Millimeter Wave Massive MIMO Downlink Per-Group Communications With Hybrid Linear Precoding

We address the problem of analyzing and classifying in groups the downlink channel environment in a millimeter- wavelength cell, accounting for path loss, multipath fading, and User Equipment (UE) blocking, by employing a hybrid propagation and multipath fading model, thus using accurate inter-group interference modeling. The base station (BS) employs a large Uniform Planar Array (UPA) to facilitate massive Multiple-Input, Multiple-Output (MIMO) communications with high efficiency. UEs are equipped with a single antenna and are distributed uniformly within the cell. The key problem is analyzing and defining groups toward precoding. Because equitable throughput is desired between groups, Combined Frequency and Spatial Division and Multiplexing (CFSDM) is found to be necessary. We show that by employing three subcarrier frequencies, the UEs can be efficiently separated into high throughput groups, with each group employing Virtual Channel Model Beams (VCMB) based inner precoding, followed by efficient Multi-User MultipleInput Multiple-Output (MU-MIMO) outer precoders. For each group, we study three different sub-grouping methods offering different advantages. We show that the improvement offered by Zero-Forcing Per-Group Precoding (ZF-PGP) over Zero-Forcing Precoding (ZFP) is very high.

Massive multiple-input multiple-output (MIMO) systems, where base stations (BSs) are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the BS in closed form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the BS to communicate with the detected UEs. The favorable propagation conditions of massive MIMO suppress interference among UEs, whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in massive MIMO networks, under uncorrelated and correlated fading channels. With $2.5 \times 10^{3}$ UEs that may simultaneously become active with probability 1%, a total of 16 frequency–time codes, and 100 antennas, a given UE is detected with probability 75%, and the accuracy of its timing estimate is on the order of few samples.

This paper considers the problem of massive multiple-input-multiple-output (MIMO) wireless communication systems with quasi-orthogonal space-time block code (QOSTBC) transmission in the presence of spatial correlation effect (SCE) and mutual coupling effect (MCE). Conventional MIMO channels with QOSTBC transmission suffer from several drawbacks. The number of transmit antennas is restricted to a few in order to achieve full transmission rate. Therefore, it is difficult to make a quasi-orthogonal space time block coded massive MIMO system to achieve full transmission rate. Moreover, MIMO channel with QOSTBC transmission is usually considered for the case where the number of user equipments (UEs) is one. We present a joint beamforming and spatial precoding method to deal with the above drawbacks. The proposed method incorporates a beamforming scheme and spatial precoding to formulate an appropriate optimization process to effectively alleviate the considered problems. The resulting optimization problem will be solved by using a cooperative coevolutionary particle swarm optimization algorithm under two proposed fitness functions. During the optimization process, the proposed method finds the optimal beamforming coefficients of the precoding matrix, the optimal normalized positions of transmit and receive antenna elements for the case of using linear antenna arrays, and the optimal angle differences of transmit and receive antenna elements for the case of using circular arrays. Based on the proposed method, we are able to cure the performance degradation of a quasi-orthogonal space time block coded massive MIMO system due to the SCE and MCE. Moreover, the proposed method makes QOSTBC MIMO communications with full transmission rate for any number of transmit antennas achievable. Several simulation examples are presented to show the superior bit error rate (BER) performances of QOSTBC wireless MIMO scenarios with linear as well as circular antenna arrays by using the proposed method as compared to the existing methods.

