The Experts below are selected from a list of 168 Experts worldwide ranked by ideXlab platform
Bjorn Ottersten - One of the best experts on this subject based on the ideXlab platform.
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Deploying Dynamic On-Board Signal Processing Schemes for Multibeam Satellite Systems
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Vahid Joroughi, Symeon Chatzinotas, Mirza Golam Kibria, Eva Lagunas, Bhavani Shankar M. R., Joel Grotz, Sina Maleki, Bjorn OtterstenAbstract:This paper designs dynamic onboard signal processing schemes in a multiple gateway multi-beam satellite system where Full Frequency Reuse pattern is considered among the beams and feeds. In particular, we deploy on-board Joint Precoding, Feed selection and Signal switching mechanism (JPFS) so that the following advantages are realized, I) No need of Channel State Information (CSI) exchange among the gateways and satellite, since the performance of precoding is highly sensitive to the quality of CSI, II) In case one gateway fails, rerouting signals through other gateways can be applied without any extra signal processing, III) Properly selecting on-board feed/s to serve each user which generates maximum gain toward corresponding user, IV) Flexibly switching the signals received from the gateways to requested users where each user can dynamically request traffic from any gateway, and V) Multiple users with multiple traffic streams can be dynamically served at each beam. However, deploying such JPFS architecture imposes high complexity to the satellite payload. To tackle this issue, this study aims at deploying JPFS that can provide affordable complexity at the payload. In addition, while increasing the data demand imposes extensive bandwidth resources requirement in the feeder link, the proposed JPFS design works efficiently with available feeder link resources even if the data demand increases. The proposed design is evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.
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Full Frequency Reuse multibeam satcoms frame based precoding and user scheduling
arXiv: Information Theory, 2014Co-Authors: Dimitrios Christopoulos, Symeon Chatzinotas, Bjorn OtterstenAbstract:The practical optimization of the forward link of a broadband multibeam satellite that aggressively Reuses the Frequency resources, is the focus of the present work. Herein, two fundamental practical challenges, namely the need to frame multiple users in a single transmission and the per-antenna transmit power limitations, are addressed. To this end, the so-called frame based precoding problem is optimally solved using the principles of physical layer multicasting to multiple co-channel groups under per-antenna constraints. In this context, a novel optimization problem that aims at maximizing the system sum rate under individual power constraints is proposed. Added to that, the formulation is further extended to include availability constraints. As a result, the high gains of the sum rate optimal design are traded off to satisfy the stringent availability requirements of satellite systems. Moreover, the elaborate throughput maximization, that acknowledges the finite granularity of the spectral efficiency to SINR mapping, is formulated and solved. Finally, a multicast aware user scheduling policy, based on the channel state information, is developed. Therefore, the gains of the rich in multiuser diversity broadband satellite systems are gleaned. Numerical results over a realistic simulation environment exhibit as much as 30% gains over conventional systems, even for 7 users per frame, without modifying the framing structure of legacy communication standards and with guaranteed service availability over the coverage.
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user scheduling for coordinated dual satellite systems with linear precoding
International Conference on Communications, 2013Co-Authors: Dimitrios Christopoulos, Symeon Chatzinotas, Bjorn OtterstenAbstract:The constantly increasing demand for interactive broadband satellite communications is driving current research towards novel system architectures that Reuse Frequency in a more aggressive manner. To this end, the topic of dual satellite systems, in which satellites share spatial (i.e. same coverage area) and spectral (i.e. Full Frequency Reuse) degrees of freedom is introduced. In each multibeam satellite, multiuser interferences are mitigated by employing zero forcing precoding with realistic per antenna power constraints. However, the two sets of users that the transmitters are separately serving, interfere. The present contribution, proposes the partial cooperation, herein referred to as coordination, between the two coexisting transmitters in order to reduce interferences and enhance the performance of the whole system, while maintaining moderate system complexity. In this direction, a heuristic, iterative, low complexity algorithm that allocates users in the two interfering sets is proposed. This novel algorithm, improves the performance of each satellite and of the overall system, simultaneously. The first is achieved by maximizing the orthogonality between users allocated in the same set, hence optimizing the zero forcing performance, whilst the second by minimizing the level of interferences between the two sets. Simulation results show that the proposed method, compared to conventional techniques, significantly increases spectral efficiency.
