from the (to our knowledge) first double- directionally resolved measurement campaign at mm-wave frequencies in a suburban microcell. The measurements are performed with a real-time channel sounder equipped with phased antenna arrays that allows electrical beam steering in microseconds, and which can measure path-loss of up to 169 dB. Exploiting the phase coherency of the measurements in the different beams, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We present statistics of channel characteristics such as path-loss, shadowing and delay spread results for line-of-sight and non-line-of-sight cases, as well as sample results for power angular spectrum and extracted multi- path components.

In this paper, we present a novel real-time MIMO channel sounder for 28 GHz. Until now, the common practice to investigate the directional characteristics of millimeter-wave channels has been using a rotating horn antenna. The sounder presented here is capable of performing horizontal and vertical beam steering with the help of phased arrays. Thanks to the fast beam-switching capability, the proposed sounder can perform measurements that are directionally resolved both at the transmitter (TX) and receiver (RX) as fast as 1.44 milliseconds compared to the minutes or even hours required for rotating horn antenna sounders. This does not only enable us to measure more points for better statistical inference but also allows us to perform directional analysis in dynamic environments. Equally important, the short measurement time combined with the high phase stability of our setup limits the phase drift between TX and RX, enabling phase-coherent sounding of all beam pairs even when TX and RX are physically separated and have no cabled connection for synchronization. This ensures that the measurement data is suitable for high-resolution parameter extraction algorithms. Along with the system design and specifications, this paper also discusses the measurements performed for verification of the sounder. Furthermore, we present sample measurements from a channel sounding campaign performed on a residential street.

Massive multi-input multi-output (MIMO) is shown to significantly increase spectral efficiency by exploiting a large number of antennas to support high-order multiuser MIMO. In 3GPP Release-13, a full-dimension MIMO (FD-MIMO) technology was introduced to address practical aspects for massive MIMO in cellular systems; extensive simulations show 2–4 times capacity gain compared with current LTE systems. FD-MIMO has been identified as one of the key 5G technologies and is being continuously improved in 3GPP new radio standards. However, several practical challenges such as interference mitigation among MIMO streams for a large number of users with limited channel feedback, hardware limitations, such as calibration errors that limit the precoding capabilities need to be addressed carefully. A proof of concept (PoC) base-station and user equipment (UE) prototype has been designed to validate the potential of FD-MIMO technology and demonstrate commercial implementation feasibility. In this paper, we present theory and architecture behind an FD-MIMO prototype and share the field test results with LTE-based UEs in a multi-user MIMO setup. We also analyze the impact of transmitter and receiver calibration errors on the performance of the FD-MIMO system.

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.

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 lognormally 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.

The 3-D multiple-input multiple-output (3-D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. In this paper, we present a low-cost test equipment (channel sounder) and post-processing algorithms suitable for investigating the 3-D MIMO channels as well as the results from a measurement campaign for obtaining elevation and azimuth characteristics in an outdoor-to-indoor (O2I) environment. Due to limitations in available antenna switches, our channel sounder consists of a hybrid switched/virtual cylindrical array with effectively 480 antenna elements at the base station. The virtual setup increased the overall MIMO measurement duration, thereby introducing phase drift errors in the measurements. Using reference antenna measurements, we estimate and correct for the phase errors during post-processing. We provide the elevation and azimuth angular spreads for the measurements done in an urban macro-cellular and urban micro-cellular environments, and study their dependence on the user equipment (UE) height. Based on the measurements done with UE placed on different floors, we study the feasibility of separating users in the elevation domain. The measured channel impulse responses are also used to study the channel hardening aspects of the massive MIMO and the optimality of the maximum ratio combining receiver.

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 (mm-wave) cellular systems typically must rely on directional beamforming (BF) in order to create a viable connection. The BF direction must, therefore, be learned-as well as used-in the initial access process for mm-wave cellular networks. This paper considers four simple but representative initial access protocols that use various combinations of directional BF 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 π/12) for UPT. Of the considered protocols, the best tradeoff between initial access delay and UPT is achieved under a fast CS protocol.

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.

