Sandra Lagen

Improper Gaussian signaling for the Z-interference channel

The optimal transmission scheme for a multiple-input multiple-output point-to-point channel (MIMO-P2P) is dependent on the type of received interference, which can be either proper or improper. We will show that the presence of improper interference is beneficial in terms of improving the achievable rate compared to a case with proper interference. These benefits are due to using widely linear precoder and receiver. Additionally, we investigate a cellular scenario where two types of users are coexisting, ones that are receiving proper interference while others which might be receiving improper interference, trying to devise the best transmission scheme to be adopted. We propose an improper Gaussian signaling-based scheme that allows controlling system performance and fairness in terms of bitrate through a single parameter.

On the Superiority of Improper Gaussian Signaling in Wireless Interference MIMO Scenarios

Recent results have elucidated the benefits of using improper Gaussian signaling (IGS) as compared with conventional proper Gaussian signaling (PGS) in terms of achievable rate for interference-limited conditions. This paper exploits majorization theory tools to formally quantify the gains of IGS along with widely linear transceivers for multiple-input multiple-output (MIMO) systems in interference-limited scenarios. The MIMO point-to-point channel with interference is analyzed, assuming that received interference can be either proper or improper, and we demonstrate that the use of the optimal IGS when received interference is improper strictly outperforms (in terms of achievable rate and mean square error) the use of the optimal PGS when interference is proper. Then, these results are extended to two practical situations. First, the MIMO Z-interference channel (Z-IC) is investigated, where a trade-off arises: with IGS, we could increase the achievable rate of the interfered user while gracefully degrading the rate of the non-interfered user. Second, these concepts are applied to a two-tier heterogeneous cellular network where macrocells and small cells coexist and multiple MIMO Z-ICs appear.

Decentralized Coordinated Precoding for Dense TDD Small Cell Networks

Cellular networks need the densification of small eNBs (SeNBs) to face the tremendous data traffic demand growth, implying an interference increase and making transmit coordination a key enabler. This article proposes a decentralized coordinated precoding (D-CoP) for downlink (DL) weighted sum-rate maximization in dense MIMO TDD small cell networks (SCNs). Each SeNB designs its own precoding matrices based on channel state information (CSI) of the served users and knowledge of the interference-cost matrix that allows managing interference towards unintended users. A protocol is proposed to acquire the interference-cost matrix by processing the uplink (UL) received signal provided that: (1) channel reciprocity can be assumed and (2) all users participating in DL can transmit in UL with an adequate transmit filter. In contrast to existing transmit coordination techniques, D-CoP is fully scalable, avoids estimation of the interfering channels, and does not require information exchange between SeNBs. In case all parameters are perfectly acquired, an iterative algorithm is presented with demonstrated monotonic convergence when all SeNBs update its transmit precoders simultaneously. Further, the problem is reformulated in order to derive a robust D-CoP under imperfect CSI conditions. Finally, simulations in 3GPP LTE-Advanced SCNs show significant user packet throughput gains, without increasing the complexity associated to transmit coordination. Robustness to imperfect CSI and non-ideal channel reciprocity is shown through simulations.

Coexisting Linear and Widely Linear Transceivers in the MIMO Interference Channel

Recent results have shown the benefits of widely linear precoding (WLP) in the MIMO interference channel (MIMO IC) assuming that all transmitters can follow the same strategy. Motivated by a transitional scenario where legacy linear transmitters coexist with widely linear ones, this work investigates the general K-user MIMO IC in a heterogeneous (linear and widely linear) transmitter deployment. In particular, we address the maximization of the weighted sum-rate (WSR) for (widely) linear transmit filters design through the use of the complex-valued formulation. Since the maximum WSR problem is non-convex, and thus difficult to be solved, we formulate an equivalent minimum weighted mean square error problem that allows deriving closed-form expressions for (widely) linear transceivers. Then an iterative procedure is proposed, which is proven to reach a stationary point of the maximum WSR problem. Simulations show that the proposed procedure allows increasing the sum-rate as compared to coordinated linear transceiver schemes. The gains are larger and significant in two different nonexclusive conditions: as the interference level increases or when the number of antennas is low.

Distributed inter-cluster interference management for CoMP-based cellular networks

This paper proposes distributed inter-cluster interference management procedures for 1 Coordinated Multi-Point (CoMP) Time Division Duplexing (TDD) cellular networks, in which a set of base stations (BSs), that form a cluster, cooperate in the downlink (DL) and uplink (UL) transmissions to communicate with a group of users while causing interference to users in neighbor clusters. In order to reduce the complications associated to the estimation of the interfering channels, we describe new iterative procedures based on channel reciprocity that avoid such estimation by exploiting the received signal in the UL, provided that transmit and receive filters in DL and UL are properly designed. Simulations show that proposed techniques attain significant DL and UL spectral efficiency gains. Furthermore, an analysis of imperfect channel state information (CSI) at both ends is included, evidencing the robustness of the proposed procedures. Considerations on convergence are included.