This paper analyzes the problem of optimum user association and sum rate maximization for software defined radio access networks (SD-RANs) with access node diversity for fifth generation (5G) wireless networks. We consider four complementary types of access nodes namely, a massive multiple- input multiple-output (MIMO) base station (BS), MIMO BSs, small cells (SCs), and indoor and outdoor distributed antenna systems (DAS). The SDRAN user association problem is solved through a novel three-stage optimization scheme called the place coverage (TPC). TPC divides the user equipment (UEs) into two sets of indoor and outdoor UEs. Initially, TPC associates indoor UEs with the indoor DAS access nodes. Next, outdoor UEs are associated with the outdoor DAS access nodes, SCs, MIMO BSs, and the massive MIMO BS. Finally, the remaining resources of indoor and outdoor access nodes are used to serve the UEs that have not been served. The scope of TPC is multifold. First, TPC reflects the closest model to a real-world diverse 5G network. Secondly, servicing the indoor UEs with iDAS antennas results in lower power load per user for the access network resulting in better coverage, quality, and data speed. Thirdly, TPC provides reduced radiation levels for indoor UEs. Numerical results show that TPC provides significant sum rate and fairness gains over the received signals strength (RSS) based association schemes [1]. Moreover, TPC provides comparable performance levels as the optimum association scheme based on exhaustive search while bringing significant complexity reduction.

We consider the allocation of spectral and power resources every subframe (1~ms) on the uplink of the Long Term Evolution (LTE) wideband orthogonal frequency division multiple access (OFDMA) cellular network. System bandwidth is divided into multiple sub-bands. The fractional power control to manage interference is allowed to be a function of the sub-band. Moreover, the channel gains from the base-station to the mobiles (user equipment (UE) in LTE) can vary with the sub-band. The power and spectral resources are to be allocated among different UEs in a cell based on channel conditions, desired fairness/QoS, and constraints on power due to interference and power limitation at a UE. For $N$ UEs and $M$ sub-bands, we exploit the structure of the underlying optimization problem to obtain an $O(NM)$ algorithm for optimal resource allocation when a UE can be allocated resources in a non-contiguous manner in frequency. To implement this in an OFDMA system, we delineate a simple control signalling structure where the UE computes in $O(M)$ time a waterfilling solution every subframe it is assigned resources. When the allocation is restricted to contiguous allocation in frequency due to single carrier constraint in LTE, we convert the solution computed above in $O(NM)$ time into a feasible solution. For typical network scenarios, the solution thus computed is numerically shown to be very close to the optimal solution with no restrictions.

We propose a cross-layer switching method of orthogonal frequency division multiple access (OFDMA) and multiuser multiple input multiple output (MU-MIMO) for the future wireless local area network systems. The proposed method, employed on the medium access control layer, switches between OFDMA and MU-MIMO as the transmission method after processing by using physical layer information such as an overhead of channel state information feedback, station number, and data length in order to improve the transmission efficiency. Simulation results show that the proposed method achieves higher total throughput than conventional OFDMA or MU-MIMO where switching is not performed.

Opportunistic usage of licensed frequency bands has been proposed as a solution to spectral crowding problem by using Cognitive Radio (CR) systems. Multicarrier techniques based on OFDMA (Orthogonal Frequency Division Multiple Access) have been the technique of choice for sharing the spectrum due to their flexibility in deactivating the sub-carriers to avoid interference to primary licensed users. However, OFDMA based techniques are associated with certain issues such as high PAPR, which is especially harmful at the user equipment (UE) end. Single Carrier FDMA (SC-FDMA) for uplink has been proposed as one of the solutions for reducing the PAPR (Peak-to-Average-Power-Ratio) at UE end. SC-FDMA may utilize localized mapping or the distributed mapping techniques to allocate the sub-carriers to the different users. In spectrum pooling, the availability of free sub-carriers depends upon spectral activity of the licensed user which is unpredictable and considered random. As a result, available carriers may be allocated to the rented or opportunistic users based on availability using a Non Contiguous OFDM (NC-OFDMA) scheme. NC-OFDMA makes efficient use of the spectrum at the cost of high PAPR at the User Equipment (UE). To be able for PAPR reduction using SC-FDMA requires certain structure to the available subcarriers; i.e. consecutive or equi-spaced, which may not be true inall cases. In this paper, we propose a spectrum allocation algorithm that takes into account system requirements for resource efficiency and proposes a DFT of appropriate size for spectrum spreading such that localized SC-FDMA may be implemented at the UE to reduce PAPR. Proposed algorithm seeks to balance the requirements of low PAPR and required spectral efficiency and provides a solution that satisfies the requirements.