The path loss model describing the power-law dependency on distance plus a log-normally distributed shadowing attenuation, is a staple of link budgets and system simulations. Determination of the parameters of this model is usually done from measurements and ray tracing. We show that the typical least-square fitting to those data points is inherently biased to give the best fitting to the link distances that happen to have more evaluation points; this bias might be highly undesirable in various types of simulations that use the resulting model. In this paper we present a weighted fitting method to address this issue. While it is unavoidable that fits are better for one distance range than another, we argue that such a decision should be made consciously, and adjusted to the type of simulation for which the path loss model should be used. We discuss the weighting functions for different purposes, and show their impact on prediction accuracy of signal level, interference level, and capacity in a hexagonal cellular grid simulation. As examples, weighted fitting models are presented for 28 GHz channels in urban macrocells, and it is shown that the fitting accuracy can be improved by our approach.

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

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.

We leverage stochastic geometry to characterize key performance metrics for neighboring Wi-Fi and LTE networks in unlicensed spectrum. Our analysis focuses on a single unlicensed frequency band, where the locations for the Wi-Fi access points and LTE eNodeBs are modeled as two independent homogeneous Poisson point processes. Three LTE coexistence mechanisms are investigated: 1) LTE with continuous transmission and no protocol modifications; 2) LTE with discontinuous transmission; and 3) LTE with listen-before-talk and random back-off. For each scenario, we derive the medium access probability, the signal-to-interference-plus-noise ratio coverage probability, the density of successful transmissions (DST), and the rate coverage probability for both Wi-Fi and LTE. Compared with the baseline scenario where one Wi-Fi network coexists with an additional Wi-Fi network, our results show that Wi-Fi performance is severely degraded when LTE transmits continuously. However, LTE is able to improve the DST and rate coverage probability of Wi-Fi while maintaining acceptable data rate performance when it adopts one or more of the following coexistence features: a shorter transmission duty cycle, lower channel access priority, or more sensitive clear channel assessment thresholds.

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.

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.

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.

In this paper, direction-of-arrival (DOA) estimation problem for massive multiple-input multiple-output (MIMO) systems with a two dimensional (2D) array is investigated, assuming no knowledge of path number, noise power, path gain correlations and bad data statistics. A novel iterative algorithm operating on tensor represented data is proposed, with integrated features of effective bad data mitigation and automatic source enumeration. Simulation results are presented to illustrate the excellent performance of the proposed algorithm in term of accuracy and robustness.

3-dimensional Multiple-Input Multiple-Output (3D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. For 3D MIMO system design, it is important to have full characterization of the 3D MIMO propagation channel, i.e., characterize both the elevation and azimuth characteristics of the wireless propagation channel, especially at the base station end. In this paper, we present first results from a measurement campaign for obtaining these characteristics. For those measurements we use a hybrid switched/virtual cylindrical array with 480 antenna elements at the base station (BS), and a switched array with 24 antenna elements at the user equipment (UE). We perform outdoor-to-indoor (O2I) channel measurements in an urban macro-cellular (UMa) and a micro-cellular (UMi) environments. We provide the elevation and azimuth angular spreads at the transmitter and receiver, and study their dependence on the UE height. With the increase in the UE height, the BS elevation spreads decreased from 1.24 deg to 1 deg and 1.15 deg to 0.78 deg respectively for UMa and UMi; the azimuth spreads remained approximately the same (7.5 deg). Based on the measurements done with the UE placed on different floors, we study the feasibility of separating users in the elevation domain. Users were separable in 44% and 54.17% scenarios respectively for the UMa and UMi environments.

With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive- MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the root mean square error (RMSE) of the DoA estimation is analytically characterized for the corresponding MIMO-OFDM system.

The future wireless networks, such as Centralized Radio Access Network (C-RAN), will need to deliver data rate about 100 times to 1000 times the current 4G technology. For C-RAN based network architecture, there is a pressing need for tremendous enhancement of the effective data rate of the Common Public Radio Interface (CPRI). Compression of CPRI data is one of the potential enhancements. We introduce a vector quantization based compression algorithm for CPRI links, utilizing Lloyd algorithm. Methods to vectorize the I/Q samples and enhanced initialization of Lloyd algorithm for codebook training are investigated for improved performance. Multi-stage vector quantization is considered to reduce codebook search complexity. Simulation results show that our solution can achieve compression of 4 times for uplink and 4.5 times for downlink, within 2% Error Vector Magnitude (EVM) distortion. Remarkably, vector quantization codebook proves to be quite robust against data modulation mismatch, fading, signal-to-noise (SNR) and Doppler spread.