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user scheduling for coordinated dual satellite systems with linear precoding
arXiv: Information Theory, 2012Co-Authors: Dimitrios Christopoulos, Symeon Chatzinotas, Bjorn OtterstenAbstract:The constantly increasing demand for interactive broadband satellite communications is driving current research to explore novel system architectures that Reuse Frequency in a more aggressive manner. To this end, the topic of dual satellite systems, in which satellites share spatial (i.e. same coverage area) and spectral (i.e. Full Frequency Reuse) degrees of freedom is introduced. In each multibeam satellite, multiuser interferences are mitigated by employing zero forcing precoding with realistic per antenna power constraints. However, the two sets of users that the transmitters are separately serving, interfere. The present contribution, proposes the partial cooperation, namely coordination between the two coexisting transmitters in order to reduce interferences and enhance the performance of the whole system, while maintaining moderate system complexity. In this direction, a heuristic, iterative, low complexity algorithm that allocates users in the two interfering sets is proposed. This novel algorithm, improves the performance of each satellite and of the overall system, simultaneously. The first is achieved by maximizing the orthogonality between users allocated in the same set, hence optimizing the zero forcing performance, whilst the second by minimizing the level of interferences between the two sets. Simulation results show that the proposed method, compared to conventional techniques, significantly increases spectral efficiency.
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Massive MIMO Transmission for LEO Satellite Communications
IEEE Journal on Selected Areas in Communications, 1Co-Authors: Ke-xin Li, Jiaheng Wang, Bjorn OtterstenAbstract:Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks, in particular 5G and beyond networks, to provide global wireless access with enhanced data rates. Massive multiple-input multipleoutput (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO transmission scheme with Full Frequency Reuse (FFR) for LEO satellite communication systems and exploit statistical channel state information (sCSI) to address the difficulty of obtaining instantaneous CSI (iCSI) at the transmitter. We first establish the massive MIMO channel model for LEO satellite communications and simplify the transmission designs via performing Doppler and delay compensations at user terminals (UTs). Then, we develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL) receiver in closed-form, aiming to maximize the average signal-to-leakage-plus-noise ratio (ASLNR) and the average signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some channel condition. Motivated by this, we propose a space angle based user grouping (SAUG) algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and Frequency resource. The proposed algorithm is asymptotically optimal in the sense that the lower and upper bounds of the achievable rate coincide when the number of satellite antennas or UT groups is sufficiently large. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems. Notably, the proposed sCSI based precoder and receiver achieve the similar performance with the iCSI based ones that are often infeasible in practice.
Kaiming Shen - One of the best experts on this subject based on the ideXlab platform.
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Spatial Deep Learning for Wireless Scheduling
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Kaiming Shen, Wei YuAbstract:The optimal scheduling of interfering links in a dense wireless network with Full Frequency Reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths and then optimizing the scheduling based on the model. This model-based method is, however, resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance-dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives a near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.
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spatial deep learning for wireless scheduling
arXiv: Signal Processing, 2018Co-Authors: Wei Cui, Kaiming ShenAbstract:The optimal scheduling of interfering links in a dense wireless network with Full Frequency Reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard, because channel estimation is expensive in dense networks; further, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers for networks in which the channels are largely functions of distance dependent path-losses. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.
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Spatial Deep Learning for Wireless Scheduling
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Kaiming Shen, Wei YuAbstract:The optimal scheduling of multiple interfering links in a densely deployed wireless network with Full Frequency Reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. This is accomplished by taking advantage of the recent advances in fractional programming that allows us to generate high- quality local optimum solutions to the scheduling problem for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering and interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution.
Wei Yu - One of the best experts on this subject based on the ideXlab platform.
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Spatial Deep Learning for Wireless Scheduling
IEEE Journal on Selected Areas in Communications, 2019Co-Authors: Kaiming Shen, Wei YuAbstract:The optimal scheduling of interfering links in a dense wireless network with Full Frequency Reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths and then optimizing the scheduling based on the model. This model-based method is, however, resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance-dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives a near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.
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Spatial Deep Learning for Wireless Scheduling
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Kaiming Shen, Wei YuAbstract:The optimal scheduling of multiple interfering links in a densely deployed wireless network with Full Frequency Reuse is a well-known challenging problem. The classical optimization approaches to this problem typically operate under the paradigm of first estimating all the interfering channel strengths then finding an optimum solution using the model. However, traditional scheduling methods are computationally and resource intensive, because channel estimation is expensive especially in dense networks, and further the optimization of link scheduling is typically a nonconvex problem. This paper takes a novel deep spatial learning approach to the scheduling problem. We show that it is possible to bypass the channel estimation stage altogether and to use a deep neural network to produce a near optimal schedule based solely on geographic locations of the transmitters and receivers in the network. This is accomplished by taking advantage of the recent advances in fractional programming that allows us to generate high- quality local optimum solutions to the scheduling problem for randomly deployed device-to-device networks as training data, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering and interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution.
Michele Zorzi - One of the best experts on this subject based on the ideXlab platform.
