Fairness In Non-orthogonal Multiple Access Research Articles

Optimization of handoff for joint uplink base station association and power control with max‐min fairness in NOMA small‐cell networks

SummaryThe handoff rate and load balancing are two important issues that have a great impact on the spectrum and energy efficiency in the small‐cell networks (SCN). This paper investigates the handoff optimization in SCN with power‐domain non‐orthogonal multiple access (NOMA) that uses successive interference cancelation (SIC), considering the fairness among base stations (BSs). We study the joint base station association and power control problem by considering the motion of mobile users (MUs) and load balancing in the SCNs. Under the maximum allowable transmit power and minimum average‐rate constraints, two optimization problems are formulated using the number of associated MUs, the number of handoffs, and the transmit power of all MUs. The total power consumption minimization and the system‐wide and handoff utility maximization problems are combined into a single‐stage optimization problem through the weighted‐sum method. We solve the formulated problem using a game theory‐based algorithm and primal decomposition theory. The simulation results show that our proposed algorithm can significantly reduce the frequent handoffs and bring a fair power‐controlled BS association in SCN.

Capacity and Fairness Maximization-Based Resource Allocation for Downlink NOMA Networks

Non-orthogonal multiple access (NOMA) is one of the leading technologies for 5G communication. User pairing (UP) and power allocation (PA) are the key controlling mechanisms for the optimization of the performance of NOMA systems. This paper presents a novel UP and PA (UPPA) technique for capacity and fairness maximization in NOMA called (CFM-UPPA). The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated. Then, based on this investigation, the PA and UP algorithms of the CFM-UPPA technique are proposed. The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness, and its maximum value is adjusted to guarantee the successive interference cancellation (SIC) constraints. The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity. The performance evaluation of the proposed CFM-UPPA technique in terms of capacity, fairness, and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access (OMA) system and that of the NOMA system with random UP. Also, the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms, such as the random UP algorithm.

MISO assisted multiple access by removing orthogonal: enabling D2D transmission and performance analysis

As a breakthrough system model adopted in the fifth generation (5G) wireless networks, multiple input single output (MISO)-based non-orthogonal multiple access (NOMA) has been introduced and investigated. With device-to-device transmission taken into account, we consider a NOMA-based downlink network under Rayleigh fading in this paper. To look main metric, we study the system outage behaviour, and close-form expressions for the exact outage probability of two NOMA users are obtained. By further evaluating the outage probability under different power allocation factors, fairness in NOMA still is guaranteed. Finally, numerical results are presented to demonstrate the validity of our analysis and show the advantages of NOMA scheme.

A New Definition of Fairness for Non-Orthogonal Multiple Access

Fairness in non-orthogonal multiple access (NOMA) may be defined in different ways, taking into account the physical layer alone or also including the process of radio resource management. The latter has been frequently used in the recent literature, implying a significant advantage of this technique compared with conventional orthogonal multiple access (OMA). In this letter, we look at the fairness issue from another angle and introduce a fundamental definition of fairness, which measures the difference between the rates that can be achieved by the users and the fair rates suggested by the power distribution among them. With this definition, the fairness issue is inexistent in OMA and it appears as a NOMA-specific problem due to interference between users and the detection process. The new fairness index we advocate incorporates the power distribution of different users. Thus, in a cell with a non-uniform power distribution, fairness implies that a user with a higher power should get a higher rate, and hence, the index should measure the rate of each user by accounting for the fraction of total power allocated to it. Our index achieves its maximum value of 1 only when all users get their fair rates, and it goes to zero when one user gets all resources in the case of uniform power distribution. We provide an asymptotic analysis of fairness at high and low signal-to-noise ratio (SNR) values and give a comprehensive illustration in the two-user case.

Power Allocation and User Clustering for Uplink MC-NOMA in D2D Underlaid Cellular Networks

In this letter, the resource allocation problem for uplink multi-carrier non-orthogonal multiple access (NOMA) in device-to-device (D2D) underlaid cellular networks is investigated. Due to the existence of D2D pairs, NOMA users are subject to varying degrees of interference. We formulate the problem as a Nash bargaining game to maximize the Nash product of each user. We propose an iterative algorithm applying Karush–Kuhn–Tucker conditions to solve the power allocation problem and modeling the user clustering as a Hedonic coalition game. Simulation results verify the convergence of the proposed algorithms. Besides, the algorithms achieve near-optimal performance compared to exhaustive search method and the throughput and fairness in NOMA are both higher than those in orthogonal multiple access.

Power Allocation for Max-Sum Rate and Max-Min Rate Proportional Fairness in NOMA

In this letter, we consider proportional fairness scheduling (PFS) for downlink non-orthogonal multiple access (NOMA) with two users. Unlike conventional multiuser diversity systems, the power allocation plays a key role in PFS for NOMA as it can support multiple users simultaneously with positive transmission rates. With different criteria, we find optimal power allocation. Among those, it is shown that the PFS scheme that maximizes the minimum normalized rate can provide not only proportional fairness, but also small variation of transmission rates.