To support downlink full-dimension (FD-MIMO) in FDD scenarios, an accurate yet reasonably efficient channel-state-information (CSI) feedback scheme is required to harness the potential beamforming gain offered by the use of a large number of antenna elements in a 2D active antenna array. In this paper, a reduced-spaced channel quantization and feedback scheme which exploits slow variation of the angle-of-departure (AoD) distribution is proposed. By perceiving the instantaneous spatial channel to be a linear combination of slowly varying subset of matrices, it is demonstrated that the proposed scheme offers up to 62.5% dimensionality reduction with negligible throughput loss in 16-antenna FD-MIMO system. When used together with vector quantization, the proposed scheme offers substantial gain over that supported by 3GPP LTE.

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.

Dual-polarized antennas provide a mechanism to achieve polarization diversity or multiplex non-interfering data streams over-the-air. Practical realization of dual-polarized transmission, however, must overcome challenges such as mobile misorientation with respect to the base station as well as non-ideal polarization isolation. With the advent of mmWave communications, dual-polarized large scale antenna arrays can be realized inexpensively. In this paper, we propose a beamforming algorithm that jointly designs the horizontal and vertical beamformers and combiners to maximize spectral efficiency in the presence of mobile misorientation and polarization leakage. First, we show that dual-polarized antenna arrays introduce channel structure that enables mobile orientation estimation without explicit channel knowledge. We derive the maximum likelihood estimate of the azimuth and elevation mobile rotation. Given these estimates, we design the horizontal and vertical beamformer and combiner with practical RF hardware constraints. We generalize the beamforming algorithm to enable multiplexing multiple data streams across different channel paths in the horizontal and vertical domain. Results show that mobile orientation estimation using dual-polarized antenna arrays achieves over 90% accuracy even below 0 dB. Using a realistic clustered channel model, spectral efficiency gains of the order of 5 dB are demonstrated over dual-polarized antennas with independent horizontal/vertical beam steering as well as single polarized antenna arrays.

In this paper, we research on an interdisciplinary field of how the Smart Grid can help energy cost reduction in wireless communications. We study how the advances in the Smart Grid such as the information on predicted electricity price can be used to help reduce the energy cost of the cellular networks. Particularly, smart energy management at cell-sites is studied to minimize the electricity cost for mobile operators by leveraging time-varying electricity prices in the Smart Grid. We first propose a theoretical framework to optimally manage energy at a single cell-site (without and with energy harvester, respectively) by judiciously choosing which power source (the Power-Grid or a storage-battery) to use, when and how much to use in the cell-site; as well as controlling when and how much to charge the storage-battery. Moreover, we extend the framework from a single cell-site to a network including multiple cell-sites with different electricity prices or energy storage capacity, etc., where cell association, power control, and scheduling can be jointly considered to provide load shifting and cell ON/OFF to achieve mobile network operation with the lowest electricity cost.

In this paper, we presented our recent characterization and analysis on the power consumption of smartphone radio components, including Wi-Fi, GPS and cellular (3G/4G) modules. Different from previous research that focused on the properties of single module under a limited number of usage scenarios, our works are performed on the statistically selected representative Apps with four generations of Samsung Galaxy series smartphones. We found that over the four characterized generations, the average power consumption of the smartphones with a connected Wi-Fi network on the selected Apps increases about 38.20%, indicating more and more energy-hungry designs. However, the raising of the power consumption is not mainly from the radio modules as the power efficiency of the Wi-Fi module indeed improves by about 21%. The 4G module consumes about 15.13% more power compared to the latest 3G module. But the power efficiency of 3G module improves by averagely 15.17% over the four generations. The GPS module also shows 25.80% power consumption improvement over the generations. We also show that the characterized power results can be combined with the real user traces for detailed daily energy usage estimation. Our research established an ethological and evolutionary power consumption trend of smartphones, which will guide the power optimization in the next-generation smartphone design.

