We propose a concept system termed distributed base station (DBS), which enables distributed transmit beamforming at large carrier wavelengths to achieve significant range extension and or increased downlink data rate, providing a low-cost infrastructure for applications such as rural broadband. We consider a frequency division duplexed (FDD) system, using feedback from the receiver to achieve the required phase coherence. At a given range, @math cooperating transmitters can achieve @math -fold increase in received power compared to that for a single transmitters, and feedback-based algorithms with near-ideal performance have been prototyped. In this paper, however, we identify and address key technical issues in translating such power gains into range extension via a DBS. First, to combat the drop in per-node SNR with extended range, we design a feedback-based adaptation strategy that is suitably robust to noise. Second, to utilize available system bandwidth, we extend narrowband adaptation algorithms to wideband channels through interpolation over OFDM subcarriers. Third, we observe that the feedback channel may become a bottleneck unless sophisticated distributed reception strategies are employed, but show that acceptable performance can still be obtained with standard uplink reception if channel time variations are slow enough. We quantify system performance compactly via outage capacity analyses.
The present paper builds on our prior conference paper @cite_5 , which introduced the DOST scheme, and also discussed extensions to wideband systems. However, it goes well beyond @cite_5 by presenting a DBS concept system built around DOST, including an OFDM system design roughly consistent with LTE, specific prescriptions for pilot design, and outage capacity analyses for downlink and uplink which compactly characterize system-level performance.
@cite_5
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In this letter, we focus on joint subcarrier and power allocation in the uplink of an OFDMA system. Our goal is to maximize the rate-sum capacity in the uplink. For the purpose, we formulate an optimization problem subject to subcarrier and power constraints and draw necessary conditions for optimality, from which we derive joint subcarrier and power allocation algorithms. Simulation results show that our proposed scheme enhances the system capacity, providing almost near optimal solutions with low computational burden. The conventional design of cellular systems prescribes the separation of uplink and downlink transmissions via time-division or frequency-division duplex. Recent advances in analog and digital domain self-interference interference cancellation challenge the need for this arrangement and open up the possibility to operate base stations, especially low-power ones, in a full-duplex mode. As a means to cope with the resulting downlink-to-uplink interference among base stations, this letter investigates the impact of the Cloud Radio Access Network (C-RAN) architecture. The analysis follows an information theoretic approach based on the classical Wyner model. The analytical results herein confirm the significant potential advantages of the C-RAN architecture in the presence of full-duplex base stations, as long as sufficient fronthaul capacity is available and appropriate mobile station scheduling, or successive interference cancellation at the mobile stations, is implemented. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. Using stochastic geometry, we develop a tractable uplink modeling paradigm for outage probability and spectral efficiency in both single and multi-tier cellular wireless networks. The analysis accounts for per user equipment (UE) power control as well as the maximum power limitations for UEs. More specifically, for interference mitigation and robust uplink communication, each UE is required to control its transmit power such that the average received signal power at its serving base station (BS) is equal to a certain threshold ρ o . Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold of ρ o . We show that there exists a transfer point in the uplink system performance that depends on the following tuple: BS intensity λ, maximum transmit power of UEs P u , and ρ o . That is, when P u is a tight operational constraint with respect to (w.r.t.) λ and ρ o , the uplink outage probability and spectral efficiency highly depend on the values of λ and ρ o . In this case, there exists an optimal cutoff threshold ρ o *, which depends on the system parameters, that minimizes the outage probability. On the other hand, when P u is not a binding operational constraint w.r.t. λ and ρ o , the uplink outage probability and spectral efficiency become independent of λ and ρ o . We obtain approximate yet accurate simple expressions for outage probability and spectral efficiency, which reduce to closed forms in some special cases. A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency @math by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around @math ; no more pilot reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system. In this paper, we consider an adaptive modulation system with multiple-input-multiple-output (MIMO) antennas in conjunction with orthogonal frequency-division multiplexing (OFDM) operating over frequency-selective Rayleigh fading environments. In particular, we consider a type of beamforming with a maximum ratio transmission maximum ratio combining (MRT-MRC) transceiver structure. For this system, we derive a central limit theorem for various block-based performance metrics. This motivates an accurate Gaussian approximation to the system data rate and the number of outages per OFDM block. In addition to the data rate and outage distributions, we also consider the subcarrier signal-to-noise ratio (SNR) as a process in the frequency domain and compute level crossing rates (LCRs) and average fade bandwidths (AFBs). Hence, we provide fundamental but novel results for the MIMO OFDM channel. The accuracy of these results is verified by Monte Carlo simulations, and applications to performance analysis and system design are discussed. Universal mobile telecommunication system (UMTS) networks should be deployed according to