Showing papers by "Mats Bengtsson published in 2016"

Subspace Estimation and Decomposition for Large Millimeter-Wave MIMO Systems

Abstract: Channel estimation and precoding in hybrid analog-digital millimeter-wave (mmWave) MIMO systems is a fundamental problem that has yet to be addressed, before any of the promised gains can be harnessed. For that matter, we propose a method (based on the well-known Arnoldi iteration) exploiting channel reciprocity in TDD systems and the sparsity of the channel’s eigenmodes, to estimate the right (resp. left) singular subspaces of the channel, at the BS (resp. MS). We first describe the algorithm in the context of conventional MIMO systems, and derive bounds on the estimation error in the presence of distortions at both BS and MS. We later identify obstacles that hinder the application of such an algorithm to the hybrid analog-digital architecture, and address them individually. In view of fulfilling the constraints imposed by the hybrid analog-digital architecture, we further propose an iterative algorithm for subspace decomposition, whereby the above estimated subspaces, are approximated by a cascade of analog and digital precoder/combiner. Finally, we evaluate the performance of our scheme against the perfect CSI, fully digital case (i.e., an equivalent conventional MIMO system), and conclude that similar performance can be achieved, especially at medium-to-high SNR (where the performance gap is less than 5%), however, with a drastically lower number of RF chains ( ${\sim}4$ to 8 times less).

Adaptive Pilot Clustering in Heterogeneous Massive MIMO Networks

Abstract: We consider the uplink of a cellular massive multiple-input multiple-output network. Acquiring channel state information at the base stations (BSs) requires uplink pilot signaling. Since the number of orthogonal pilot sequences is limited by the channel coherence, pilot reuse across cells is necessary to achieve high spectral efficiency. However, finding efficient pilot reuse patterns is non-trivial, especially in practical asymmetric BS deployments. We approach this problem using the coalitional game theory. Each BS has a few unique pilots and can form coalitions with other BSs to gain access to more pilots. The BSs in a coalition, thus, benefit from serving more users in their cells at the expense of higher pilot contamination and interference. Given that a cell’s average spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional game model is in the partition form. We develop a low-complexity distributed coalition formation based on individual stability. By incorporating a BS intercommunication budget constraint, we are able to control the overhead in message exchange between the BSs and ensure the algorithm’s convergence to a solution of the game called individually stable coalition structure. Simulation results reveal fast algorithmic convergence and substantial performance gains over the baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.

Virtual Full-Duplex Buffer-Aided Relaying in the Presence of Inter-Relay Interference

Abstract: In this paper, we study virtual full-duplex (FD) buffer-aided relaying to recover the loss of multiplexing gain caused by half-duplex (HD) relaying in a multiple relay network, where each relay is equipped with a buffer and multiple antennas, through joint opportunistic relay selection (RS) and beamforming (BF) design. The main idea behind virtual FD buffer-aided relaying is that the source and one of the relays simultaneously transmit their own information to another relay and the destination, respectively. In such networks, interrelay interference (IRI) is a crucial problem, which has to be resolved like self-interference in the FD relaying. In contrast to previous work that neglected IRI, we propose joint RS and BF schemes taking IRI into consideration by using multiple antennas at the relays. To maximize average end-to-end rate, we propose a weighted sum-rate maximization strategy assuming that adaptive rate transmission is employed in both the source to relay and relay to destination links. Then, we propose several BF schemes cancelling or suppressing IRI in order to maximize the weighted sum-rate. Numerical results show that our proposed optimal, zero-forcing, and minimum mean square error BF-based RS schemes asymptotically approach the ideal FD relaying upper bound when increasing the number of antennas and/or the number of relays.

Distributed CSI Acquisition and Coordinated Precoding for TDD Multicell MIMO Systems

Abstract: Several distributed coordinated precoding methods exist in the downlink multicell multiple-input–multiple-output (MIMO) literature, many of which assume perfect knowledge of received signal covariance and local effective channels. In this paper, we let the notion of channel state information (CSI) encompass this knowledge of covariances and effective channels. We analyze what local CSI is required in the weighted minimization of the mean square error (WMMSE) algorithm for distributed coordinated precoding, and we study how this required CSI can be obtained in a distributed fashion. Based on pilot-assisted channel estimation, we propose three CSI acquisition methods with different tradeoffs between feedback and signaling, backhaul use, and computational complexity. One of the proposed methods is fully distributed, meaning that it only depends on over-the-air signaling but requires no backhaul, and it results in a fully distributed joint system when coupled with the WMMSE algorithm. Naively applying the WMMSE algorithm together with the fully distributed CSI acquisition results in catastrophic performance however; therefore, we propose a robustified WMMSE algorithm (RB-WMMSE) based on the well-known diagonal loading framework. By enforcing properties of the WMMSE solutions with perfect CSI onto the problem with imperfect CSI, the resulting diagonally loaded spatial filters are shown to perform significantly better than the naive filters. The proposed robust and distributed system is evaluated using numerical simulations and is shown to perform well compared with benchmarks. Under centralized CSI acquisition, the proposed algorithm performs on par with other existing centralized robust WMMSE algorithms. When evaluated in a large-scale fading environment, the performance of the proposed system is promising.

