Showing papers by "Mats Bengtsson published in 2019"
14 Aug 2019
TL;DR: This paper proposes a link adaptation approach that overcomes the shortcomings of OLLA through a novel learning scheme that relies on contextual multi-armed bandits (MAB), where the context vector is composed of the instantaneous wireless channel state along with side information about the link.
Abstract: Cellular networks dynamically adjust the transmission parameters for a wireless link in response to its time-varying channel state. This is known as link adaptation, where the typical goal is to maximize the link throughput. State-of-the-art outer loop link adaptation (OLLA) selects the optimal transmission parameters based on an approximate, offline, model of the wireless link. Further, OLLA refines the offline model by dynamically compensating any deviations from the observed link performance. However, in practice, OLLA suffers from slow convergence and a sub-optimal link throughput. In this paper, we propose a link adaptation approach that overcomes the shortcomings of OLLA through a novel learning scheme. Our approach relies on contextual multi-armed bandits (MAB), where the context vector is composed of the instantaneous wireless channel state along with side information about the link. For a given context, our approach learns the success probability for each of the available transmission parameters, which is then exploited to select the throughput-maximizing parameters. Through numerical experiments, we show that our approach converges faster than OLLA and achieves a higher steady-state link throughput. For frequent and infrequent channel reports respectively, our scheme outperforms OLLA by 15% and 25% in terms of the steady-state link throughput.
19 citations
TL;DR: In this paper, several distributed approaches for CB-CoMP are introduced, which rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling, and elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI.
Abstract: CoMP transmission and reception have been considered in cellular networks for enabling larger coverage, improved rates, and interference mitigation. To harness the gains of coordinated beamforming, fast information exchange over a backhaul connecting the cooperating BSs is required. In practice, the bandwidth and delay limitations of the backhaul may not be able to meet such stringent demands. These impairments motivate the study of cooperative approaches based only on local CSI that require minimal or no information exchange between the BSs. To this end, several distributed approaches are introduced for CB-CoMP. The proposed methods rely on the channel reciprocity and iterative spatially precoded over-the-air pilot signaling. We elaborate how F-B training facilitates distributed CB by allowing BSs and UEs to iteratively optimize their respective transmitters/receivers based on only locally measured CSI. The trade-off due to the overhead from the F-B iterations is discussed. We also consider the challenge of dynamic TDD where the UE-UE channel knowledge cannot be acquired at the BSs by exploiting channel reciprocity. Finally, standardization activities and practical requirements for enabling the proposed F-B training schemes in 5G radio access are discussed.
18 citations
01 Mar 2019
TL;DR: This work studies a cooperative network with a buffer-aided multi-antenna source, multiple half-duplex (HD) buffer- aided relays and a single destination that could represent a cellular downlink sce.
Abstract: We study a cooperative network with a buffer-aided multi-antenna source, multiple half-duplex (HD) buffer-aided relays and a single destination. Such a setup could represent a cellular downlink sce ...
10 citations
11 Jul 2019
TL;DR: In this paper, a multi-hypothesis distributed detection technique with non-identical local detectors is investigated and a receive beamforming algorithm, based on a modification of Lozano's algorithm, is proposed to equalize the channel gains from different sensors.
Abstract: In this paper, a multi-hypothesis distributed detection technique with non-identical local detectors is investigated. Here, for a global event, some of the sensors/detectors can observe the whole set of hypotheses, whereas the remaining sensors can either see only some aspects of the global event or infer more than one hypothesis as a single hypothesis. Another possible option is that different sensors provide complementary information. The local decisions are sent over a multiple access radio channel so that the data fusion is formed in the air before reaching the decision fusion center (DFC). An optimal energy fusion rule is formulated by considering the radio channel effects and the reliability of the sensors together, and a closed-form solution is derived. A receive beamforming algorithm, based on a modification of Lozano's algorithm, is proposed to equalize the channel gains from different sensors. Sensors with limited detection capabilities are found to boost the overall system performance when they are used along with fully capable sensors. The additional transmit power used by these sensors is compensated by the designed fusion rule and the antenna array gain. Additionally, the DFC, equipped with a large antenna array, can reduce the overall transmit energy consumption without sacrificing the detection performance.
