Marian Codreanu

Cooperative MIMO-OFDM Cellular System with Soft Handover Between Distributed Base Station Antennas

Cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base stations (BS) is considered for users located within a soft handover (SHO) region. The downlink resource allocation problem with different BS power constraints is studied for the orthogonal frequency division multiplexing system with adaptive MIMO transmission. Joint design of the linear transmit and receive beamformers in a MIMO multiuser transmission subject to per BS power constraints is considered. A solution for the weighted sum rate maximization problem is proposed. The proposed algorithm is shown to provide a very efficient solution despite of the fact that the global optimality cannot be guaranteed due to the non-convexity of the optimization problem. Moreover, efficient resource allocation method based on zero forcing transmission is provided. The impact of the size of a SHO region, the overhead from the increased resource utilization, and different inter-cell interference distributions due to the SHO are evaluated by system level simulations. Although the overhead from the SHO processing can be significant, it can be mitigated by using space division multiple access for users having an identical SHO active set composition. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing.

Joint Design of Tx-Rx Beamformers in MIMO Downlink Channel

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria, including weighted sum rate maximization, weighted sum mean square error minimization, minimum signal-to-interference-plus-noise ratio (SINR) maximization and sum power minimization under a minimum SINR constraint. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically nontractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. Even though each subproblem is optimally solved, there is no guarantee that the global optimum has been found due to the nonconvexity of the problem. However, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient.

Weighted Sum-Rate Maximization for MISO Downlink Cellular Networks via Branch and Bound

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multiple-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between them falls below a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated by numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on WSRMax problem. The method proposed here can be easily extended to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

Linear Multiuser MIMO Transceiver Design With Quality of Service and Per-Antenna Power Constraints

Joint design of linear multiuser multiple-input-multiple-output (MIMO) transceiver subject to different quality of service (QoS) constraints per user and with per-antenna or antenna group power constraints is considered. Solutions for two linear transceiver optimization problems are proposed, i.e., balancing the weighted signal-to-interference-plus-noise ratio (SINR) per data stream and balancing the weighted rate for each scheduled user with minimum rate requirements per user. The proposed joint transceiver optimization algorithms are compared to corresponding optimal nonlinear transmission methods as well as to generalized zero-forcing transmission solutions. Unlike the optimal nonlinear schemes, the optimization problems employed in the linear multiuser MIMO transceiver design are not convex in general. However, the proposed algorithms are shown to provide very efficient solutions despite of the fact that the global optimum cannot be guaranteed due to nonconvexity of the problems.

Resource Allocation for Cross-Layer Utility Maximization in Wireless Networks

The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the networks links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided.

Age of information with packet management

We consider a system in which random status updates arrive at a source node, and should be transmitted through a wireless network to the intended destination node. The status updates are samples of a random process, transmitted as packets, containing the time stamp to identify the moment the sample was generated. The time it takes to successfully transmit a packet to the destination is modeled as an exponentially distributed service time. The status update age at the receiver is the time elapsed since the last received update was generated. In this paper, we analyze the age in the case that the source node has the capability to manage the arriving samples and decide which packets will be transmitted to the destination. In addition to the average age, we investigate the average of a new metric, called peak age, which provides information about the maximum value of the Age, achieved immediately before receiving an update.

Weighted sum-rate maximization for a set of interfering links via branch and bound

We consider the problem of weighted sum-rate maximization (WSRMax) for an arbitrary set of interfering links. This problem is known to be NP-hard; therefore, it is extremely difficult to solve even for a relative small number of links. The main contribution of this paper is to provide a solution method, based on the branch and bound technique, which solves WSRMax problem with an optimality certificate. At each step of the proposed algorithm, an upper and a lower bound are computed for the optimal value of the underlying problem. The algorithm terminates when the difference between the upper and the lower bound is smaller than a specified tolerance. The proposed algorithm can be further used to provide other performance benchmarks by back-substituting it into any network design method which relies on WSRMax. It is also very useful for evaluating the performance loss encountered by any heuristic algorithm.

On The Age Of Information In Status Update Systems With Packet Management

We consider a communication system in which status updates arrive at a source node, and should be transmitted through a network to the intended destination node. The status updates are samples of a random process under observation, transmitted as packets, which also contain the time stamp to identify when the sample was generated. The age of the information available to the destination node is the time elapsed, since the last received update was generated. In this paper, we model the source-destination link using the queuing theory, and we assume that the time it takes to successfully transmit a packet to the destination is an exponentially distributed service time. We analyze the age of information in the case that the source node has the capability to manage the arriving samples, possibly discarding packets in order to avoid wasting network resources with the transmission of stale information. In addition to characterizing the average age, we propose a new metric, called peak age, which provides information about the maximum value of the age, achieved immediately before receiving an update.

WLC14-2: Adaptive MIMO-OFDM Cellular System with Soft Handover between Distributed Base Station Antennas

The joint cooperative processing of transmitted signal from several multiple-input multiple-output (MIMO) base station (BS) antenna heads is considered for users located within a soft handover (SHO) region. The system level gains and trade-offs from cooperative SHO processing are investigated. The impact of the size of the SHO region, overhead from the increased hardware and physical (time, frequency) resource utilization, and different non-reciprocal inter-cell interference distributions due to SHO are evaluated. Practical user, bit and power allocation method with different BS power constraints is provided for the proposed cooperative multiuser MIMO transmission. The overhead from SHO processing can be significant, and the call blocking probability can be dramatically increased. However, the overhead can be mitigated by using space division multiple access for users that have identical SHO active set composition. Also, the dropping probability is decreased, and thus, the total outage probability with SHO is less than without SHO. The users located at the SHO region may enjoy from greatly increased transmission rates. This translates to significant overall system level gains from the cooperative SHO processing. The proposed soft handover scheme can be used to provide more evenly distributed service over the entire cellular network.

Weighted Sum-Rate Maximization in Wireless Networks: A Review

The weighted sum-rate maximization (WSRMax) problem plays a central role in many network control and optimization methods, such as power control, link scheduling, cross-layer control, network utility maximization. The problem is NP-hard in general. In Weighted Sum-Rate Maximization in Wireless Networks: A Review, a cohesive discussion of the existing solution methods associated with the WSRMax problem, including global, fast local, as well as decentralized methods is presented. In addition, general optimization approaches, such as branch and bound methods, complementary geometric programming, and decomposition methods, are discussed in depth to address the problem. Through a number of numerical examples, the applicability of the resulting algorithms in various application domains is demonstrated. The presented algorithms and the associated numerical results can be very useful for network engineers or researchers with an interest in network design.