Full-dimension multiple-input multiple-output (FD-MIMO) systems, in which base stations are equipped with a large number of antennas in a two-dimensional panel, has received considerable attention from academia researchers and industry practitioners. Compared with legacy cellular communication systems, FD-MIMO systems can achieve significantly higher spectral efficiency with high order multi-user MIMO (MU-MIMO) transmissions. However, as high-order MU-MIMO also incurs high precoding and scheduling complexity, it is critical to reduce complexity of these operations in order to realize throughput potential of FD-MIMO systems in practice. In this paper, we propose a reduced complexity algorithm to realize the high performance precoding technique, signal-to-leakage plus noise ratio (SLNR) precoding, and propose an efficient scheduling algorithm to enable high-order MU-MIMO transmissions in FD-MIMO systems. We further demonstrate the effectiveness of the proposed algorithms by using system level simulations.

There has been an increasing interest in Full- Dimension (FD) MIMO, i.e., exploiting the elevation domain (in addition to the azimuth domain) for increasing the capacity of multi-antenna systems. A first prerequisite for designing FD MIMO is an understanding of the elevation characteristics of wireless propagation channels, in particular elevation at the base station. This paper utilizes a state-of-art ray-tracing simulation software to investigate and quantify these elevation characteristics in urban macro cellular (UMa) environments. We study in detail the height and distance dependency of these channel parameters such as mean elevation angle and elevation spread. More importantly we find that several of these parameters exhibit markedly different characteristics depending on whether the UEs are close to exterior walls or they are deeper inside the buildings. Thus, the traditional method of "factoring" the propagation into an outdoor part and an indoor part is not applicable, and we propose more accurate fitting equations.

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

Mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation at the base station, 2D “Massive MIMO”, combined with OFDM, has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a broadband wireless system. In 2D broadband MIMO-OFDM systems, a base station will rely on the spatial information extracted from uplink sounding reference signals to perform downlink MIMO beam-forming. Accordingly, multi-dimensional parameter estimation of a ray-based multipath wireless channel becomes crucial for such systems to realize the predicted capacity gains. In this paper, we study joint angle and delay estimation for 2D broadband MIMO-OFDM systems and analytically assess its estimation performance. To be specific, a tensor-based standard ESPRIT algorithm is naturally applied to estimate the corresponding 3D channel parameters. Further, we give out the closed-form mean square error expressions for DoA estimation. It is found that the dimensionality of the antenna array at the base station, as well as the implementation of OFDM, play an important role in determining the estimation performance. Simulation is conducted to evaluate the performance of the tensor-based ESPRIT algorithm, and the empirical results match very well with that from the derived analytical expressions. These insights will be useful for designing practical 2D broadband MIMO-OFDM systems in future mobile wireless communications.

Mobile data traffic is expected to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, two-dimensional (2D) “massive MIMO” has been proposed as one of the enabling technologies for future wireless systems. In 2D “massive MIMO” systems, a base station will rely on the uplink sounding signals to figure out the downlink spatial channel information to perform MIMO precoding. Accordingly, direction-of-arrival (DoA) estimation of the underlying three-dimensional (3D) channel at the base station becomes essential for 2D “massive MIMO” systems to realize the predicted capacity gains. In this paper, we will analyze the performance of DoA estimation based on ESPRIT methods and study its impact on the capacity of 2D “massive MIMO” systems. To be specific, for ESPRIT-type algorithms, we will derive the closed-form expressions for the mean square errors of the elevation and azimuth angle estimations. These results will be used to obtain design intuitions for 2D antenna arrays at the base station as well as the capacity of the underlying 2D “massive MIMO” systems.

This paper considers an uplink massive MIMO scheme that employs a pilot reuse scheme. For the massive MIMO scheme, a non-asymptotic cell throughput lower bound that is valid for any M is derived and the bound is shown to be tight by simulations. The lower bound is dependent upon three interference terms: intra-cell interference, inter-cell interference, and interference due to pilot contamination. A set of conditions on which each interference term dominates is examined by analysis and simulations, where the conditions are defined in terms of network topology, pilot reuse number and M. For example, for a dense network with M = 100, the largest cell throughput is achieved with pilot reuse number 2, in which case the traditional inter-cell interference dominates rather than the pilot contamination.