cost-effective strategies that optimize a cost objective and satisfy target quality-of-service (QoS) requirements. In this paper, we propose novel algorithms for joint uplink downlink UMTS radio planning with the objective of minimizing total power consumption in the network. Specifically, we define two components of the radio planning problem: 1) continuous-based site placement and 2) integer-based site selection. In the site-placement problem, our goal is to find the optimal locations of UMTS base stations (BSs) in a certain geographic area with a given user distribution to minimize the total power expenditure such that a satisfactory level of downlink and uplink signal-to-interference ratio (SIR) is maintained with bounded outage constraints. We model the problem as a constrained optimization problem with SIR-based uplink and downlink power control scheme. An algorithm is proposed and implemented using pattern search techniques for derivative-free optimization with augmented Lagrange multiplier estimates to support general constraints. In the site-selection problem, we aim to select the minimum set of BSs from a fixed set of candidate sites that satisfies quality and outage constraints. We develop an efficient elimination algorithm by proposing a method for classifying BSs that are critical for network coverage and QoS. Finally, the problem is reformulated to take care of location constraints whereby the placement of BSs in a subset of the deployment area is not permitted due to, e.g., private property limitations or electromagnetic radiation constraints. Experimental results and optimal tradeoff curves are presented and analyzed for various scenarios. We consider a distributed antenna system where L antenna terminals (ATs) are connected to a Central Processor (CP) via digital error-free links of finite capacity R 0 , and serve K user terminals (UTs). This system model has been widely investigated both for the uplink and the downlink, which are instances of the general multiple-access relay and broadcast relay networks. In this work we focus on the downlink, and propose a novel downlink precoding scheme nicknamed “Reverse Quantized Compute and Forward”(RQCoF). For this scheme we obtain achievable rates and compare with the state-of-the-art available in the literature. We also provide simulation results for a realistic network with fading with K > L UTs, and show that channel-based user selection produces large benefits and essentially removes the problem of rank deficiency in the system matrix.1 Over the last decade, the growing amount of uplink (UL) and downlink (DL) mobile data traffic has been characterized by substantial asymmetry and time variations. Dynamic time-division duplex (TDD) has the capability to accommodate to the traffic asymmetry by adapting the UL DL configuration to the current traffic demands. In this work, we study a two-tier heterogeneous cellular network (HCN) where the macro tier and small cell tier operate according to a dynamic TDD scheme on orthogonal frequency bands. To offload the network infrastructure, mobile users in proximity can engage in device-to-device (D2D) communications, whose activity is determined by a carrier sensing multiple access (CSMA) scheme to protect the ongoing infrastructure-based and D2D transmissions. We present an analytical framework for evaluating the network performance in terms of load-aware coverage probability and network throughput. The proposed framework allows quantification of the effect on the coverage probability of the most important TDD system parameters, such as the UL DL configuration, the base station density, and the bias factor. In addition, we evaluate how the bandwidth partition and the D2D network access scheme affect the total network throughput. Through the study of the tradeoff between coverage probability and D2D user activity, we provide guidelines for the optimal design of D2D network access. We consider the uplink (UL) and downlink (DL) of non-cooperative multi-cellular time-division duplexing (TDD) systems, assuming that the number N of antennas per base station (BS) and the number K of user terminals (UTs) per cell are large. Our system model accounts for channel estimation, pilot contamination, and an arbitrary path loss and antenna correlation for each link. We derive approximations of achievable rates with several linear precoders and detectors which are proven to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations. It is known from previous work assuming uncorrelated channels, that as N→∞ while K is fixed, the system performance is limited by pilot contamination, the simplest precoders detectors, i.e., eigenbeamforming (BF) and matched filter (MF), are optimal, and the transmit power can be made arbitrarily small. We analyze to which extent these conclusions hold in the more realistic setting where N is not extremely large compared to K. In particular, we derive how many antennas per UT are needed to achieve η of the ultimate performance limit with infinitely many antennas and how many more antennas are needed with MF and BF to achieve the performance of minimum mean-square error (MMSE) detection and regularized zero-forcing (RZF), respectively. In this paper, we study the performance of a combined link- and physical-layer approach for the downlink of a dynamic OFDM-frequency division multiple access transmission system delivering MPEG-4 video streams. The approach consists of three different mechanisms-queue management, resource allocation, and subcarrier assignment-which can be used separately as well as in combination. This cross-layer approach allows to utilize channel-related knowledge and semantic information of the streams' packets. Consecutively adding these mechanisms to the studied system, we judge their respective impact on system performance. To do so, we present a new metric for quality assessment of long video transmissions (considerably longer than 10 s) and apply it to the simulation results. In particular, we introduce a local distortion threshold-based metric instead of an overall average judgment. We find that our combined system approach increases the number of supportable terminals per cell by up to 300 . This paper investigates multiple access