Decentralized widely linear precoding design for the MIMO interference channel

This paper addresses the interference management in a MIMO interference channel (MIMO-IC) by proposing a decentralized transmit and receive beamformer optimization using improper (or circularly asymmetric complex) Gaussian signaling. For the ease of exposition, the downlink (DL) of a cellular network is considered. In order to generate improper Gaussian signals, widely linear precoding (WLP) is adopted at transmission, while at reception we consider that users might apply either widely linear estimation (WLE) or linear estimation. The coordination between transmitters for WLP design is attained by taking into account the received signal in the uplink (UL), provided that propagation channel reciprocity can be assumed and that transmit filters in the UL are appropriately designed. In this way the estimation of the interfering channels is avoided, while we can take advantage of both DL transmit coordination to adjust transmit power and beamformers and the use of improper Gaussian signaling to exploit the real and imaginary dimensions of the MIMO channel. Simulations show that the proposed decentralized technique allows reducing the mean square error (MSE) and increasing user throughput in highly interfered scenarios.

Distributed User-Centric Clustering and Precoding Design for CoMP Joint Transmission

This paper addresses distributed interference management in downlink of multi-cell multiple- input multiple-output (MIMO) time division duplex (TDD) networks. Each user is associated with a user-centric cluster of base stations (BSs), which cooperatively serve the user through CoMP joint transmission. Clusters of different users possibly share some BSs such that they may overlap, being coupled by interference and transmit power constraints at each BS. Our objective is the design of BSs clustering and precoding matrices per-user in order to maximize the weighted sum- rate of the system by controlling the interference and the power spent at BSs. In contrast to previous works where all channel matrices in the system are needed, we propose a distributed procedure whereby only channel matrices towards a limited number of candidate BSs per user are required while interference is still controlled by using the signal received from an uplink transmission. For an LTE-compliant dense deployment of 2x2 MIMO BSs/users, results show gains of 6-16% in terms of sum-rate and 49-84% in terms of 5%-tile per-user rate (depending on the maximum cluster size) as compared to distributed BS-disjoint clustering schemes.

Network-MIMO backhauling for QOS-constrained relay transmission

A relay station (RS)-based cellular system deployment is considered, where multiple base stations (BS) cooperate in the BS-RS in-band transmission for the downlink. With the joint optimization of the resources allocated to the wireless backhaul and to the RS-MS access, it is possible to exploit the benefits of networked-MIMO along with combating the pathloss and shadowing effects, and thus obtain significantly enhanced spectral efficiency and coverage homogeneity. The problem is shown to be convex under conventional QoS objective functions. Suboptimal low complexity solutions are also proposed and evaluated.

Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

This paper addresses the energy-latency tradeoff in distributed application offloading, in which an energy-limited handset offloads totally or partially an application to one or several virtual machines (VMs) located in remote locations or access points (APs) close to the mobile terminal (MT). One of the APs (the serving AP) provides radio access to the MT and is connected to the VMs through nonideal backhaul (BH) links. In this setting, we optimize the offloading strategy (including the joint optimization of radio and computational resources) to minimize the energy consumption at the MT subject to a maximum latency constraint. In addition, we propose robust designs to cope with imperfect acquisition of the channel state information (CSI) and the BH parameters. Our findings show that as far as the energy-latency tradeoff is concerned, the optimal order of activation of the VMs does not depend on their processing capabilities but the delays of the BH links. However, once a VM is selected to participate in the processing, the optimal amount of processing allocated to such VM depends on its computational capabilities as well as on the features (capacity and delay) of the BH link. Additionally, offloading decisions become more conservative as the uncertainty in CSI and BH parameters increases.

Long-term provisioning of radio resources based on their utilization in dense OFDMA networks

This paper presents long-term resource provisioning schemes for dense multi-cell OFDMA-based networks, where multiple cells with possibly overlapping coverage areas compete for the same set of resources. The optimization is done over the long term, being independent of the specific users connected to each cell but dynamic enough to follow significant variations of the traffic load. In this sense, we focus on the average resource utilization (RU) and propose schemes to minimize the maximum RU of all cells. Firstly, we consider orthogonal resource usage among cells. Optimal closed-form expressions for the long-term resource provisioning are derived for this case. Secondly, we assume that resources can be reused at non-overlapping cells. In this case, the resource provisioning is solved in two steps: i) the number of resources required per cell is obtained by discretizing the optimal solution of a convex problem, and then ii) the specific resources to be utilized by each cell are determined by using graph coloring. In contrast to previous works, graph coloring can be applied to get an implementable solution for any condition of the cell loads. Simulation results show a significant reduction of the maximum RU, which translates into an increase of the served traffic and a reduction of the packet delay, as compared to static resource provisioning schemes.This paper presents long-term resource provisioning schemes for dense multi-cell OFDMA-based networks, where multiple cells with possibly overlapping coverage areas compete for the same set of resources. The optimization is done over the long term, being independent of the specific users connected to each cell but dynamic enough to follow significant variations of the traffic load. In this sense, we focus on the average resource utilization (RU) and propose schemes to minimize the maximum RU of all cells. Firstly, we consider orthogonal resource usage among cells. Optimal closed-form expressions for the long-term resource provisioning are derived for this case. Secondly, we assume that resources can be reused at non-overlapping cells. In this case, the resource provisioning is solved in two steps: i) the number of resources required per cell is obtained by discretizing the optimal solution of a convex problem, and then ii) the specific resources to be utilized by each cell are determined by using graph coloring. In contrast to previous works, graph coloring can be applied to get an implementable solution for any condition of the cell loads. Simulation results show a significant reduction of the maximum RU, which translates into an increase of the served traffic and a reduction of the packet delay, as compared to static resource provisioning schemes.