A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment

By Zhiguo Ding, Robert Schober, H. Vincent Poor. IEEE Transactions on Wireless Communications, 2015

565 citations recorded by the source corpus.

The application of multiple-input multiple-output (MIMO) techniques to nonorthogonal multiple access (NOMA) systems is important to enhance the performance gains of NOMA. In this paper, a novel MIMO-NOMA framework for downlink and uplink transmission is proposed by applying the concept of signal alignment. By using stochastic geometry, closed-form analytical results are developed to facilitate the performance evaluation of the proposed framework for randomly deployed users and interferers. The impact of different power allocation strategies, namely fixed power allocation and cognitive radio inspired power allocation, on the performance of MIMO-NOMA is also investigated. Computer simulation results are provided to demonstrate the performance of the proposed framework and the accuracy of the developed analytical results.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/93b91f9b1dd25a1cfa629f39c3acdc06066675ee

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Citations in the corpus

Exploiting Intelligent Reflecting Surfaces in NOMA Networks: Joint Beamforming Optimization (2019, methodology): “Furthermore, a general framework for MIMO-NOMA in both downlink and uplink transmission was proposed in [23].”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, methodology): “It is worth noting that although NOMA is not always capacity-achieving for the general Gaussian MISO/MIMO BC, it is still an effective transmission strategy, especially for the overloaded regime(3) [26], [27], where the number of available antennas at the BS is smaller than the total number of user ”
A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems (2016, background): “The use of the total power constraint is therefore useful to measure inter-group interference [14].”
A Survey of Rate-Optimal Power Domain NOMA With Enabling Technologies of Future Wireless Networks (2019, background): “to find the ideal user clustering even with an exhaustive search of all possible solutions [201] because acceptable solutions may not exist.”
On Optimal Power Allocation for Downlink Non-Orthogonal Multiple Access Systems (2017, background): “In [13] and [14], the authors discussed an combination of NOMA with MIMO technologies.”
Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems (2016, methodology): “In [9], a transmission framework based on signal alignment was proposed for multiple-input multiple-output (MIMO) NOMA systems.”
Modulation and Multiple Access for 5G Networks (2017, background): “MU-MISO Two stage BF Transmit power/rate Optimal power allocation [34] MU-MISO Restrictive BF Throughput Less constraints at the BS [35] MU-MISO ZF-BF + DPC Power consumption & OP Precoding with low complexity & user pairing [36] MU-MIMO Random BF Sum rates No perfect CSI required [31] MU-MIMO ZF-BF”
Enhancing the Physical Layer Security of Non-Orthogonal Multiple Access in Large-Scale Networks (2016, methodology): “) can be developed for further enhancing the attainable performance of the networks considered [41], [42], but this is beyond the scope of this treatise.”
Grant-Free Non-Orthogonal Multiple Access for IoT: A Survey (2019, background): “In UL scenarios where large number of users transmit to a BS with many antennas, the BS can exploit the spatial diversity to perform MUD [230]–[232].”
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends (2017, methodology): “As shown in [77] and [78], the k-th user’s M×1 channel vector, denoted by hk, can be expressed as follows: hk = √ M aka(θk) √ 1 + dαk , (13) where M is the number of antennas at the BS, a(θ) = 1√ M [ 1 e−jπθ · · · e−jπ(M−1)θ ]T , θk denotes the normalized direction of the line-of-sight (LOS) path, d”
Resource Allocation for Downlink NOMA Systems: Key Techniques and Open Issues (2017, methodology): “MIMO-NOMA AND MIMO-OMA In this section, based on the system model in [17], we conduct a performance comparison between MIMO-NOMA and MIMO-OMA when each user has a minimum rate requirement (QoS).”
Capacity Comparison Between MIMO-NOMA and MIMO-OMA With Multiple Users in a Cluster (2017, methodology): “[12], [13] investigate the performance of MIMO-NOMA when there are multiple clusters in the system and, through simulations, validate the superiority of MIMO-NOMA over MIMO-OMA.”
A Survey of NOMA: Current Status and Open Research Challenges (2019, unknown): “[19], [20], [21], [22] and UL [21], [22], [23], [24], to incorporate the typical data transmission methods such as hybrid automatic repeat request (HARQ) to the cases using”