This paper presents the link-level performance of downlink non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) receiver, considering the influence of error vector magnitude (EVM). In our evaluations, for the case of a 1-by-2 transmission and the number of NOMA multiplexed users is two, we clarify the performance of NOMA for cell-center user equipment (UE) with codeword level SIC (CWIC) receiver and the performance of the cell-edge UE, which does not apply SIC. Based on link-level performance evaluations, we show that the performance of CWIC receiver is almost the same as that of ideal SIC when the allocated transmit power ratio to cell-center UE is less than 0.35, the modulation and coding scheme (MCS) of cell-center UE is 16QAM (R = 0.51) or 64QAM (R = 0.47), and the MCS of cell-edge UE is QPSK (R = 0.51). Furthermore, we also show that, when EVM value is 4 % and the allocated power ratio to cell-center UE is between 0.2 and 0.37, the level of degradation due to the influence of EVM is below 0.6 dB, which is almost the same level of degradation as that of orthogonal frequency division multiple access (OFDMA) for the EVM value required by 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE). On the other hand, we show that the influence of EVM is marginal for cell-edge UE compared to cell-center UE. However, when the cell-center UE uses a higher order modulation such as 64QAM, we show that the required SNR of cell-edge UE increases due to the increase in inter-user interference. Based on these results, we clarify that it is important for the base station (BS) to take into account the target BLER and influence of EVM when selecting the MCS and allocated transmit power ratio of NOMA UEs.

Solving the main optimization problem, including a large number of access points and user equipment in ultra-dense networks (UDNs) using the Munkres algorithm, is a time-consuming solution. In this paper, we propose two new ideas for selecting the appropriate access points (APs) in UDNs that reduce the complexity order and find the suboptimum solution efficiently. Applying the first idea, all user equipment (UE) that are out of access points' service area and all access points that cannot find any user equipment in their service area are removed. In this case, the solutions of the conventional and proposed version of the Munkres algorithm are the same, which offers a high total sum value of the signal to noise ratios (SNRs). Still, it does not guarantee minimum interference. Hence, as the second idea, we consider estimation of the signal to interference plus noise ratio (SINR), entitled by average-SINR and random-SINR, and solve the optimization problem, including a large number of access points and a variable number of user equipment densely distributed. The simulation results show the effectiveness of the proposed algorithms in the view of sum-rate, the number of successful user equipment, and computational complexity for an area, including 250 APs and a variable number of UEs.

5G massive MIMO deployments up to 64T64R (64 Transmit and 64 Receive) antenna arrays are gaining traction, in part due to the smaller size of such arrays suitable for mid-band spectrum (2-5 GHz). A scheduling algorithm that can aggressively pair many UEs (User Equipment) on the same time-frequency resources is essential to fully derive the benefits of such an array. In this paper, an uplink scheduler design is proposed based on RME (Recursive Maximal Expansion) that ensures contiguous resource allocation and power constraint for each UE, while maximizing MU (Multi-User) MIMO (Multiple-Input Multiple-Output) gains by selecting and pairing UE groups based on the sum-PF (Proportional Fair) metric. We show, using realistic system level simulations, that up to 100% average cell-throughput gains can be achieved by MU-MIMO over SU (Single-User) MIMO in dense urban micro cell deployment scenarios presenting an attractive proposition for massive MIMO array deployments. It is observed that power control parameters play a critical role and should be optimized considering scheduler behavior, especially for cell-edge throughput improvement.