techniques for wireless downlink in cloud radio access networks (CRANs) where all base stations (BSs) are connected via a cloud. We first propose a power allocation scheme that makes use of non-orthogonal multiple access to allocate the power at the BSs based on their relative wireless distances to the cloud. Specifically, a successive interference cancellation mechanism is designed at the cloud to lay multiple BSs over each other in the power domain. We then analyse the achievable throughput of the NOMA in the CRANs. The derived expressions not only show the effectiveness of the proposed NOMA over the conventional orthogonal frequency division multiple access (OFDMA) but also allow us to develop an algorithm to find the optimal number of BSs in the CRANs. Finally, numerical results are provided to show an improvement of up to eight times of the sum data rate and a higher number of BSs that could be maintained with the proposed NOMA over the OFDMA scheme as well as evaluating the impact of the wireless propagation environment and channel quality on the system throughput and cloud-edge throughput performance. In this paper, we consider the downlink signal-to-interference-plus-noise ratio (SINR) analysis in a heterogeneous cellular network with K tiers. Each tier is characterized by a base-station (BS) arrangement according to a homogeneous Poisson point process with certain BS density, transmission power, random shadow fading factors with arbitrary distribution, arbitrary path-loss exponent and a certain bias towards admitting the mobile-station (MS). The MS associates with the BS that has the maximum instantaneous biased received power under the open access cell association scheme. For such a general setting, we provide an analytical characterization of the coverage probability at the MS. The 5G New Radio (NR) access technology defines multiple numerologies to support a wide range of carrier frequencies, deployment scenarios, and variety of use cases. In this paper, we consider a resource allocation problem to efficiently support multiple numerologies simultaneously. We assume frequency division multiplexing (FDM) of numerologies in a time division duplex (TDD) system with a self-contained slot format. We focus on optimizing the numerology subband (SB) configuration, as well as the duplexing ratio between downlink (DL) and uplink (UL) directions within each SB. The optimization problem minimizes the weighted sum of the normalized load (NL) for each SB in each direction. We prove that our optimization problem is convex and, furthermore, we derive the optimal closed-form expressions for the numerology SB configuration and the DL-UL duplexing ratio per SB. The effectiveness of the proposed resource allocation is validated through an end-to-end ns-3 based simulator, which shows how the optimization of the NLs is translated into an improved throughput and delay performance. The performance of cloud radio access network (C-RAN) is constrained by the limited fronthaul link capacity under future heavy data traffic. To tackle this problem, extensive efforts have been devoted to design efficient signal quantization compression techniques in the fronthaul to maximize the network throughput. However, most of the previous results are based on information-theoretical quantization methods, which are hard to implement practically due to the high complexity. In this paper, we propose using practical uniform scalar quantization in the uplink communication of an orthogonal frequency division multiple access (OFDMA) based C-RAN system, where the mobile users are assigned with orthogonal sub-carriers for transmission. In particular, we study the joint wireless power control and fronthaul quantization design over the sub-carriers to maximize the system throughput. Efficient algorithms are proposed to solve the joint optimization problem when either information-theoretical or practical fronthaul quantization method is applied. We show that the fronthaul capacity constraints have significant impact to the optimal wireless power control policy. As a result, the joint optimization shows significant performance gain compared with optimizing only wireless power control or fronthaul quantization. Besides, we also show that the proposed simple uniform quantization scheme performs very close to the throughput performance upper bound, and in fact overlaps with the upper bound when the fronthaul capacity is sufficiently large. Overall, our results reveal practically achievable throughput performance of C-RAN for its efficient deployment in the next-generation wireless communication systems. We propose joint spatial division and multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional channel state information at the transmitter (CSIT). JSDM achieves significant savings both in the downlink training and in the CSIT uplink feedback, thus making the use of large antenna arrays at the base station potentially suitable also for frequency division duplexing (FDD) systems, for which uplink downlink channel reciprocity cannot be exploited. In the proposed scheme, the multiuser MIMO downlink precoder is obtained by concatenating a prebeamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional “effective” channel matrix. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is approached in the large number of antennas limit. For this case, we use Szego's asymptotic theory of Toeplitz matrices to show that a DFT-based prebeamforming matrix is near-optimal, requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a 2-D base station antenna array, with 3-D beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the prebeamforming optimization and calculate the system spectral efficiency under proportional fairness and max-min fairness criteria, showing extremely attractive performance. Our numerical results are obtained via asymptotic random matrix theory, avoiding lengthy Monte Carlo simulations and providing accurate results for realistic (finite) number of antennas and users. As society continues to integrate information-based technologies into daily life, there is an increased need for small, powerful mobile phones. Recently, relaying technologies have been researched for standardization of the next