Tarcisio F. Maciel

On the Performance, Complexity, and Fairness of Suboptimal Resource Allocation for Multiuser MIMO–OFDMA Systems

The combination of multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) is a promising solution to the flexible and spectrally efficient provision of data services in future wireless communication systems. However, adaptive resource allocation (RA) in frequency, time, and space in multiuser MIMO-OFDMA systems is very complex due to the inclusion of the space dimension and the large number of resources to be managed. Indeed, an optimal RA for maximizing the sum rate is usually too complex for practical application, and suboptimal strategies are required. In this paper, the performance, complexity, and fairness of suboptimal RA strategies aiming at the maximization of the sum rate are investigated. A model for suboptimal RA strategies is proposed, and two new RA strategies are introduced. The proposed strategies are compared in terms of the sum rate, complexity, and throughput fairness and are shown to present better performance-complexity and performance-fairness tradeoffs than some existing suboptimal strategies, as well as to achieve almost the same sum rate as that obtained through an exhaustive search.

A resource allocation strategy for SDMA/OFDMA systems

Optimum resource allocation (RA) in space division multiple access (SDMA)/orthogonal frequency division multiple access (OFDMA) systems is a prohibitively complex task for which efficient sub-optimal strategies are preferable. In this work, a new space-frequency/time resource allocation (S-FT RA) is proposed, which divides RA in two tasks: the SDMA grouping, for which a new SDMA grouping algorithm is proposed, and the joint frequency/time RA, which is solved using Munkres algorithm. It is shown that the proposed strategy is flexible and that it achieves a considerable fraction of the maximum achievable average system capacity.

A Convex Quadratic SDMA Grouping Algorithm Based on Spatial Correlation

Space Division Multiple Access (SDMA) is a promising solution to improve the spectral efficiency of future mobile radio systems. However, finding the group of MSs that maximizes system capacity using SDMA is a complex combinatorial problem, which can only be assuredly solved through an Exhaustive Search (ES). Because an ES is usually too complex, there are several sub- optimal SDMA grouping algorithms to solve this problem. Such algorithms, however, usually depend on the preceding matrices of candidate SDMA groups and are also considerably complex. In this work, an SDMA grouping algorithm is proposed for the downlink of multi-user multiple input multiple output systems. It is based on the spatial correlation and gains of the MSs channels in the SDMA group, thus not depending on preceding and having low complexity. The proposed algorithm is formulated as a convex quadratic optimization problem and is efficiently solved by convex optimization methods. It is analyzed considering zero-forcing precoding and it is shown to almost achieve the performance of an ES for the SDMA group that maximizes the system capacity.

Resource Assignment for Rate Maximization With QoS Guarantees in Multiservice Wireless Systems

We formulate the resource assignment problem of maximizing the spectral efficiency of a wireless system subject to user satisfaction constraints in the multiservice scenario that is a challenging resource allocation problem from the system operators point of view. We show that although this optimization problem is nonlinear, it can be converted to an integer linear program. This way, the optimal solution can be obtained by standard techniques. Due to the high computational complexity to obtain the optimal solution, we propose a fast suboptimal algorithm named Reallocation-based Assignment for Improved Spectral Efficiency and Satisfaction (RAISES). Simulation results show that our proposal achieves near-optimal performance in low and medium loads with a much lower computational complexity compared with the algorithm used to obtain the optimal solution. Therefore, the RAISES algorithm achieves a good tradeoff between performance and computational complexity.

A Low-Complexity SDMA Grouping Strategy for the Downlink of Multi-User MIMO Systems

In this work, a sub-optimal space division multiple access (SDMA) grouping strategy is proposed for the downlink of multi-user MIMO systems. It uses a new spatial compatibility check metric to estimate the grouping efficiency without needing to compute MIMO filter weights, thus reducing the complexity of SDMA grouping. Moreover, an iterative variant of the block diagonalization for throughput maximization algorithm is proposed, which can be employed to select the number of streams allocated to each user as to increase the sum capacity of the SDMA group. The proposed SDMA strategy is compared through simulation with single-user transmission, random grouping, and exhaustive search, and it is shown to have a good performance-complexity trade-off