In Band Full Duplex (IBFD) is an emerging radio technology that can double Spectral Efficiency (SE), one of the key enhancement item for 5G. Due to complex processing and hardware required to realize IBFD capability, it is generally not recommended for User Equipments (UEs), while the Base Stations (BSs) can accommodate these changes. This results in Hybrid IBFD Cellular Network (HICN) that restricts IBFD capability strictly to BS and continues with legacy Half Duplex (HD) UEs. Due to this IBFD capability mismatch, frequency allocation is a challenging task in HICN and, to the best of our knowledge, a generic allocation algorithm to maximize sum-SE of a cell and work across different cell sizes is not reported yet in literature. In this paper, we propose the frequency allocation algorithm for HICN that maximizes sum-SE of a cell and shown to work independent of cell size. An adaptive and robust UE grouping algorithm is introduced that, with the knowledge of location information of UEs, shares frequencies among UEs based on their distance separation. To characterize the performance of the proposed algorithm, we have used frequency reuse efficiency (i·) that signifies the amount of frequency reuse and sum-SE for the accumulated SE achieved by all UEs in UL and DL. We have obtained closed-form analytical expression for i· as well as bounds on number of frequencies required. Simulation results reveal that the frequency reuse is doubled with increase in number of UEs, leading to doubling sum-SE for Macro, Micro and Pico BS classes.

Channel quality feedback is crucial for the operation of 4G and 5G radio networks, as it allows to control User Equipment (UE) connectivity, transmission scheduling, and the modulation and rate of the data transmitted over the wireless link. However, when such feedback is frequent and the number of UEs in a cell is large, the channel may be overloaded by signalling messages, resulting in lower throughput and data loss. Optimizing this signaling process thus represents a key challenge. In this paper, a focus on Channel Quality Indicator (CQI) reports that are periodically sent from a UE to the base station, and propose mechanisms to optimize the reporting process with the aim of reducing signaling overhead and avoiding the associated channel overloads, particularly when channel conditions are stable. To this end, we apply machine learning mechanisms to predict channel stability, which can be used to decide if the CQI of a UE is necessary to be reported, and in turn to control the reporting frequency. For this purpose,four machine learning models,namely Support Vector Machines (SVM),K-Nearest Neighbour (KNN),Random Forest and Neural Networks (ANN). Simulation results shows that both provide a high prediction accuracy, with ANN consistently outperforming, especially as CQI reporting frequency reduces. Keywords: ANN,KNN,SVM,Random Forest,CQI,5G,UE

Cell-free massive MIMO (mMIMO) is expected to be a novel network architecture to support massive connections, ultra-high transmission rate, and ultra-reliability low-latency transmission. Therefore, applications in ultra-reliable low-latency communications (URLLC) can be served by the cell-free mMIMO network in the future. This paper considers a multiuser downlink cell-free massive MIMO communication system and defines two events to evaluate the system’s failure in latency and reliability performance of the transmission. The first kind of event is defined as Transmission Error, where the value of the downlink rate becomes too high to be supported by the channel between the access points (APs) and user equipments (UEs), while the second event is defined as Time Overflow, where the time of packet delivery violates the hard deadline. The probabilities of these events can help evaluate the URLLC performance of the system because the probability of Transmission Error reflects the reliability of the transmission while the probability of Time Overflow reflects the latency of the transmission. We derive the expressions of the probabilities of these two events and the final probability system outage, with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, based on the properties of Gamma distribution and the approximation for non-isotropic vectors. We finally provide the numerical results by system-level simulations to show that the expressions are accurate and discuss the influence of the noise variance, the number of UEs, and the precoding scheme on the URLLC performance.

In orthogonal frequency-division multiple access (OFDMA) downlink systems, user equipments over large delay spread channels reduce the cell spectral efficiency due to intersymbol interference (ISI). Channel shortening is an attractive filtering technique to mitigate ISI over such large delay spread scenarios, but the filter should be designed carefully to prevent subsequent spectrum null phenomenon. In this paper, we propose subband interference suppression (SIS) in channel shortening for OFDMA downlink systems. In our framework, we first develop the minimum subband interference (MSI) filter which reduces the interference and noise enhancement within the subcarriers of interest. To reduce its complexity, we present the delay preselection method, which predetermines significant delay candidates on the basis of estimation results. Simulation results show that the proposed SIS framework is effective over cellular scenarios, where conventional schemes do not work properly.

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes—a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., $k$ -nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner.