1,721,116 research outputs found
User-centric C-RAN architecture for ultra-dense 5G networks: Challenges and methodologies
No full textRobust Beamforming Design for Ultra-dense User-Centric C-RAN in the Face of Realistic Pilot Contamination and Limited Feedback
Full text linkCCBY The ultra-dense cloud radio access network (UDCRAN), in which remote radio heads (RRHs) are densely deployed in the network, is considered. To reduce the channel estimation overhead, we focus on the design of robust transmit beamforming for user-centric frequency division duplex (FDD) UD-CRANs, where only limited channel state information (CSI) is available. Specifically, we conceive a complete procedure for acquiring the CSI that includes two key steps: channel estimation and channel quantization. The phase ambiguity (PA) is also quantized for coherent cooperative transmission. Based on the imperfect CSI, we aim for optimizing the beamforming vectors in order to minimize the total transmit power subject to users’ rate requirements and fronthaul capacity constraints. We derive the closed-form expression of the achievable data rate by exploiting the statistical properties of multiple uncertain terms. Then, we propose a low-complexity iterative algorithm for solving this problem based on the successive convex approximation technique. In each iteration, the Lagrange dual decomposition method is employed for obtaining the optimal beamforming vector. Furthermore, a pair of low-complexity user selection algorithms are provided to guarantee the feasibility of the problem. Simulation results confirm the accuracy of our robust algorithm in terms of meeting the rate requirements. Finally, our simulation results verify that using a single bit for quantizing the PA is capable of achieving good performance
Weighted sum-rate maximization for the ultra-dense user-centric TDD C-RAN downlink relying on imperfect CSI
Full text linkThe weighted sum-rate maximization problem of ultra-dense cloud radio access networks is considered. The user-centric clustering is adopted for reducing the complexity. To reduce the training overhead, one only needs to estimate the intra-cluster channel-state information (CSI), while only the large-scale channel gains are available outside the cluster. We first derive the rate lower bound (LB) relying on Jensen’s inequality. For the special case of non-overlapping clusters, the accurate data rate expression is derived in the closed form. The simulation results show the tightness of the LB for both the overlapped and non-overlapped cases. Then, we consider an alternative problem where the actual data rate is replaced by its LB, which constitutes a non-convex optimization problem. First, the globally optimal solution is obtained by applying the high-complexity outer polyblock approximation (OPA) algorithm. Then, we invoke the reduced-complexity modified weighted minimum mean square error (WMMSE) algorithm for mitigating the deleterious effects of the realistic imperfect CSI. For the subproblem solved by each WMMSE iteration, the beamforming vectors are derived in the closed form relying on the Lagrangian dual decomposition method. Finally, our simulation results show that the modified WMMSE algorithm’s performance is comparable to that of the high-complexity OPA algorithm, which outperforms other benchmark algorithms
The Non-Coherent Ultra-Dense C-RAN Is Capable of Outperforming Its Coherent Counterpart at a Limited Fronthaul Capacity
Full text linkThe weighted sum rate maximization problem of ultra-dense cloud radio access networks (C-RANs) is considered, where realistic fronthaul capacity constraints are incorporated. To reduce the training overhead, pilot reuse is adopted and the transmit-beamforming used is designed to be robust to the channel estimation errors. In contrast to the conventional C-RAN where the remote radio heads (RRHs) coherently transmit their data symbols to the user, we consider their non-coherent transmission, where no strict phase-synchronization is required. By exploiting the classic successive interference cancellation (SIC) technique, we first derive the closed-form expressions of the individual data rates from each serving RRH to the user and the overall data rate for each user that is not related to their decoding order. Then, we adopt the reweighted l1 -norm technique to approximate the l0 -norm in the fronthaul capacity constraints as the weighted power constraints. A low-complexity algorithm based on a novel sequential convex approximation (SCA) algorithm is developed to solve the resultant optimization problem with convergence guarantee. A beneficial initialization method is proposed to find the initial points of the SCA algorithm. Our simulation results show that in the high fronthaul capacity regime, the coherent transmission is superior to the non-coherent one in terms of its weighted sum rate. However, significant performance gains can be achieved by the non-coherent transmission over the non-coherent one in the low fronthaul capacity regime, which is the case in ultra-dense C-RANs, where mmWave fronthaul links with stringent capacity requirements are employed