Uplink Power Control with Variable Target SINR for D2D Communications Underlying Cellular Networks

Researches in Power Control (PC) schemes are fundamental to provide efficient algorithms and understand their behavior in Device-to-Device (D2D) scenario. The aim of the current study is to investigate Open Loop Power Control (OLPC), Closed Loop Power Control (CLPC) and indicate Soft Dropping Power Control (SDPC) like an alternative PC scheme to D2D communications underlaying a cellular network. We briefly review PC schemes, suggest a new point of dynamic offset compensation factor σ setting the issue in CLPC and evaluate its performance in terms of spectral and power efficiency. Results demonstrate that SDPC keeps a reasonable spectral efficiency and provides a gain of 70% in power efficiency in relation Long Term Evolution (LTE) PC schemes for cellular communications, and the factor σ = 0.8 can modify the behavior of CLPC because increases the Signal to Interference-plus-Noise Ratio (SINR) of the worst users.

Improved Spectral Efficiency With Acceptable Service Provision in Multiuser MIMO Scenarios

In this paper, we study the downlink resource assignment problem of maximizing the total data rate subject to minimum satisfaction guarantees in multiservice scenarios assuming the use of multiuser (MU) multiple-input-multiple-output (MIMO) techniques. With the use of MU MIMO techniques, the same frequency resource can be shared by different terminals at the same time by assigning different spatial channels to each terminal. Different from single-user (SU) access per frequency resource where the resource assignment problem consists of searching for the best association between frequency resources and terminals, with MU access per resource, we aim at finding which space-division multiple-access (SDMA) group should be associated with each frequency resource. The first contribution of this paper is the formal presentation of this generalized problem as an optimization problem. Then, after some mathematical manipulations, we managed to convert this problem from a nonlinear integer to an integer linear problem (ILP) that can be optimally solved by standard techniques instead of resorting to brute-force methods. Motivated by the high computational complexity of the optimal solution, we propose a low-complexity algorithm. By analyzing outage rate and total data rate performance metrics, we show that the proposed solution presents a good performance-complexity tradeoff compared with the method for obtaining the optimal solution.

Clustering-based Assignment within CoMP systems

More and more Coordinated Multi-Point (CoMP) systems are attracting interest as a means to improve the performance of conventional cellular networks, particularly thanks to the joint processing approach. However, the large amount of signaling and computer operations required deserves special attention regarding both backhaul and radio resources constraints. Partitioning the set of transmission points into clusters is likely to reduce this coordination cost, but the search for the best formations of clusters may lead to expressive further computational efforts. Herein we introduce a novel approach, the Clustering-based Assignment Algorithm (CbAA), to cluster dynamically the transmission points on the downlink in order to transmit coherently to multiple users. When compared to static clustering, the proposed CbAA provided expressive gains in terms of system spectral efficiency. Compared to state-of-the-art dynamic clustering approaches, CbAA can waive the exhaustive search over all possible formations as well as the repetitive precoder calculations during the clustering procedure.

Interference Mitigation Using Band Selection for Network-Assisted D2D Communications

For systems with reuse factor less than one, the co- channel interference can drastically reduce the gain of primary networks, which limits the whole system performance. This paper exploits the selection of Downlink (DL) or Uplink (UL) band to be reused by a Device-to-Device (D2D) link. The band selected by the method is based on a radio distance metric, such as received power. Despite being a simple concept, results have shown that band selection can effectively mitigate the interference caused by D2D communications in a Long Term Evolution (LTE) network. Therefore, the coexistence of cellular and D2D communication modes becomes possible in time, frequency, and space.

Network-Assisted Neighbor Discovery Based on Power Vectors for D2D Communications

Device-to-Device (D2D) communication is seen as a promising technology to increase the capacity of current wireless systems without extra electromagnetic spectrum bands. However, before starting a D2D communication, Mobile Stations (MSs) must be aware of their neighbors, through a discovery process. While operating in cellular networks, such process may benefit from the network-assistance. In this paper, we propose a method based on the available network power measures to improve the discovery process. Results show that our proposal is less complex but still outperforms traditional methods when considering the time to detect all neighbors.