An Integrated Approach of ACM and CDMA in a Novel Multi User Spatial Modulation Scheme

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 7 Issue 4, July-August 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD59660 | Volume – 7 | Issue – 4 | Jul-Aug 2023 Page 220
An Integrated Approach of ACM and CDMA in a
Novel Multi-User Spatial Modulation Scheme
Kumari Kanchana1
, Dr. Prabhat Sharma2
1
M Tech Scholar, Department of Electronics and Communication Engineering,
2
Research Guide, Department of Electronics and Communication Engineering,
1,2
Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India
ABSTRACT
The research focus in wireless communication access technologies is
driven by the demand for high peak data rates and significantly
improved spectral efficiencies, as well as the support for specific
quality of service (QoS) requirements. By leveraging the
orthogonality of space constellations and signal constellations, well-
established digital signal modulation schemes can be employed. The
spatial multiplexing gain is achieved through the concurrent
transmission of spatially encoded bits. This thesis investigates and
compares the analytical and numerical performance of Spatial
Modulation (SM) under various channel conditions, taking into
account practical channel considerations, in comparison to existing
MIMO techniques. The results demonstrate that SM achieves a low
bit error ratio (BER) while substantially reducing receiver
complexity, all while maintaining spectral efficiency.
KEYWORDS: MIMO, Spatial Modulation, BER, Bit Error Rate,
Modulation Technique etc.
How to cite this paper: Kumari
Kanchana | Dr. Prabhat Sharma "An
Integrated Approach of ACM and
CDMA in a Novel Multi-User Spatial
Modulation Scheme" Published in
International
Journal of Trend in
Scientific Research
and Development
(ijtsrd), ISSN:
2456-6470,
Volume-7 | Issue-4,
August 2023,
pp.220-228, URL:
www.ijtsrd.com/papers/ijtsrd59660.pdf
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International Journal of Trend in
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I. INTRODUCTION
The rise of wireless communication systems like
vehicle-to-vehicle (V2V) communication [1] and
wireless high-definition (HD) television has
stimulated research in MIMO technology. MIMO has
emerged as a key technique for enhancing data
throughput, link reliability, and spectral efficiency [2-
4]. MIMO techniques can be broadly categorized into
spatial diversity and spatial multiplexing schemes.
Spatial diversity techniques [5, 6] enhance link
reliability by transmitting multiple redundant copies
of data over independent channels to the receiver.
Alamouti's scheme [6] is a popular transmit diversity
technique that utilizes a pair of transmit antennas to
achieve complete transmit diversity. However, this
approach trades diversity gains for low spectral
efficiency, which remains unchanged compared to a
single-input multiple-output (SIMO) system [7].
Among the various existing technologies, multiple-
input multiple-output (MIMO) with adaptive coding
and modulation holds promise as a candidate for
future wireless systems. MIMO systems enhance
spectral efficiency by utilizing multiple transmit
antennas to simultaneously transmit data to the
receiver. Adaptive modulation and coding enable
robust and efficient transmission over channels that
vary over time. The fundamental principle is to
estimate the channel at the receiver and provide this
estimate as feedback to the transmitter, allowing the
transmission scheme to adapt to the channel
characteristics. The concept involves treating the
transmit antenna array as a spatial constellation
diagram, where the actual antennas serve as
constellation points. Each spatial constellation point
is mapped to a distinct bit sequence. At any given
moment, only one antenna transmits energy, and the
relevant information is encoded in the physical
location of the transmitting antenna or spatial
constellation point. Naturally, this necessitates a
IJTSRD59660

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
novel detection process at the receiver, known as
antenna detection.
RELATED WORK
Space shift keying (SSK) modulation in [28] can be
regarded as a specific instance of Spatial Modulation
(SM), where only the transmit antenna indices convey
information. The SSK scheme eliminates the need for
conventional modulation techniques, reducing
receiver complexity compared to SM while
maintaining performance gains. Similar to SM, SSK
schemes are limited to a number of transmit antennas
that are a power of two. When the transmit antenna
constraint cannot be met, the generalized SSK
(GSSK) scheme [29] offers a viable solution. In [29],
GSSK utilizes combinations of antenna indices to
transmit information, allowing for application to any
antenna configuration. However, the flexibility of
GSSK comes at the cost of reduced performance
compared to SSK [29].