Distributed Long-Term Base Station Clustering in Cellular Networks Using Coalition Formation

Abstract: Interference alignment (IA) is a promising technique for interference mitigation in multicell networks due to its ability to completely cancel the intercell interference through linear precoding and receive filtering. In small networks, the amount of required channel state information (CSI) is modest and IA is, therefore, typically applied jointly over all base stations. In large networks, where the channel coherence time is short in comparison to the time needed to obtain the required CSI, base station clustering must be applied however. We model such clustered multicell networks as a set of coalitions, where CSI acquisition and IA precoding are performed independently within each coalition. We develop a long-term throughput model, which includes both CSI acquisition overhead and the level of interference mitigation ability as a function of the coalition structure. Given the throughput model, we formulate a coalitional game where the involved base stations are the rational players. Allowing for individual deviations by the players, we formulate a distributed coalition formation algorithm with low complexity and low communication overhead that leads to an individually stable coalition structure. The dynamic clustering is performed using only long-term CSI, but we also provide a robust short-term precoding algorithm, which accounts for the intercoalition interference when spectrum sharing is applied between coalitions. Numerical simulations show that the distributed coalition formation is generally able to reach long-term sum throughputs within 10% of the global optimum.

Subspace estimation and hybrid precoding for wideband millimeter-wave MIMO systems

Abstract: There has been growing interest in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, which would likely employ hybrid analog-digital precoding with large-scale analog arrays deployed at wide bandwidths. Primary challenges here are how to efficiently estimate the large-dimensional frequency-selective channels and customize the wideband hybrid analog-digital precoders and combiners. To address these challenges, we propose a low-overhead channel subspace estimation technique for the wideband hybrid analog-digital MIMO precoding systems. We first show that the Gram matrix of the frequency-selective channel can be decomposed into frequency-flat and frequency-selective components. Based on this, the Arnoldi approach, leveraging channel reciprocity and time-reversed echoing, is employed to estimate a frequency-flat approximation of the frequency-selective mmWave channels, which is used to design the analog parts. After the analog precoder and combiner design, the low-dimensional frequency-selective channels are estimated using conventional pilot-based channel sounding. Numerical results show that considerable improvement in data-rate performance is possible.

Globally Optimal Base Station Clustering in Interference Alignment-Based Multicell Networks

Abstract: Coordinated precoding based on interference alignment is a promising technique for improving the throughputs in future wireless multicell networks. In small networks, all base stations can typically jointly coordinate their precoding. In large networks, however, base station clustering is necessary due to the otherwise overwhelmingly high channel state information (CSI) acquisition overhead. In this work, we provide a branch and bound algorithm for finding the globally optimal base station clustering. The algorithm is mainly intended for benchmarking existing suboptimal clustering schemes. We propose a general model for the user throughputs, which only depends on the long-term CSI statistics. The model assumes intracluster interference alignment and is able to account for the CSI acquisition overhead. By enumerating a search tree using a best-first search and pruning sub-trees in which the optimal solution provably cannot be, the proposed method converges to the optimal solution. The pruning is done using specifically derived bounds, which exploit some assumed structure in the throughput model. It is empirically shown that the proposed method has an average complexity that is orders of magnitude lower than that of exhaustive search.

An energy efficient communication technique for medical implants/micro robots

Abstract: Biomedical implanted sensors and medical micro robots are emerging devices which facilitate health monitoring, medical therapies, and minimally invasive surgeries. The success of these devices highly relies on establishing reliable communication links between inside and outside the body in the presence of severe constraints on energy consumptions. We develop an energy efficient pilot-assisted data transmission technique for communication between a single-antenna medical sensor/micro-robot inside the body to multi-antenna receiver on the body surface though non-homogeneous propagation environment.

Separability and Sum-Rate Maximization in MIMO Interfering Networks.

Abstract: MIMO systems in the lower part of the millimetre-wave spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum-rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this work, we propose algorithms for tackling the sum-rate maximization problem, that are designed to address the above limitations. We derive a lower bound on the sum-rate, a so-called DLT bound (i.e., a difference of log and trace), shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems, that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-off streams exhibiting low-SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the millimeter wave spectrum. Moreover, our results also suggest that interference management still brings about significant performance gains, especially in dense deployments.

Stable Matching with Externalities for Beamforming and User Assignment in Multi-cell MISO Systems

Abstract: We consider the problem of distributed joint user association and beamforming in multi-cell multiple-input single-output systems. Assuming perfect local channel state information, each base station applies a distributed beamforming scheme called WSLNR-MAX which depends on the user association in the network. We determine the user association by a proposed stable matching with externalities algorithm which also takes the beamforming vectors at the base stations into account. The merit in the stable matching model is the distributed implementation aspects. Each user asks to be matched with a base station according to his preferences, and each base station decides independently which users to accept. Simulation results reveal efficient distributed operation of the system compared to matching without externalities.