generation of mobile communication systems, including third-Generation Partnership Project (3GPP) LTE-Advanced, IEEE 802.16j, and IEEE 802.16m. Especially, LTE-Advanced is an evolutionary version of IMT-2000 defined by the ITU. To satisfy these requirements, relaying technology is considered as a powerful candidate scheme with carrier aggregation, MIMO, and CoMP. Relaying technology has been introduced to guarantee high data rates to multiple users. It can also extend cell coverage or effectively increase the average throughput of the cell by installing relay nodes at cell edges or in shadow areas. Thus, in this paper, we propose a method for boosting reception performance using the downlink transmission method of the LTE system, which is the next-generation mobile communication technology standard currently underway in 3GPP. At the moment, orthogonal frequency division multiplexing (OFDM), which is suitable for high-speed data transmission and multipath, is commonly used in an LTE downlink system. However, the OFDM method has a disadvantage of displaying a relatively higher PAPR at the terminal since it basically uses a multi-carrier. To this end, single carrier division multiple access (SC-FDMA) is used in an LTE uplink system in order to compensate for this defect related to high PAPR of OFDM at such an important terminal where power efficiency is important. However, when the channels in the frequency domain deteriorate signals, SC-FDMA reveals a defect in that the impact of deteriorated parts spreads and causes performance degradation. To this end, we propose that a relay be installed between the station and terminal, the distance between BS and RS be set at 500 or 1,000 m, and orthogonal frequency division multiple access (OFDMA) and SC-FDMA be chosen as transmission methods of RS. This paper found SC-FDMA to be a better choice when RS is closer to BS, whereas OFDMA is a better choice when the distance between BS and RS is farther. The system's reception performance improved when the transmission method fit the circumstances in the middle between BS and MS. With the exponential increase in high rate traffic given by a new generation of wireless devices, data is expected to overwhelm cellular network capacity in the near future. Femtocell networks have been recently proposed as an efficient and cost-effective approach to provide unprecedented levels of network capacity and coverage. However, the dense and random deployment of femtocells and their uncoordinated operation raise important questions concerning interference pollution and spectrum allocation. Motivated by the flexible subchannel allocation capabilities of cognitive radio, we propose a cognitive hybrid division duplex (CHDD) that is suitable for heterogeneous networks in future mobile communication systems. Specifically, our CHDD scheme has a pair of frequency bands to perform frequency division duplex (FDD) on the macrocell, while time division duplex (TDD) is simultaneously operated in these bands by underlaid cognitive femtocells. By doing so, the proposed CHDD scheme exploits the advantages of both FDD and TDD schemes: operating in FDD at the macrocell tier controls inter-tier interference, whereas operating in TDD at the femtocell tier provides to femtocells the flexibility of adjusting uplink and downlink rates together with opportunistic access benefits. Using tools from stochastic geometry, we provide a methodology on how to design efficient switching mechanisms for cognitive TDD operation of femtocells. In particular, we derive closed-form expressions for the success probability and the area spectral efficiency of the proposed CHDD scheme when the macro tier is in downlink and uplink mode. Furthermore, we propose an open access policy as a means to improve the performance of macrocell transmissions. Our analysis and numerical results show the effectiveness of introducing cognition in femtocells so as to improve the system performance of two-tier femtocell networks. We study a distributed antenna system where L antenna terminals (ATs) are connected to a central processor (CP) via digital error-free links of finite capacity R0, and serve K user terminals (UTs). This model has been widely investigated both for the uplink (UTs to CP) and for the downlink (CP to UTs), which are instances of the general multiple-access relay and broadcast relay networks. We contribute to the subject in the following ways: 1) For the uplink, we consider the recently proposed “compute and forward” (CoF) approach and examine the corresponding system optimization at finite SNR. 2) For the downlink, we propose a novel precoding scheme nicknamed “reverse compute and forward” (RCoF). 3) In both cases, we present low-complexity versions of CoF and RCoF based on standard scalar quantization at the receivers, that lead to discrete-input discrete-output symmetric memoryless channel models for which near-optimal performance can be achieved by standard single-user linear coding. 4) We provide extensive numerical results and finite SNR comparison with other “state of the art” information theoretic techniques, in scenarios including fading and shadowing. The proposed uplink and downlink system optimization focuses specifically on the ATs and UTs selection problem. In both cases, for a given set of transmitters, the goal consists of selecting a subset of the receivers such that the corresponding system matrix has full rank and the sum rate is maximized. We present low-complexity ATs and UTs selection schemes and demonstrate through Monte Carlo simulation that the proposed schemes essentially eliminate the problem of rank deficiency of the system matrix and greatly mitigate the noninteger penalty affecting CoF RCoF at high SNR. Comparison with other state-of-the art information theoretic schemes, show competitive performance of the proposed approaches with significantly lower complexity. This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base stations (BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP first decodes the quantization codewords and then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, to establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise-ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.