Fractional bit encoded spatial modulation (FBE-SM)
in [30] represents a more versatile SM scheme based
on modulus conversion theory. The FBE-SM
approach enables the transmitter to operate with an
arbitrary number of antennas, making it suitable for
compact mobile devices with limited space for
transmit antennas. Numerical results demonstrate that
FBE-SM provides design flexibility and the necessary
degrees of freedom to balance performance and
capacity [30].
Trellis coded spatial modulation (TCSM) in [31]
incorporates trellis coded modulation (TCM) into the
antenna constellation points of SM. This increases the
distance between antenna constellation points, leading
to improved performance over spatially correlated
channels. The TCSM scheme is analyzed in [32],
which proposes an analytical framework for
performance evaluation over correlated fading
channels.
Soft-output maximum likelihood (ML) detection in
[33] introduces a soft-output ML detector for SM
orthogonal frequency division multiplexing (OFDM)
systems, surpassing the performance of conventional
hard decision-based SM detectors.
SM with partial channel state information (CSI) at the
receiver in [34] develops and analyzes an SM detector
with an unknown phase reference at the receiver.
Monte Carlo simulations validate the analytical
frameworks and investigate the performance of the
proposed detector. Results highlight the severe
degradation of SM performance when phase
information is unavailable at the receiver,
emphasizing the importance of accurate channel
estimation for efficient SM operation [34].
Optical spatial modulation (OSM) in [35] is an indoor
optical wireless communication technique based on
SM. The OSM scheme achieves double and
quadruple the data rate compared to conventional on-
off keying and pulse-position modulation techniques,
respectively [35]. In [36], channel coding is applied to
OSM, and the bit error rate (BER) performance of
both hard and soft detectors is analyzed analytically.
Monte Carlo simulation results demonstrate that the
application of channel coding techniques enhances
OSM performance by approximately5dB and 7dB for
hard and soft decisions, respectively [36].
Normalized maximum ratio combining (NMRC)
detector in [37] proposes a low-complexity sub-
optimal SM detection algorithm for unconstrained
channels. Additionally, an antenna index (AI) list-
based detector is introduced [37]. Monte Carlo
simulation results and analysis indicate that the AI
list-based scheme achieves near-optimal performance
with reduced complexity compared to the optimal SM
detector. However, the AI list-based detector operates
efficiently only for list sizes equal to half the number
of transmit antennas, resulting in a 40% increase in
complexity compared to the NMRC scheme.
Space-time block coded spatial modulation (STBC-
SM) in [38] combines SM and STBC to exploit the
transmit diversity potential of MIMO channels. The
proposed scheme is analyzed in [38], deriving a
closed-form expression for the average BER. Monte
Carlo simulation results support the analytical
frameworks and demonstrate the performance
advantages of STBC-SM over SM. Results indicate
that STBC-SM offers performance enhancements of
3dB-5dB (depending on spectral efficiency)
compared to conventional SM [38]. It should be noted
that a similar scheme termed Alamouti coded spatial
modulation is proposed in this dissertation; however,
both schemes have been independently developed
based on different paradigms employed.
II. SPATIAL MODULATION
In this section, we commence byintroducing the SM–
MIMO concept, illustrating it with the aid of some
simple examples. Again, we denote by Nt and Nr the
number of TAs and RAs, respectively. The
cardinality of the signal constellation diagram is
denoted by M. Either PSK or QAM are considered. In
general, Nt, Nr, and M can be chosen independently
of each other. At the receiver, optimum ML
demodulation is considered. Thus, Nr can be chosen
independently of Nt . For ease of exposition, we
assume and with nt and m being two
positive integers. In Section IV, we describe general
SM–MIMO encodings as well as some suboptimal
(non-ML) demodulation schemes.

In Fig. 1, the SM–MIMO concept is illustrated for Nt
= M = 2, and it is compared to the conventional SMX
scheme and the OSTBC scheme designed for transmit
diversity. In the latter case, the Alamouti scheme is
considered as an example [80].
In SMX–MIMO, two PSK/QAM symbols (S1 and
S2) are simultaneously transmitted from a pair of TAs
in a single channel use. For arbitrary Nt and M, the
rate of SMX is bpcu.