Multi-user MIMO is the enabling technology for LTE-Advanced systems to meet IMT-Advanced targets. The gain of multi-user MIMO is achieved partially through advanced user-grouping, user-scheduling, and precoding. Traditionally, multiuser MIMO scheduling focuses solely on spectral-efficiency [1]. That is, the scheduler will strike to balance the cell-edge user spectral-efficiency as well as the cell-average spectral-efficiency. Similar to spectral-efficiency, energy-efficiency is becoming increasingly important for wireless communications. The energy efficiency is measured by a classical measure, “throughput per Joule”, while both RF transmit power and device electronic circuit power consumptions are considered. In this paper, an energy-efficient proportional-fair scheduling is proposed for downlink multi-user MIMO systems. To specific, the scheduling algorithm is proposed to balance cell-edge energy-efficiency and the cell-average energy-efficiency. The energy-efficient proportional-fair metric is defined and the optimal power allocation maximizing the performance measure is identified. System level evaluation suggests that multi-user MIMO could improve the energy-efficiency of a wireless communication system significantly.

In this paper, we investigate a typical LTE-Advanced multi-user MIMO system where mobile stations (MSs) feedback limited channel state information using codebook based approaches. A joint optimization of multi-user scheduling, linear transmit beam-forming, and receive combining is conducted at the base station to combat the intra-cell interference created by multi-user MIMO operation. Low complexity coordinated beam-forming schemes based on proportional-fair metric is proposed for jointly designing the beam-forming and receive combining vectors. Feedback schemes complied with LTE-Advanced specification is also introduced for the proposed coordinated beam-forming scheme. System level evaluation is conducted to show that the proposed coordinated beam-forming can help improving the performance of LTE-Advanced systems significantly.

We propose a method to improve traditional character-based PPM text compression algorithm [1] for natural languages. Consider a text file as a sequence of alternating words and non-words, the basic idea of our algorithm is to encode nonwords and prefixes of words using character-based context models and encode suffixes of words using dictionary models. By using dictionary models, the algorithm can encode multiple characters as a whole, and thus enhance the compression efficiency. The advantages of the proposed algorithm are: 1) it does not require any text preprocessing, 2) it does not need any explicit codeword to identify switch between context and dictionary models, 3) it can be applied to any character-based PPM algorithms without incurring much additional computational cost.

Energy efficiency is becoming increasingly important for mobile devices because battery technology has not kept up with the growing demand of ubiquitous multimedia communications. Since multi-user multiple-input multiple-output (MU-MIMO) is a key technology in next-generation wireless communications, this paper addresses optimal energy-efficient design for MU-MIMO. The energy efficiency is measured by a classic metric, ``throughput per Joule'', while both RF transmit power and device electronic circuit power are considered. We define the energy efficiency (EE) capacity for MU-MIMO and study the power allocation that achieves this capacity. We show that user antennas should be used only when the corresponding subchannels are sufficiently good and using them improves the overall network EE. Based on theoretical analysis, we further develop low-complexity yet globally optimal energy-efficient power allocation algorithms that converge to the optimum exponentially. Finally comprehensive simulation results are provided to demonstrate the significant gain in network energy efficiency.

The LTE-Advanced (LTE-A) system is currently under development to allow for significantly higher spectral efficiency and data throughput than LTE systems. In a wireless system based on orthogonal frequency division multiplexing (OFDM) with frequency reuse factor one such as LTE, the achievable cell spectral efficiency is often limited by the inter-cell interference or coverage shortage of base stations. Hence in LTE-A, coordinated multi-point (CoMP) transmission/reception (a.k.a. multi-cell MIMO or base station cooperation) and relaying technologies are being introduced to clear these major performance hurdles. In this paper, overall picture of cooperative communication technologies being discussed in LTE-A systems including CoMP and relaying is presented, together with considerations on system design.