4G/5G spectrum sharing: efficient 5G deployment to serve enhanced mobile broadband and Internet of Things applications
No full textIntelligent reflecting surface aided MIMO broadcasting for simultaneous wireless information and power transfer
Full text linkAn intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternatively optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications
4G/5G spectrum sharing for enhanced mobile broad-band and IoT services
No full text5G has been developed for supporting diverse services, such as enhanced mobile broadband (eMBB), massive machine type communication (mMTC) and ultra-reliable low latency communication (URLLC). The latter two constitute enablers of the Internet of Things (IoT). The new spectrum released for 5G deployments, primarily above 3 GHz, unfortunately has a relatively high path-loss, which limits the coverage, especially for the uplink (UL). The high propagation loss, the limited number of UL slots in a TDD frame and the limited user-power gravely limit the UL coverage, but this is where bandwidth is available. Moreover, the stringent requirements of eMBB and IoT applications lead to grave 5G challenges, such as site-planning, ensuring seamless coverage, adapting the TDD DL/UL slot ratio and the frame structure for maintaining a low bit error rate (BER) as well as low latency, etc. This paper addresses some of those challenges with the aid of a unified spectrum sharing mechanism, and by means of an UL/DL decoupling solution based on 4G/5G frequency sharing. The key concept is to accommodate the UL resources in an LTE FDD frequency band as a supplemental UL carrier in addition to the New Radio (NR) operation in the TDD band above 3 GHz. With the advent of this concept, the conflicting requirements of high transmission efficiency, large coverage area and low latency can be beneficially balanced. We demonstrate that the unified 5G spectrum exploitation mechanism is capable of seamlessly supporting compelling IoT and eMBB services.<br/
Fifty years of noise modeling and mitigation in power-line communications
No full textBuilding on the ubiquity of electric power infrastructure, power line communications (PLC) has been successfully used in diverse application scenarios, including the smart grid and in-home broadband communications systems as well as industrial and home automation. However, the power line channel exhibits deleterious properties, one of which is its hostile noise environment. This article aims for providing a review of noisemodeling and mitigation techniques in PLC. Specifically, a comprehensive review of representative noise models developed over the past fifty years is presented, including both the empirical models based on measurement campaigns and simplified mathematical models. Following this, we provide an extensive survey of the suite of noise mitigation schemes, categorizing them into mitigation at the transmitter as well as parametric and nonparametric techniques employed at the receiver. Furthermore,since the accuracy of channel estimation in PLC is affected by noise, we review the literature of joint noise mitigation and channel estimation solutions. Finally, a number of directions are outlined for future research on both noise modeling and mitigation in PLC
Dynamic aerial base station placement for minimum-delay communications
No full textQueuing delay is of essential importance in the Internet-of-Things scenarios where the buffer sizes of devices are limited. The existing cross-layer research contributions aiming at minimizing the queuing delay usually rely on either trans- mit power control or dynamic spectrum allocation. Bearing in mind that the transmission throughput is dependent on the distance between the transmitter and the receiver, in this context we exploit the agility of the unmanned aerial vehicle (UAV)- mounted base stations for proactively adjusting the aerial base station (ABS)’s placement in accordance with wireless tele-traffic dynamics. Specifically, we formulate a minimum-delay ABS placement problem for UAV-enabled networks, subject to realistic constraints on the ABS’s battery life and velocity. Its solutions are technically realized under three different assumptions in regard to the wireless tele-traffic dynamics. The backward induction technique is invoked for both the scenario where the full knowledge of the wireless tele-traffic dynamics is available, and for the case where only their statistical knowledge is available. By contrast, a reinforcement learning aided approach is invoked for the case when neither the exact number of arriving packets nor that of their statistical knowledge is available. The numerical results demonstrate that our proposed algorithms are capable of improving the system’s performance compared to the benchmark schemes in terms of both the average delay and of the buffer overflow probability
Multicell MIMO communications relying on intelligent reflecting surfaces
Full text linkIntelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance. </p
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