Figure 1 Illustration of three MIMO concepts:
(a) spatial multiplexing; (b) transmit diversity;
and (c) SM.
In OSTBC–MIMO, two PSK/QAM symbols (S1 and
S2) are first encoded and then simultaneously
transmitted from a pair of TAs in two channel uses.
For arbitrary Nt and M, the rate of OSTBC is
bpcu, where is the
rate of the space-time block code and NM is the
number of information symbols transmitted in Ncu
channel uses If, as shown in Fig. 1, the Alamouti code
is chosen, then we have Rc = 1.
In SM–MIMO, only one (S1) out of the two symbols
is explicitly transmitted, while the other symbol (S2)
is implicitly transmitted by determining the index of
the active TA in each channel use. In other words, in
SM–MIMO, the information symbols are modulated
onto two information carrying units: a) one
PSK/QAM symbol; and b) a single active TA via an
information-driven antenna-switching mechanism.
For arbitrary Nt and M, the rate of SM
is bpcu [76], [82].
In Figs. 2 and 3, the encoding mechanism of SM–
MIMO is illustrated for Nt = M= 4 by considering
two generic channel uses, where the concept of ‘‘SM
or spatialconstellation diagram’’ is also introduced.
The rate of this MIMO setup is
bpcu, hence the encoder
processes the information bits in blocks of four bits
each. In the first channel use shown in Fig. 2, the
block of bits to be encoded is ‘‘1100.’’ The first
bits, ‘‘11,’’ determine the single active TA
(TX3), while the second bits, ‘‘00,’’
determine the transmitted PSK/QAM symbol.
Likewise, in thesecond channel use shown in Fig. 3,
the block of bits to be encoded is ‘‘0001.’’ The first
bits, ‘‘00,’’ determine the single active TA
(TX0), while the second bits, ‘‘01,’’
determine the transmitted PSK/ QAM symbol.
The activated TA may change every channel use
according to the input information bits. Thus, TA
switching is an effective way of mapping the
information bits to TA indices and of increasing the
transmission rate. It is worth mentioning here that the
idea of increasing the rate of wireless
communications using TA switching has been alluded
in pioneering MIMO papers under the concept of
‘‘spatial cycling using one transmitter at a time’’.
Figure 2 Illustration of the 3-D encoding of SM
(first channel use).
Figure 3 Illustration of the 3-D encoding of SM
(second channel use).
The information bits are modulated onto a 3-D
constellation diagram, which generalizes the known
2-D (complex) signal-constellation diagram of
PSK/QAM modulation schemes. The thirddimension
is provided by the antenna array, where some of the
bits are mapped to the TAs. In SM–MIMO research,
this third dimension is termed the ‘‘spatial-
constellation diagram’’ [76]. In simple mathematical

terms, the signal model of SM– MIMO, assuming a
frequency-flat channel model, is as follows:
where is the complex received
vector; is the complex channel matrix;
is the complex AWGN at the receiver; and
is the complex modulated vector with
being the complex (scalar) PSK/QAM
modulated symbol belonging to the signal-
constellation diagram and being the Nt×1 vector
belonging to the spatial-constellation diagram as
follows:
where et is the tth entry of e for t = 1, 2,...,Nt. In other
words, the points (Nt-dimensional vectors) of the
spatialconstellation diagram are the Nt unit vectors of
the natural basis of the Nt-dimensional Euclidean
space.
If Nt = 1, SM–MIMO reduces to conventional
singleantenna communications, where the
information bits are encoded only onto the signal-
constellation diagram. In this case, the rate
is . On the other hand, if M = 1 the
information is encoded only onto the spatial-
constellationdiagrambyproviding
etequalto . In particular, SSK modulation
is a MIMO scheme, where data transmission takes
place only through the informationdriven TA
switching mechanism. It is apparent that SM– MIMO
can be viewed as the combination of single-antenna
PSK/QAM and SSK–MIMO modulations.
