Lossy Compression for Compute-and-Forward in Limited Backhaul Uplink Multicell Processing

We study the transmission over a cloud radio access network in which multiple base stations (BS) are connected to a central processor (CP) via finite-capacity backhaul links. We propose two lattice-based coding schemes. In the first scheme, the base stations decode linear combinations of the transmitted messages, in the spirit of compute-and-forward (CoF), but differs from it essentially in that the decoded equations are remapped to linear combinations of the channel input symbols, sent compressed in a lossy manner to the central processor, and are not required to be linearly independent. Also, by opposition to the standard CoF, an appropriate multi-user decoder is utilized to recover the sent messages. The second coding scheme generalizes the first one by also allowing, at each relay node, a joint compression of the decoded equation and the received signal. Both schemes apply in general, but are more suited for situations in which there are more users than base stations. We show that both schemes can outperform standard CoF and successive Wyner-Ziv schemes in certain regimes, and illustrate the gains through some numerical examples.Comment: Submitted to IEEE Transactions on Communication

Optimal Channel Training in Uplink Network MIMO Systems

We consider a multi-cell frequency-selective fading uplink channel (network MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations (BSs) with M antennas each. The BSs, assumed to be oblivious of the applied codebooks, forward compressed versions of their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since the BSs and the CS are assumed to have no prior channel state information (CSI), the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training, taking different path losses between the UTs and the BSs into account. We then study how the optimal training length is impacted by the backhaul capacity. Although our analytical results are based on a large system limit, we show by simulations that they provide very accurate approximations for even small system dimensions.Comment: 15 pages, 7 figures. To appear in the IEEE Transactions on Signal Processin

A stochastic approach for resource allocation with backhaul and energy harvesting constraints

We propose a novel stochastic radio-resource-allocation strategy that achieves long-term fairness considering backhaul and air-interface capacity limitations. The base station (BS) is powered only with a finite battery that is recharged by an energy harvester. The energy harvesting is also taken into account in the proposed resource-allocation strategy. The constrained scenario is often found in remote rural areas where the backhaul connection is limited, and the BSs are fed with solar panels of reduced size. Our results show that the proposed scheme achieves higher fairness among the users and provides greater worst user rate and sum rate if an average backhaul constraint is considered.Peer ReviewedPostprint (published version

Coordinated Multipoint Communications In Heterogeneous Networks

As users' demands on cellular service escalate rapidly, operators are required to deploy technologies with wider and more sophisticated techniques. In order to meet the future service needs, the standardization body 3rd Generation Partnership Project (3GPP) has standardized Long Term Evolution (LTE) and it has been working on enhancement of LTE and LTE-Advanced. The two key enabling technologies of LTE-Advanced are Heterogeneous Networks (HetNets) and Coordinated Multipoint (CoMP) communications. The former is aimed to improve inconsistent user experience and its basic feature is standardized in 3GPP release 11. The latter one where small cells are deployed within macro-cellular networks has been considered to enhance coverage and capacity. This thesis presents a concise literature survey of cooperative communications and CoMP technologies. Furthermore, a detailed Matlab-based simulation study on CoMP between macro and small cells in HetNets is presented. Comparative analyses and evaluations are also made for different CoMP schemes under different deployed scenarios. At the same time, a new CoMP UE selection criterion is proposed to fit the modified round robin scheduling deployed in simulation and optimize the resource allocation among CoMP and non-CoMP UEs

Cloud-aided wireless systems: communications and radar applications

This dissertation focuses on cloud-assisted radio technologies for communication, including mobile cloud computing and Cloud Radio Access Network (C-RAN), and for radar systems. This dissertation first concentrates on cloud-aided communications. Mobile cloud computing, which allows mobile users to run computationally heavy applications on battery limited devices, such as cell phones, is considered initially. Mobile cloud computing enables the offloading of computation-intensive applications from a mobile device to a cloud processor via a wireless interface. The interplay between offloading decisions at the application layer and physical-layer parameters, which determine the energy and latency associated with the mobile-cloud communication, motivates the inter-layer optimization of fine-grained task offloading across both layers. This problem is modeled by using application call graphs, and the joint optimization of application-layer and physical-layer parameters is carried out via a message passing algorithm by minimizing the total energy expenditure of the mobile user. The concept of cloud radio is also being considered for the development of two cellular architectures known as Distributed RAN (D-RAN) and C-RAN, whereby the baseband processing of base stations is carried out in a remote Baseband Processing Unit (BBU). These architectures can reduce the capital and operating expenses of dense deployments at the cost of increasing the communication latency. The effect of this latency, which is due to the fronthaul transmission between the Remote Radio Head (RRH) and the BBU, is then studied for implementation of Hybrid Automatic Repeat Request (HARQ) protocols. Specifically, two novel solutions are proposed, which are based on the control-data separation architecture. The trade-offs involving resources such as the number of transmitting and receiving antennas, transmission power and the blocklength of the transmitted codeword, and the performance of the proposed solutions is investigated in analysis and numerical results. The detection of a target in radar systems requires processing of the signal that is received by the sensors. Similar to cloud radio access networks in communications, this processing of the signals can be carried out in a remote Fusion Center (FC) that is connected to all sensors via limited-capacity fronthaul links. The last part of this dissertation is dedicated to exploring the application of cloud radio to radar systems. In particular, the problem of maximizing the detection performance at the FC jointly over the code vector used by the transmitting antenna and over the statistics of the noise introduced by quantization at the sensors for fronthaul transmission is investigated by adopting the information-theoretic criterion of the Bhattacharyya distance and information-theoretic bounds on the quantization rate