9 citations
01 Feb 2019
TL;DR: A fusion rule based on minimization of variance of the local mis-detection is proposed and the presence of sensors with limited detection capabilities is found to have a positive impact on the overall system performance, both in terms of probability of detection and transmit power consumption.
Abstract: There has been very little exploration when it comes to design distributed detection techniques and data fusion rules with non-identical sensors. This concept can be utilized in many possible applications within industrial automation, surveillance and safety. Here, for a global event, some of the sensors/detectors in the network can observe the full set of the hypotheses, whereas the remaining sensors infer more than one hypotheses as a single hypothesis. The local decisions are sent to the decision fusion center (DFC) over a multiple access wireless channel. In this paper, a fusion rule based on minimization of variance of the local mis-detection is proposed. The presence of sensors with limited detection capabilities is found to have a positive impact on the overall system performance, both in terms of probability of detection and transmit power consumption. Additionally, when the DFC is equipped with a large antenna array, the overall transmit power consumption can be reduced without sacrificing the detection performance.
3 citations
02 Jul 2019
TL;DR: Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask and typically needs 3 iterations to achieve similar results at the expense of a slightly increased computational complexity.
Abstract: This paper proposes a new large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems. In this paper, we first consider a previously proposed mask-compliant spectral precoding scheme that utilizes a generic convex optimization solver which suffers from high computational complexity, notably in large-scale systems. To mitigate the complexity of computing the LS-MSP, we propose a divide-and-conquer approach that breaks the original problem into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution. Based on these solutions, we develop three specialized first-order low-complexity algorithms, based on 1) projection on convex sets and 2) the alternating direction method of multipliers. We also develop an algorithm that capitalizes on the closed-form solutions for the rank 1 quadratic constraints, which is referred to as 3) semianalytical spectral precoding. Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask. The results also indicate that 3) typically needs 3 iterations to achieve similar results as 1) and 2) at the expense of a slightly increased computational complexity.
3 citations
Posted Content•
TL;DR: In this article, a large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems is proposed, where the original problem is decomposed into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution.
Abstract: This paper proposes a new large-scale mask-compliant spectral precoder (LS-MSP) for orthogonal frequency division multiplexing systems. In this paper, we first consider a previously proposed mask-compliant spectral precoding scheme that utilizes a generic convex optimization solver which suffers from high computational complexity, notably in large-scale systems. To mitigate the complexity of computing the LS-MSP, we propose a divide-and-conquer approach that breaks the original problem into smaller rank 1 quadratic-constraint problems and each small problem yields closed-form solution. Based on these solutions, we develop three specialized first-order low-complexity algorithms, based on 1) projection on convex sets and 2) the alternating direction method of multipliers. We also develop an algorithm that capitalizes on the closed-form solutions for the rank 1 quadratic constraints, which is referred to as 3) semi-analytical spectral precoding. Numerical results show that the proposed LS-MSP techniques outperform previously proposed techniques in terms of the computational burden while complying with the spectrum mask. The results also indicate that 3) typically needs 3 iterations to achieve similar results as 1) and 2) at the expense of a slightly increased computational complexity.
1 citations
01 Nov 2019
TL;DR: This paper presents an antenna selection and two bit allocation methods for general spatial modulation, all based on channel state information, and shows that the presented methods have lower computational complexity and perform better than the existing approaches.
Abstract: This paper presents an antenna selection and two bit allocation methods for general spatial modulation, all based on channel state information. The proposed antenna selection method maximizes the Euclidean distance between the possible antenna selections in order to decrease the bit error rate. The bit allocation methods aim at determining the neighboring symbols and minimizing the Hamming distance in between them. In some scenarios, we show, through Monte Carlo simulations, that the presented methods have lower computational complexity and perform better than the existing approaches.