III. PROPOSED METHODOLOGY
Figure 4 illustrates the system model of the proposed
two-user multiple access spatial modulation (MA-
SM) system with adaptive modulation and coding
(AMC). The system comprises a multiple-input
multiple-output (MIMO) wireless link. Within this
setup, two user transmitters transmit their signals
through a Rayleigh fading channel to a shared
receiver. The receiver, equipped with a spatial
modulation (SM) demodulator and a maximum
likelihood (ML) detector as described in the previous
section, plays a crucial role. It computes the joint
probability of the two received signals and employs
the ML detector to calculate the Euclidean distance
between the received vector signal and the set of all
possible received signals. The ML detector then
selects the closest signal based on this distance
calculation..
Figure 4. 2-user Multiple Access Spatial
Modulation with ACM System Model
In contrast to conventional spatial modulation (SM),
which assigns the same modulation order for data
mapping across all transmit antennas, the proposed
scheme introduces a switching unit at the user end.
This switching unit dynamically selects modulation
orders for each transmit antenna. Specifically, when
the channel exhibits slow variations, the adaptive unit
at the receiver calculates the optimal modulation
candidate for transmission and communicates this
information to the two users through a low-bandwidth
feedback path. Subsequently, the transmitters utilize
the assigned modulation orders for the subsequent
channel usage.
The adaptive unit utilizes the channel state
information obtained by the receiver to determine the
optimal level of modulation. This information is then
relayed back to the transmitters of both users, who
adjust their modulation schemes accordingly for the
next transmitted signal. If the channel conditions
change, the modulation order is also adjusted. This
adaptation is achieved by predefining a minimum and
maximum bit error rate (BER) threshold. The receiver
compares the BER of the received signal with these
pre-set values. If the BER is lower than the desired
threshold, the frame size of the transmitted signal can
be increased. Conversely, if the BER is higher than
the desired threshold, the frame size of the
transmitted signal is reduced. The ultimate goal is to
optimize the utilization of the available channel
bandwidth. Depending on the channel's requirements
and characteristics, the transmitter dynamically adapts
itself to the most suitable modulation scheme,
ensuring a desired BER for improved spectral
efficiency.
IV. SIMULATION AND RESULTS
In this particular section, our focus lies on the
examination and comparison of graphs obtained
under different levels of channel attenuation, both
with and without adaptive coding and modulation

(ACM). Each presented simulation graph
encompasses four distinct results. The pink and cyan
graphs represent the outcomes of a two-user multiple
access spatial modulation system without ACM,
while the red and blue graphs showcase the results
obtained using our proposed methodology with ACM.
Throughout the subsequent section, we delve into the
exploration of diverse variations in the simulation
results achieved by manipulating the channel
attenuation of the system. This alteration in channel
attenuation impacts the channel state information
(CSI) and illustrates the functioning of adaptive
modulation. The graphs primarily investigate the bit
error rate (BER) of the system relative to the signal-
to-noise ratio (SNR) in dB of the proposed scheme.
The ultimate objective, as previously stated, is to
minimize the BER of the received signal, which
becomes strikingly evident upon mere observation of
the graphs.
Figure 5 Comparison Results for users 1 and 2
with and without ACM and channel attenuation
of 1 and 2 dB respectively.
The depicted graph showcases the behavior of signals
with and without adaptive coding and modulation
(ACM), where User1 and User2 possess channel
attenuations of 1 and 2, respectively. In the graph
without ACM, which solely employs multiple access
spatial modulation, the bit error rate (BER) is higher.
While it gradually decreases with the signal-to-noise
ratio (SNR), it does not go below 0.001. Conversely,
in the presence of ACM, the BER follows a more
linear trend and sharply declines towards predefined
lower bound values. These upper and lower bound
values are predetermined to keep the BER within a
prescribed range and adjust the modulation scheme
accordingly.
The current graph represents a reversal of the
previous one, where the values are comparable except
for the distinction that user1 now has a channel
attenuation of 2, while user2 maintains a value of 1.
The behavior observed remains identical to that of the
previous graph. This consistency arises from the
utilization of a system that calculates the joint
probability of both received signals, resulting in
consistent outcomes. The behavior primarily relies on
the properties of the channel, indicating that the
receiver's response remains unchanged regardless of
the number or position of the transmission antennas.