WIRELESS INFORMATION AND ENERGY TRANSFER: TRADEOFF FOR FAIR RESOURCE ALLOCATION

In this paper, we study two schemes for the fair resource allocation in wireless powered communication networks (WPCNs): a non-orthogonal multiple access (NOMA) scheme, and a proportional fair (PF) scheduling scheme. The considered WPCN consists of a base station (BS) that broadcast radio frequency (RF) energy over the downlink, and N energy harvesting users (EHUs). If NOMA is employed, all EHUs concurrently transmit information over the uplink with successive interference cancellation employed at the BS. If PF scheduling is employed, a single EHU is selected for uplink transmission in each frame. For both schemes, we arrive at optimal allocations for the BS transmit power and the time sharing between uplink and downlink transmissions that maximize the uplink sum-rate, while maintaining high level of system fairness. For the PF scheme, we also derive the optimal scheduling policy. Compared to the state-of-the art schemes based upon time division multiple access (TDMA), both schemes significantly improve the system fairness at the expense of minor (or nonexistent) rate degradation. Key words: energy harvesting; wireless powered communication networks; non-orthogonal multiple access; successive interference cancelation; proportional fair scheduling REFERENCES: [1] P. Grover, A. Sahai: Shannon meets Tesla: wireless information and power transfer, Proc. IEEE ISIT 2010, pp. 2363–2367, Austin, USA, June 2010. [2] D. Gunduz, K. Stamatiou, N. Michelusi, M. Zorzi: Designing intelligent energy harvesting communication systems, IEEE Commun. Magazine, 52, 1, 210–216 (Jan.2014). [3] C. K. Ho, R. Zhang: Optimal energy allocation for wireless communications with energy harvesting constraints, IEEE Trans. Signal Proccessing, 60, 9, 4808–4818 (May 2012). [4] H. Ju, R. Zhang: Throughput maximization in wireless powered communication networks, IEEE Trans. Wireless Commun., 13, 1, 418–428 (Jan. 2014). [5] X. Kang, C. Ho Keong, S. Sun: Optimal time allocation for dynamic-TDMA-based wireless powered communication networks, Proc. IEEE Globecom 2014, Austin, USA, Dec. 2014. [6] H. Ju, R. Zhang: Optimal resource allocation in full-duplex wireless-powered communication network, IEEE Trans. on Commun., 62, 10, 3528–3540 (Oct. 2014). [7] T. Takeda, K. Higuchi: Enhanced user fairness using non-orthogonal access with SIC in cellular uplink, VTC 2011, San Francisco, USA, pp. 1–5, 2011. [8] Z. Ding, Z. Yang, P. Fan, H. V. Poor: On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users, IEEE Signal Process. Lett., 21, 12, 1501–1505 (2014). [9] S. Timotheou, I. Krikidis: Fairness for non-orthogonal multiple access in 5G systems, IEEE Signal Process. Lett., 22, 10, 1462–1465 (2015). [10] H. Chingoska, Z. Hadzi-Velkov, I. Nikoloska, N. Zlatanov: Resource Allocation in Wireless Powered Communication Networks with Non-Orthogonal Multiple Access, IEEE Wireless Communications Letters, 5 (6), 684–687 (2016). [11] P. Viswanath, D. N. Tse, R. Laroia: Opportunistic beamforming using dumb antennas, IEEE Trans. Information Theory, 46, 6, 1277–1294 (June 2002). [12] N. Tekbiyik, T. Girici, E. Uysal-Biyikoglu, K. Leblebicioglu: Proportional fair resource allocation on an energy harvesting downlink, IEEE Trans. Wireless Communications, 12, 4, 1699–1711 (April 2013). [13] H. Chingoska, I. Nikoloska, Z. Hadzi-Velkov, N. Zlatanov: Proportional fair scheduling in wireless powered communication networks, 23rd International Conference on Telecommunications (ICT), May 2013. [14] Z. Hadzi-Velkov, I. Nikoloska, H. Chingoska, N. Zlatanov, Proportional fair scheduling in wireless networks with RF energy harvesting and processing cost, IEEE Comm. Letters, 20, 10, 2107–2110 (2016). [15] T.-D. Nguyen, Y. Han: A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems, IEEE Comm. Letters, 10, 11 (Nov. 2006). [16] Z. Hadzi-Velkov, I. Nikoloska, G. K. Karagiannidis, T. Q. Duong: Wireless networks with energy harvesting and power transfer: joint power and time allocation, IEEE Signal Process. Letters, 23, 1, 50–54 (Jan. 2016). [17] R. Jain, D. Chiu, W. Hawe: A Quantitative measure of fairness and discrimination for resource allocation in shared computer systems, Tech. Rep. TR-301, DEC, September 1984. [18] W. Yu, R. Lui: Dual methods for nonconvex spectrum optimization of multicarrier systems, IEEE Trans. Commun., 54, 7, 1310–1322 (Jul. 2006). [19] L. Liu, R. Zhang, K.-C. Chua: Wireless information transfer with opportunistic energy harvesting, IEEE Trans. Wireless. Commun., 12, 1, 288–300 (Jan. 2013).

Fairness for Non-Orthogonal Multiple Access in 5G Systems

In non-orthogonal multiple access (NOMA) downlink, multiple data flows are superimposed in the power domain and user decoding is based on successive interference cancellation. NOMA's performance highly depends on the power split among the data flows and the associated power allocation (PA) problem. In this letter, we study NOMA from a fairness standpoint and we investigate PA techniques that ensure fairness for the downlink users under i) instantaneous channel state information (CSI) at the transmitter, and ii) average CSI. Although the formulated problems are non-convex, we have developed low-complexity polynomial algorithms that yield the optimal solution in both cases considered.