In this paper, a streamlined MIMO Kalman equalizer architecture is proposed to extract the commonality in the data path by jointly considering the displacement structure of the transition matrix and the block-Toeplitz structure of the channel matrix. Finally, an iterative conjugate-gradient based algorithm is proposed to avoid the inverse of the Hermitian symmetric innovation correlation matrix in Kalman gain processor. The proposed architecture not only reduces the numerical complexity to O(F log F) per chip, but also facilitates the parallel and pipelined VLSI implementation in real-time processing.

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).

We present an efficient LMMSE chip equalizer to suppress the interference caused by the multipath fading channel in the MIMO multi-code CDMA downlink. The block-Toeplitz structure in the correlation matrix is approximated with a block circulant matrix. An FFT-based algorithm is applied to avoid the direct-matrix-inverse (DMI) in the system equation. Hermitian optimization is proposed to further reduce the complexity. A comparative study in both performance and complexity with the conjugate-gradient (CG) algorithm is then presented. The simulation shows very promising results for the FFT-based equalizer compared with both the DMI and CG algorithms.

The evaluation of the performance of a cellular system using a simulation environment usually involves a link-to-system mapping based on a channel quality indication. The mapping is done using a set of link curves characterizing the link behavior, that are generated offline. In some cases, the static performance of a link is a sufficient indicator. But in high mobility, the block-fading assumption introduces inaccuracies in the link-to-system mapping. This paper proposes a dual-variable mapping technique that can be used in high-mobility conditions, using the mean and the variance of the SINR seen during a frame. Simulations show that, using this methodology, the mapping can be created independent of the velocity and the multipath profile of the channel. This mapping technique can also be used as a channel quality indication for link adaptation purposes.

In this paper, we propose a novel channel quality indicator (CQI) measure for the space-time jointly encoded MIMO-CDMA systems in frequency selective channels. The CQI proposed here is derived from a so-called per-Walsh code joint detection structure consisting of a front-end linear filter followed by joint symbol detection across all streams. The linear filter is designed to convert the multipath channel into a single-path channel to restore orthogonality of Walsh codes and to avoid joint sequence detection. We derive a class of filter that maximizes the so-called constrained mutual information, and show that the conventional LMMSE and MVDR equalizers belong to this class. Similar to the notion of generalized SNR (GSNR), this constrained mutual information provides us with a CQI measure describing the MIMO link quality. Such a CQI measure is essential in providing a simple one-dimensional mapping for both link adaptation and link-to-system mapping for jointly encoded MIMO-CDMA system.

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.

Selective broadcast schemes for point-to-multiple point transmission of identical information are studied for a CDMA wireless system. The channel states for all selected users are assumed known at both the transmitter and the receivers. The goal is to find signaling schemes that satisfy the received SNR requirement at all desired users while minimizing total transmit power. We solve for the optimal transmit signature code and receive filters by developing Iterative Least Distance Programming (ILDP) and Linear Programming (LP) algorithms. We show that the optimal selective broadcast scheme is more power efficient than simple broadcast schemes and is robust against modest channel estimate delays.

We propose a low complexity receiver structure for a multiple transmit antenna, multiple receive antenna (MIMO) system in the EDGE environment. In such a MIMO system, independent data streams are transmitted through different transmit antennas. In the proposed low complexity receiver structure, these data streams are separated and detected individually. The signal separation at the receiver is performed by the space-time interference cancellation filters. Each filter has a target data stream and intends to maximize the signal to interference and noise ratio (SINR) of that target data stream. Each filter is followed by a prefilter and a decision feedback sequence estimator (DFSE) which includes a feedback filter and a MAP sequence estimator. The maximum SINR (MSINR) interference cancellation comes in two flavors: blind and explicit methods. The blind MSINR method was first proposed in Liang et al. (1997) as a two-stage co-channel interference (CCI) suppression method with multiple receive antennas. We show that the blind MSINR method can be directly applied in the MIMO context and also derive another variation of the MSINR method, which we term the explicit MSINR method. While both methods achieve decent performance at low complexity, the explicit method utilizes the knowledge of all the training sequences and performs better than the blind method.