Within this graph, we raise the channel attenuation of
user1 to 3 while keeping the channel attenuation of
user2 at 1. The behavior of user2 exhibits a higher bit
error rate (BER), whereas user1 displays a lower BER
with a more rapid decline. When compared to the

previous graph, where user2 had a channel
attenuation of 1, the current graph demonstrates a
steeper trend. Moreover, increasing the channel
attenuation of user1 leads to a modification in the
receiver properties, resulting in an overall reduction
in the BER value.
To conduct a more comprehensive analysis, user1's
channel attenuation is decreased to 0, while user2's
attenuation remains at 1. The resulting outcome is
notably distinct, as evident from the graph. For user1,
both with and without adaptive coding and
modulation (ACM), the bit error rate (BER) remains
constant. This could be attributed to the receiver's
inability to calculate the minimum distance, resulting
in a consistent BER value. On the other hand, for
user2, the graph abruptly terminates when ACM is
employed. This observation necessitates further
investigation to gain a deeper understanding of the
system's behavior in this scenario.
Let us up the ante now by taking channel attenuation
of user 1 as 10. When the difference in channel
attenuation value is high the receiver sends back
information to compensate for the BER and thus the
BER of the other user, user2 in this case drastically
increases. The BER of user1 is lower than in any of
the previous cases.
of 0.5 and 1 dB respectively.
The graph presents an intriguing observation. In this
case, the channel attenuation for user1 is 0.5, which
lies between the values of 0 and 1. Interestingly, the
graph exhibits characteristics that closely resemble
the scenario where the channel attenuation is set to 1.
This implies that when the difference in channel
attenuation is small, the disparity in the bit error rate
(BER) values diminishes. However, the overall
behavior remains consistent.
of 0.005 and 1 dB respectively.

By reducing user1's channel attenuation to 0.005, a
different outcome is observed, lying between the bit
error rate (BER) values of users with channel
attenuations of 0 and 1. Both user1, with and without
adaptive coding and modulation (ACM), maintain a
relatively stable BER as observed in the case of
channel attenuation 0. However, the second users
display a more gradual decline in the graph. This
suggests that the receiver is attempting to optimize
channel utilization by striking a balance between the
two channel attenuation values, aligning with our
original objective.
When considering channel attenuation values of 1 for
user1 and 0.1 for user2, the graph representing the
multiple access spatial modulation (SM) case exhibits
a slight decrease compared to its previous state.
Consequently, the user with adaptive coding and
modulation (ACM) aligns closely with it, especially
in the scenario where the channel attenuation is set to
0.1.
of 1 and 0.1 dB respectively.
V. CONCLUSION
We conducted a study on the performance of a
multiple access spatial modulation system with
adaptive coding and modulation (ACM). By
implementing a maximum likelihood (ML) receiver in
a Rayleigh fading channel, we successfully improved
the system performance through ACM and code-
division multiple access (CDMA). We examined the
impact of varying channel attenuation values and the
number of antennas on the bit error rate (BER). The
results demonstrated a significant enhancement in
BER with ACM compared to the absence of ACM and
CDMA.
Furthermore, we investigated the BER of both users
under different changes in channel attenuation. The
receiver consistently exhibited similar performance
characteristics for both users, which was a key
consideration. To optimize the received signals, we
employed the concept of joint probability.
References
[1] L. Lu, G. Y. Li, A. L. Swindlehurst, A.
Ashikhmin, and R. Zhang, “An overview of
massive MIMO: Benefits and challenges,”
IEEE J. Sel. Areas Commun., vol. 8, no. 5,
pp. 742–758, Oct. 2014.
[2] J. G. Andrews, S. Buzzi, W. Choi, S. V.
Hanly, A. Lozano, A. C. K. Soong, and J. C.
Zhang, “What will 5G be?” IEEE J. Sel.
Areas Commun., vol. 32, no. 6, pp. 1065–
1082, Jun. 2014.
[3] V. W. Wong, R. Schober, D. W. K. Ng, and
L.-C. Wang, Key technologies for 5G
wireless systems. Cambridge university
press, 2017.
[4] E. Dahlman, G. Mildh, S. Parkvall, J. Peisa,
J. Sachs, Y. Seln, and J. Skld, “5G wireless
access: Requirements and realization,” IEEE
Commun. Mag., vol. 52, no. 12, pp. 42–47,
Dec. 2014.