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Power-shaped advanced resource assignment (PSARA) for fixed broadband wireless access systems
IEEE Transactions on Wireless Communications, 2004Co-Authors: Velio Tralli, Rodolfo Veronesi, Michele ZorziAbstract:We propose and investigate a new resource allocation technique for the downlink of time-division multiple access (TDMA)-based fixed broadband wireless access systems (FBWA) with Full Frequency Reuse. This technique, named power-shaped advanced resource assignment exploits an appropriate set of power profiles which limit (or shape) the power transmitted in each slot of the frame and are suitably Reused among the cells, in order to efficiently distribute the intercell and intersector interference inside the frame and to make it partially predictable. In systems where base stations assign radio resources in an uncoordinated fashion, this allows the allocation algorithm to assign time slots to users on the basis of the power required to fulfill a predefined carrier-to-interference ratio, since worst case interference can be suitably estimated; moreover, different degrees of protection against interference are provided across the slots to efficiently accommodate users with different location-dependent channel conditions. Simulation results for a typical cellular FBWA system show that this technique significantly improves the capacity with respect to other techniques recently proposed, e.g., the enhanced staggered resource assignment, even when they use power control. Moreover, an analytical framework useful to understand the concept of power shaping and to discuss some guidelines for the design of power profiles is provided.
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Resource allocation with power-shaping in fixed broadband wireless access systems
Global Telecommunications Conference 2002. GLOBECOM '02. IEEE, 2002Co-Authors: Velio Tralli, Rodolfo Veronesi, Michele ZorziAbstract:This paper investigates a new resource allocation technique for TDMA-based fixed broadband wireless access systems (FBWA) with Full Frequency Reuse where base stations assign radio resources in an uncoordinated fashion. This technique, named power-shaped advanced resource assignment (PSARA) exploits an appropriate set of power profiles that limit (or shape) the power transmitted in each slot of the frame with the aim of helping the allocation algorithm to efficiently distribute the intercell and intersector interference inside the frame. Simulation results for a typical cellular FBWA system show that this technique significantly improves the capacity with respect to other techniques previously proposed, e.g. the enhanced staggered resource assignment (ESRA), even when they use power control. Moreover, an analytical framework useful to understand the concept of power-shaping and to discuss some guidelines for the design of power profiles is provided.
G. De Veciana - One of the best experts on this subject based on the ideXlab platform.
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architecture and abstractions for environment and traffic aware system level coordination of wireless networks
IEEE ACM Transactions on Networking, 2011Co-Authors: B. Rengarajan, G. De VecianaAbstract:This paper presents a system-level approach to interference management in an infrastructure-based wireless network with Full Frequency Reuse. The key idea is to use loose base-station coordination that is tailored to the spatial load distribution and the propagation environment to exploit the diversity in a user population's sensitivity to interference. System architecture and abstractions to enable such coordination are developed for both the downlink and the uplink cases, which present differing interference characteristics. The basis for the approach is clustering and aggregation of traffic loads into classes of users with similar interference sensitivities that enable coarse-grained information exchange among base stations with greatly reduced communication overheads. This paper explores ways to model and optimize the system under dynamic traffic loads where users come and go, resulting in interference-induced performance coupling across base stations. Based on extensive system-level simulations, we demonstrate load-dependent reductions in file transfer delay ranging from 20%-80% as compared to a simple baseline not unlike systems used in the field today while simultaneously providing more uniform coverage. Average savings in user power consumption of up to 75% is achieved. Performance results under heterogeneous spatial loads illustrate the importance of being traffic- and environment-aware.
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Architecture and Abstractions for Environment and Traffic Aware System-Level Coordination of Wireless Networks: The Downlink Case
IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 2008Co-Authors: B. Rengarajan, G. De VecianaAbstract:Two ways to substantially enhance wireless broadband capacity are Full Frequency Reuse and smaller cells, both of which result in operational regimes that are highly dynamic and interference limited. This paper presents a system-level approach to interference management, that has reasonable backhaul communication and computation requirements. The basis for the approach is clustering and aggregation of measurements of the spatial diversity in sensitivity to interference associated with average user populations. This enables the system to exchange information and optimize coordinated transmission schedules using only coarse grained data. The paper explores various ways of optimizing such schedules: from a static, decoupled version to a dynamic version capturing user-level scheduling, fluctuating loads and inter-cell interference that couples base stations' performance. Based on extensive system-level simulations, we demonstrate reductions in file transfer delay ranging from 20-80%, from light to heavy loads, as compared to a simple baseline not unlike those in the field today. This improvement is achieved while providing more uniform coverage, and reducing base station power consumption by up to 45%.
