Resource Allocation in Service Area based Networks

By applying joint transmission in the downlink and joint detection in the uplink, the novel service area architecture allows multiple mobile stations to be simultaneously active on the same OFDM subcarrier without causing interference to each other. Moreover, the proposed adaptive subcarrier and power allocation techniques are shown to be able to improve the spectral efficiency significantly in service area based networks. The significance of the frequency selectivity of wireless channels, the correlation among users’ spatial signatures and the presence of interferences to resource allocation is also assessed through simulations.Durch den Einsatz von Joint Detection in der Aufwärtsstrecke und Joint Transmission in der Abwärtsstrecke ermöglicht die neuartige Service Area Architektur es mehreren Mobilstationen in dem selben OFDM-Subträger gleichzeitig interferenzfrei aktiv zu sein. Darüber hinaus wrid gezeigt, dass die vorgeschlagenen adaptiven Subträger- und Leistungsallokationstechniken die spektrale Effizienz eines Service Area basierten Mobilfunksystems erheblich erhöhen können. Die Bedeutung der Frequnzselektivität der Funkkanäle, der Korrelation zwischen räumlichen Signaturen der Teinehmer und der Existenz der Interferenz für die adaptive Ressourcenallokation wird ebenfalls durch Computersimulationen bewertet

FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification

We propose a novel method for massive multiple-input multiple-output (massive MIMO) in frequency division duplexing (FDD) systems. Due to the large frequency separation between uplink (UL) and downlink (DL) in FDD systems, channel reciprocity does not hold. Hence, in order to provide DL channel state information to the base station (BS), closed-loop DL channel probing, and channel state information (CSI) feedback is needed. In massive MIMO, this typically incurs a large training overhead. For example, in a typical configuration with M ≃200 BS antennas and fading coherence block of T ≃ 200 symbols, the resulting rate penalty factor due to the DL training overhead, given by max{0, 1 - M/T }, is close to 0. To reduce this overhead, we build upon the well-known fact that the angular scattering function of the user channels is invariant over frequency intervals whose size is small with respect to the carrier frequency (as in current FDD cellular standards). This allows us to estimate the users' DL channel covariance matrix from UL pilots without additional overhead. Based on this covariance information, we propose a novel sparsifying precoder in order to maximize the rank of the effective sparsified channel matrix subject to the condition that each effective user channel has sparsity not larger than some desired DL pilot dimension T dl , resulting in the DL training overhead factor max{0, 1 - T dl /T } and CSI feedback cost of Tdl pilot measurements. The optimization of the sparsifying precoder is formulated as a mixed integer linear program, that can be efficiently solved. Extensive simulation results demonstrate the superiority of the proposed approach with respect to the concurrent state-of-the-art schemes based on compressed sensing or UL/DL dictionary learning

FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification

We propose a novel method for massive multiple-input multiple-output (massive MIMO) in frequency division duplexing (FDD) systems. Due to the large frequency separation between uplink (UL) and downlink (DL) in FDD systems, channel reciprocity does not hold. Hence, in order to provide DL channel state information to the base station (BS), closed-loop DL channel probing, and channel state information (CSI) feedback is needed. In massive MIMO, this typically incurs a large training overhead. For example, in a typical configuration with M ≃200 BS antennas and fading coherence block of T ≃ 200 symbols, the resulting rate penalty factor due to the DL training overhead, given by max{0, 1 - M/T }, is close to 0. To reduce this overhead, we build upon the well-known fact that the angular scattering function of the user channels is invariant over frequency intervals whose size is small with respect to the carrier frequency (as in current FDD cellular standards). This allows us to estimate the users' DL channel covariance matrix from UL pilots without additional overhead. Based on this covariance information, we propose a novel sparsifying precoder in order to maximize the rank of the effective sparsified channel matrix subject to the condition that each effective user channel has sparsity not larger than some desired DL pilot dimension T dl , resulting in the DL training overhead factor max{0, 1 - T dl /T } and CSI feedback cost of Tdl pilot measurements. The optimization of the sparsifying precoder is formulated as a mixed integer linear program, that can be efficiently solved. Extensive simulation results demonstrate the superiority of the proposed approach with respect to the concurrent state-of-the-art schemes based on compressed sensing or UL/DL dictionary learning

A cross-layer cooperation strategy for cellular networks.

PhDCooperation is seen as a means to improve the signal in OFDMA wireless networks by overcoming the inter-cell interference. Such co-operation can be deployed in both the physical layer and the MAC layer. In this thesis, a cross-layer cooperation strategy is considered. Firstly, in the physical layer, a cooperative coding scheme with private information sharing is proposed based on dirty paper coding; this is analyzed in a scenario with two transmitters and two receivers. To implement the cooperation, a rate limited link is deployed at the transmitters’ side in order to share the information. A new achievable rate region is established in both strong interference regime and weak interference regime. Secondly, in the MAC layer, a graph-based dynamic coordinated clustering scheme is proposed. An interference weighted graph is constructed to assist dynamic coordinated clustering for inter-cell interference mitigation and to improve the cell-edge user performance. Only 2 bits are allowed for the signalling exchange between transmitters and this reduces the overhead of the approach. The system throughput and cell-edge throughput with different user distributions are used to evaluate the performance. Thirdly, a transmit antenna selection algorithm is presented to optimize system performance with the constraint of fairness. A graph is generated by using the channel condition between the transmit antennas and Mobile Stations. Based on the graph, a heuristic algorithm is proposed to choose the transmit antenna for each user in order to improve the system performance and guarantee the user fairness. Finally, combining the cooperative coding scheme and cooperative clustering scheme, a cross-layer cooperation scheme is presented. In the physical layer, the cooperation coding scheme mitigates the interference and increases the transmission rate; in the MAC layer, the cooperative clustering scheme provides efficient cooperative transmission. Simulation results show that the proposed scheme can effectively increase both the system throughput and cell-edge throughput