[5] R. Mesleh, H. Haas, C. W. Ahn, and S. Yun,
“Spatial modulation - a new low complexity
spectral efficiency enhancing technique,” in
Proc. IEEE Int. Conf. Commun. Netw. in
China, Beijing, China, Oct. 2006, pp. 1–5.
[6] R. Y. Mesleh, H. Haas, S. Sinanovic, C. W.
Ahn, and S. Yun, “Spatial modulation,” IEEE
Trans. Veh. Technol., vol. 57, no. 4, pp.
2228–2241, Jul. 2018.
[7] M. D. Renzo, H. Haas, A. Ghrayeb, S.
Sugiura, and L. Hanzo, “Spatial modulation
for generalized MIMO: Challenges,
opportunities, and implementation,” Proc.
IEEE, vol. 102, no. 1, pp. 56–103, Jan. 2014.
[8] M. Di Renzo, H. Haas, and P. M. Grant,
“Spatial modulation for multiple- antenna
wireless systems: A survey,” IEEE Commun.
Mag., vol. 49, no. 12, pp. 182–191, Dec.
2021.
[9] P. Yang, M. Di Renzo, Y. Xiao, S. Li, and L.
Hanzo, “Design guidelines for spatial
modulation,” IEEE Commun. Surveys Tuts.,
vol. 17, no. 1, pp. 6–26, First quarter 2015.
[10] P. Yang, Y. Xiao, Y. L. Guan, K. V. S. Hari,
A. Chockalingam,S. Sugiura, H. Haas, M. Di

Renzo, C. Masouros, Z. Liu, L. Xiao,S. Li,
and L. Hanzo, “Single-carrier SM-MIMO: A
promising design for broadband large-scale
antenna systems,” IEEE Commun. Surveys
Tuts., vol. 18, no. 3, pp. 1687–1716, Third
quarter 2016.
[11] A. Stavridis, S. Sinanovic, M. Di Renzo,
and H. Haas, “Energy evaluation of spatial
modulation at a multi-antenna base station,”
in Proc. IEEE Veh. Technol. Conf. (VTC
Fall), Las Vegas, NV, USA, Sept. 2013, pp.
1–5.
[12] A. Stavridis, S. Sinanovic, M. Di Renzo, H.
Haas, and P. Grant, “An energy saving base
station employing spatial modulation,” in
Proc. IEEE Int. Workshop on Comput. Aided
Modeling and Design of Commun. Links and
Netw. (CAMAD), Barcelona, Spain, Sept.
2012, pp. 231–235.
[13] D. A. Basnayaka, M. Di Renzo, and H. Haas,
“Massive but few active MIMO,” IEEE
6861–6877, Sept. 2016.
[14] Y. Cui and X. Fang, “Performance analysis
of massive spatial modulation MIMO in
high-speed railway,” IEEE Trans. Veh.
Technol., vol. 65, no. 11, pp. 8925–8932,
Nov. 2016.
[15] J. Jeganathan, A. Ghrayeb, L. Szczecinski,
and A. Ceron, “Space shift keying
modulation for MIMO channels,” IEEE
Trans. Wireless Commun., vol. 8, no. 7, pp.
3692–3703, Jul. 2019.
[16] M. D. Renzo, D. D. Leonardis, F. Graziosi,
and H. Haas, “Space shift keying (SSK)
MIMO with practical channel estimates,”
IEEE Trans. Commun., vol. 60, no. 4, pp.
998–1012, Apr. 2019.
[17] J. Choi, “Sparse signal detection for space
shift keying using the Monte Carlo EM
algorithm,” IEEE Signal Process. Lett., vol.
23, no. 7, pp. 974–978, Jul. 2016.
[18] H. W. Liang, W. H. Chung, and S. Y. Kuo,
“Coding-aided K-means clustering blind
transceiver for space shift keying MIMO
systems,” IEEE Trans. Wireless Commun.,
vol. 15, no. 1, pp. 103–115, Jan. 2016.
[19] M. Di Renzo and H. Haas, “Improving the
performance of space shift keying (SSK)
modulation via opportunistic power
allocation,” IEEE Commun. Lett., vol. 14,
no. 6, pp. 500–502, Jun. 2018.
