Placeholder image for staff profile

Dr Alexandr Kuzminsky


Senior Research Fellow

My publications

Highlights

A.M.Kuzminskiy, P.Xiao, R.Tafazolli, “Spectrum Sharing with Decentralized Occupation Control in Rule Regulated Networks,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 2, pp. 281- 294, June 2019.

A.Kuzminskiy, P.Xiao, R.Tafazolli, “'Good Neighbor Distributed Beam Scheduling in Coexisting Multi-RAT Networks,” in Proc. WCNC, Barcelona, Spain, Apr. 2018.

A.Kuzminskiy, Y.Abramovich, P.Xiao, R.Tafazolli, “'Spectrum sharing efficiency analysis in rule regulated networks with decentralized occupation control,” in Proc. PIMRC, Valencia, Spain, Sept. 2016.

A.Kuzminskiy, Y.Abramovich, P.Xiao, R.Tafazolli, “Uniform expected likelihood solution for Interference rejection combining regularization,” in Proc. ICASSP, Shanghai, China, March 2016.

Publications

Alexandr Kuzminskiy, Pei Xiao, Rahim Tafazolli (2018)Good Neighbor Distributed Beam Scheduling in Coexisting Multi-RAT Networks, In: Proceedings of WCNC 2018 IEEE

Spectrum sharing and employing highly directional antennas in the mm-wave bands are considered among the key enablers for 5G networks. Conventional interference avoidance techniques like listen-before-talk (LBT) may not be efficient for such coexisting networks. In this paper, we address a coexistence mechanism by means of distributed beam scheduling with minimum cooperation between spectrum sharing subsystems without any direct data exchange between them. We extend a “Good Neighbor” (GN) principle initially developed for decentralized spectrum allocation to the distributed beam scheduling problem. To do that, we introduce relative performance targets, develop a GN beam scheduling algorithm, and demonstrate its efficiency in terms of performance/complexity trade off compared to that of the conventional selfish (SLF) and recently proposed distributed learning scheduling (DLS) solutions by means of simulations in highly directional antenna mm-wave scenarios.

Alexandr Kuzminskiy, Pei Xiao, Rahim Tafazolli (2020)Good Neighbor Alternative to Best Response and Machine Learning Based Beamforming and Power Adaptation for MIMO Ad Hoc Networks, In: 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications: Track 2: Networking and MAC Institute of Electrical and Electronics Engineers

Decentralized joint transmit power and beam- forming selection for multiple antenna wireless ad hoc net- works operating in a multi-user interference environment is considered. An important feature of the considered environ- ment is that altering the transmit beamforming pattern at some node generally creates more signicant changes to in- terference scenarios for neighboring nodes than variation of the transmit power. Based on this premise, a good neighbor algorithm is formulated in the way that at the sensing node, a new beamformer is selected only if it needs less than the given portion of the transmit power required for the current beamformer. Otherwise, it keeps the current beamformer and achieves the performance target only by means of power adaptation. Equilibrium performance and convergence be- havior of the proposed algorithm compared to the best re- sponse and regret matching solutions is demonstrated by means of semi-analytic Markov chain performance analysis for small scale and simulations for large scale networks.

ALEXANDR KUZMINSKIY, Yuri I. Abramovich, PEI XIAO, RAHIM TAFAZOLLI, Jinliang Huang (2021)Maximum Likelihood Optimization of Adaptive Asynchronous Interference Mitigation Beamformer, In: IEEE Transactions on Signal Processing Institute of Electrical and Electronics Engineers (IEEE)

In asynchronous (intermittent) interference scenarios, the content of co-channel interference sources over the data interval may be different from the interferers content over the training interval, typically with extra interference sources presented over the data interval. Under such conditions, conventional adaptive beamformer designed over the training interval may lose its efficiency when applied to the data interval. In this paper, we address the problem by 1) formulating a family of the second order statistics adaptive beamformers regularized by the covariance matrix estimated over the data interval; 2) proposing a maximum likelihood methodology for optimization of the combined (mixed) covariance matrix based on maximization of a product of a likelihood ratio that checks the accuracy of the recovered training signals and a likelihood ratio on equality of the eigenvalues in complementary to the signal subspace defined over the data interval; 3) demonstrating efficiency and robustness of the proposed solution as a linear adaptive beamformer and as an initialization for iterative beamformer with projections to the finite alphabet in different asynchronous interference scenarios comparing with the basic training and data based interference rejection combining receivers.

Alexandr Kuzminskiy, Y Abramovich, Pei Xiao, Rahim Tafazolli (2016)Spectrum Sharing Efficiency Analysis in Rule Regulated Networks with Decentralized Occupation Control, In: IEEE PIMRC 2016 Proceedings

Decentralized dynamic spectrum allocation (DSA) that exploit adaptive antenna array interference mitigation (IM) diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over band allocation of other BSs sharing the same bands. To this end, “good neighbor” (GN) rules allow effective trade off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets corresponding to the potentially “interference free” DSA; define the semi-analytic absorbing Markov chain model for the GN DSA with occupation control and study the convergence properties including effects of possible breaks of the GN rules; and for higher-dimension networks, develop the simplified search GN algorithms with occupation and power control (PC) and demonstrate their efficiency by means of simulations in the scenario with unlimited requested network occupation.

Alexandr M. Kuzminskiy, Pei Xiao, Rahim Tafazolli (2019)Spectrum Sharing with Decentralized Occupation Control in Rule Regulated Networks, In: IEEE Transactions on Cognitive Communications and Networking(99) Institute of Electrical and Electronics Engineers (IEEE)

Decentralized dynamic spectrum allocation (DSA) that exploits adaptive antenna array interference mitigation diversity at the receiver, is studied for interference-limited environments with high level of frequency reuse. The system consists of base stations (BSs) that can optimize uplink frequency allocation to their user equipments (UEs) to minimize impact of interference on the useful signal, assuming no control over resource allocation of other BSs sharing the same bands. To this end“, good neighbor” (GN) rules allow effective trade-off between the equilibrium and transient decentralized DSA behavior if the performance targets are adequate to the interference scenario. In this paper, we 1) extend the GN rules by including a spectrum occupation control that allows adaptive selection of the performance targets; 2) derive estimates of absorbing state statistics that allow formulation of applicability areas for different DSA algorithms; 3) define a semi-analytic absorbing Markov chain model and study convergence probabilities and rates of DSA with occupation control including networks with possible partial breaks of the GN rules. For higher-dimension networks, we develop simplified search GN algorithms with occupation and power control and demonstrate their efficiency by means of simulations.

Additional publications