Pilot Precoding and Combining in Multiuser MIMO Networks

Abstract: Although the benefits of precoding and combining of data signals are widely recognized, the potential of these techniques for pilot transmission is not fully understood. This is particularly relevant for multiuser multiple-input multiple-output (MU-MIMO) cellular systems using millimeter-wave (mmWave) communications, where multiple antennas will have to be used both at the transmitter and the receiver to overcome the severe path loss. In this paper, we characterize the gains of pilot precoding and combining in terms of channel estimation quality and achievable data rate. Specifically, we consider three uplink pilot transmission scenarios in a mmWave MU-MIMO cellular system: 1) non-precoded and uncombined, 2) precoded but uncombined, and 3) precoded and combined. We show that a simple precoder that utilizes only the second-order statistics of the channel reduces the variance of the channel estimation error by a factor that is proportional to the number of user equipment (UE) antennas. We also show that using a linear combiner designed based on the second-order statistics of the channel significantly reduces multiuser interference and provides the possibility of reusing some pilots. Specifically, in the large antenna regime, pilot precoding and combining help to accommodate a large number of UEs in one cell, significantly improve channel estimation quality, boost the signal-to-noise ratio of the UEs located close to the cell edges, alleviate pilot contamination, and address the imbalanced coverage of pilot and data signals.

Sum-rate Maximization in Sub-28 GHz Millimeter-Wave MIMO Interfering Networks

Abstract: MIMO systems in the lower part of the millimeter-wave spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum-rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this work, we propose algorithms for tackling the sum-rate maximization problem, that are designed to address the above limitations. We derive a lower bound on the sum-rate, a so-called DLT bound (i.e., a difference of log and trace), shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems, that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-off streams exhibiting low-SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the millimeter wave spectrum. Moreover, our results also suggest that interference management still brings about significant performance gains, especially in dense deployments.

Globally Optimal Base Station Clustering in Interference Alignment-Based Multicell Networks

Abstract: Coordinated precoding based on interference alignment is a promising technique for improving the throughputs in future wireless multicell networks. In small networks, all base stations can typically jointly coordinate their precoding. In large networks however, base station clustering is necessary due to the otherwise overwhelmingly high channel state information (CSI) acquisition overhead. In this work, we provide a branch and bound algorithm for finding the globally optimal base station clustering. The algorithm is mainly intended for benchmarking existing suboptimal clustering schemes. We propose a general model for the user throughputs, which only depends on the long-term CSI statistics. The model assumes intracluster interference alignment and is able to account for the CSI acquisition overhead. By enumerating a search tree using a best-first search and pruning sub-trees in which the optimal solution provably cannot be, the proposed method converges to the optimal solution. The pruning is done using specifically derived bounds, which exploit some assumed structure in the throughput model. It is empirically shown that the proposed method has an average complexity which is orders of magnitude lower than that of exhaustive search.

Joint Coordinated Precoding and Discrete Rate Selection in Multicell MIMO Networks

Abstract: Many practical wireless communications systems select their transmit rate from a finite set of modulation and coding schemes, which correspond to a set of discrete rates. In this paper, we therefore formulate a joint coordinated precoding and discrete rate selection problem for multiple-input multiple-output (MIMO) multicell networks. Compared to the common assumption of using the continuous Shannon rates as the user utilities, explicitly accounting for the discrete rates more accurately models practical wireless communication systems. The optimization problem that we formulate is combinatorial and non-convex, however, and is thus hard to solve. We therefore rewrite the problem using a discontinuous rate function, which we then bound using its concave envelope in some domain. Based on block coordinate descent, we provide a convergent resource allocation algorithm which can be implemented in a semi-distributed fashion. Numerical performance evaluation shows performance gains when the discrete rates are optimized using our model, as compared to the traditional methods which use the continuous Shannon rates as the user utilities.

Relaying and wireless network coding

Abstract: Relaying and network coding are powerful techniques that improve the performance of a cellular network, for example by extending the network coverage, by increasing the system capacity or by enhanc ...

Distributed Long-Term Base Station Clustering in Cellular Networks using Coalition Formation

Abstract: Interference alignment (IA) is a promising technique for interference mitigation in multicell networks due to its ability to completely cancel the intercell interference through linear precoding and receive filtering. In small networks, the amount of required channel state information (CSI) is modest and IA is therefore typically applied jointly over all base stations. In large networks, where the channel coherence time is short in comparison to the time needed to obtain the required CSI, base station clustering must be applied however. We model such clustered multicell networks as a set of coalitions, where CSI acquisition and IA precoding is performed independently within each coalition. We develop a long-term throughput model which includes both CSI acquisition overhead and the level of interference mitigation ability as a function of the coalition structure. Given the throughput model, we formulate a coalitional game where the involved base stations are the rational players. Allowing for individual deviations by the players, we formulate a distributed coalition formation algorithm with low complexity and low communication overhead that leads to an individually stable coalition structure. The dynamic clustering is performed using only long-term CSI, but we also provide a robust short-term precoding algorithm which accounts for the intercoalition interference when spectrum sharing is applied between coalitions. Numerical simulations show that the distributed coalition formation is generally able to reach long-term sum throughputs within 10 % of the global optimum.