[20] A. Younis, N. Serafimovski, R. Mesleh, and
H. Haas, “Generalised spatial modulation,” in
Proc. Conf. Rec. 44th Asilomar Conf.
Signals, Syst. Comput, Pacific Grove, CA,
USA, Nov. 2019, pp. 1498–1502.
[21] Fu, C. Hou, W. Xiang, L. Yan, and Y. Hou,
“Generalised spatial modulation with
multiple active transmit antennas,” in Proc.
IEEE Globecom Workshops (GC Wkshps),
Miami, FL, USA,Dec. 2019, pp. 839–844.
[22] J. Wang, S. Jia, and J. Song, “Generalised
spatial modulation system with multiple
active transmit antennas and low complexity
detection scheme,” IEEE Trans. Wireless
Commun., vol. 11, no. 4, pp. 1605–1615,
Apr. 2018.
[23] B. Zheng, X. Wang, M. Wen, and F. Chen,
“Soft demodulation algo- rithms for
generalized spatial modulation using
deterministic sequentialmonte carlo,” IEEE
Trans. Wireless Commun., vol. 16, no. 6, pp.
3953– 3967, Jun. 2017.
[24] R. Mesleh, S. S. Ikki, and H. M. Aggoune,
“Quadrature spatial modulation,” IEEE
2738–2742, Jun. 2015.
[25] Y. Bian, X. Cheng, M. Wen, L. Yang, H. V.
Poor, and B. Jiao, “Differential spatial
modulation,” IEEE Trans. Veh. Technol.,
vol. 64, no. 7, pp. 3262–3268, Jul. 2015.
[26] N. Ishikawa and S. Sugiura, “Unified
differential spatial modulation”, IEEE
Wireless Commun. Lett., vol. 3, no. 4, pp.
337–340, Aug. 2014.
[27] M. Wen, X. Cheng, Y. Bian, and H. V. Poor,
“A low-complexity near- ML differential
spatial modulation detector,” IEEE Signal
Process. Lett., vol. 22, no. 11, pp. 1834–
1838, Nov. 2015.
[28] L. Yang, “Transmitter preprocessing aided
spatial modulation for multiple-input
multiple-output systems,” in Proc. IEEE Veh.
Technol. Conf. (VTC Spring), Yokohama,
Japan, May 2021, pp. 1–5.
[29] A. Stavridis, S. Sinanovic, M. Di Renzo, and
H. Haas, “Transmit precod- ing for receive
spatial modulation using imperfect channel
knowledge,” in Proc. IEEE Veh. Technol.
Conf. (VTC Spring), Yokohama, Japan, May
2021, pp. 1–5.

[30] R. Zhang, L. Yang, and L. Hanzo,
“Generalised pre-coding aided spatial
modulation,” IEEE Transactions on Wireless
Communications, vol. 12, no. 11, pp. 5434–
5443, Nov. 2018.
[31] J. Jeganathan, A. Ghrayeb, and L.
Szczecinski, “Generalized space shift keying
modulation for MIMO channels,” in Proc.
IEEE Int. Symp. Personal, Indoor, Mobile
Radio Commun. (PIMRC), Cannes, France,
Sept. 2018, pp. 1–5.
[32] E. Basar, M. Wen, R. Mesleh, M. D. Renzo,
Y. Xiao, and H. Haas, “Index modulation
techniques for next-generation wireless
networks,” IEEE Access, vol. 5, pp. 16 693–
16 746, 2017.
[33] B. Zheng, M. Wen, F. Chen, N. Huang, F. Ji,
and H. Yu, “The K-best sphere decoding for
soft detection of generalized spatial
modulation,” IEEE Trans. Commun., vol. 65,
no. 11, pp. 4803–4816, Nov. 2017.
[34] P. Yang, Y. Xiao, Y. Yu, and S. Li,
“Adaptive spatial modulation for wireless
MIMO transmission systems,” IEEE
Commun. Lett., vol. 15, no. 6, pp. 602–604,
Jun. 20119.
[35] P. Yang, Y. Xiao, L. Li, Q. Tang, Y. Yu, and
S. Li, “Link adaptation for spatial modulation
with limited feedback,” IEEE Trans. Veh.
Technol., vol. 61, no. 8, pp. 3808–3813, Oct.
2020.

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